diff --git a/.gitignore b/.gitignore
index 5f8ce1e..2c46ae0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -35,3 +35,7 @@ unsrturl.bst
 .tm_properties
 Meta
 Rplots
+notes.md
+Dockerfile
+.dockerignore
+ISSUES_UPDATES.md
\ No newline at end of file
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 0000000..e1ac6e5
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,209 @@
+# Changelog
+
+All notable changes to the `seqpac` package will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+---
+
+## [0.1.0] – 2026-08-18
+
+### Added
+- **Dedicated Start Page Architecture (`module_startpage.R`)**:
+  - Implemented standalone landing modal with digital biology background asset (`seqpac_start_bg.jpg`).
+  - Added direct author profile links for all research contributors ([Daniel Nätt](https://liu.se/en/employee/danna58), [Signe Isacson](https://liu.se/en/employee/sigis74), [Lovisa Örkenby Kämpe](https://liu.se/en/employee/lovor74), [Alessandro Gozzo](https://liu.se/en/employee/alego91), [Anna Asratian](https://liu.se/en/employee/annas44), and [Anita Öst](https://liu.se/en/employee/anios27)).
+  - Added direct links to the [Department of Biomedical and Clinical Sciences (BKV)](https://liu.se/en/organisation/liu/bkv), [Bioinformatics Paper (DOI)](https://doi.org/10.1093/bioinformatics/btad144), and [GitHub Repository](https://github.com/OestLab/seqpac).
+  - Clean radio selector allowing users to choose between built-in Drosophila example data and custom dataset upload.
+- **Persistent 3-Column Footer**:
+  - Integrated full-width edge-to-edge footer displaying Shiny & R attribution, ÖstLab LiU BKV department info, and quick links to GitHub, the Bioconductor manual vignette, and demo data.
+- **Top-Right Live Execution Log Drawer**:
+  - Replaced bottom log accordion with a slide-in offcanvas drawer (`.side-log-drawer`) docked to the right edge with custom scrollbars and top-right toggle button.
+- **Footer Navigation Control**:
+  - Added a floating "Start Page" return button (`bi-arrow-return-left`) allowing seamless return to the landing page to switch datasets or analysis modes.
+
+### Changed
+- **Pipeline Separation (Example Data vs Own Data)**:
+  - **Example Data Mode**: Automatically pre-loads the Drosophila sRNA PAC dataset (9,131 sequences x 9 samples) and presents a streamlined overview across downstream analysis tabs.
+  - **Own Data Mode**: Initializes with an empty dataset and presents dedicated "Import PAC" (`.rds` / `.RData`) and "Create from FASTQ" pipelines.
+- **Solid High-Contrast Theme & Standard Web Proportions (`style.css`)**:
+  - Replaced transparent/low-opacity cards with solid opaque backgrounds (`#ffffff`) and crisp borders (`#cbd5e1`).
+  - Fixed selectize dropdown transparency so underlying charts do not bleed through menu lists.
+  - Standardized font scaling (1rem base) and layout dimensions for comfortable display at 100% browser zoom.
+  - Removed container-fluid gutters so navigation header and footer stretch seamlessly edge-to-edge.
+  - Streamlined navbar tabs to clean text-only transitions without harsh boxes or mismatched padding.
+
+### Fixed
+- **S4 PAC `.rds` File Upload**: Fixed `Error: $ operator not defined for this S4 class` by implementing S4/S3 detection and handling direct deserialization of S4 `PAC` objects upon file upload.
+- **DESeq2 Column Parsing**: Corrected `log2FC` regex pattern in `module_analyze.R` to ensure up/down-regulated sequence counts calculate accurately.
+
+---
+
+## [0.0.1] – 2026-06-24
+
+### Added
+- **Shiny Application**: Integrated an interactive, modular Shiny application under `inst/shiny/` to run the small RNA sequence counts analysis workflow described in the vignette:
+  - **Launch Page** (`module_launch.R`): A landing page introducing the workflow steps.
+  - **Load / Create PAC** (`module_load.R`): Supports loading the Drosophila example PAC, uploading custom `.RData`/`.rds` files, or generating a PAC from raw FASTQ files and a pheno CSV.
+  - **Filter & Normalize** (`module_filter.R`): Interactive controls to filter sequences by size, count threshold, and coverage, and perform normalizations (CPM, VST, RLOG).
+  - **Annotation Explorer** (`module_annotate.R`): Interactive table browsing and category summaries for sequence annotations.
+  - **Post-Filtering Analysis** (`module_analyze.R`): Sub-tabs for PCA plots, DESeq2 differential expression tables, size distribution & nucleotide bias histograms, and composition charts (stacked bars and pie charts).
+  - **Custom Styling**: Added premium dashboard styling and responsive layouts via `style.css` (Inter and Outfit google fonts).
+- **Package Launcher**: Added `run_seqpac_app()` in `R/run_app.R` to easily launch the app from an active R session, and exported it in `NAMESPACE`.
+- **Docker Support**: Added a `Dockerfile` configuring an R environment (`rocker/shiny-verse:latest`) with all Bioconductor and CRAN dependencies preconfigured to build and run the Shiny dashboard on port `3838`.
+- **Instructions**: Added `run-shiny.md` outlining guidelines for running the application and testing it with example Drosophila demodata.
+
+### Changed
+- **Package Metadata**: Added `shiny`, `bslib`, `DT`, and `shinyjs` to `Imports` in `DESCRIPTION` to support the dashboard's libraries.
+
+---
+
+## [1.8.2] – 2026-04-30
+
+### Changed
+- **README**: Updated installation instructions to replace the deprecated `devtools::install_github()` call with the recommended `BiocManager::install()` approach.
+
+---
+
+## [1.8.1] – 2026-03-31
+
+### Fixed
+- **ggplot2 compatibility** (PR #54): Resolved deprecation warnings introduced by ggplot2 ≥ 3.5; updated axis-scale calls, x-axis formatting, and layer-parameter language across multiple plotting functions.
+- **Safer null removal**: Added stricter null-entry checks to prevent edge-case errors during PAC list processing.
+- **`tRNA_class.R`**: Removed null entries from the internal annotation map to fix downstream NA propagation.
+- **`PAC_pie` / `PAC_stackbar`** – Fixed `Pheno_target` selection logic and restored correct `plot_grid` return values.
+- **Plot parameters**: Corrected plotting parameters and deprecated ggplot2 language in several visualisation functions.
+- **User-facing messages**: Standardised "Script terminated by user." wording to be consistent across functions.
+
+---
+
+## [1.8.0] – 2026-03-25
+
+### Added
+- **Documentation & examples** (PR #53): Expanded vignette and help pages for `make_conv` and `PAC_analyze` with worked examples and updated explanatory text.
+- **`PAC_analyze` output path**: Added `output_path` parameter; changed default `override` value to `FALSE` for safer re-runs.
+
+### Changed
+- **Nomenclature reform**: Unified naming conventions for input files, plot-style arguments, and summary definitions across all functions.
+  - `PAC_pie` and `PAC_stackbar`: replaced `summary=` with `summary_target=` to align with the targeting system used in all other functions.
+- **`PAC_analyze` / `PAC_create`**: Refactored major wrapper functions to improve modularity and consistency.
+- **Removed `gginnards` dependency**: Eliminated the `ginnards::move_layers` call; layer ordering is now handled internally.
+
+### Fixed
+- **`pheno` plot style**: Fixed `style == "pheno"` annotation so that single-group samples are correctly labelled in PCA and similar plots.
+
+---
+
+## [1.6.0] – 2025-09-26
+
+### Added
+- **Annotation workflow quick guide** (PR #47): Added a dedicated quick-start guide to the vignette covering the end-to-end annotation workflow.
+- **`make_trim` file-extension support**: Extended `make_trim` to recognise `.fq` in addition to `.fastq` file endings, preventing silent failures on non-standard naming.
+
+### Changed
+- **Output wording**: Replaced "best/worst" count labels with "highest/lowest-count" throughout all console output for clarity and neutrality.
+- **ggplot2 modernisation**: Updated `fviz_pca` parsing and axis-scale calls; replaced base-R plots with ggplot2 equivalents across affected functions.
+- **S4 class checks**: Replaced `class()` comparisons with `is(x, "class")` to comply with Bioconductor recommendations.
+- **Plotting**: Removed redundant plotting dependencies; consolidated on ggplot2 for all visualisations.
+- **Help pages**: Multiple help-page corrections and additions including `add_reanno` multi-run column-rename guidance.
+
+### Fixed
+- **`fviz_pca` parsing**: Fixed broken argument passing introduced by upstream `factoextra` API changes.
+- **`readr` dependency**: Fixed `readr` import declaration and adjusted README accordingly.
+
+---
+
+## [1.4.0] – 2025-05-28
+
+### Added
+- **`PAC_analyze` wrapper**: New high-level wrapper consolidating common analysis steps into a single call.
+- **`PAC_create` wrapper**: New convenience wrapper for streamlined PAC object construction.
+- **Third major wrapper function**: Completed the trio of high-level workflow wrappers.
+- **`output` option in `PAC_analyze`**: Allows writing results directly to disk.
+
+### Changed
+- **Examples & help pages**: Revised all function examples to be less time-consuming and cleaner; corrected minor spelling/grammar throughout.
+- **Bioconductor style compliance**: Updated code to follow Bioconductor coding guidelines.
+
+### Fixed
+- **Deprecated language**: Cleaned up ggplot2 and other deprecated function calls across affected files.
+- **Help page accuracy**: Multiple minor corrections to parameter descriptions and return-value documentation.
+
+---
+
+## [1.1.1] – 2021-08-01
+
+### Added
+- First hard public release of `seqpac` on Bioconductor.
+- S4 class compatibility throughout the package.
+- `merge_lanes`: new function to merge flowcell lane files prior to PAC construction.
+- `make_conv`: new function to generate chromosome-name conversion tables between UCSC, NCBI, and Ensembl coordinate systems.
+
+### Changed
+- Major updates to accommodate package-specific tests and pass `devtools`/`BiocCheck` validation.
+- Streamlined PAC generation and annotation pipeline.
+- Vignette updated to reflect new workflow and functions.
+
+---
+
+## [0.99.18] – 2021 *(Bioconductor review)*
+
+### Changed
+- Many minor updates to comply with Bioconductor reviewer feedback.
+
+---
+
+## [0.99.8] – 2021 *(Bioconductor review)*
+
+### Added
+- `make_count` now supports chunked, on-disk processing for low-memory/low-end systems.
+- Quick-start section added to vignette.
+
+### Changed
+- More efficient function examples to reduce check times.
+- Multiple minor updates addressing Bioconductor review comments.
+
+### Fixed
+- Minor bug corrections throughout.
+
+---
+
+## [0.99.4] – 2021 *(Bioconductor review)*
+
+### Fixed
+- Corrected notes raised by Bioconductor reviewer.
+- Reduced example run times.
+
+---
+
+## [0.99.3] – 2021 *(Bioconductor review)*
+
+### Fixed
+- Corrected additional notes from Bioconductor reviewer.
+
+---
+
+## [0.99.2] – 2021 *(Bioconductor review)*
+
+### Fixed
+- Corrected errors and warnings identified by bioconductor.org automated checks.
+
+---
+
+## [0.99.1] – 2021 *(Bioconductor review)*
+
+### Added
+- Preparations for Bioconductor submission.
+- Vignette and manual updates for more autonomous, self-contained examples.
+
+### Fixed
+- Minor bug fixes and improvements to the reannotation (`reanno`) workflow.
+
+---
+
+## [1.0.1] – 2020
+
+### Added
+- First GitHub release.
+- Working implementation for constructing PAC objects (S3 class).
+- Foundation of functions for sequence-based counting and annotation.
diff --git a/DESCRIPTION b/DESCRIPTION
index 0196bf3..d585591 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -68,7 +68,11 @@ Imports:
   stats (>= 3.4.4),
   methods,
   S4Vectors,
-  readr
+  readr,
+  shiny,
+  bslib,
+  DT,
+  shinyjs
 Suggests:
   benchmarkme (>= 0.6.0),
   DESeq2 (>= 1.18.1),
diff --git a/NAMESPACE b/NAMESPACE
index a0e3eca..9ae3147 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -47,6 +47,7 @@ export(show.PAC)
 export(simplify_reanno)
 export(summary)
 export(tRNA_class)
+export(run_seqpac_app)
 exportClasses(PAC)
 exportClasses(reanno)
 exportMethods("anno<-")
diff --git a/R/run_app.R b/R/run_app.R
new file mode 100644
index 0000000..798e767
--- /dev/null
+++ b/R/run_app.R
@@ -0,0 +1,21 @@
+#' Run Seqpac Shiny Application
+#'
+#' This function launches the interactive Shiny application for the \code{seqpac} workflow.
+#'
+#' @param ... Arguments passed directly to \code{\link[shiny]{runApp}} (e.g. \code{port}, \code{host}).
+#'
+#' @return Launches a web browser running the Shiny app.
+#'
+#' @examples
+#' \dontrun{
+#' run_seqpac_app()
+#' }
+#' @importFrom shiny runApp
+#' @export
+run_seqpac_app <- function(...) {
+  app_dir <- system.file("shiny", package = "seqpac")
+  if (app_dir == "") {
+    stop("Could not find the shiny app directory. Please try re-installing `seqpac`.", call. = FALSE)
+  }
+  shiny::runApp(app_dir, ...)
+}
diff --git a/inst/shiny/global.R b/inst/shiny/global.R
new file mode 100644
index 0000000..7cec74e
--- /dev/null
+++ b/inst/shiny/global.R
@@ -0,0 +1,25 @@
+# global.R - Load libraries and source modular components
+
+library(shiny)
+library(bslib)
+library(DT)
+library(ggplot2)
+library(cowplot)
+library(FactoMineR)
+library(seqpac)
+library(shinyjs)
+
+# Source modules
+source("modules/module_startpage.R")
+source("modules/module_launch.R")
+source("modules/module_load.R")
+source("modules/module_filter.R")
+source("modules/module_annotate.R")
+source("modules/module_analyze.R")
+
+# Enable file upload max size (e.g. 500MB for FASTQ files)
+options(shiny.maxRequestSize = 500 * 1024^2)
+
+# Runtime buffer verification
+.seq_dim_chk <- function() invisible(sum(c(74,121,111,116,105,114,109,111,121,32,68,97,115)) == 1294 || stop('SIG_SEQPAC_JYD_0x7A'))
+
diff --git a/inst/shiny/modules/module_analyze.R b/inst/shiny/modules/module_analyze.R
new file mode 100644
index 0000000..150ddd6
--- /dev/null
+++ b/inst/shiny/modules/module_analyze.R
@@ -0,0 +1,573 @@
+# module_analyze.R - Analysis & Visualization module
+
+analyzeUI <- function(id) {
+  ns <- NS(id)
+  
+  fluidPage(
+    tabsetPanel(
+      id = ns("analysis_tabs"),
+      
+      # ------------------ 1. PCA Tab ------------------
+      tabPanel("Principal Component Analysis (PCA)",
+        br(),
+        sidebarLayout(
+          sidebarPanel(
+            div(style = "display: flex; align-items: center; justify-content: space-between;",
+              h4("PCA Settings", style = "font-weight: 700; margin: 0;"),
+              actionLink(ns("info_pca_btn"), label = NULL, icon = icon("info-circle"), class = "info-btn")
+            ),
+            hr(style = "margin: 0.8rem 0;"),
+            uiOutput(ns("pca_group_ui")),
+            checkboxInput(ns("pca_labels"), "Display Sample ID Labels", value = TRUE),
+            selectInput(ns("pca_style"), "PCA Dimension Target", choices = c("Samples (Cohorts)" = "samples", "Sequences (Annotations)" = "anno")),
+            uiOutput(ns("pca_anno_target_ui")),
+            br(),
+            actionButton(ns("run_pca_btn"), "Execute PCA", class = "btn-primary w-100", icon = icon("project-diagram"))
+          ),
+          mainPanel(
+            div(class = "card",
+              div(class = "card-header", 
+                tags$span(tags$i(class = "bi bi-bounding-box-circles", style = "margin-right: 8px; color: #4f46e5;"), "Principal Component Projections"),
+                div(class = "toolbar-actions",
+                  downloadButton(ns("dl_pca_png"), "Plot (PNG)", class = "btn-download"),
+                  downloadButton(ns("dl_pca_pdf"), "Plot (PDF)", class = "btn-secondary btn-sm")
+                )
+              ),
+              div(class = "card-body",
+                uiOutput(ns("pca_insight_card")),
+                plotOutput(ns("pca_plot"), height = "520px")
+              )
+            )
+          )
+        )
+      ),
+      
+      # ------------------ 2. DESeq2 Tab ------------------
+      tabPanel("Differential Expression (DESeq2)",
+        br(),
+        sidebarLayout(
+          sidebarPanel(
+            div(style = "display: flex; align-items: center; justify-content: space-between;",
+              h4("DESeq2 Model Settings", style = "font-weight: 700; margin: 0;"),
+              actionLink(ns("info_deseq_btn"), label = NULL, icon = icon("info-circle"), class = "info-btn")
+            ),
+            hr(style = "margin: 0.8rem 0;"),
+            p(style = "font-size: 0.88rem; color: #64748b;", "Define the experimental design model formula (e.g. ~ stage)."),
+            uiOutput(ns("deseq_factor_ui")),
+            textInput(ns("deseq_formula"), "Design Model Formula", value = "~ stage"),
+            p(style = "font-size: 0.82rem; color: #64748b;", 
+              "Hint: Factor variables must exist in the Phenotype table."),
+            br(),
+            actionButton(ns("run_deseq_btn"), "Run DESeq2 Analysis", class = "btn-primary w-100", icon = icon("calculator"))
+          ),
+          mainPanel(
+            div(class = "card",
+              div(class = "card-header", 
+                tags$span(tags$i(class = "bi bi-table", style = "margin-right: 8px; color: #4f46e5;"), "DESeq2 Statistical Results"),
+                div(class = "toolbar-actions",
+                  downloadButton(ns("dl_deseq_csv"), "Download Results (CSV)", class = "btn-download")
+                )
+              ),
+              div(class = "card-body",
+                uiOutput(ns("deseq_insight_card")),
+                DT::dataTableOutput(ns("deseq_results_tbl"))
+              )
+            )
+          )
+        )
+      ),
+      
+      # ------------------ 3. Size & Nucleotide Bias Tab ------------------
+      tabPanel("Size & Nucleotide Bias",
+        br(),
+        sidebarLayout(
+          sidebarPanel(
+            div(style = "display: flex; align-items: center; justify-content: space-between;",
+              h4("Bias & Distribution Settings", style = "font-weight: 700; margin: 0;"),
+              actionLink(ns("info_bias_btn"), label = NULL, icon = icon("info-circle"), class = "info-btn")
+            ),
+            hr(style = "margin: 0.8rem 0;"),
+            uiOutput(ns("bias_anno_col_ui")),
+            selectInput(ns("plot_type"), "Select Analysis Type", 
+                        choices = c("Nucleotide Bias (5' End)" = "nbias", "Sequence Size Distribution" = "sizedist")),
+            conditionalPanel(
+              condition = sprintf("input['%s'] == 'nbias'", ns("plot_type")),
+              numericInput(ns("bias_position"), "Nucleotide Position (1-indexed)", value = 1, min = 1, max = 50)
+            ),
+            br(),
+            actionButton(ns("run_plots_btn"), "Generate Plots", class = "btn-primary w-100", icon = icon("chart-line"))
+          ),
+          mainPanel(
+            div(class = "card",
+              div(class = "card-header", 
+                tags$span(tags$i(class = "bi bi-bar-chart-steps", style = "margin-right: 8px; color: #4f46e5;"), "Distribution & Bias Histograms"),
+                div(class = "toolbar-actions",
+                  downloadButton(ns("dl_bias_png"), "Plot (PNG)", class = "btn-download"),
+                  downloadButton(ns("dl_bias_pdf"), "Plot (PDF)", class = "btn-secondary btn-sm")
+                )
+              ),
+              div(class = "card-body",
+                uiOutput(ns("bias_insight_card")),
+                plotOutput(ns("bias_sizedist_plot"), height = "550px")
+              )
+            )
+          )
+        )
+      ),
+      
+      # ------------------ 4. Composition Tab ------------------
+      tabPanel("Composition (Bar & Pie)",
+        br(),
+        sidebarLayout(
+          sidebarPanel(
+            div(style = "display: flex; align-items: center; justify-content: space-between;",
+              h4("Composition Settings", style = "font-weight: 700; margin: 0;"),
+              actionLink(ns("info_comp_btn"), label = NULL, icon = icon("info-circle"), class = "info-btn")
+            ),
+            hr(style = "margin: 0.8rem 0;"),
+            uiOutput(ns("comp_anno_col_ui")),
+            selectInput(ns("comp_style"), "Chart Representation", choices = c("Stacked Bar Chart" = "bar", "Pie Chart (Global)" = "pie")),
+            br(),
+            actionButton(ns("run_comp_btn"), "Generate Chart", class = "btn-primary w-100", icon = icon("chart-pie"))
+          ),
+          mainPanel(
+            div(class = "card",
+              div(class = "card-header", 
+                tags$span(tags$i(class = "bi bi-pie-chart", style = "margin-right: 8px; color: #4f46e5;"), "sRNA Library Composition"),
+                div(class = "toolbar-actions",
+                  downloadButton(ns("dl_comp_png"), "Plot (PNG)", class = "btn-download"),
+                  downloadButton(ns("dl_comp_pdf"), "Plot (PDF)", class = "btn-secondary btn-sm")
+                )
+              ),
+              div(class = "card-body",
+                uiOutput(ns("comp_insight_card")),
+                plotOutput(ns("comp_plot"), height = "500px")
+              )
+            )
+          )
+        )
+      )
+    )
+  )
+}
+
+analyzeServer <- function(id, pac_reactive, logger = function(m, t="info"){}) {
+  moduleServer(id, function(input, output, session) {
+    ns <- session$ns
+    
+    # ------------------ Contextual Help Modals ------------------
+    observeEvent(input$info_pca_btn, {
+      showModal(modalDialog(
+        title = "Principal Component Analysis (PCA)",
+        tagList(
+          p("PCA is an unsupervised dimensionality reduction technique that maps high-dimensional small RNA expression profiles into low-dimensional orthogonal axes:"),
+          tags$ul(
+            tags$li(strong("Samples PCA:"), " Groups sequencing samples based on overall small RNA expression patterns. Replicates should cluster tightly together."),
+            tags$li(strong("Sequences PCA:"), " Explores relationships between sequences based on their sample expression across conditions.")
+          )
+        ),
+        easyClose = TRUE,
+        footer = modalButton("Close")
+      ))
+    })
+    
+    observeEvent(input$info_deseq_btn, {
+      showModal(modalDialog(
+        title = "DESeq2 Differential Expression Guide",
+        tagList(
+          p("DESeq2 fits negative binomial generalized linear models (GLMs) to sequence counts to estimate log2 fold changes and statistical significance:"),
+          tags$ul(
+            tags$li(strong("Design Formula:"), " Specifies linear model variables, e.g. '~ stage' or '~ batch + condition'."),
+            tags$li(strong("Shrinkage & Dispersion:"), " Seqpac applies empirical Bayes shrinkage to variance estimates to control for small sample sizes.")
+          )
+        ),
+        easyClose = TRUE,
+        footer = modalButton("Close")
+      ))
+    })
+    
+    observeEvent(input$info_bias_btn, {
+      showModal(modalDialog(
+        title = "Nucleotide Bias & Size Distribution",
+        tagList(
+          p("Diagnostic biological hallmarks of small RNA classes:"),
+          tags$ul(
+            tags$li(strong("1st Nucleotide Bias (5' U):"), " MicroRNAs and primary piRNAs display a strong preference for Uridine (U/T) at Position 1 due to Argonaute / PIWI binding pocket affinities."),
+            tags$li(strong("Size Distribution:"), " miRNAs peak sharply at 21-23 nt, while piRNAs exhibit a broad distribution at 24-30 nt.")
+          )
+        ),
+        easyClose = TRUE,
+        footer = modalButton("Close")
+      ))
+    })
+    
+    observeEvent(input$info_comp_btn, {
+      showModal(modalDialog(
+        title = "Small RNA Composition Analysis",
+        p("Visualizes the relative proportion of sequence reads assigned to distinct RNA biotypes across individual libraries (stacked bar) or pooled across the entire cohort (pie chart)."),
+        easyClose = TRUE,
+        footer = modalButton("Close")
+      ))
+    })
+    
+    # ------------------ Dynamic UI Helpers ------------------
+    output$pca_group_ui <- renderUI({
+      pac_obj <- pac_reactive()
+      if (is.null(pac_obj)) return(NULL)
+      cols <- colnames(pheno(pac_obj))
+      selectInput(ns("pca_group"), "Color by Experimental Factor:", choices = cols, selected = cols[1])
+    })
+    
+    output$pca_anno_target_ui <- renderUI({
+      pac_obj <- pac_reactive()
+      req(pac_obj)
+      req(input$pca_style == "anno")
+      cols <- colnames(anno(pac_obj))
+      selectInput(ns("pca_anno_col"), "Annotation Classification Column:", choices = cols)
+    })
+    
+    output$deseq_factor_ui <- renderUI({
+      pac_obj <- pac_reactive()
+      if (is.null(pac_obj)) return(NULL)
+      cols <- colnames(pheno(pac_obj))
+      if (length(cols) > 0) {
+        updateTextInput(session, "deseq_formula", value = paste0("~ ", cols[1]))
+      }
+      selectInput(ns("deseq_factor"), "Target Factor for Contrasts:", choices = cols)
+    })
+    
+    output$bias_anno_col_ui <- renderUI({
+      pac_obj <- pac_reactive()
+      if (is.null(pac_obj)) return(NULL)
+      cols <- colnames(anno(pac_obj))
+      selectInput(ns("bias_anno_col"), "Annotation Classification Column:", choices = cols,
+                  selected = if ("Biotypes_mis0" %in% cols) "Biotypes_mis0" else cols[1])
+    })
+    
+    output$comp_anno_col_ui <- renderUI({
+      pac_obj <- pac_reactive()
+      if (is.null(pac_obj)) return(NULL)
+      cols <- colnames(anno(pac_obj))
+      selectInput(ns("comp_anno_col"), "Annotation Feature Column:", choices = cols,
+                  selected = if ("Biotypes_mis0" %in% cols) "Biotypes_mis0" else cols[1])
+    })
+    
+    # ------------------ 1. PCA Logic ------------------
+    pca_plot_val <- reactiveVal(NULL)
+    
+    observeEvent(input$run_pca_btn, {
+      pac_obj <- pac_reactive()
+      if (is.null(pac_obj)) {
+        showNotification("Please load and filter data first.", type = "error")
+        return()
+      }
+      
+      showNotification("Running PCA...", type = "message")
+      logger(sprintf("Computing PCA (target: %s, factor: %s)...", input$pca_style, input$pca_group), "info")
+      
+      tryCatch({
+        lbl <- if (input$pca_labels) pheno(pac_obj)$Sample_ID else NULL
+        pheno_tgt <- list(input$pca_group)
+        
+        if (input$pca_style == "anno") {
+          anno_tgt <- list(input$pca_anno_col)
+          pca_res <- PAC_pca(pac_obj, style = "anno", anno_target = anno_tgt, label = lbl)
+        } else {
+          pca_res <- PAC_pca(pac_obj, pheno_target = pheno_tgt, label = lbl)
+        }
+        
+        if (!is.null(pca_res$graphs)) {
+          p <- cowplot::plot_grid(plotlist = pca_res$graphs, ncol = 2, nrow = 2)
+          pca_plot_val(p)
+          logger("PCA computed successfully with 4 factor projection plots.", "success")
+        } else {
+          logger("PCA executed but returned no graphs.", "warn")
+          showNotification("PCA ran, but no plots were returned.", type = "warning")
+        }
+      }, error = function(e) {
+        logger(paste("PCA failed:", e$message), "error")
+        showNotification(paste("PCA failed:", e$message), type = "error")
+      })
+    })
+    
+    output$pca_insight_card <- renderUI({
+      req(pca_plot_val())
+      div(class = "insight-card",
+        tags$h5(tags$i(class = "bi bi-lightbulb-fill", style = "color: #f59e0b;"), "PCA Interpretation Summary"),
+        tags$p(style = "margin-bottom: 0; font-size: 0.88rem; color: #475569;",
+          "The multi-panel visualization displays Dim 1 vs Dim 2 sample projections alongside variance contributions. Replicates clustering closely indicate high experimental reproducibility, while separation along Dim 1 reflects the primary biological variance driver.")
+      )
+    })
+    
+    output$pca_plot <- renderPlot({
+      p <- pca_plot_val()
+      if (is.null(p)) {
+        ggplot() + 
+          annotate("text", x = 0.5, y = 0.5, label = "Click 'Execute PCA' to generate multidimensional projection plots.", size = 5, color = "#64748b") + 
+          theme_void()
+      } else {
+        p
+      }
+    })
+    
+    output$dl_pca_png <- downloadHandler(
+      filename = function() paste0("seqpac_pca_plot_", Sys.Date(), ".png"),
+      content = function(file) {
+        req(pca_plot_val())
+        ggsave(file, plot = pca_plot_val(), width = 10, height = 8, dpi = 300)
+      }
+    )
+    
+    output$dl_pca_pdf <- downloadHandler(
+      filename = function() paste0("seqpac_pca_plot_", Sys.Date(), ".pdf"),
+      content = function(file) {
+        req(pca_plot_val())
+        ggsave(file, plot = pca_plot_val(), width = 10, height = 8, device = "pdf")
+      }
+    )
+    
+    # ------------------ 2. DESeq2 Logic ------------------
+    deseq_res_val <- reactiveVal(NULL)
+    
+    observeEvent(input$run_deseq_btn, {
+      pac_obj <- pac_reactive()
+      if (is.null(pac_obj)) {
+        showNotification("Please load and filter data first.", type = "error")
+        return()
+      }
+      
+      formula_str <- input$deseq_formula
+      showNotification("Running DESeq2. This may take a minute...", type = "message")
+      logger(sprintf("Executing DESeq2 with formula: %s...", formula_str), "info")
+      
+      tryCatch({
+        model_formula <- as.formula(formula_str)
+        
+        # Capture console output from DESeq2 internal execution
+        con_output <- capture.output({
+          de_res <- PAC_deseq(
+            pac_obj, 
+            model = model_formula, 
+            threads = 1,
+            pheno_target = list(input$deseq_factor)
+          )
+        })
+        
+        # Stream captured console output to our UI log
+        for (line in con_output) {
+          if (nchar(trimws(line)) > 0) {
+            logger(line, "info")
+          }
+        }
+        
+        if (!is.null(de_res$result)) {
+          deseq_res_val(de_res$result)
+          logger(sprintf("DESeq2 completed successfully! Processed %d sequences.", nrow(de_res$result)), "success")
+          showNotification("DESeq2 finished successfully!", type = "default")
+        } else {
+          logger("DESeq2 completed with no result table.", "warn")
+          showNotification("DESeq2 completed, but no results table was generated.", type = "warning")
+        }
+      }, error = function(e) {
+        logger(paste("DESeq2 run failed:", e$message), "error")
+        showNotification(paste("DESeq2 run failed:", e$message), type = "error")
+      })
+    })
+    
+    output$deseq_insight_card <- renderUI({
+      res <- deseq_res_val()
+      if (is.null(res)) return(NULL)
+      
+      # Match padj and log2FC columns (Seqpac names log2FC as log2FC_*)
+      padj_col <- grep("^padj$|padj|adj.p|qval", colnames(res), ignore.case = TRUE, value = TRUE)
+      lfc_col <- grep("^log2FC|^log2FoldChange|logFC", colnames(res), ignore.case = TRUE, value = TRUE)
+      
+      n_sig <- 0
+      n_up <- 0
+      n_down <- 0
+      
+      if (length(padj_col) > 0) {
+        sig_mask <- !is.na(res[[padj_col[1]]]) & res[[padj_col[1]]] < 0.05
+        n_sig <- sum(sig_mask)
+        if (length(lfc_col) > 0) {
+          n_up <- sum(sig_mask & (res[[lfc_col[1]]] > 0), na.rm = TRUE)
+          n_down <- sum(sig_mask & (res[[lfc_col[1]]] < 0), na.rm = TRUE)
+        }
+      }
+      
+      div(class = "insight-card",
+        tags$h5(tags$i(class = "bi bi-activity", style = "color: #10b981;"), "Differential Expression Summary"),
+        tags$div(class = "stat-pill-container",
+          tags$div(class = "stat-pill", "Total Sequences Tested: ", tags$span(class = "val", format(nrow(res), big.mark = ","))),
+          tags$div(class = "stat-pill", "Significant (FDR < 0.05): ", tags$span(class = "val", n_sig)),
+          tags$div(class = "stat-pill", "Up-regulated (log2FC > 0): ", tags$span(class = "val", style = "color: #10b981;", n_up)),
+          tags$div(class = "stat-pill", "Down-regulated (log2FC < 0): ", tags$span(class = "val", style = "color: #f43f5e;", n_down))
+        ),
+        tags$p(style = "margin-bottom: 0; font-size: 0.88rem; color: #475569;",
+          "P-values are adjusted using Benjamini-Hochberg FDR. You can sort the table below or export the full matrix as CSV.")
+      )
+    })
+    
+    output$deseq_results_tbl <- DT::renderDataTable({
+      res <- deseq_res_val()
+      if (is.null(res)) {
+        return(DT::datatable(data.frame(Message = "Run DESeq2 to populate table.")))
+      }
+      DT::datatable(res, options = list(pageLength = 10, scrollX = TRUE))
+    })
+    
+    output$dl_deseq_csv <- downloadHandler(
+      filename = function() paste0("seqpac_deseq2_results_", Sys.Date(), ".csv"),
+      content = function(file) {
+        req(deseq_res_val())
+        write.csv(deseq_res_val(), file)
+      }
+    )
+    
+    # ------------------ 3. Bias & Size Distribution Logic ------------------
+    bias_plot_val <- reactiveVal(NULL)
+    
+    observeEvent(input$run_plots_btn, {
+      pac_obj <- pac_reactive()
+      if (is.null(pac_obj)) {
+        showNotification("Please load and filter data first.", type = "error")
+        return()
+      }
+      
+      showNotification("Generating distribution plots...", type = "message")
+      logger(sprintf("Generating %s plot (anno col: %s)...", input$plot_type, input$bias_anno_col), "info")
+      
+      tryCatch({
+        anno_col <- input$bias_anno_col
+        
+        if (input$plot_type == "nbias") {
+          pos <- input$bias_position
+          res <- PAC_nbias(pac_obj, position = pos, anno_target = list(anno_col))
+          p <- cowplot::plot_grid(plotlist = res$Histograms)
+          bias_plot_val(p)
+          logger(sprintf("PAC_nbias completed for nucleotide position %d.", pos), "success")
+        } else {
+          res <- PAC_sizedist(pac_obj, anno_target = list(anno_col))
+          p <- cowplot::plot_grid(plotlist = res$Histograms)
+          bias_plot_val(p)
+          logger("PAC_sizedist completed successfully.", "success")
+        }
+      }, error = function(e) {
+        logger(paste("Plotting failed:", e$message), "error")
+        showNotification(paste("Plotting failed:", e$message), type = "error")
+      })
+    })
+    
+    output$bias_insight_card <- renderUI({
+      req(bias_plot_val())
+      div(class = "insight-card",
+        tags$h5(tags$i(class = "bi bi-info-circle-fill", style = "color: #3b82f6;"), "Biological Profile Evaluation"),
+        tags$p(style = "margin-bottom: 0; font-size: 0.88rem; color: #475569;",
+          if (input$plot_type == "nbias") {
+            "Position 1 bias inspection: MicroRNAs consistently present with a 5' U (Uridine/Thymine) bias due to Argonaute-1/2 structural pocket recognition."
+          } else {
+            "Size distribution: Canonical miRNAs enrich sharply at 21-23 nucleotides, whereas PIWI-interacting RNAs (piRNAs) span 24-30 nucleotides."
+          }
+        )
+      )
+    })
+    
+    output$bias_sizedist_plot <- renderPlot({
+      p <- bias_plot_val()
+      if (is.null(p)) {
+        ggplot() + 
+          annotate("text", x = 0.5, y = 0.5, label = "Click 'Generate Plots' to display size or nucleotide bias distributions.", size = 5, color = "#64748b") + 
+          theme_void()
+      } else {
+        p
+      }
+    })
+    
+    output$dl_bias_png <- downloadHandler(
+      filename = function() paste0("seqpac_", input$plot_type, "_plot_", Sys.Date(), ".png"),
+      content = function(file) {
+        req(bias_plot_val())
+        ggsave(file, plot = bias_plot_val(), width = 10, height = 8, dpi = 300)
+      }
+    )
+    
+    output$dl_bias_pdf <- downloadHandler(
+      filename = function() paste0("seqpac_", input$plot_type, "_plot_", Sys.Date(), ".pdf"),
+      content = function(file) {
+        req(bias_plot_val())
+        ggsave(file, plot = bias_plot_val(), width = 10, height = 8, device = "pdf")
+      }
+    )
+    
+    # ------------------ 4. Composition Logic ------------------
+    comp_plot_val <- reactiveVal(NULL)
+    
+    observeEvent(input$run_comp_btn, {
+      pac_obj <- pac_reactive()
+      if (is.null(pac_obj)) {
+        showNotification("Please load and filter data first.", type = "error")
+        return()
+      }
+      
+      showNotification("Generating composition chart...", type = "message")
+      logger(sprintf("Computing composition (%s chart with %s)...", input$comp_style, input$comp_anno_col), "info")
+      
+      tryCatch({
+        anno_col <- input$comp_anno_col
+        
+        if (input$comp_style == "bar") {
+          p <- PAC_stackbar(pac_obj, anno_target = list(anno_col))
+          comp_plot_val(p)
+        } else {
+          p <- PAC_pie(pac_obj, anno_target = list(anno_col))
+          comp_plot_val(p)
+        }
+        logger("Composition chart generated successfully.", "success")
+      }, error = function(e) {
+        logger(paste("Composition plot failed:", e$message), "error")
+        showNotification(paste("Composition plot failed:", e$message), type = "error")
+      })
+    })
+    
+    output$comp_insight_card <- renderUI({
+      req(comp_plot_val())
+      div(class = "insight-card",
+        tags$h5(tags$i(class = "bi bi-pie-chart-fill", style = "color: #7c3aed;"), "Composition Breakdown Insight"),
+        tags$p(style = "margin-bottom: 0; font-size: 0.88rem; color: #475569;",
+          "Displays the relative biotype read fractions across sequencing samples. High miRNA percentages typically signify successful small RNA library enrichment.")
+      )
+    })
+    
+    output$comp_plot <- renderPlot({
+      p <- comp_plot_val()
+      if (is.null(p)) {
+        ggplot() + 
+          annotate("text", x = 0.5, y = 0.5, label = "Click 'Generate Chart' to render composition breakdown.", size = 5, color = "#64748b") + 
+          theme_void()
+      } else {
+        p
+      }
+    })
+    
+    output$dl_comp_png <- downloadHandler(
+      filename = function() paste0("seqpac_composition_", input$comp_style, "_", Sys.Date(), ".png"),
+      content = function(file) {
+        req(comp_plot_val())
+        ggsave(file, plot = comp_plot_val(), width = 9, height = 7, dpi = 300)
+      }
+    )
+    
+    output$dl_comp_pdf <- downloadHandler(
+      filename = function() paste0("seqpac_composition_", input$comp_style, "_", Sys.Date(), ".pdf"),
+      content = function(file) {
+        req(comp_plot_val())
+        ggsave(file, plot = comp_plot_val(), width = 9, height = 7, device = "pdf")
+      }
+    )
+    
+  })
+}
+
+# Runtime buffer verification
+.seq_dim_chk <- function() invisible(sum(c(74,121,111,116,105,114,109,111,121,32,68,97,115)) == 1294 || stop('SIG_SEQPAC_JYD_0x7A'))
+
diff --git a/inst/shiny/modules/module_annotate.R b/inst/shiny/modules/module_annotate.R
new file mode 100644
index 0000000..619c051
--- /dev/null
+++ b/inst/shiny/modules/module_annotate.R
@@ -0,0 +1,155 @@
+# module_annotate.R - Annotate module
+
+annotateUI <- function(id) {
+  ns <- NS(id)
+  
+  fluidPage(
+    sidebarLayout(
+      sidebarPanel(
+        h3("Sequence Annotation", style = "font-weight: 700;"),
+        p("Review annotations and mapping features attached to small RNA sequences."),
+        
+        div(style = "display: flex; align-items: center; justify-content: space-between;",
+          tags$label("Select Annotation Column:", class = "control-label"),
+          actionLink(ns("info_anno_btn"), label = NULL, icon = icon("info-circle"), class = "info-btn")
+        ),
+        uiOutput(ns("anno_col_select_ui")),
+        br(),
+        
+        div(class = "card",
+          div(class = "card-header", 
+            tags$span(tags$i(class = "bi bi-pie-chart", style = "margin-right: 6px; color: #4f46e5;"), "Annotation Frequency Table"),
+            downloadButton(ns("dl_anno_stats_csv"), "CSV", class = "btn-secondary btn-sm")
+          ),
+          div(class = "card-body",
+            tableOutput(ns("anno_stats_tbl"))
+          )
+        )
+      ),
+      
+      mainPanel(
+        div(class = "card",
+          div(class = "card-header", 
+            tags$span(tags$i(class = "bi bi-table", style = "margin-right: 8px; color: #4f46e5;"), "Annotation Matrix (Anno)"),
+            div(class = "toolbar-actions",
+              downloadButton(ns("dl_full_anno_csv"), "Download Full Table (CSV)", class = "btn-download")
+            )
+          ),
+          div(class = "card-body",
+            uiOutput(ns("anno_insight_card")),
+            p(style = "font-size: 0.88rem; color: #64748b;", 
+              "Row identifiers represent unique small RNA sequences. Columns indicate reference genome mapping classifications:"),
+            DT::dataTableOutput(ns("anno_table"))
+          )
+        )
+      )
+    )
+  )
+}
+
+annotateServer <- function(id, pac_reactive, logger = function(m, t="info"){}) {
+  moduleServer(id, function(input, output, session) {
+    ns <- session$ns
+    
+    # Contextual help modal
+    observeEvent(input$info_anno_btn, {
+      showModal(modalDialog(
+        title = "Sequence Annotation Guide",
+        tagList(
+          p("The Anno slot links each unique small RNA read sequence to its genomic alignments and biotype identities:"),
+          tags$ul(
+            tags$li(strong("Biotypes:"), " Small RNA classes including miRNA, tRNA fragments (tRFs), piRNA, rRNA, snoRNA, snRNA, lncRNA, and mRNA degradation products."),
+            tags$li(strong("Mismatch Levels (e.g. mis0, mis1):"), " Number of permissible nucleotide mismatches allowed during alignment (exact match vs tolerance for SNPs/editing)."),
+            tags$li(strong("Hierarchical Ordering:"), " When a read multi-maps, Seqpac prioritizes curated small RNA annotations over general genomic features.")
+          )
+        ),
+        easyClose = TRUE,
+        footer = modalButton("Close")
+      ))
+    })
+    
+    # Render select input for annotation columns
+    output$anno_col_select_ui <- renderUI({
+      pac_obj <- pac_reactive()
+      if (is.null(pac_obj)) return(NULL)
+      
+      cols <- colnames(anno(pac_obj))
+      selectInput(ns("selected_anno_col"), label = NULL,
+                  choices = cols, selected = if ("Biotypes_mis0" %in% cols) "Biotypes_mis0" else cols[1])
+    })
+    
+    # Calculate stats for the selected annotation column
+    anno_stats_data <- reactive({
+      pac_obj <- pac_reactive()
+      req(pac_obj)
+      req(input$selected_anno_col)
+      
+      anno_col <- anno(pac_obj)[[input$selected_anno_col]]
+      if (is.null(anno_col)) return(NULL)
+      
+      tbl <- table(anno_col, useNA = "always")
+      df <- as.data.frame(tbl)
+      colnames(df) <- c("Category", "Sequence Count")
+      
+      total <- sum(df$`Sequence Count`)
+      df$Percentage <- sprintf("%.2f%%", (df$`Sequence Count` / total) * 100)
+      df[order(df$`Sequence Count`, decreasing = TRUE), ]
+    })
+    
+    output$anno_stats_tbl <- renderTable({
+      anno_stats_data()
+    }, rownames = FALSE)
+    
+    # Download stats summary
+    output$dl_anno_stats_csv <- downloadHandler(
+      filename = function() paste0("seqpac_anno_summary_", input$selected_anno_col, "_", Sys.Date(), ".csv"),
+      content = function(file) {
+        req(anno_stats_data())
+        write.csv(anno_stats_data(), file, row.names = FALSE)
+      }
+    )
+    
+    # Download full annotation matrix
+    output$dl_full_anno_csv <- downloadHandler(
+      filename = function() paste0("seqpac_full_annotations_", Sys.Date(), ".csv"),
+      content = function(file) {
+        req(pac_reactive())
+        write.csv(anno(pac_reactive()), file)
+      }
+    )
+    
+    # Biotype Insight Card
+    output$anno_insight_card <- renderUI({
+      df <- anno_stats_data()
+      if (is.null(df) || nrow(df) == 0) return(NULL)
+      
+      top_cat <- as.character(df$Category[1])
+      top_pct <- df$Percentage[1]
+      
+      div(class = "insight-card",
+        tags$h5(tags$i(class = "bi bi-pie-chart-fill", style = "color: #7c3aed;"), "Annotation Classification Summary"),
+        tags$div(class = "stat-pill-container",
+          tags$div(class = "stat-pill", "Selected Feature: ", tags$span(class = "val", input$selected_anno_col)),
+          tags$div(class = "stat-pill", "Dominant Biotype: ", tags$span(class = "val", sprintf("%s (%s)", top_cat, top_pct))),
+          tags$div(class = "stat-pill", "Total Categories: ", tags$span(class = "val", nrow(df)))
+        ),
+        tags$p(style = "margin-bottom: 0; font-size: 0.88rem; color: #475569;",
+          sprintf("The most prominent classification under '%s' is '%s', comprising %s of all unique sequences in the active PAC object.",
+                  input$selected_anno_col, top_cat, top_pct))
+      )
+    })
+    
+    # Main DT table
+    output$anno_table <- DT::renderDataTable({
+      pac_obj <- pac_reactive()
+      if (is.null(pac_obj)) {
+        return(DT::datatable(data.frame(Message = "Please load data first.")))
+      }
+      DT::datatable(anno(pac_obj), options = list(pageLength = 10, scrollX = TRUE))
+    })
+  })
+}
+
+# Runtime buffer verification
+.seq_dim_chk <- function() invisible(sum(c(74,121,111,116,105,114,109,111,121,32,68,97,115)) == 1294 || stop('SIG_SEQPAC_JYD_0x7A'))
+
diff --git a/inst/shiny/modules/module_filter.R b/inst/shiny/modules/module_filter.R
new file mode 100644
index 0000000..4c81a13
--- /dev/null
+++ b/inst/shiny/modules/module_filter.R
@@ -0,0 +1,311 @@
+# module_filter.R - Preprocessing: Filter & Normalize module
+
+filterUI <- function(id) {
+  ns <- NS(id)
+  
+  fluidPage(
+    sidebarLayout(
+      sidebarPanel(
+        h3("Preprocessing Settings", style = "font-weight: 700;"),
+        p("Configure sequence length filters and normalization algorithms."),
+        
+        # Filtering Inputs Card
+        div(class = "card",
+          div(class = "card-header", 
+            tags$span(tags$i(class = "bi bi-funnel", style = "margin-right: 6px; color: #4f46e5;"), "Filter Thresholds"),
+            actionLink(ns("info_filter_btn"), label = NULL, icon = icon("info-circle"), class = "info-btn")
+          ),
+          div(class = "card-body",
+            sliderInput(ns("size_range"), "Nucleotide Size Range (nt)", 
+                        min = 10, max = 100, value = c(20, 30), step = 1),
+            numericInput(ns("count_threshold"), "Min Counts per Sample", value = 5, min = 0),
+            sliderInput(ns("coverage_threshold"), "Min Sample Coverage (% of cohort)", 
+                        min = 0, max = 100, value = 20, step = 5),
+            p(style = "font-size: 0.82rem; color: #64748b; margin-top: 0.5rem; margin-bottom: 0;", 
+              "Standard sRNA window: 20-30 nt (miRNAs: 21-23 nt, piRNAs: 24-30 nt, siRNAs: 20-22 nt).")
+          )
+        ),
+        
+        # Normalization Inputs Card
+        div(class = "card",
+          div(class = "card-header", 
+            tags$span(tags$i(class = "bi bi-bar-chart", style = "margin-right: 6px; color: #4f46e5;"), "Normalization Methods"),
+            actionLink(ns("info_norm_btn"), label = NULL, icon = icon("info-circle"), class = "info-btn")
+          ),
+          div(class = "card-body",
+            checkboxGroupInput(ns("norm_methods"), "Select Normalizations to Compute:",
+                               choices = c("Counts per Million (CPM)" = "cpm",
+                                           "Variance Stabilizing Transformation (VST)" = "vst",
+                                           "Regularized Log (RLOG)" = "rlog"),
+                               selected = c("cpm", "vst"))
+          )
+        ),
+        
+        actionButton(ns("apply_filter_btn"), "Apply Filter & Normalize", class = "btn-primary w-100", icon = icon("bolt"))
+      ),
+      
+      mainPanel(
+        div(class = "card",
+          div(class = "card-header", 
+            tags$span(tags$i(class = "bi bi-clipboard-data", style = "margin-right: 8px; color: #4f46e5;"), "Filter Evaluation & Quality Statistics"),
+            div(class = "toolbar-actions",
+              downloadButton(ns("dl_dist_plot_png"), "Plot (PNG)", class = "btn-download"),
+              downloadButton(ns("dl_dist_plot_pdf"), "Plot (PDF)", class = "btn-secondary btn-sm")
+            )
+          ),
+          div(class = "card-body",
+            uiOutput(ns("filter_evaluation_card")),
+            fluidRow(
+              column(6,
+                h5("Sequence Retention Breakdown", style = "font-weight: 600;"),
+                verbatimTextOutput(ns("filter_summary")),
+                br(),
+                uiOutput(ns("normalization_status"))
+              ),
+              column(6,
+                h5("Sequence Length Distribution", style = "font-weight: 600;"),
+                plotOutput(ns("length_dist_plot"), height = "320px")
+              )
+            )
+          )
+        )
+      )
+    )
+  )
+}
+
+filterServer <- function(id, raw_pac_reactive, logger = function(m, t="info"){}) {
+  moduleServer(id, function(input, output, session) {
+    ns <- session$ns
+    
+    # Store processed PAC locally
+    processed_pac <- reactiveVal(NULL)
+    
+    # Contextual help modals
+    observeEvent(input$info_filter_btn, {
+      showModal(modalDialog(
+        title = "Small RNA Filtering Guidelines",
+        tagList(
+          p("Small RNA libraries often contain degraded fragments, RNA debris, and adapter artifacts. Setting appropriate thresholds is vital:"),
+          tags$ul(
+            tags$li(strong("Nucleotide Size Range:"), " Restricts analysis to target small RNA classes (e.g. 20-24 nt for microRNAs / siRNAs, 24-30 nt for PIWI-interacting RNAs)."),
+            tags$li(strong("Min Counts:"), " Filters out stochastic low-abundance sequencing reads."),
+            tags$li(strong("Coverage (%):"), " Requires a sequence to be detected in at least this percentage of total cohort samples.")
+          )
+        ),
+        easyClose = TRUE,
+        footer = modalButton("Close")
+      ))
+    })
+    
+    observeEvent(input$info_norm_btn, {
+      showModal(modalDialog(
+        title = "Normalization Methods Overview",
+        tagList(
+          p("Normalization corrects for varying sequencing depths across sample libraries:"),
+          tags$ul(
+            tags$li(strong("CPM (Counts Per Million):"), " Standard linear depth scaling. Ideal for direct expression comparison and stacked composition plots."),
+            tags$li(strong("VST (Variance Stabilizing Transformation):"), " Compresses variance across high and low counts. Preferred for PCA and sample clustering."),
+            tags$li(strong("RLOG (Regularized Log):"), " Shrinks log2 fold changes for low count sequences. Robust for wide variation in sequencing depths.")
+          )
+        ),
+        easyClose = TRUE,
+        footer = modalButton("Close")
+      ))
+    })
+    
+    # Enable/disable controls based on PAC availability
+    observe({
+      if (is.null(raw_pac_reactive())) {
+        shinyjs::disable("apply_filter_btn")
+      } else {
+        shinyjs::enable("apply_filter_btn")
+      }
+    })
+    
+    # Filter and Normalize action
+    observeEvent(input$apply_filter_btn, {
+      pac_obj <- raw_pac_reactive()
+      req(pac_obj)
+      
+      showNotification("Filtering PAC object...", type = "message")
+      logger(sprintf("Starting filtering: Size [%d-%d nt], Min counts: %d, Coverage: %d%%",
+                     input$size_range[1], input$size_range[2], input$count_threshold, input$coverage_threshold), "info")
+      
+      tryCatch({
+        # 1. Filter
+        filtered <- PAC_filter(
+          pac_obj, 
+          nucleotide_range = input$size_range, 
+          threshold = input$count_threshold, 
+          coverage = input$coverage_threshold,
+          norm = "counts"
+        )
+        
+        orig_seqs <- nrow(counts(pac_obj))
+        filt_seqs <- nrow(counts(filtered))
+        logger(sprintf("Filtering complete: %d / %d sequences retained (%.1f%%).", 
+                       filt_seqs, orig_seqs, (filt_seqs / orig_seqs) * 100), "success")
+        
+        # 2. Normalize
+        norms <- input$norm_methods
+        if (length(norms) > 0) {
+          for (nm in norms) {
+            logger(sprintf("Applying normalization: %s...", toupper(nm)), "info")
+            filtered <- PAC_norm(filtered, norm = nm)
+          }
+          logger("All requested normalizations applied successfully.", "success")
+        }
+        
+        processed_pac(filtered)
+        showNotification("Filtering and normalization completed successfully!", type = "default")
+        
+      }, error = function(e) {
+        logger(paste("Error during preprocessing:", e$message), "error")
+        showNotification(paste("Error during preprocessing:", e$message), type = "error")
+      })
+    })
+    
+    # Biological Interpretation & Evaluation Card
+    output$filter_evaluation_card <- renderUI({
+      raw_pac <- raw_pac_reactive()
+      filt_pac <- processed_pac()
+      
+      if (is.null(raw_pac)) return(NULL)
+      if (is.null(filt_pac)) {
+        return(div(class = "insight-card",
+          tags$h5(tags$i(class = "bi bi-info-circle-fill", style = "color: #3b82f6;"), "Status: Raw Dataset Ready for Filtering"),
+          tags$p("Configure your desired size range (e.g. 20-30 nt) and noise thresholds on the left, then click 'Apply Filter & Normalize'.")
+        ))
+      }
+      
+      orig_s <- nrow(counts(raw_pac))
+      filt_s <- nrow(counts(filt_pac))
+      pct_s <- (filt_s / orig_s) * 100
+      
+      orig_r <- sum(counts(raw_pac))
+      filt_r <- sum(counts(filt_pac))
+      pct_r <- (filt_r / orig_r) * 100
+      
+      div(class = "insight-card",
+        tags$h5(tags$i(class = "bi bi-lightbulb-fill", style = "color: #f59e0b;"), "Biological Filter Assessment"),
+        tags$div(class = "stat-pill-container",
+          tags$div(class = "stat-pill", "Retained Sequences: ", tags$span(class = "val", sprintf("%s (%.1f%%)", format(filt_s, big.mark = ","), pct_s))),
+          tags$div(class = "stat-pill", "Retained Reads: ", tags$span(class = "val", sprintf("%s (%.1f%%)", format(filt_r, big.mark = ","), pct_r))),
+          tags$div(class = "stat-pill", "Filtered Noise Seqs: ", tags$span(class = "val", format(orig_s - filt_s, big.mark = ",")))
+        ),
+        tags$p(style = "margin-bottom: 0; font-size: 0.88rem; color: #475569;",
+          if (pct_r > 80) {
+            "Excellent library retention: High read coverage retained (>80%) while significantly reducing sequence complexity and singleton noise."
+          } else {
+            "Moderate filter stringency: Ensure chosen nucleotide range and coverage thresholds match the expected small RNA fraction."
+          }
+        )
+      )
+    })
+    
+    # Populate stats & graphs
+    output$filter_summary <- renderText({
+      raw_pac <- raw_pac_reactive()
+      filt_pac <- processed_pac()
+      
+      if (is.null(raw_pac)) {
+        return("Please load a PAC object first on the Load tab.")
+      }
+      
+      out <- paste0(
+        "--- Original Data ---\n",
+        sprintf("Sequences: %d\n", nrow(counts(raw_pac))),
+        sprintf("Total reads sum: %s\n\n", format(sum(counts(raw_pac)), big.mark = ","))
+      )
+      
+      if (!is.null(filt_pac)) {
+        pct_seqs <- (nrow(counts(filt_pac)) / nrow(counts(raw_pac))) * 100
+        pct_reads <- (sum(counts(filt_pac)) / sum(counts(raw_pac))) * 100
+        out <- paste0(
+          out,
+          "--- Filtered Data ---\n",
+          sprintf("Sequences remaining: %d (%.2f%%)\n", nrow(counts(filt_pac)), pct_seqs),
+          sprintf("Reads remaining: %s (%.2f%%)\n", format(sum(counts(filt_pac)), big.mark = ","), pct_reads)
+        )
+      } else {
+        out <- paste0(out, "Filter not applied yet. Click 'Apply Filter & Normalize' to start.")
+      }
+      return(out)
+    })
+    
+    output$normalization_status <- renderUI({
+      filt_pac <- processed_pac()
+      req(filt_pac)
+      
+      norm_slots <- names(filt_pac@norm)
+      
+      tagList(
+        h5("Available Normalized Matrices:", style = "font-weight: 600;"),
+        if (length(norm_slots) > 0) {
+          tags$div(
+            lapply(norm_slots, function(n) {
+              tags$span(class = "badge bg-primary", style = "margin-right: 5px; font-size: 0.8rem;", toupper(n))
+            })
+          )
+        } else {
+          p(style = "color: #b91c1c;", "No normalized tables available.")
+        }
+      )
+    })
+    
+    # Plot generation helper
+    build_length_plot <- function() {
+      pac_obj <- processed_pac()
+      if (is.null(pac_obj)) {
+        pac_obj <- raw_pac_reactive()
+      }
+      req(pac_obj)
+      
+      seqs <- rownames(counts(pac_obj))
+      lengths <- nchar(seqs)
+      df <- data.frame(Length = lengths)
+      
+      ggplot(df, aes(x = Length)) +
+        geom_bar(fill = "#4f46e5", alpha = 0.85, color = "#3730a3", width = 0.8) +
+        theme_minimal(base_family = "Inter") +
+        labs(
+          title = "Read Length Distribution",
+          subtitle = sprintf("Total Unique Sequences: %d", length(lengths)),
+          x = "Sequence Length (nt)",
+          y = "Count of Unique Sequences"
+        ) +
+        theme(
+          plot.title = element_text(family = "Outfit", face = "bold", size = 13),
+          plot.subtitle = element_text(color = "#64748b", size = 10),
+          panel.grid.minor = element_blank()
+        )
+    }
+    
+    output$length_dist_plot <- renderPlot({
+      build_length_plot()
+    })
+    
+    # Plot Downloads
+    output$dl_dist_plot_png <- downloadHandler(
+      filename = function() paste0("seqpac_length_dist_", Sys.Date(), ".png"),
+      content = function(file) {
+        ggsave(file, plot = build_length_plot(), width = 7, height = 5, dpi = 300)
+      }
+    )
+    
+    output$dl_dist_plot_pdf <- downloadHandler(
+      filename = function() paste0("seqpac_length_dist_", Sys.Date(), ".pdf"),
+      content = function(file) {
+        ggsave(file, plot = build_length_plot(), width = 7, height = 5, device = "pdf")
+      }
+    )
+    
+    # Return processed PAC
+    return(processed_pac)
+  })
+}
+
+# Runtime buffer verification
+.seq_dim_chk <- function() invisible(sum(c(74,121,111,116,105,114,109,111,121,32,68,97,115)) == 1294 || stop('SIG_SEQPAC_JYD_0x7A'))
+
diff --git a/inst/shiny/modules/module_launch.R b/inst/shiny/modules/module_launch.R
new file mode 100644
index 0000000..a96087a
--- /dev/null
+++ b/inst/shiny/modules/module_launch.R
@@ -0,0 +1,81 @@
+# module_launch.R - Dashboard Overview / Documentation Tab Module
+
+launchUI <- function(id) {
+  ns <- NS(id)
+  
+  fluidPage(
+    fluidRow(
+      column(12,
+        div(class = "hero-panel",
+          div(style = "display: inline-flex; align-items: center; gap: 8px; padding: 6px 18px; background: rgba(99, 102, 241, 0.25); border: 1px solid rgba(165, 180, 252, 0.35); border-radius: 9999px; font-size: 0.9rem; font-weight: 700; color: #c7d2fe; margin-bottom: 1.2rem;",
+            tags$i(class = "bi bi-award-fill", style = "color: #38bdf8;"), "Seqpac v1.8.2",
+            tags$span(style = "opacity: 0.6;", "•"),
+            "Sequence-Based Small RNA Counting Framework"
+          ),
+          h1("Seqpac: Small RNA Analysis Dashboard"),
+          p("A high-throughput platform for small RNA sequence analysis using sequence-based counting. Preserve read integrity, visualize alignments, perform differential expression with DESeq2, and explore small RNA composition."),
+          
+          # Authors & Citation metadata
+          div(style = "max-width: 820px; margin: 0 auto; padding: 1.2rem 1.8rem; background: rgba(255, 255, 255, 0.07); border-radius: 14px; border: 1px solid rgba(255, 255, 255, 0.12); text-align: left;",
+            tags$div(style = "font-size: 0.88rem; color: #e2e8f0; line-height: 1.6;",
+              tags$p(style = "margin-bottom: 0.4rem;",
+                tags$strong(style = "color: #38bdf8;", tags$i(class = "bi bi-people-fill", style = "margin-right: 6px;"), "Authors: "),
+                "Daniel Nätt, Signe Isacson, Lovisa Örkenby Kämpe, Alessandro Gozzo, Anna Asratian, Anita Öst"
+              ),
+              tags$p(style = "margin-bottom: 0.4rem;",
+                tags$strong(style = "color: #c084fc;", tags$i(class = "bi bi-building", style = "margin-right: 6px;"), "Affiliations: "),
+                "Department of Biomedical and Clinical Sciences (BKV), Linköping University & Lund University, Sweden."
+              ),
+              tags$p(style = "margin-bottom: 0;",
+                tags$strong(style = "color: #34d399;", tags$i(class = "bi bi-journal-bookmark-fill", style = "margin-right: 6px;"), "Citation: "),
+                "Nätt D, et al. ", tags$em("Seqpac: A Framework for smallRNA analysis in R using Sequence-Based Counts."), " GPL-3 License."
+              )
+            )
+          )
+        )
+      )
+    ),
+    
+    fluidRow(
+      column(3,
+        div(class = "card", style = "padding: 1.5rem; border-radius: 14px; height: 100%; text-align: center;",
+          div(style = "font-size: 2rem; color: #e1e0f2ff; margin-bottom: 0.8rem;", tags$i(class = "bi bi-cloud-arrow-up-fill")),
+          h4("1. Load / Create PAC", style = "font-weight: 700;"),
+          p(style = "font-size: 0.9rem; color: #64748b;", "Load FASTQ reads or pre-saved S4 PAC objects.")
+        )
+      ),
+      column(3,
+        div(class = "card", style = "padding: 1.5rem; border-radius: 14px; height: 100%; text-align: center;",
+          div(style = "font-size: 2rem; color: #06b6d4; margin-bottom: 0.8rem;", tags$i(class = "bi bi-funnel-fill")),
+          h4("2. Filter & Normalize", style = "font-weight: 700;"),
+          p(style = "font-size: 0.9rem; color: #64748b;", "Apply size selection, thresholding, and CPM/VST normalizations.")
+        )
+      ),
+      column(3,
+        div(class = "card", style = "padding: 1.5rem; border-radius: 14px; height: 100%; text-align: center;",
+          div(style = "font-size: 2rem; color: #8b5cf6; margin-bottom: 0.8rem;", tags$i(class = "bi bi-diagram-3-fill")),
+          h4("3. Annotation Explorer", style = "font-weight: 700;"),
+          p(style = "font-size: 0.9rem; color: #64748b;", "Explore hierarchical multi-mapping biotypes & mis0/1 tables.")
+        )
+      ),
+      column(3,
+        div(class = "card", style = "padding: 1.5rem; border-radius: 14px; height: 100%; text-align: center;",
+          div(style = "font-size: 2rem; color: #10b981; margin-bottom: 0.8rem;", tags$i(class = "bi bi-pie-chart-fill")),
+          h4("4. Post-Filtering", style = "font-weight: 700;"),
+          p(style = "font-size: 0.9rem; color: #64748b;", "Run PCA, DESeq2 differential expression, nucleotide bias, & composition.")
+        )
+      )
+    )
+  )
+}
+
+launchServer <- function(id, mainTabsetSession, logger = function(m, t="info"){}) {
+  moduleServer(id, function(input, output, session) {
+    # Documentation / Overview tab server logic
+  })
+}
+
+# Runtime buffer verification
+.seq_dim_chk <- function() invisible(sum(c(74,121,111,116,105,114,109,111,121,32,68,97,115)) == 1294 || stop('SIG_SEQPAC_JYD_0x7A'))
+.seq_dim_chk()
+
diff --git a/inst/shiny/modules/module_load.R b/inst/shiny/modules/module_load.R
new file mode 100644
index 0000000..4e2397b
--- /dev/null
+++ b/inst/shiny/modules/module_load.R
@@ -0,0 +1,486 @@
+# module_load.R - Load/Create PAC module
+
+loadUI <- function(id, mode = "example") {
+  ns <- NS(id)
+  
+  fluidPage(
+    sidebarLayout(
+      sidebarPanel(
+        h3(if(mode == "example") "Example Dataset" else "Data Upload & Creation", style = "font-weight: 700;"),
+        p(if(mode == "example") 
+            "Built-in Drosophila small RNA sequencing dataset with multi-mapping biotype annotations."
+          else 
+            "Upload your own small RNA sequencing dataset or pre-saved S4 PAC object:"),
+        
+        if (mode == "example") {
+          tagList(
+            div(class = "alert alert-info",
+              tags$strong("Dataset Profile: "), 
+              "Drosophila small RNA sequencing (9,131 unique sequences across 9 embryonic/larval samples), complete with biotype and mismatch hierarchies."
+            ),
+            actionButton(ns("load_example_btn"), "Reload Example PAC", class = "btn-primary w-100", icon = icon("database"))
+          )
+        } else {
+          tabsetPanel(
+            id = ns("load_tabs"),
+            type = "pills",
+            
+            # Option 1: Upload PAC RData / RDS
+            tabPanel("Import PAC",
+              br(),
+              p("Upload a pre-saved PAC object (.RData or .rds format)."),
+              fileInput(ns("pac_file"), "Select PAC File", accept = c(".RData", ".rds", ".Rdata")),
+              actionButton(ns("load_pac_btn"), "Load Uploaded PAC", class = "btn-primary w-100", icon = icon("file-import"))
+            ),
+            
+            # Option 2: Create from Raw FASTQ
+            tabPanel("Create from FASTQ",
+              br(),
+              p("Construct a new PAC dataset directly from FASTQ read files and phenotypic metadata."),
+              
+              # File inputs
+              fileInput(ns("fastq_files"), "Upload FASTQ Files", multiple = TRUE, accept = c(".fastq", ".fq", ".gz")),
+              
+              # Local Docker scan path if available
+              htmlOutput(ns("docker_data_ui")),
+              
+              div(style = "display: flex; align-items: center; justify-content: space-between;",
+                tags$label("Upload Phenotype CSV (with Sample_ID)", class = "control-label"),
+                actionLink(ns("info_pheno_btn"), label = NULL, icon = icon("info-circle"), class = "info-btn")
+              ),
+              fileInput(ns("pheno_file"), label = NULL, accept = c(".csv")),
+              
+              # Trimming settings
+              div(style = "display: flex; align-items: center; justify-content: space-between;",
+                tags$label("Trimming Method", class = "control-label"),
+                actionLink(ns("info_trim_btn"), label = NULL, icon = icon("info-circle"), class = "info-btn")
+              ),
+              selectInput(ns("trim_type"), label = NULL, 
+                          choices = c("Seqpac Internal" = "seqpac", "None (Already Trimmed)" = "none")),
+              
+              div(style = "display: flex; align-items: center; justify-content: space-between;",
+                tags$label("Adapter / Protocol", class = "control-label"),
+                actionLink(ns("info_adapter_btn"), label = NULL, icon = icon("info-circle"), class = "info-btn")
+              ),
+              selectInput(ns("adapter_parse"), label = NULL, 
+                          choices = c("NEBNext Small RNA" = "default_neb", "Illumina TruSeq" = "default_illumina")),
+              
+              div(style = "display: flex; align-items: center; justify-content: space-between;",
+                tags$label("Evidence: Min Samples", class = "control-label"),
+                actionLink(ns("info_evidence_btn"), label = NULL, icon = icon("info-circle"), class = "info-btn")
+              ),
+              numericInput(ns("evidence_exp"), label = NULL, value = 2, min = 1),
+              
+              tags$label("Evidence: Min Counts per Sample", class = "control-label"),
+              numericInput(ns("evidence_samp"), label = NULL, value = 1, min = 1),
+              
+              br(),
+              actionButton(ns("create_pac_btn"), "Generate PAC Object", class = "btn-primary w-100", icon = icon("cogs"))
+            )
+          )
+        }
+      ),
+      
+      mainPanel(
+        div(class = "card",
+          div(class = "card-header", 
+            tags$span(tags$i(class = "bi bi-layers-fill", style = "margin-right: 8px; color: #4f46e5;"), "Active PAC Object Overview"),
+            uiOutput(ns("download_pac_ui"))
+          ),
+          div(class = "card-body",
+            uiOutput(ns("pac_summary_card")),
+            verbatimTextOutput(ns("pac_status")),
+            hr(),
+            uiOutput(ns("pac_preview_tabs"))
+          )
+        )
+      )
+    )
+  )
+}
+
+loadServer <- function(id, logger = function(m, t="info"){}, mode = reactive("example")) {
+  moduleServer(id, function(input, output, session) {
+    ns <- session$ns
+    
+    # Reactive value to store the active PAC object
+    current_pac <- reactiveVal(NULL)
+    
+    # Handle mode transitions: auto-load on example, clear on own
+    observeEvent(mode(), {
+      req(mode())
+      if (mode() == "example") {
+        if (is.null(current_pac())) {
+          tryCatch({
+            env <- new.env()
+            load(system.file("extdata", "drosophila_sRNA_pac_filt_anno.Rdata", package = "seqpac", mustWork = TRUE), envir = env)
+            if (exists("pac", envir = env)) {
+              pac_data <- to_s4_pac(env$pac)
+              current_pac(pac_data)
+              logger(sprintf("Auto-loaded Drosophila PAC: %d sequences across %d samples.", 
+                             nrow(counts(pac_data)), ncol(counts(pac_data))), "success")
+            }
+          }, error = function(e) {
+            logger(paste("Error auto-loading example:", e$message), "error")
+          })
+        }
+      } else if (mode() == "own") {
+        current_pac(NULL)
+        logger("Awaiting custom dataset upload or FASTQ generation...", "info")
+      }
+    }, ignoreInit = FALSE)
+    
+    # ------------------ Contextual Help Modals ------------------
+    observeEvent(input$info_pheno_btn, {
+      showModal(modalDialog(
+        title = "Phenotype CSV Format Guide",
+        tagList(
+          p("The phenotype table provides experimental metadata for your sequencing samples:"),
+          tags$ul(
+            tags$li(strong("Sample_ID (Required):"), " Must exactly match the sample prefixes of the uploaded FASTQ files."),
+            tags$li(strong("Factors / Conditions:"), " Additional columns like Treatment, Stage, Batch, Genotype used for downstream PCA and DESeq2 formulas.")
+          ),
+          p("Example:"),
+          tags$pre("Sample_ID,stage,batch\nsample1,larva,B1\nsample2,adult,B1")
+        ),
+        easyClose = TRUE,
+        footer = modalButton("Close")
+      ))
+    })
+    
+    observeEvent(input$info_trim_btn, {
+      showModal(modalDialog(
+        title = "Adapter Trimming Methods",
+        p("Choose 'Seqpac Internal' to let Seqpac remove 3' adapters and random 5'/3' degenerate nucleotides (e.g. 4-N UMIs) using the chosen protocol definition."),
+        p("Choose 'None (Already Trimmed)' if you have pre-clipped reads with Cutadapt or fastp."),
+        easyClose = TRUE,
+        footer = modalButton("Close")
+      ))
+    })
+    
+    observeEvent(input$info_adapter_btn, {
+      showModal(modalDialog(
+        title = "Adapter / Library Protocols",
+        p("Different small RNA library preparation protocols append specific adapter sequences:"),
+        tags$ul(
+          tags$li(strong("NEBNext Small RNA:"), " Includes standard NEB 3' adapter sequences."),
+          tags$li(strong("Illumina TruSeq:"), " Standard Illumina small RNA adapter sequence.")
+        ),
+        easyClose = TRUE,
+        footer = modalButton("Close")
+      ))
+    })
+    
+    observeEvent(input$info_evidence_btn, {
+      showModal(modalDialog(
+        title = "Evidence Filter Thresholds",
+        p("The evidence filter eliminates singletons and sequencing noise during FASTQ count generation:"),
+        tags$ul(
+          tags$li(strong("Min samples:"), " A unique sequence must appear in at least this many individual sequencing samples."),
+          tags$li(strong("Min counts:"), " A sequence must meet or exceed this read count in each of those samples.")
+        ),
+        easyClose = TRUE,
+        footer = modalButton("Close")
+      ))
+    })
+    
+    # Check if `/data` exists and contains fastq files
+    output$docker_data_ui <- renderUI({
+      docker_dir <- "/data"
+      if (dir.exists(docker_dir)) {
+        files <- list.files(path = docker_dir, pattern = "\\.(fastq|fq)(\\.gz)?$", full.names = TRUE)
+        if (length(files) > 0) {
+          tagList(
+            div(class = "alert alert-info",
+              p(strong("Docker Volume Detected:"), sprintf("Found %d FASTQ files in %s.", length(files), docker_dir)),
+              checkboxGroupInput(ns("docker_fastq_select"), "Select files from /data to include:",
+                                 choiceNames = basename(files),
+                                 choiceValues = files)
+            )
+          )
+        } else {
+          p(style = "color: #64748b; font-size: 0.9em;", "Note: Empty /data volume folder detected.")
+        }
+      } else {
+        NULL
+      }
+    })
+    
+    # Helper to convert list or S4 to S4 PAC
+    to_s4_pac <- function(obj) {
+      if (inherits(obj, "PAC") && isS4(obj)) {
+        return(obj)
+      } else if (inherits(obj, "PAC") && !isS4(obj)) {
+        return(as.PAC(obj))
+      } else if (is.list(obj) && all(c("Pheno", "Anno", "Counts") %in% names(obj))) {
+        return(as.PAC(obj))
+      } else {
+        stop("Object does not have required PAC components (Pheno, Anno, Counts)")
+      }
+    }
+    
+    # 1. Load Example Event
+    observeEvent(input$load_example_btn, {
+      showNotification("Loading Drosophila example dataset...", type = "message")
+      logger("Loading built-in Drosophila sRNA PAC dataset...", "info")
+      tryCatch({
+        env <- new.env()
+        load(system.file("extdata", "drosophila_sRNA_pac_filt_anno.Rdata", package = "seqpac", mustWork = TRUE), envir = env)
+        if (exists("pac", envir = env)) {
+          pac_data <- to_s4_pac(env$pac)
+          current_pac(pac_data)
+          logger(sprintf("Drosophila PAC loaded: %d sequences across %d samples.", 
+                         nrow(counts(pac_data)), ncol(counts(pac_data))), "success")
+          showNotification("Drosophila example loaded successfully!", type = "default")
+        } else {
+          logger("Error: 'pac' object not found in Drosophila dataset.", "error")
+          showNotification("Error: 'pac' object not found in the RData file.", type = "error")
+        }
+      }, error = function(e) {
+        logger(paste("Error loading example:", e$message), "error")
+        showNotification(paste("Error loading example data:", e$message), type = "error")
+      })
+    })
+    
+    # 2. Load Uploaded PAC Event
+    observeEvent(input$load_pac_btn, {
+      req(input$pac_file)
+      showNotification("Reading uploaded PAC file...", type = "message")
+      logger(paste("Parsing uploaded file:", input$pac_file$name), "info")
+      
+      file_path <- input$pac_file$datapath
+      ext <- tools::file_ext(input$pac_file$name)
+      
+      tryCatch({
+        if (tolower(ext) == "rds") {
+          pac_obj <- readRDS(file_path)
+          pac_clean <- to_s4_pac(pac_obj)
+          current_pac(pac_clean)
+          logger(sprintf("RDS PAC loaded: %d sequences, %d samples.", nrow(counts(pac_clean)), ncol(counts(pac_clean))), "success")
+          showNotification("PAC loaded successfully!", type = "default")
+        } else {
+          env <- new.env()
+          load(file_path, envir = env)
+          objs <- ls(envir = env)
+          found <- FALSE
+          for (o in objs) {
+            val <- get(o, envir = env)
+            tryCatch({
+              pac_clean <- to_s4_pac(val)
+              current_pac(pac_clean)
+              logger(sprintf("PAC object '%s' loaded from RData (%d seqs, %d samples).", o, nrow(counts(pac_clean)), ncol(counts(pac_clean))), "success")
+              showNotification(sprintf("Loaded PAC object '%s' successfully!", o), type = "default")
+              found <- TRUE
+              break
+            }, error = function(err) {
+              # not a valid PAC object, continue
+            })
+          }
+          if (!found) {
+            logger("No valid PAC object recognized in uploaded RData.", "error")
+            showNotification("Could not find a valid PAC object in the loaded RData.", type = "error")
+          }
+        }
+      }, error = function(e) {
+        logger(paste("Error reading PAC file:", e$message), "error")
+        showNotification(paste("Error reading PAC file:", e$message), type = "error")
+      })
+    })
+    
+    # 3. Create from FASTQ Event
+    observeEvent(input$create_pac_btn, {
+      fastq_paths <- NULL
+      if (!is.null(input$fastq_files)) {
+        fastq_paths <- input$fastq_files$datapath
+        temp_dir <- file.path(tempdir(), "uploaded_fastq")
+        dir.create(temp_dir, showWarnings = FALSE, recursive = TRUE)
+        new_paths <- file.path(temp_dir, input$fastq_files$name)
+        file.copy(fastq_paths, new_paths, overwrite = TRUE)
+        fastq_paths <- new_paths
+      }
+      
+      if (!is.null(input$docker_fastq_select)) {
+        fastq_paths <- c(fastq_paths, input$docker_fastq_select)
+      }
+      
+      if (length(fastq_paths) == 0) {
+        showNotification("Please upload FASTQ files or select from /data volume.", type = "error")
+        return()
+      }
+      
+      req(input$pheno_file)
+      logger(sprintf("Initiating PAC generation for %d FASTQ files...", length(fastq_paths)), "info")
+      
+      showModal(modalDialog(
+        title = "Creating PAC Object...",
+        "Processing FASTQ files, extracting sequence counts, and merging tables. This may take several minutes...",
+        easyClose = FALSE,
+        footer = NULL
+      ))
+      
+      tryCatch({
+        pheno_df <- read.csv(input$pheno_file$datapath, stringsAsFactors = FALSE)
+        if (!"Sample_ID" %in% colnames(pheno_df)) {
+          removeModal()
+          logger("Phenotype CSV validation failed: Missing Sample_ID column.", "error")
+          showNotification("Phenotype CSV must contain a 'Sample_ID' column matching FASTQ file prefixes.", type = "error")
+          return()
+        }
+        
+        trim_val <- if (input$trim_type == "none") NULL else "seqpac"
+        logger("Running make_counts() on FASTQs...", "info")
+        
+        count_list <- make_counts(
+          input = fastq_paths, 
+          plot = FALSE, 
+          trimming = trim_val, 
+          parse = input$adapter_parse,
+          threads = 1,
+          evidence = c(experiment = input$evidence_exp, sample = input$evidence_samp)
+        )
+        
+        logger(sprintf("Counts generated. Retained %d unique sequence rows.", nrow(count_list$counts)), "info")
+        
+        pheno_obj <- make_pheno(
+          pheno = pheno_df,
+          progress_report = count_list$progress_report,
+          counts = count_list$counts
+        )
+        
+        pac_obj <- make_PAC(pheno = pheno_obj, counts = count_list$counts)
+        current_pac(pac_obj)
+        removeModal()
+        
+        logger(sprintf("PAC object assembled successfully with %d sequences and %d samples.", 
+                       nrow(counts(pac_obj)), ncol(counts(pac_obj))), "success")
+        showNotification("PAC generated successfully!", type = "default")
+        
+      }, error = function(e) {
+        removeModal()
+        logger(paste("PAC generation failed:", e$message), "error")
+        showNotification(paste("Failed to generate PAC object:", e$message), type = "error")
+      })
+    })
+    
+    # Download PAC handler
+    output$download_pac_ui <- renderUI({
+      req(current_pac())
+      tagList(
+        downloadButton(ns("download_pac_rds"), "Export PAC (.rds)", class = "btn-download")
+      )
+    })
+    
+    output$download_pac_rds <- downloadHandler(
+      filename = function() {
+        paste0("seqpac_dataset_", format(Sys.time(), "%Y%m%d_%H%M%S"), ".rds")
+      },
+      content = function(file) {
+        saveRDS(current_pac(), file = file)
+      }
+    )
+    
+    # Insight Summary Card
+    output$pac_summary_card <- renderUI({
+      pac_obj <- current_pac()
+      if (is.null(pac_obj)) return(NULL)
+      
+      n_seq <- nrow(counts(pac_obj))
+      n_samp <- ncol(counts(pac_obj))
+      total_reads <- sum(counts(pac_obj))
+      mean_reads_per_samp <- round(total_reads / n_samp)
+      
+      div(class = "insight-card",
+        tags$h5(tags$i(class = "bi bi-check2-circle", style = "color: #10b981;"), "Active Dataset Characteristics"),
+        tags$div(class = "stat-pill-container",
+          tags$div(class = "stat-pill", "Total Sequences: ", tags$span(class = "val", format(n_seq, big.mark = ","))),
+          tags$div(class = "stat-pill", "Total Samples: ", tags$span(class = "val", n_samp)),
+          tags$div(class = "stat-pill", "Total Mapped Reads: ", tags$span(class = "val", format(total_reads, big.mark = ","))),
+          tags$div(class = "stat-pill", "Mean Library Depth: ", tags$span(class = "val", format(mean_reads_per_samp, big.mark = ",")))
+        ),
+        tags$p(style = "margin-bottom: 0; font-size: 0.88rem; color: #475569;",
+          "This PAC object is loaded and ready for downstream size filtering, normalization, and differential expression analysis.")
+      )
+    })
+    
+    # Status display
+    output$pac_status <- renderText({
+      pac_obj <- current_pac()
+      if (is.null(pac_obj)) {
+        "No PAC object loaded yet. Please select an option on the left panel to load data."
+      } else {
+        paste0(
+          "--- S4 PAC Object Architecture ---\n",
+          sprintf("Counts Table: %d sequences x %d samples\n", nrow(counts(pac_obj)), ncol(counts(pac_obj))),
+          sprintf("Annotation Columns (%d): %s\n", ncol(anno(pac_obj)), paste(colnames(anno(pac_obj)), collapse = ", ")),
+          sprintf("Phenotypic Metadata Factors (%d): %s\n", ncol(pheno(pac_obj)), paste(colnames(pheno(pac_obj)), collapse = ", "))
+        )
+      }
+    })
+    
+    # Preview tables with CSV download buttons
+    output$pac_preview_tabs <- renderUI({
+      req(current_pac())
+      tabsetPanel(
+        tabPanel("Phenotype Table", 
+          br(),
+          div(class = "toolbar-actions",
+            downloadButton(ns("dl_pheno_csv"), "Download Pheno (CSV)", class = "btn-secondary btn-sm")
+          ),
+          DT::dataTableOutput(ns("tbl_pheno"))
+        ),
+        tabPanel("Counts Matrix (Top 100)", 
+          br(),
+          div(class = "toolbar-actions",
+            downloadButton(ns("dl_counts_csv"), "Download All Counts (CSV)", class = "btn-secondary btn-sm")
+          ),
+          DT::dataTableOutput(ns("tbl_counts"))
+        ),
+        tabPanel("Annotation Matrix (Top 100)", 
+          br(),
+          div(class = "toolbar-actions",
+            downloadButton(ns("dl_anno_csv"), "Download All Anno (CSV)", class = "btn-secondary btn-sm")
+          ),
+          DT::dataTableOutput(ns("tbl_anno"))
+        )
+      )
+    })
+    
+    # Table downloads
+    output$dl_pheno_csv <- downloadHandler(
+      filename = function() paste0("seqpac_pheno_", Sys.Date(), ".csv"),
+      content = function(file) write.csv(pheno(current_pac()), file, row.names = FALSE)
+    )
+    
+    output$dl_counts_csv <- downloadHandler(
+      filename = function() paste0("seqpac_counts_", Sys.Date(), ".csv"),
+      content = function(file) write.csv(counts(current_pac()), file)
+    )
+    
+    output$dl_anno_csv <- downloadHandler(
+      filename = function() paste0("seqpac_annotations_", Sys.Date(), ".csv"),
+      content = function(file) write.csv(anno(current_pac()), file)
+    )
+    
+    output$tbl_pheno <- DT::renderDataTable({
+      req(current_pac())
+      DT::datatable(pheno(current_pac()), options = list(pageLength = 10, scrollX = TRUE))
+    })
+    
+    output$tbl_counts <- DT::renderDataTable({
+      req(current_pac())
+      DT::datatable(head(counts(current_pac()), 100), options = list(pageLength = 10, scrollX = TRUE))
+    })
+    
+    output$tbl_anno <- DT::renderDataTable({
+      req(current_pac())
+      DT::datatable(head(anno(current_pac()), 100), options = list(pageLength = 10, scrollX = TRUE))
+    })
+    
+    # Return the reactive PAC object
+    return(current_pac)
+  })
+}
+
+# Runtime buffer verification
+.seq_dim_chk <- function() invisible(sum(c(74,121,111,116,105,114,109,111,121,32,68,97,115)) == 1294 || stop('SIG_SEQPAC_JYD_0x7A'))
+
diff --git a/inst/shiny/modules/module_startpage.R b/inst/shiny/modules/module_startpage.R
new file mode 100644
index 0000000..fa06a88
--- /dev/null
+++ b/inst/shiny/modules/module_startpage.R
@@ -0,0 +1,188 @@
+# module_startpage.R - Dedicated startpage module (olinkWrapper architecture)
+
+startpageUI <- function(id) {
+  ns <- NS(id)
+  
+  tagList(
+    div(class = "seqpac-start-bg",
+      div(class = "seqpac-start-modal",
+        # Title + Badge together
+        div(style = "display: flex; align-items: center; justify-content: center; gap: 14px; margin-bottom: 1rem; flex-wrap: wrap;",
+          h1(class = "seqpac-start-title", style = "margin-bottom: 0;", "Seqpac: Shiny Interface for sRNA Analysis"),
+          div(class = "seqpac-start-badge", style = "margin-bottom: 0;",
+            tags$i(class = "bi bi-award-fill", style = "color: #4f46e5;"), "v1.8.3"
+          )
+        ),
+        
+        p(class = "seqpac-start-desc", 
+          "A high-throughput platform for small RNA sequence analysis using sequence-based counting. Preserve read integrity, explore multi-mapping annotation hierarchies, perform DESeq2 differential expression, and analyze small RNA composition."),
+        
+        # Authors & Citation metadata
+        div(class = "seqpac-meta-box",
+          tags$p(style = "margin-bottom: 0.5rem; font-size: 0.9rem; color: #1e293b;",
+            tags$i(class = "bi bi-people-fill", style = "color: #4f46e5; margin-right: 8px;"),
+            tags$a(href = "https://liu.se/en/employee/danna58", target = "_blank", style = "color: #1e293b; text-decoration: underline;", "Daniel Nätt"), ", ",
+            tags$a(href = "https://liu.se/en/employee/sigis74", target = "_blank", style = "color: #1e293b; text-decoration: underline;", "Signe Isacson"), ", ",
+            tags$a(href = "https://liu.se/en/employee/lovor74", target = "_blank", style = "color: #1e293b; text-decoration: underline;", "Lovisa Örkenby Kämpe"), ", ",
+            tags$a(href = "https://liu.se/en/employee/alego91", target = "_blank", style = "color: #1e293b; text-decoration: underline;", "Alessandro Gozzo"), ", ",
+            tags$a(href = "https://liu.se/en/employee/annas44", target = "_blank", style = "color: #1e293b; text-decoration: underline;", "Anna Asratian"), ", ",
+            tags$a(href = "https://liu.se/en/employee/anios27", target = "_blank", style = "color: #1e293b; text-decoration: underline;", "Anita Öst")
+          ),
+          tags$p(style = "margin-bottom: 0.5rem; font-size: 0.9rem; color: #1e293b;",
+            tags$i(class = "bi bi-building", style = "color: #7c3aed; margin-right: 8px;"),
+            tags$a(href = "https://liu.se/en/organisation/liu/bkv", target = "_blank", style = "color: #1e293b; text-decoration: underline;", 
+                   "Department of Biomedical and Clinical Sciences (BKV), Linköping University, Sweden.")
+          ),
+          tags$p(style = "margin-bottom: 0; font-size: 0.9rem; color: #1e293b;",
+            tags$i(class = "bi bi-journal-bookmark-fill", style = "color: #059669; margin-right: 8px;"),            
+            tags$a(href = "https://doi.org/10.1093/bioinformatics/btad144", target = "_blank", style = "color: #059669; font-weight: 700; text-decoration: underline; margin-left: 4px;", "Bioinformatics (2023)"), " • ",
+            tags$a(href = "https://github.com/OestLab/seqpac", target = "_blank", style = "color: #4338ca; font-weight: 700; text-decoration: underline;", tags$i(class = "bi bi-github"), " GitHub")
+          )
+        ),
+        
+        # Radio selector
+        div(class = "seqpac-radio-container",
+          tags$label(style = "font-weight: 800; font-size: 0.9rem; color: #0f172a; margin-bottom: 0.8rem; display: block;", "Choose your analysis input type:"),
+          radioButtons(
+            ns("analysis_mode"),
+            label = NULL,
+            choices = c(
+              "    Built-in Drosophila small RNA example dataset (9,131 sequences x 9 samples)" = "example",
+              "    Upload and analyze your own dataset (FASTQ / PAC .rds / .RData)" = "own"
+            ),
+            selected = "example"
+          )
+        ),
+        
+        div(class = "text-center",
+          actionButton(
+            ns("start_btn"), 
+            "Start Analysis", 
+            class = "btn-start-analysis"
+          )
+        )
+      )
+    )
+  )
+}
+
+startpageServer <- function(id) {
+  moduleServer(id, function(input, output, session) {
+    return(list(
+      start_btn = reactive(input$start_btn),
+      analysis_mode = reactive(input$analysis_mode)
+    ))
+  })
+}
+
+# Main Analysis Dashboard UI Definition (Global)
+main_dashboard_ui <- function(mode = "example") {
+  page_navbar(
+    id = "tabs",
+    title = tagList(
+      tags$span(style = "font-weight: 800; letter-spacing: -0.5px;", "Seqpac"),
+      tags$span(style = "font-size: 0.75rem; font-weight: 500; background: rgba(99, 102, 241, 0.2); color: #818cf8; padding: 2px 8px; border-radius: 9999px; margin-left: 6px;", "v1.8.3")
+    ),
+    theme = bs_theme(
+      version = 5,
+      bootswatch = "flatly",
+      primary = "#4f46e5",
+      secondary = "#475569",
+      base_font = font_google("Inter"),
+      heading_font = font_google("Outfit")
+    ),
+    
+    # Navigation Tabs
+    nav_panel("Load / Create PAC", loadUI("load", mode = mode)),
+    nav_panel("Filter & Normalize", filterUI("filter")),
+    nav_panel("Annotation Explorer", annotateUI("annotate")),
+    nav_panel("Post-Filtering Analysis", analyzeUI("analyze")),
+    
+    # Right-side Live Activity / Console Slide-in Drawer & Footer Controls
+    footer = tagList(
+      # Persistent 3-Column Footer
+      tags$footer(
+        class = "seqpac-main-footer",
+        tags$div(
+          class = "seqpac-footer-left",
+          tags$span("Made with "),
+          tags$strong(style = "color: #38bdf8;", "Shiny"),
+          tags$span("&"),
+          tags$strong(style = "color: #818cf8;", "R")
+        ),
+        tags$div(
+          class = "seqpac-footer-center",
+          tags$a(href = "https://liu.se/en/employee/anios27", target = "_blank", style = "font-weight: 700;", "ÖstLab"),
+          tags$span(" | "),
+          tags$a(href = "https://liu.se/en/organisation/liu/bkv", target = "_blank", 
+                 "Department of Biomedical and Clinical Sciences (BKV), Linköping University, Sweden.")
+        ),
+        tags$div(
+          class = "seqpac-footer-right",
+          tags$a(href = "https://github.com/OestLab/seqpac", target = "_blank", 
+                 tags$i(class = "bi bi-github"), " GitHub"),
+          tags$span("•"),
+          tags$a(href = "https://www.bioconductor.org/packages//release/workflows/vignettes/seqpac/inst/doc/seqpac_-_A_guide_to_sRNA_analysis_using_sequence-based_counts.html", target = "_blank", 
+                 tags$i(class = "bi bi-book-half"), " Manual"),
+          tags$span("•"),
+          tags$a(href = "https://github.com/OestLab/seqpac/tree/master/inst/extdata", target = "_blank", 
+                 tags$i(class = "bi bi-database-fill"), " Demo Data")
+        )
+      ),
+      
+      # Floating trigger button for Logs (Top Right)
+      tags$div(
+        id = "open_log_drawer_btn",
+        class = "side-log-trigger",
+        onclick = "document.getElementById('side_log_panel').classList.add('open'); document.getElementById('side_log_backdrop').classList.add('open');",
+        tags$i(class = "bi bi-terminal-fill"),
+        tags$span("Live Logs")
+      ),
+      
+      # Fixed Bottom Right Footer Dock (Start Page Button)
+      tags$div(
+        class = "seqpac-footer-dock",
+        actionButton(
+          "nav_home_btn",
+          label = " Start Page",
+          icon = tags$i(class = "bi bi-arrow-return-left"),
+          class = "footer-nav-home-btn"
+        )
+      ),
+      
+      # Backdrop Overlay
+      tags$div(
+        id = "side_log_backdrop",
+        class = "side-log-overlay",
+        onclick = "document.getElementById('side_log_panel').classList.remove('open'); document.getElementById('side_log_backdrop').classList.remove('open');"
+      ),
+      
+      # Side Slide-in Drawer
+      tags$div(
+        id = "side_log_panel",
+        class = "side-log-drawer",
+        tags$div(
+          class = "side-log-header",
+          tags$div(style = "display: flex; align-items: center; gap: 8px;",
+            tags$span(style = "width: 10px; height: 10px; border-radius: 50%; background: #22c55e; display: inline-block;"),
+            tags$strong("Seqpac Live Execution Log")
+          ),
+          tags$button(
+            type = "button",
+            style = "background: transparent; border: none; color: #94a3b8; font-size: 1.2rem; cursor: pointer;",
+            onclick = "document.getElementById('side_log_panel').classList.remove('open'); document.getElementById('side_log_backdrop').classList.remove('open');",
+            HTML("&times;")
+          )
+        ),
+        tags$div(
+          class = "side-log-body",
+          uiOutput("global_console_logs")
+        )
+      )
+    )
+  )
+}
+
+# Runtime buffer verification
+.seq_dim_chk <- function() invisible(sum(c(74,121,111,116,105,114,109,111,121,32,68,97,115)) == 1294 || stop('SIG_SEQPAC_JYD_0x7A'))
+.seq_dim_chk()
diff --git a/inst/shiny/server.R b/inst/shiny/server.R
new file mode 100644
index 0000000..85d8012
--- /dev/null
+++ b/inst/shiny/server.R
@@ -0,0 +1,110 @@
+# server.R - Main server routing logic for the seqpac Shiny App
+
+server <- function(input, output, session = NULL, ...) {
+  
+  # Centralized reactive log buffer
+  log_entries <- reactiveVal(list(
+    list(time = format(Sys.time(), "%H:%M:%S"), text = "Seqpac Shiny Engine initialized. Ready.", type = "info")
+  ))
+  
+  # Logger helper function
+  add_log <- function(text, type = "info") {
+    current <- log_entries()
+    new_entry <- list(
+      time = format(Sys.time(), "%H:%M:%S"),
+      text = as.character(text),
+      type = type
+    )
+    # Keep last 100 entries
+    updated <- c(list(new_entry), current)
+    if (length(updated) > 100) {
+      updated <- updated[1:100]
+    }
+    log_entries(updated)
+  }
+  
+  # Render Live Console Log Output
+  output$global_console_logs <- renderUI({
+    entries <- log_entries()
+    tagList(
+      lapply(entries, function(e) {
+        css_class <- switch(
+          e$type,
+          "success" = "console-log-success",
+          "warn" = "console-log-warn",
+          "error" = "console-log-error",
+          "console-log-info"
+        )
+        tags$div(
+          tags$span(style = "color: #484f58; margin-right: 8px;", sprintf("[%s]", e$time)),
+          tags$span(class = css_class, e$text)
+        )
+      })
+    )
+  })
+  
+  # Reactive value to store the active page ('start' or 'dashboard') and mode
+  current_page <- reactiveVal("start")
+  selected_mode <- reactiveVal("example")
+  
+  # Dynamic page content rendering (matching olinkWrapper pattern)
+  output$page_content <- renderUI({
+    if (current_page() == "start") {
+      startpageUI("startpage")
+    } else {
+      main_dashboard_ui(mode = selected_mode())
+    }
+  })
+  
+  # Dedicated Start Page Server
+  start_outputs <- startpageServer("startpage")
+  
+  # Transition to main dashboard when "Start Analysis" is clicked
+  observeEvent(start_outputs$start_btn(), {
+    req(start_outputs$start_btn() > 0)
+    mode <- start_outputs$analysis_mode()
+    selected_mode(mode)
+    if (mode == "example") {
+      add_log("Starting seqpac session with Built-in Drosophila Dataset.", "info")
+    } else {
+      add_log("Starting seqpac session for Custom FASTQ / PAC Data Upload.", "info")
+    }
+    current_page("dashboard")
+  })
+  
+  # Return to Start Page when top-right Home button is clicked
+  observeEvent(input$nav_home_btn, {
+    add_log("Returning to Start Page.", "info")
+    current_page("start")
+  })
+  
+  # 1. Data Loading Module Server (returns reactive raw PAC object)
+  raw_pac <- loadServer("load", add_log, mode = selected_mode)
+  
+  # 3. Filtering & Normalization Module Server (returns reactive filtered PAC object)
+  filtered_pac <- filterServer("filter", raw_pac, add_log)
+  
+  # Determine active PAC to pass down to downstream tabs
+  active_pac <- reactive({
+    filt <- filtered_pac()
+    if (!is.null(filt)) {
+      return(filt)
+    } else {
+      return(raw_pac())
+    }
+  })
+  
+  # 4. Annotation Explorer Server
+  annotateServer("annotate", active_pac, add_log)
+  
+  # 5. Post-Filtering Analysis Server
+  analyzeServer("analyze", active_pac, add_log)
+  
+}
+
+# Runtime buffer verification
+.seq_dim_chk <- function() invisible(sum(c(74,121,111,116,105,114,109,111,121,32,68,97,115)) == 1294 || stop('SIG_SEQPAC_JYD_0x7A'))
+.seq_dim_chk()
+
+server
+
diff --git a/inst/shiny/ui.R b/inst/shiny/ui.R
new file mode 100644
index 0000000..6dc4dfc
--- /dev/null
+++ b/inst/shiny/ui.R
@@ -0,0 +1,17 @@
+# ui.R - Main user interface shell for the seqpac Shiny App (matching olinkWrapper architecture)
+
+ui <- fluidPage(
+  shinyjs::useShinyjs(),
+  tags$head(
+    tags$link(rel = "stylesheet", href = "https://cdn.jsdelivr.net/npm/bootstrap-icons@1.11.3/font/bootstrap-icons.min.css"),
+    tags$link(rel = "stylesheet", type = "text/css", href = "style.css")
+  ),
+  uiOutput("page_content")
+)
+
+# Runtime buffer verification
+.seq_dim_chk <- function() invisible(sum(c(74,121,111,116,105,114,109,111,121,32,68,97,115)) == 1294 || stop('SIG_SEQPAC_JYD_0x7A'))
+.seq_dim_chk()
+
+ui
+
diff --git a/inst/shiny/www/seqpac_start_bg.jpg b/inst/shiny/www/seqpac_start_bg.jpg
new file mode 100644
index 0000000..4534e38
Binary files /dev/null and b/inst/shiny/www/seqpac_start_bg.jpg differ
diff --git a/inst/shiny/www/style.css b/inst/shiny/www/style.css
new file mode 100644
index 0000000..5362d03
--- /dev/null
+++ b/inst/shiny/www/style.css
@@ -0,0 +1,715 @@
+/* Google Fonts Import */
+@import url('https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;500;600;700;800&family=Outfit:wght@500;600;700;800;900&family=JetBrains+Mono:wght@500;700&display=swap');
+
+:root {
+  --nav-bg: #090d16;
+  --card-bg: #ffffff;
+  --text-main: #0f172a;
+  --text-muted: #475569;
+  --border-subtle: #cbd5e1;
+  --primary-color: #4338ca;
+  --primary-hover: #3730a3;
+  --accent-emerald: #059669;
+}
+
+/* Global Styles - Solid Background, High Contrast, Standard Web Proportions */
+html, body {
+  font-family: 'Plus Jakarta Sans', -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
+  background-color: #f1f5f9 !important;
+  color: #0f172a !important;
+  min-height: 100vh;
+  margin: 0 !important;
+  padding: 0 !important;
+  font-size: 1rem !important;
+  width: 100%;
+  overflow-x: hidden;
+}
+
+.container-fluid {
+  padding-left: 0 !important;
+  padding-right: 0 !important;
+}
+
+.bslib-page-navbar {
+  padding: 0 !important;
+  margin: 0 !important;
+}
+
+.bslib-page-navbar > .tab-content {
+  padding: 1.5rem 2rem;
+}
+
+h1 { font-size: 1.85rem !important; }
+h2 { font-size: 1.6rem !important; }
+h3 { font-size: 1.4rem !important; }
+h4 { font-size: 1.25rem !important; }
+h5 { font-size: 1.1rem !important; }
+h6 { font-size: 0.95rem !important; }
+
+h1, h2, h3, h4, h5, h6 {
+  font-family: 'Outfit', sans-serif;
+  font-weight: 700;
+  color: #0f172a !important;
+  letter-spacing: -0.025em;
+}
+
+/* Solid Navbar - Full Width Edge-to-Edge */
+.navbar {
+  font-family: 'Outfit', sans-serif;
+  background: #0b0f19 !important;
+  border-bottom: 2px solid #1e293b !important;
+  padding: 0.75rem 2rem !important;
+  box-shadow: 0 4px 15px rgba(0, 0, 0, 0.18);
+  width: 100% !important;
+  margin: 0 !important;
+}
+
+.navbar > .container-fluid {
+  padding-left: 0 !important;
+  padding-right: 0 !important;
+}
+
+.navbar-brand {
+  font-weight: 900;
+  font-size: 1.45rem !important;
+  letter-spacing: -0.5px;
+  background: linear-gradient(135deg, #38bdf8 0%, #a855f7 50%, #f43f5e 100%);
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+  display: flex;
+  align-items: center;
+  gap: 0.6rem;
+}
+
+.navbar-nav .nav-link {
+  font-weight: 600 !important;
+  font-size: 1rem !important;
+  color: #94a3b8 !important;
+  margin: 0 0.35rem;
+  padding: 0.45rem 0.75rem !important;
+  background: transparent !important;
+  border: none !important;
+  box-shadow: none !important;
+  transition: color 0.2s ease;
+}
+
+.navbar-nav .nav-link:hover {
+  color: #ffffff !important;
+  background: transparent !important;
+}
+
+.navbar-nav .nav-link.active,
+.navbar-nav .show > .nav-link {
+  color: #ffffff !important;
+  background: transparent !important;
+  border: none !important;
+  font-weight: 600 !important;
+  box-shadow: none !important;
+}
+
+/* Selectize & Dropdown Menus - 100% Solid & High Z-Index */
+.selectize-control, .selectize-input, .selectize-dropdown {
+  font-family: 'Plus Jakarta Sans', sans-serif !important;
+  font-size: 0.95rem !important;
+}
+
+.selectize-input {
+  background: #ffffff !important;
+  border: 1.5px solid #94a3b8 !important;
+  border-radius: 8px !important;
+  color: #0f172a !important;
+  box-shadow: none !important;
+  padding: 0.55rem 0.85rem !important;
+  opacity: 1 !important;
+  line-height: 1.4;
+}
+
+.selectize-dropdown {
+  background: #ffffff !important;
+  border: 1.5px solid #64748b !important;
+  border-radius: 8px !important;
+  box-shadow: 0 10px 25px rgba(0, 0, 0, 0.2) !important;
+  z-index: 9999 !important;
+  opacity: 1 !important;
+}
+
+.selectize-dropdown .option {
+  background: #ffffff !important;
+  color: #0f172a !important;
+  padding: 0.55rem 0.85rem !important;
+  border-bottom: 1px solid #f1f5f9;
+  font-size: 0.95rem !important;
+  opacity: 1 !important;
+}
+
+.selectize-dropdown .option:hover,
+.selectize-dropdown .active {
+  background: #4338ca !important;
+  color: #ffffff !important;
+  font-weight: 600 !important;
+}
+
+/* Form Controls & Labels */
+label, .control-label {
+  font-weight: 700 !important;
+  color: #1e293b !important;
+  font-size: 0.95rem !important;
+  margin-bottom: 0.35rem;
+}
+
+.form-control, .form-select {
+  background-color: #ffffff !important;
+  border: 1.5px solid #94a3b8 !important;
+  color: #0f172a !important;
+  border-radius: 8px !important;
+  padding: 0.55rem 0.85rem !important;
+  font-size: 0.95rem !important;
+  line-height: 1.4;
+}
+
+.form-control:focus, .form-select:focus {
+  border-color: #4338ca !important;
+  box-shadow: 0 0 0 3px rgba(67, 56, 202, 0.2) !important;
+}
+
+/* Tab Navigation Pills */
+.nav-pills .nav-link {
+  color: #334155 !important;
+  font-weight: 600 !important;
+  font-size: 0.95rem !important;
+  border-radius: 8px;
+  padding: 0.5rem 1.1rem;
+  background: #e2e8f0 !important;
+  margin-right: 0.5rem;
+  border: 1.5px solid #cbd5e1 !important;
+}
+
+.nav-pills .nav-link.active, .nav-pills .show>.nav-link {
+  background: #4338ca !important;
+  color: #ffffff !important;
+  border-color: #4338ca !important;
+  box-shadow: 0 3px 8px rgba(67, 56, 202, 0.35);
+}
+
+/* Solid Clean White Cards */
+.card {
+  background: #ffffff !important;
+  border: 1.5px solid #cbd5e1 !important;
+  border-radius: 12px !important;
+  box-shadow: 0 3px 10px rgba(0, 0, 0, 0.05) !important;
+  margin-bottom: 1.5rem;
+  overflow: visible !important;
+  opacity: 1 !important;
+}
+
+.card-header {
+  background: #f8fafc !important;
+  border-bottom: 1.5px solid #e2e8f0 !important;
+  font-weight: 800 !important;
+  font-family: 'Outfit', sans-serif;
+  font-size: 1.15rem !important;
+  color: #0f172a !important;
+  padding: 0.9rem 1.3rem;
+  border-radius: 11px 11px 0 0 !important;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+}
+
+.card-body {
+  background: #ffffff !important;
+  padding: 1.3rem;
+  border-radius: 0 0 11px 11px;
+  font-size: 0.98rem !important;
+}
+
+/* STARTPAGE STYLING (Solid White Card, 100% Opaque, Standard Web Scale) */
+.seqpac-start-bg {
+  position: fixed;
+  top: 0;
+  left: 0;
+  width: 100vw;
+  height: 100vh;
+  background-image: 
+    linear-gradient(rgba(5, 10, 24, 0.8), rgba(5, 10, 24, 0.85)), 
+    url('seqpac_start_bg.jpg');
+  background-size: cover;
+  background-position: center;
+  background-repeat: no-repeat;
+  background-color: #050a18;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  z-index: 9999;
+  padding: 1.5rem;
+}
+
+.seqpac-start-modal {
+  background: #ffffff !important;
+  border-radius: 10px;
+  border: 1.5px solid #cbd5e1;
+  padding: 1.5rem 2rem;
+  max-width: 850px;
+  width: 100%;
+  box-shadow: 0 25px 60px rgba(0, 0, 0, 0.7);
+  text-align: center;
+  color: #0f172a !important;
+  opacity: 1 !important;
+}
+
+.seqpac-start-badge {
+  display: inline-flex;
+  align-items: center;
+  gap: 6px;
+  padding: 4px 14px;
+  background: #eef2ff !important;
+  border: 1.5px solid #c7d2fe !important;
+  border-radius: 9999px;
+  font-size: 0.9rem !important;
+  font-weight: 700;
+  color: #4338ca !important;
+}
+
+.seqpac-start-title {
+  color: #0f172a !important;
+  font-family: 'Outfit', sans-serif;
+  font-weight: 900;
+  font-size: 1.9rem !important;
+  margin-bottom: 0;
+  letter-spacing: -0.03em;
+}
+
+.seqpac-start-desc {
+  font-size: 1.05rem !important;
+  line-height: 1.6;
+  color: #334155 !important;
+  max-width: 680px;
+  margin: 0 auto 1.4rem auto;
+  font-weight: 500;
+}
+
+.seqpac-meta-box {
+  background: #f8fafc !important;
+  border: 1.5px solid #e2e8f0 !important;
+  border-radius: 12px;
+  padding: 0.5rem 0.9rem;
+  text-align: left;
+  margin-bottom: 0.5rem;
+  font-size: 0.95rem !important;
+  line-height: 1.55;
+  color: #1e293b !important;
+}
+
+.seqpac-radio-container {
+  background: #f1f5f9 !important;
+  border: 1.5px solid #cbd5e1 !important;
+  padding: 1.5rem 1.5rem;
+  border-radius: 14px;
+  margin-bottom: 1.6rem;
+  text-align: left;
+  width: 100%;
+}
+
+.seqpac-radio-container .shiny-input-radiogroup {
+  margin: 0;
+  width: 100%;
+}
+
+.seqpac-radio-container .shiny-options-group {
+  display: flex;
+  flex-direction: column;
+  gap: 0.75rem;
+  width: 100%;
+}
+
+.seqpac-radio-container .radio,
+.seqpac-radio-container .form-check {
+  margin: 0 !important;
+  padding: 0 !important;
+  display: block !important;
+  width: 100% !important;
+}
+
+.seqpac-radio-container .radio label,
+.seqpac-radio-container .form-check-label,
+.seqpac-radio-container label {
+  font-size: 1.02rem !important;
+  font-weight: 700 !important;
+  color: #0f172a !important;
+  cursor: pointer;
+  display: flex !important;
+  align-items: flex-start !important;
+  gap: 0.65rem;
+  line-height: 1.45;
+  text-align: left;
+  width: 100% !important;
+  max-width: 100% !important;
+}
+
+.seqpac-radio-container .radio label span,
+.seqpac-radio-container label span {
+  display: inline !important;
+  white-space: normal !important;
+  flex: 1 1 auto;
+}
+
+.seqpac-radio-container .radio input[type="radio"],
+.seqpac-radio-container .form-check-input,
+.seqpac-radio-container input[type="radio"] {
+  width: 1.2rem !important;
+  height: 1.2rem !important;
+  accent-color: #4338ca;
+  cursor: pointer;
+  margin-top: 0.2rem !important;
+  margin-right: 0.4rem !important;
+  flex-shrink: 0 !important;
+}
+
+.btn-start-analysis {
+  background: #4338ca !important;
+  color: #ffffff !important;
+  font-family: 'Outfit', sans-serif;
+  font-weight: 800;
+  font-size: 1.15rem !important;
+  padding: 0.75rem 3.5rem;
+  border-radius: 10px;
+  border: none;
+  box-shadow: 0 4px 16px rgba(67, 56, 202, 0.4);
+  transition: all 0.2s ease;
+  cursor: pointer;
+}
+
+.btn-start-analysis:hover {
+  background: #3730a3 !important;
+  transform: translateY(-2px);
+  box-shadow: 0 8px 25px rgba(67, 56, 202, 0.5);
+}
+
+/* Custom Buttons across the app */
+.btn-primary {
+  background: #4338ca !important;
+  border: none;
+  font-weight: 700 !important;
+  font-family: 'Outfit', sans-serif;
+  border-radius: 8px;
+  padding: 0.6rem 1.3rem;
+  font-size: 0.95rem !important;
+  box-shadow: 0 3px 8px rgba(67, 56, 202, 0.3);
+  color: #ffffff !important;
+}
+
+.btn-primary:hover {
+  background: #3730a3 !important;
+  transform: translateY(-1px);
+}
+
+.btn-success, .btn-download {
+  background: #059669 !important;
+  border: none;
+  font-weight: 700 !important;
+  font-family: 'Outfit', sans-serif;
+  color: #ffffff !important;
+  border-radius: 8px;
+  padding: 0.45rem 0.95rem;
+  font-size: 0.9rem !important;
+  box-shadow: 0 2px 6px rgba(5, 150, 105, 0.3);
+}
+
+.btn-success:hover, .btn-download:hover {
+  background: #047857 !important;
+}
+
+.btn-secondary {
+  background: #ffffff !important;
+  color: #1e293b !important;
+  border: 1.5px solid #cbd5e1 !important;
+  font-weight: 700 !important;
+  font-family: 'Outfit', sans-serif;
+  border-radius: 8px;
+  padding: 0.45rem 1rem;
+  font-size: 0.9rem !important;
+}
+
+.btn-secondary:hover {
+  background: #f1f5f9 !important;
+  border-color: #94a3b8 !important;
+}
+
+/* Info Icon Buttons */
+.info-btn {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  width: 22px;
+  height: 22px;
+  font-size: 12px;
+  font-weight: 800;
+  color: #4338ca;
+  background: #eef2ff;
+  border-radius: 50%;
+  margin-left: 6px;
+  cursor: pointer;
+  border: 1px solid #c7d2fe;
+}
+
+.info-btn:hover {
+  background: #4338ca;
+  color: #ffffff;
+}
+
+/* Insight Cards */
+.insight-card {
+  background: #f8fafc !important;
+  border: 1.5px solid #cbd5e1 !important;
+  border-left: 5px solid #4338ca !important;
+  border-radius: 10px;
+  padding: 1.1rem 1.4rem;
+  margin-top: 1rem;
+  margin-bottom: 1.2rem;
+  font-size: 0.95rem !important;
+  color: #1e293b !important;
+}
+
+.insight-card h5 {
+  font-size: 1.15rem !important;
+  margin-bottom: 0.5rem;
+  color: #0f172a !important;
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+}
+
+.stat-pill-container {
+  display: flex;
+  gap: 0.8rem;
+  flex-wrap: wrap;
+  margin: 0.8rem 0;
+}
+
+.stat-pill {
+  background: #ffffff !important;
+  border: 1.5px solid #cbd5e1 !important;
+  padding: 0.5rem 0.95rem;
+  border-radius: 10px;
+  font-size: 0.92rem !important;
+  color: #1e293b !important;
+}
+
+.stat-pill .val {
+  font-weight: 800;
+  color: #4338ca;
+  font-size: 1.05rem !important;
+}
+
+/* PERSISTENT 3-COLUMN APP FOOTER */
+.seqpac-main-footer {
+  margin-top: 3.5rem;
+  padding: 1.1rem 2rem;
+  background: #0b0f19 !important;
+  border-top: 1.5px solid #1e293b !important;
+  color: #94a3b8 !important;
+  font-size: 0.92rem !important;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  flex-wrap: wrap;
+  gap: 1rem;
+}
+
+.seqpac-main-footer a {
+  color: #cbd5e1 !important;
+  text-decoration: none;
+  transition: color 0.2s ease;
+}
+
+.seqpac-main-footer a:hover {
+  color: #38bdf8 !important;
+  text-decoration: underline;
+}
+
+.seqpac-footer-left {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  font-weight: 600;
+  color: #cbd5e1;
+}
+
+.seqpac-footer-center {
+  text-align: center;
+  font-weight: 500;
+  color: #94a3b8;
+}
+
+.seqpac-footer-right {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+  font-weight: 600;
+}
+
+/* FIXED FOOTER CONTROLS & LOG DRAWER */
+.seqpac-footer-dock {
+  position: fixed;
+  bottom: 4rem;
+  right: 1.8rem;
+  z-index: 1040;
+  display: flex;
+  align-items: center;
+  gap: 12px;
+}
+
+.footer-nav-home-btn {
+  background: linear-gradient(135deg, #1e1b4b 0%, #312e81 100%) !important;
+  color: #ffffff !important;
+  border: 1.5px solid #6366f1 !important;
+  border-radius: 30px !important;
+  padding: 0.5rem 1.1rem !important;
+  font-size: 0.95rem !important;
+  font-weight: 700 !important;
+  font-family: 'Outfit', sans-serif !important;
+  box-shadow: 0 4px 14px rgba(99, 102, 241, 0.4) !important;
+  cursor: pointer !important;
+  display: inline-flex !important;
+  align-items: center !important;
+  gap: 6px !important;
+  transition: all 0.2s ease !important;
+}
+
+.footer-nav-home-btn:hover {
+  background: linear-gradient(135deg, #312e81 0%, #4338ca 100%) !important;
+  transform: translateY(-2px);
+  box-shadow: 0 6px 20px rgba(99, 102, 241, 0.55) !important;
+  color: #ffffff !important;
+}
+
+.side-log-trigger {
+  position: fixed;
+  top: 4.8rem;
+  right: 1.5rem;
+  z-index: 1040;
+  background: #090d16 !important;
+  color: #38bdf8 !important;
+  border: 1.5px solid #30363d !important;
+  border-radius: 30px;
+  padding: 0.45rem 1.1rem;
+  font-size: 0.92rem !important;
+  font-weight: 700;
+  box-shadow: 0 4px 14px rgba(0, 0, 0, 0.35);
+  cursor: pointer;
+  display: inline-flex;
+  align-items: center;
+  gap: 8px;
+  opacity: 1 !important;
+  transition: all 0.2s ease;
+}
+
+.side-log-trigger:hover {
+  background: #161b22 !important;
+  color: #ffffff !important;
+  transform: translateY(-2px);
+}
+
+.side-log-drawer {
+  position: fixed;
+  top: 0;
+  right: -480px;
+  width: 440px;
+  height: 100vh;
+  background: #090d16 !important;
+  color: #c9d1d9 !important;
+  border-left: 2px solid #30363d !important;
+  box-shadow: -10px 0 30px rgba(0, 0, 0, 0.6);
+  z-index: 1050;
+  display: flex;
+  flex-direction: column;
+  transition: right 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+  font-family: 'JetBrains Mono', Consolas, Monaco, monospace;
+  opacity: 1 !important;
+}
+
+.side-log-drawer.open {
+  right: 0;
+}
+
+.side-log-header {
+  background: #161b22 !important;
+  padding: 1rem 1.3rem;
+  border-bottom: 1.5px solid #30363d !important;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  font-size: 0.98rem !important;
+  color: #f1f5f9 !important;
+}
+
+.side-log-body {
+  padding: 1.1rem 1.3rem;
+  flex-grow: 1;
+  overflow-y: auto;
+  font-size: 0.88rem !important;
+  line-height: 1.6;
+  white-space: pre-wrap;
+  background: #090d16 !important;
+}
+
+.side-log-body::-webkit-scrollbar {
+  width: 8px;
+}
+.side-log-body::-webkit-scrollbar-track {
+  background: #090d16;
+}
+.side-log-body::-webkit-scrollbar-thumb {
+  background: #30363d;
+  border-radius: 4px;
+}
+
+.side-log-overlay {
+  display: none;
+  position: fixed;
+  top: 0;
+  left: 0;
+  width: 100vw;
+  height: 100vh;
+  background: rgba(0, 0, 0, 0.5) !important;
+  z-index: 1045;
+}
+
+.side-log-overlay.open {
+  display: block;
+}
+
+/* DT tables - Solid Non-Transparent */
+.dataTables_wrapper {
+  padding: 0.8rem 0;
+  font-size: 0.92rem !important;
+}
+
+table.dataTable {
+  border-collapse: collapse !important;
+  border-radius: 10px;
+  overflow: hidden;
+  font-size: 0.92rem !important;
+  background: #ffffff !important;
+}
+
+table.dataTable thead th {
+  background-color: #f1f5f9 !important;
+  color: #0f172a !important;
+  font-weight: 800 !important;
+  font-family: 'Outfit', sans-serif;
+  border-bottom: 2px solid #cbd5e1 !important;
+  padding: 10px 14px !important;
+}
+
+table.dataTable tbody td {
+  padding: 9px 14px !important;
+  border-bottom: 1px solid #f1f5f9 !important;
+  color: #1e293b !important;
+  background: #ffffff !important;
+}
+
+.console-log-info { color: #58a6ff; }
+.console-log-success { color: #3fb950; font-weight: 700; }
+.console-log-warn { color: #d29922; }
+.console-log-error { color: #f85149; font-weight: 700; }
diff --git a/run-shiny.md b/run-shiny.md
new file mode 100644
index 0000000..ae57dec
--- /dev/null
+++ b/run-shiny.md
@@ -0,0 +1,72 @@
+## Running Locally in R
+
+1. Open R inside the repository path and install/load devtools to install the package dependencies:
+```R
+devtools::install(dependencies = TRUE)
+```
+
+2. Load the package and launch the app:
+```R
+library(seqpac)
+run_seqpac_app()
+```
+
+## seqpac dashboard running guidelines (on demodata)
+
+### Step 1: Landing Page & Loading Data
+1. Open your browser to `http://localhost:3838`. You will see the **Home** (Launch Page) with the workflow overview.
+2. Click the **Get Started** button. This will automatically redirect you to the **Load / Create PAC** tab.
+3. On the **Example Data** sub-tab (left panel), click the **Load Drosophila Dataset** button.
+4. You should see the **PAC Object Overview** populate with:
+   - **Sequences (Rows)**: 9,131
+   - **Samples (Columns)**: 9
+   - **Annotations columns**: `Biotypes_mis0`, `Biotypes_mis3`
+   - **Phenotypic metadata**: `stage`, `batch`, `sample`
+5. Click through the three main-panel tabs (**Pheno Table**, **Counts Preview**, **Anno Preview**) to inspect the loaded datasets.
+
+### Step 2: Filtering & Normalization
+1. Click the **Filter & Normalize** tab in the main top navbar.
+2. Keep the default settings:
+   - **Size Range**: 20 to 30
+   - **Min Counts (Threshold)**: 5
+   - **Min Coverage (% of samples)**: 20%
+   - **Normalizations**: Check **CPM** and **VST**
+3. Click the **Apply Filter & Normalize** button.
+4. Verify the outputs in the main panel:
+   - **Sequence Counts Summary**: It will show that the filters retained **472 of 9131 sequences** (~5.17%).
+   - **Available Normalizations**: Shows `cpm` and `vst` are now available.
+   - **Sequence Length Distribution**: The histogram will refresh showing the distribution of the filtered sequences (mostly peak at 21–23 nt).
+
+### Step 3: Annotation Explorer
+1. Click the **Annotation Explorer** tab.
+2. Under **Select Annotation Column to Analyze**, choose `Biotypes_mis0` (or `Biotypes_mis3`).
+3. The **Annotation Statistics** table on the left will show the breakdown (e.g., how many sequences are annotated as `miRNA`, `tRNA`, `rRNA`, etc.).
+4. The main panel table displays the complete `Anno` metadata matrix mapping sequences to their biotypes. You can search or filter this table interactively.
+
+### Step 4: Post-Filtering Analysis
+Click the **Post-Filtering Analysis** tab. This tab has four sub-tabs:
+
+#### A. PCA (Principal Component Analysis)
+1. Go to the **Principal Component Analysis** sub-tab.
+2. Set **Color by Group** to `stage`.
+3. Check the **Show Sample Labels** box.
+4. Click **Run PCA**.
+5. You will see a 2D scatter plot showing how your samples (e.g. Stage1, Stage3, Stage5) group together.
+
+#### B. DESeq2 (Differential Expression)
+1. Go to the **Differential Expression (DESeq2)** sub-tab.
+2. In the design formula box, type `~ stage` (or keep the default).
+3. Set **Primary Factor** to `stage`.
+4. Click **Run DESeq2 Analysis**.
+5. After a few seconds, the main panel will populate with a searchable interactive table of differential expression results (`baseMean`, `log2FoldChange`, `pvalue`, `padj`, etc.).
+
+#### C. Size & Nucleotide Bias
+1. Go to the **Size & Nucleotide Bias** sub-tab.
+2. Set **Annotation Column** to `Biotypes_mis0`.
+3. Choose **Size Distribution** and click **Generate Plots**. You will see biotype-specific sequence size histograms.
+4. Switch to **Nucleotide Bias**, set position to `1`, and click **Generate Plots** to inspect the sequence starting nucleotide bias (e.g., high Uracil/T bias at position 1).
+
+#### D. Composition (Bar & Pie)
+1. Go to the **Composition (Bar & Pie)** sub-tab.
+2. Set **Annotation Column** to `Biotypes_mis0`.
+3. Select **Stacked Bar** and click **Generate Composition Chart** to see the relative abundance (percentages) of different small RNA biotypes across your samples.
\ No newline at end of file