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assembly graph deconvolution

A repository for work on deconvoluting assembly graphs containing strain mixtures using time-series abundance information.

Quick start

Assuming you are starting in a directory containing a collection of paired-end Illumina read files, with names ending in the usual R?.fastq.gz, the software can be run as follows:

export BBMAP=/path/to/bbmap
export GDECONHOME=/path/to/this/repo
find `pwd` -maxdepth 1 -name "*R1.fastq.gz" | sort > read1_files.txt
find `pwd` -maxdepth 1 -name "*R2.fastq.gz" | sort > read2_files.txt
$GDECONHOME/btools/gdecon.nf --readlist1=read1_files.txt --readlist2=read2_files.txt --r1suffix=R1.fastq.gz

If the workflow has completed successfully then the assembled strain genomes will appear in the directory out/.

Dependencies and prerequisites

  • Linux, kernel 2.6.32 or later
  • nextflow, version 0.24 or later
  • python 3
  • gfapy
  • several others

Major components of the workflow

The workflow first constructs a compacted de Bruijn graph using bcalm, then trims the dBg using btrim. The abundance of each path (unitig) in each sample is then estimated via tigops. Next, the abundance data and graph structure are given to a Bayesian graph deconvolution model. The posterior output from the model is summarised and the number of strain genomes as well as their sequences is then recorded.

Bayesian assembly graph deconvolution implemented in Stan

A model has been implemented in Stan code to carry out assembly graph deconvolution. The input file for the model must contains the following information about the unitig graph:

  • V: the number of unitigs
  • S: the number of samples
  • depths: a list of length VxS containing the depth of k-mer coverage for each unitig in each sample. Sample depths are given in succession for each unitig, e.g. if we denote the depth for unitig i in sample j as i.j, the depths are given as 1.1, 1.2, 1.3, ... , 2.1, 2.2, 2.3 and so on.
  • adj1count: the number of unitigs with a single outgoing edge on an end
  • adj1source: the source unitig ID for each unitig with a single outgoing edge on an end. Negative values indicate an outgoing edge coming from the end of the reverse-complement unitig sequence.
  • adj1dest: the destination unitig ID corresponding to each of the source nodes listed in adj1source
  • adj2count: the number of unitigs with two outgoing edges on an end
  • adj2source: unitig IDs with two outgoing edges on an end
  • adj2dest1: the first destination edge for the corresponding unitig given in adj2source
  • adj2dest2: the second destination edge for the corresponding unitig given in adj2source
  • lengths: the lengths of the V unitigs

The MEGAHIT hack

We also created a hacked-up version of megahit, designed to take multiple samples. It uses the same command-line interface as standard megahit. Two additional output files are generated in addition to the usual megahit outputs. The first is intermediate_contigs/k*.unitigs.fa, which contains the unitig sequences. This can optionally be processed with the megahit toolkit contigs2fastg program to generate a graph viewable in BANDAGE. The second output is intermediate_contigs/k*.unitig_depths.Rdata. This file is as described above.

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deconvolution of species & strain genome mixtures in assembly graphs

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assembly graph deconvolution
A repository for work on deconvoluting assembly graphs containing strain mixtures using time-series abundance information.
Quick start
Assuming you are starting in a directory containing a collection of paired-end Illumina read files, with names ending in the usual R?.fastq.gz, the software can be run as follows:
export BBMAP=/path/to/bbmap
export GDECONHOME=/path/to/this/repo
find `pwd` -maxdepth 1 -name "*R1.fastq.gz" | sort > read1_files.txt
find `pwd` -maxdepth 1 -name "*R2.fastq.gz" | sort > read2_files.txt
$GDECONHOME/btools/gdecon.nf --readlist1=read1_files.txt --readlist2=read2_files.txt --r1suffix=R1.fastq.gz

If the workflow has completed successfully then the assembled strain genomes will appear in the directory out/.
Dependencies and prerequisites
  • Linux, kernel 2.6.32 or later
  • nextflow, version 0.24 or later
  • python 3
  • gfapy
  • several others
Major components of the workflow
The workflow first constructs a compacted de Bruijn graph using bcalm, then trims the dBg using btrim. The abundance of each path (unitig) in each sample is then estimated via tigops.
Next, the abundance data and graph structure are given to a Bayesian graph deconvolution model.
The posterior output from the model is summarised and the number of strain genomes as well as their sequences is then recorded.
Bayesian assembly graph deconvolution implemented in Stan
A model has been implemented in Stan code to carry out assembly graph deconvolution.
The input file for the model must contains the following information about the unitig graph:
  • V: the number of unitigs
  • S: the number of samples
  • depths: a list of length VxS containing the depth of k-mer coverage for each unitig in each sample. Sample depths are given in succession for each unitig, e.g. if we denote the depth for unitig i in sample j as i.j, the depths are given as 1.1, 1.2, 1.3, ... , 2.1, 2.2, 2.3 and so on.
  • adj1count: the number of unitigs with a single outgoing edge on an end
  • adj1source: the source unitig ID for each unitig with a single outgoing edge on an end. Negative values indicate an outgoing edge coming from the end of the reverse-complement unitig sequence.
  • adj1dest: the destination unitig ID corresponding to each of the source nodes listed in adj1source
  • adj2count: the number of unitigs with two outgoing edges on an end
  • adj2source: unitig IDs with two outgoing edges on an end
  • adj2dest1: the first destination edge for the corresponding unitig given in adj2source
  • adj2dest2: the second destination edge for the corresponding unitig given in adj2source
  • lengths: the lengths of the V unitigs
The MEGAHIT hack
We also created a hacked-up version of megahit, designed to take multiple samples. It uses the same command-line interface as standard megahit. Two additional output files are generated in addition to the usual megahit outputs. The first is intermediate_contigs/k*.unitigs.fa, which contains the unitig sequences. This can optionally be processed with the megahit toolkit contigs2fastg program to generate a graph viewable in BANDAGE. The second output is intermediate_contigs/k*.unitig_depths.Rdata. This file is as described above.
About
deconvolution of species & strain genome mixtures in assembly graphs
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assembly graph deconvolution
A repository for work on deconvoluting assembly graphs containing strain mixtures using time-series abundance information.
Quick start
Assuming you are starting in a directory containing a collection of paired-end Illumina read files, with names ending in the usual R?.fastq.gz, the software can be run as follows:
export BBMAP=/path/to/bbmap
export GDECONHOME=/path/to/this/repo
find `pwd` -maxdepth 1 -name "*R1.fastq.gz" | sort > read1_files.txt
find `pwd` -maxdepth 1 -name "*R2.fastq.gz" | sort > read2_files.txt
$GDECONHOME/btools/gdecon.nf --readlist1=read1_files.txt --readlist2=read2_files.txt --r1suffix=R1.fastq.gz

If the workflow has completed successfully then the assembled strain genomes will appear in the directory out/.
Dependencies and prerequisites
  • Linux, kernel 2.6.32 or later
  • nextflow, version 0.24 or later
  • python 3
  • gfapy
  • several others
Major components of the workflow
The workflow first constructs a compacted de Bruijn graph using bcalm, then trims the dBg using btrim. The abundance of each path (unitig) in each sample is then estimated via tigops.
Next, the abundance data and graph structure are given to a Bayesian graph deconvolution model.
The posterior output from the model is summarised and the number of strain genomes as well as their sequences is then recorded.
Bayesian assembly graph deconvolution implemented in Stan
A model has been implemented in Stan code to carry out assembly graph deconvolution.
The input file for the model must contains the following information about the unitig graph:
  • V: the number of unitigs
  • S: the number of samples
  • depths: a list of length VxS containing the depth of k-mer coverage for each unitig in each sample. Sample depths are given in succession for each unitig, e.g. if we denote the depth for unitig i in sample j as i.j, the depths are given as 1.1, 1.2, 1.3, ... , 2.1, 2.2, 2.3 and so on.
  • adj1count: the number of unitigs with a single outgoing edge on an end
  • adj1source: the source unitig ID for each unitig with a single outgoing edge on an end. Negative values indicate an outgoing edge coming from the end of the reverse-complement unitig sequence.
  • adj1dest: the destination unitig ID corresponding to each of the source nodes listed in adj1source
  • adj2count: the number of unitigs with two outgoing edges on an end
  • adj2source: unitig IDs with two outgoing edges on an end
  • adj2dest1: the first destination edge for the corresponding unitig given in adj2source
  • adj2dest2: the second destination edge for the corresponding unitig given in adj2source
  • lengths: the lengths of the V unitigs
The MEGAHIT hack
We also created a hacked-up version of megahit, designed to take multiple samples. It uses the same command-line interface as standard megahit. Two additional output files are generated in addition to the usual megahit outputs. The first is intermediate_contigs/k*.unitigs.fa, which contains the unitig sequences. This can optionally be processed with the megahit toolkit contigs2fastg program to generate a graph viewable in BANDAGE. The second output is intermediate_contigs/k*.unitig_depths.Rdata. This file is as described above.
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assembly graph deconvolution
A repository for work on deconvoluting assembly graphs containing strain mixtures using time-series abundance information.
Quick start
Assuming you are starting in a directory containing a collection of paired-end Illumina read files, with names ending in the usual R?.fastq.gz, the software can be run as follows:
export BBMAP=/path/to/bbmap
export GDECONHOME=/path/to/this/repo
find `pwd` -maxdepth 1 -name "*R1.fastq.gz" | sort > read1_files.txt
find `pwd` -maxdepth 1 -name "*R2.fastq.gz" | sort > read2_files.txt
$GDECONHOME/btools/gdecon.nf --readlist1=read1_files.txt --readlist2=read2_files.txt --r1suffix=R1.fastq.gz

If the workflow has completed successfully then the assembled strain genomes will appear in the directory out/.
Dependencies and prerequisites
  • Linux, kernel 2.6.32 or later
  • nextflow, version 0.24 or later
  • python 3
  • gfapy
  • several others
Major components of the workflow
The workflow first constructs a compacted de Bruijn graph using bcalm, then trims the dBg using btrim. The abundance of each path (unitig) in each sample is then estimated via tigops.
Next, the abundance data and graph structure are given to a Bayesian graph deconvolution model.
The posterior output from the model is summarised and the number of strain genomes as well as their sequences is then recorded.
Bayesian assembly graph deconvolution implemented in Stan
A model has been implemented in Stan code to carry out assembly graph deconvolution.
The input file for the model must contains the following information about the unitig graph:
  • V: the number of unitigs
  • S: the number of samples
  • depths: a list of length VxS containing the depth of k-mer coverage for each unitig in each sample. Sample depths are given in succession for each unitig, e.g. if we denote the depth for unitig i in sample j as i.j, the depths are given as 1.1, 1.2, 1.3, ... , 2.1, 2.2, 2.3 and so on.
  • adj1count: the number of unitigs with a single outgoing edge on an end
  • adj1source: the source unitig ID for each unitig with a single outgoing edge on an end. Negative values indicate an outgoing edge coming from the end of the reverse-complement unitig sequence.
  • adj1dest: the destination unitig ID corresponding to each of the source nodes listed in adj1source
  • adj2count: the number of unitigs with two outgoing edges on an end
  • adj2source: unitig IDs with two outgoing edges on an end
  • adj2dest1: the first destination edge for the corresponding unitig given in adj2source
  • adj2dest2: the second destination edge for the corresponding unitig given in adj2source
  • lengths: the lengths of the V unitigs
The MEGAHIT hack
We also created a hacked-up version of megahit, designed to take multiple samples. It uses the same command-line interface as standard megahit. Two additional output files are generated in addition to the usual megahit outputs. The first is intermediate_contigs/k*.unitigs.fa, which contains the unitig sequences. This can optionally be processed with the megahit toolkit contigs2fastg program to generate a graph viewable in BANDAGE. The second output is intermediate_contigs/k*.unitig_depths.Rdata. This file is as described above.
About
deconvolution of species & strain genome mixtures in assembly graphs
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assembly graph deconvolution
A repository for work on deconvoluting assembly graphs containing strain mixtures using time-series abundance information.
Quick start
Assuming you are starting in a directory containing a collection of paired-end Illumina read files, with names ending in the usual R?.fastq.gz, the software can be run as follows:
export BBMAP=/path/to/bbmap
export GDECONHOME=/path/to/this/repo
find `pwd` -maxdepth 1 -name "*R1.fastq.gz" | sort > read1_files.txt
find `pwd` -maxdepth 1 -name "*R2.fastq.gz" | sort > read2_files.txt
$GDECONHOME/btools/gdecon.nf --readlist1=read1_files.txt --readlist2=read2_files.txt --r1suffix=R1.fastq.gz

If the workflow has completed successfully then the assembled strain genomes will appear in the directory out/.
Dependencies and prerequisites
  • Linux, kernel 2.6.32 or later
  • nextflow, version 0.24 or later
  • python 3
  • gfapy
  • several others
Major components of the workflow
The workflow first constructs a compacted de Bruijn graph using bcalm, then trims the dBg using btrim. The abundance of each path (unitig) in each sample is then estimated via tigops.
Next, the abundance data and graph structure are given to a Bayesian graph deconvolution model.
The posterior output from the model is summarised and the number of strain genomes as well as their sequences is then recorded.
Bayesian assembly graph deconvolution implemented in Stan
A model has been implemented in Stan code to carry out assembly graph deconvolution.
The input file for the model must contains the following information about the unitig graph:
  • V: the number of unitigs
  • S: the number of samples
  • depths: a list of length VxS containing the depth of k-mer coverage for each unitig in each sample. Sample depths are given in succession for each unitig, e.g. if we denote the depth for unitig i in sample j as i.j, the depths are given as 1.1, 1.2, 1.3, ... , 2.1, 2.2, 2.3 and so on.
  • adj1count: the number of unitigs with a single outgoing edge on an end
  • adj1source: the source unitig ID for each unitig with a single outgoing edge on an end. Negative values indicate an outgoing edge coming from the end of the reverse-complement unitig sequence.
  • adj1dest: the destination unitig ID corresponding to each of the source nodes listed in adj1source
  • adj2count: the number of unitigs with two outgoing edges on an end
  • adj2source: unitig IDs with two outgoing edges on an end
  • adj2dest1: the first destination edge for the corresponding unitig given in adj2source
  • adj2dest2: the second destination edge for the corresponding unitig given in adj2source
  • lengths: the lengths of the V unitigs
The MEGAHIT hack
We also created a hacked-up version of megahit, designed to take multiple samples. It uses the same command-line interface as standard megahit. Two additional output files are generated in addition to the usual megahit outputs. The first is intermediate_contigs/k*.unitigs.fa, which contains the unitig sequences. This can optionally be processed with the megahit toolkit contigs2fastg program to generate a graph viewable in BANDAGE. The second output is intermediate_contigs/k*.unitig_depths.Rdata. This file is as described above.
About
deconvolution of species & strain genome mixtures in assembly graphs
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assembly graph deconvolution
A repository for work on deconvoluting assembly graphs containing strain mixtures using time-series abundance information.
Quick start
Assuming you are starting in a directory containing a collection of paired-end Illumina read files, with names ending in the usual R?.fastq.gz, the software can be run as follows:
export BBMAP=/path/to/bbmap
export GDECONHOME=/path/to/this/repo
find `pwd` -maxdepth 1 -name "*R1.fastq.gz" | sort > read1_files.txt
find `pwd` -maxdepth 1 -name "*R2.fastq.gz" | sort > read2_files.txt
$GDECONHOME/btools/gdecon.nf --readlist1=read1_files.txt --readlist2=read2_files.txt --r1suffix=R1.fastq.gz

If the workflow has completed successfully then the assembled strain genomes will appear in the directory out/.
Dependencies and prerequisites
  • Linux, kernel 2.6.32 or later
  • nextflow, version 0.24 or later
  • python 3
  • gfapy
  • several others
Major components of the workflow
The workflow first constructs a compacted de Bruijn graph using bcalm, then trims the dBg using btrim. The abundance of each path (unitig) in each sample is then estimated via tigops.
Next, the abundance data and graph structure are given to a Bayesian graph deconvolution model.
The posterior output from the model is summarised and the number of strain genomes as well as their sequences is then recorded.
Bayesian assembly graph deconvolution implemented in Stan
A model has been implemented in Stan code to carry out assembly graph deconvolution.
The input file for the model must contains the following information about the unitig graph:
  • V: the number of unitigs
  • S: the number of samples
  • depths: a list of length VxS containing the depth of k-mer coverage for each unitig in each sample. Sample depths are given in succession for each unitig, e.g. if we denote the depth for unitig i in sample j as i.j, the depths are given as 1.1, 1.2, 1.3, ... , 2.1, 2.2, 2.3 and so on.
  • adj1count: the number of unitigs with a single outgoing edge on an end
  • adj1source: the source unitig ID for each unitig with a single outgoing edge on an end. Negative values indicate an outgoing edge coming from the end of the reverse-complement unitig sequence.
  • adj1dest: the destination unitig ID corresponding to each of the source nodes listed in adj1source
  • adj2count: the number of unitigs with two outgoing edges on an end
  • adj2source: unitig IDs with two outgoing edges on an end
  • adj2dest1: the first destination edge for the corresponding unitig given in adj2source
  • adj2dest2: the second destination edge for the corresponding unitig given in adj2source
  • lengths: the lengths of the V unitigs
The MEGAHIT hack
We also created a hacked-up version of megahit, designed to take multiple samples. It uses the same command-line interface as standard megahit. Two additional output files are generated in addition to the usual megahit outputs. The first is intermediate_contigs/k*.unitigs.fa, which contains the unitig sequences. This can optionally be processed with the megahit toolkit contigs2fastg program to generate a graph viewable in BANDAGE. The second output is intermediate_contigs/k*.unitig_depths.Rdata. This file is as described above.
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GitHub - koadman/graphdeconvolution: deconvolution of species & strain genome mixtures in assembly graphs · GitHub
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assembly graph deconvolution
A repository for work on deconvoluting assembly graphs containing strain mixtures using time-series abundance information.
Quick start
Assuming you are starting in a directory containing a collection of paired-end Illumina read files, with names ending in the usual R?.fastq.gz, the software can be run as follows:
export BBMAP=/path/to/bbmap
export GDECONHOME=/path/to/this/repo
find `pwd` -maxdepth 1 -name "*R1.fastq.gz" | sort > read1_files.txt
find `pwd` -maxdepth 1 -name "*R2.fastq.gz" | sort > read2_files.txt
$GDECONHOME/btools/gdecon.nf --readlist1=read1_files.txt --readlist2=read2_files.txt --r1suffix=R1.fastq.gz

If the workflow has completed successfully then the assembled strain genomes will appear in the directory out/.
Dependencies and prerequisites
  • Linux, kernel 2.6.32 or later
  • nextflow, version 0.24 or later
  • python 3
  • gfapy
  • several others
Major components of the workflow
The workflow first constructs a compacted de Bruijn graph using bcalm, then trims the dBg using btrim. The abundance of each path (unitig) in each sample is then estimated via tigops.
Next, the abundance data and graph structure are given to a Bayesian graph deconvolution model.
The posterior output from the model is summarised and the number of strain genomes as well as their sequences is then recorded.
Bayesian assembly graph deconvolution implemented in Stan
A model has been implemented in Stan code to carry out assembly graph deconvolution.
The input file for the model must contains the following information about the unitig graph:
  • V: the number of unitigs
  • S: the number of samples
  • depths: a list of length VxS containing the depth of k-mer coverage for each unitig in each sample. Sample depths are given in succession for each unitig, e.g. if we denote the depth for unitig i in sample j as i.j, the depths are given as 1.1, 1.2, 1.3, ... , 2.1, 2.2, 2.3 and so on.
  • adj1count: the number of unitigs with a single outgoing edge on an end
  • adj1source: the source unitig ID for each unitig with a single outgoing edge on an end. Negative values indicate an outgoing edge coming from the end of the reverse-complement unitig sequence.
  • adj1dest: the destination unitig ID corresponding to each of the source nodes listed in adj1source
  • adj2count: the number of unitigs with two outgoing edges on an end
  • adj2source: unitig IDs with two outgoing edges on an end
  • adj2dest1: the first destination edge for the corresponding unitig given in adj2source
  • adj2dest2: the second destination edge for the corresponding unitig given in adj2source
  • lengths: the lengths of the V unitigs
The MEGAHIT hack
We also created a hacked-up version of megahit, designed to take multiple samples. It uses the same command-line interface as standard megahit. Two additional output files are generated in addition to the usual megahit outputs. The first is intermediate_contigs/k*.unitigs.fa, which contains the unitig sequences. This can optionally be processed with the megahit toolkit contigs2fastg program to generate a graph viewable in BANDAGE. The second output is intermediate_contigs/k*.unitig_depths.Rdata. This file is as described above.
About
deconvolution of species & strain genome mixtures in assembly graphs
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GitHub - koadman/graphdeconvolution: deconvolution of species & strain genome mixtures in assembly graphs · GitHub
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btools
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megahit
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stan_models
stan_models
test_data
test_data
README.md
README.md
Repository files navigation
assembly graph deconvolution
A repository for work on deconvoluting assembly graphs containing strain mixtures using time-series abundance information.
Quick start
Assuming you are starting in a directory containing a collection of paired-end Illumina read files, with names ending in the usual R?.fastq.gz, the software can be run as follows:
export BBMAP=/path/to/bbmap
export GDECONHOME=/path/to/this/repo
find `pwd` -maxdepth 1 -name "*R1.fastq.gz" | sort > read1_files.txt
find `pwd` -maxdepth 1 -name "*R2.fastq.gz" | sort > read2_files.txt
$GDECONHOME/btools/gdecon.nf --readlist1=read1_files.txt --readlist2=read2_files.txt --r1suffix=R1.fastq.gz

If the workflow has completed successfully then the assembled strain genomes will appear in the directory out/.
Dependencies and prerequisites
  • Linux, kernel 2.6.32 or later
  • nextflow, version 0.24 or later
  • python 3
  • gfapy
  • several others
Major components of the workflow
The workflow first constructs a compacted de Bruijn graph using bcalm, then trims the dBg using btrim. The abundance of each path (unitig) in each sample is then estimated via tigops.
Next, the abundance data and graph structure are given to a Bayesian graph deconvolution model.
The posterior output from the model is summarised and the number of strain genomes as well as their sequences is then recorded.
Bayesian assembly graph deconvolution implemented in Stan
A model has been implemented in Stan code to carry out assembly graph deconvolution.
The input file for the model must contains the following information about the unitig graph:
  • V: the number of unitigs
  • S: the number of samples
  • depths: a list of length VxS containing the depth of k-mer coverage for each unitig in each sample. Sample depths are given in succession for each unitig, e.g. if we denote the depth for unitig i in sample j as i.j, the depths are given as 1.1, 1.2, 1.3, ... , 2.1, 2.2, 2.3 and so on.
  • adj1count: the number of unitigs with a single outgoing edge on an end
  • adj1source: the source unitig ID for each unitig with a single outgoing edge on an end. Negative values indicate an outgoing edge coming from the end of the reverse-complement unitig sequence.
  • adj1dest: the destination unitig ID corresponding to each of the source nodes listed in adj1source
  • adj2count: the number of unitigs with two outgoing edges on an end
  • adj2source: unitig IDs with two outgoing edges on an end
  • adj2dest1: the first destination edge for the corresponding unitig given in adj2source
  • adj2dest2: the second destination edge for the corresponding unitig given in adj2source
  • lengths: the lengths of the V unitigs
The MEGAHIT hack
We also created a hacked-up version of megahit, designed to take multiple samples. It uses the same command-line interface as standard megahit. Two additional output files are generated in addition to the usual megahit outputs. The first is intermediate_contigs/k*.unitigs.fa, which contains the unitig sequences. This can optionally be processed with the megahit toolkit contigs2fastg program to generate a graph viewable in BANDAGE. The second output is intermediate_contigs/k*.unitig_depths.Rdata. This file is as described above.
About
deconvolution of species & strain genome mixtures in assembly graphs
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Watchers
9 watching
Forks
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