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Benchmark: chloroplast assembly from genomic data

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10 check points from Weber et al. "Essential guidelines for computational method benchmarking" (2018) arXiv

  1. Define the purpose and scope of the benchmark.

automatic assembly tools extracting whole chloroplast genomes from mixed (plastid+genome) sequencing data

  1. Include all relevant methods.

GetOrganelle, fast-plast, org-asm, NOVOPlasty, chloroExtractor, IOGA TODO: is there another one we are missing?

  1. Select (or design) representative datasets.

We plan to use simulated data (at different chloro:genome ratios) and real datasets with existing reference chloroplasts TODO: select exact list of chloros TODO: produce simulated datasets

  1. Choose appropriate parameter values and software versions.

Latest version of each (wrapped into a docker container), default parameters as possible TODO: update all docker containers TODO: select default parameters for each tool

  1. Evaluate and rank methods according to key quantitative performance metrics.

We currently only have qualitative metrics (success, failure, incomplete, ...) TODO: design quantitative metrics (reference guided: completeness, continuity, correctness) TODO: write script to gather these metrics from output

  1. Evaluate secondary measures including runtimes and computational requirements,user-friendliness, code quality, and documentation quality.

we have a script to track all performence metrics with docker TODO: separate performence benchmarking runs with docker TODO: find objective (as objective as possible) measures for requirements, user-friendliness, code quality and documentation TODO: assign these metrics to all tools

  1. Interpret results and provide guidelines or recommendations from both user and method developer perspectives.

in addition to pure metrics keep an eye on complementarity, maybe recommend ensemble methods TODO

  1. Publish and distribute results in an accessible format.

GitHub, zenodo, DockerHub, biorXiv, BMC TODO

  1. Design the benchmark to enable future extensions.

We have that with the docker setup and having scripted everything TODO: documentation on GitHub on how to reproduce the benchmarking (incl. extension)

  1. Follow reproducible research best practices, in particular by making all code and data publicly available.

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GitHub - chloroExtractorTeam/benchmark · GitHub
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Benchmark: chloroplast assembly from genomic data
Status
10 check points from Weber et al. "Essential guidelines for computational method benchmarking" (2018) arXiv
  1. Define the purpose and scope of the benchmark.
automatic assembly tools extracting whole chloroplast genomes from mixed (plastid+genome) sequencing data
  1. Include all relevant methods.
GetOrganelle, fast-plast, org-asm, NOVOPlasty, chloroExtractor, IOGA
TODO: is there another one we are missing?
  1. Select (or design) representative datasets.
We plan to use simulated data (at different chloro:genome ratios) and real datasets with existing reference chloroplasts
TODO: select exact list of chloros
TODO: produce simulated datasets
  1. Choose appropriate parameter values and software versions.
Latest version of each (wrapped into a docker container), default parameters as possible
TODO: update all docker containers
TODO: select default parameters for each tool
  1. Evaluate and rank methods according to key quantitative performance metrics.
We currently only have qualitative metrics (success, failure, incomplete, ...)
TODO: design quantitative metrics (reference guided: completeness, continuity, correctness)
TODO: write script to gather these metrics from output
  1. Evaluate secondary measures including runtimes and computational requirements,user-friendliness, code quality, and documentation quality.
we have a script to track all performence metrics with docker
TODO: separate performence benchmarking runs with docker
TODO: find objective (as objective as possible) measures for requirements, user-friendliness, code quality and documentation
TODO: assign these metrics to all tools
  1. Interpret results and provide guidelines or recommendations from both user and method developer perspectives.
in addition to pure metrics keep an eye on complementarity, maybe recommend ensemble methods
TODO
  1. Publish and distribute results in an accessible format.
GitHub, zenodo, DockerHub, biorXiv, BMC
TODO
  1. Design the benchmark to enable future extensions.
We have that with the docker setup and having scripted everything
TODO: documentation on GitHub on how to reproduce the benchmarking (incl. extension)
  1. Follow reproducible research best practices, in particular by making all code and data publicly available.
Already covered with all previous points
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GitHub - chloroExtractorTeam/benchmark · GitHub
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Benchmark: chloroplast assembly from genomic data
Status
10 check points from Weber et al. "Essential guidelines for computational method benchmarking" (2018) arXiv
  1. Define the purpose and scope of the benchmark.
automatic assembly tools extracting whole chloroplast genomes from mixed (plastid+genome) sequencing data
  1. Include all relevant methods.
GetOrganelle, fast-plast, org-asm, NOVOPlasty, chloroExtractor, IOGA
TODO: is there another one we are missing?
  1. Select (or design) representative datasets.
We plan to use simulated data (at different chloro:genome ratios) and real datasets with existing reference chloroplasts
TODO: select exact list of chloros
TODO: produce simulated datasets
  1. Choose appropriate parameter values and software versions.
Latest version of each (wrapped into a docker container), default parameters as possible
TODO: update all docker containers
TODO: select default parameters for each tool
  1. Evaluate and rank methods according to key quantitative performance metrics.
We currently only have qualitative metrics (success, failure, incomplete, ...)
TODO: design quantitative metrics (reference guided: completeness, continuity, correctness)
TODO: write script to gather these metrics from output
  1. Evaluate secondary measures including runtimes and computational requirements,user-friendliness, code quality, and documentation quality.
we have a script to track all performence metrics with docker
TODO: separate performence benchmarking runs with docker
TODO: find objective (as objective as possible) measures for requirements, user-friendliness, code quality and documentation
TODO: assign these metrics to all tools
  1. Interpret results and provide guidelines or recommendations from both user and method developer perspectives.
in addition to pure metrics keep an eye on complementarity, maybe recommend ensemble methods
TODO
  1. Publish and distribute results in an accessible format.
GitHub, zenodo, DockerHub, biorXiv, BMC
TODO
  1. Design the benchmark to enable future extensions.
We have that with the docker setup and having scripted everything
TODO: documentation on GitHub on how to reproduce the benchmarking (incl. extension)
  1. Follow reproducible research best practices, in particular by making all code and data publicly available.
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GitHub - chloroExtractorTeam/benchmark · GitHub
Skip to content
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Repository files navigation
Benchmark: chloroplast assembly from genomic data
Status
10 check points from Weber et al. "Essential guidelines for computational method benchmarking" (2018) arXiv
  1. Define the purpose and scope of the benchmark.
automatic assembly tools extracting whole chloroplast genomes from mixed (plastid+genome) sequencing data
  1. Include all relevant methods.
GetOrganelle, fast-plast, org-asm, NOVOPlasty, chloroExtractor, IOGA
TODO: is there another one we are missing?
  1. Select (or design) representative datasets.
We plan to use simulated data (at different chloro:genome ratios) and real datasets with existing reference chloroplasts
TODO: select exact list of chloros
TODO: produce simulated datasets
  1. Choose appropriate parameter values and software versions.
Latest version of each (wrapped into a docker container), default parameters as possible
TODO: update all docker containers
TODO: select default parameters for each tool
  1. Evaluate and rank methods according to key quantitative performance metrics.
We currently only have qualitative metrics (success, failure, incomplete, ...)
TODO: design quantitative metrics (reference guided: completeness, continuity, correctness)
TODO: write script to gather these metrics from output
  1. Evaluate secondary measures including runtimes and computational requirements,user-friendliness, code quality, and documentation quality.
we have a script to track all performence metrics with docker
TODO: separate performence benchmarking runs with docker
TODO: find objective (as objective as possible) measures for requirements, user-friendliness, code quality and documentation
TODO: assign these metrics to all tools
  1. Interpret results and provide guidelines or recommendations from both user and method developer perspectives.
in addition to pure metrics keep an eye on complementarity, maybe recommend ensemble methods
TODO
  1. Publish and distribute results in an accessible format.
GitHub, zenodo, DockerHub, biorXiv, BMC
TODO
  1. Design the benchmark to enable future extensions.
We have that with the docker setup and having scripted everything
TODO: documentation on GitHub on how to reproduce the benchmarking (incl. extension)
  1. Follow reproducible research best practices, in particular by making all code and data publicly available.
Already covered with all previous points
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GitHub - chloroExtractorTeam/benchmark · GitHub
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BranchesTags
Repository files navigation
Benchmark: chloroplast assembly from genomic data
Status
10 check points from Weber et al. "Essential guidelines for computational method benchmarking" (2018) arXiv
  1. Define the purpose and scope of the benchmark.
automatic assembly tools extracting whole chloroplast genomes from mixed (plastid+genome) sequencing data
  1. Include all relevant methods.
GetOrganelle, fast-plast, org-asm, NOVOPlasty, chloroExtractor, IOGA
TODO: is there another one we are missing?
  1. Select (or design) representative datasets.
We plan to use simulated data (at different chloro:genome ratios) and real datasets with existing reference chloroplasts
TODO: select exact list of chloros
TODO: produce simulated datasets
  1. Choose appropriate parameter values and software versions.
Latest version of each (wrapped into a docker container), default parameters as possible
TODO: update all docker containers
TODO: select default parameters for each tool
  1. Evaluate and rank methods according to key quantitative performance metrics.
We currently only have qualitative metrics (success, failure, incomplete, ...)
TODO: design quantitative metrics (reference guided: completeness, continuity, correctness)
TODO: write script to gather these metrics from output
  1. Evaluate secondary measures including runtimes and computational requirements,user-friendliness, code quality, and documentation quality.
we have a script to track all performence metrics with docker
TODO: separate performence benchmarking runs with docker
TODO: find objective (as objective as possible) measures for requirements, user-friendliness, code quality and documentation
TODO: assign these metrics to all tools
  1. Interpret results and provide guidelines or recommendations from both user and method developer perspectives.
in addition to pure metrics keep an eye on complementarity, maybe recommend ensemble methods
TODO
  1. Publish and distribute results in an accessible format.
GitHub, zenodo, DockerHub, biorXiv, BMC
TODO
  1. Design the benchmark to enable future extensions.
We have that with the docker setup and having scripted everything
TODO: documentation on GitHub on how to reproduce the benchmarking (incl. extension)
  1. Follow reproducible research best practices, in particular by making all code and data publicly available.
Already covered with all previous points
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GitHub - chloroExtractorTeam/benchmark · GitHub
Skip to content
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Repository files navigation
Benchmark: chloroplast assembly from genomic data
Status
10 check points from Weber et al. "Essential guidelines for computational method benchmarking" (2018) arXiv
  1. Define the purpose and scope of the benchmark.
automatic assembly tools extracting whole chloroplast genomes from mixed (plastid+genome) sequencing data
  1. Include all relevant methods.
GetOrganelle, fast-plast, org-asm, NOVOPlasty, chloroExtractor, IOGA
TODO: is there another one we are missing?
  1. Select (or design) representative datasets.
We plan to use simulated data (at different chloro:genome ratios) and real datasets with existing reference chloroplasts
TODO: select exact list of chloros
TODO: produce simulated datasets
  1. Choose appropriate parameter values and software versions.
Latest version of each (wrapped into a docker container), default parameters as possible
TODO: update all docker containers
TODO: select default parameters for each tool
  1. Evaluate and rank methods according to key quantitative performance metrics.
We currently only have qualitative metrics (success, failure, incomplete, ...)
TODO: design quantitative metrics (reference guided: completeness, continuity, correctness)
TODO: write script to gather these metrics from output
  1. Evaluate secondary measures including runtimes and computational requirements,user-friendliness, code quality, and documentation quality.
we have a script to track all performence metrics with docker
TODO: separate performence benchmarking runs with docker
TODO: find objective (as objective as possible) measures for requirements, user-friendliness, code quality and documentation
TODO: assign these metrics to all tools
  1. Interpret results and provide guidelines or recommendations from both user and method developer perspectives.
in addition to pure metrics keep an eye on complementarity, maybe recommend ensemble methods
TODO
  1. Publish and distribute results in an accessible format.
GitHub, zenodo, DockerHub, biorXiv, BMC
TODO
  1. Design the benchmark to enable future extensions.
We have that with the docker setup and having scripted everything
TODO: documentation on GitHub on how to reproduce the benchmarking (incl. extension)
  1. Follow reproducible research best practices, in particular by making all code and data publicly available.
Already covered with all previous points
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GitHub - chloroExtractorTeam/benchmark · GitHub
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Repository files navigation
Benchmark: chloroplast assembly from genomic data
Status
10 check points from Weber et al. "Essential guidelines for computational method benchmarking" (2018) arXiv
  1. Define the purpose and scope of the benchmark.
automatic assembly tools extracting whole chloroplast genomes from mixed (plastid+genome) sequencing data
  1. Include all relevant methods.
GetOrganelle, fast-plast, org-asm, NOVOPlasty, chloroExtractor, IOGA
TODO: is there another one we are missing?
  1. Select (or design) representative datasets.
We plan to use simulated data (at different chloro:genome ratios) and real datasets with existing reference chloroplasts
TODO: select exact list of chloros
TODO: produce simulated datasets
  1. Choose appropriate parameter values and software versions.
Latest version of each (wrapped into a docker container), default parameters as possible
TODO: update all docker containers
TODO: select default parameters for each tool
  1. Evaluate and rank methods according to key quantitative performance metrics.
We currently only have qualitative metrics (success, failure, incomplete, ...)
TODO: design quantitative metrics (reference guided: completeness, continuity, correctness)
TODO: write script to gather these metrics from output
  1. Evaluate secondary measures including runtimes and computational requirements,user-friendliness, code quality, and documentation quality.
we have a script to track all performence metrics with docker
TODO: separate performence benchmarking runs with docker
TODO: find objective (as objective as possible) measures for requirements, user-friendliness, code quality and documentation
TODO: assign these metrics to all tools
  1. Interpret results and provide guidelines or recommendations from both user and method developer perspectives.
in addition to pure metrics keep an eye on complementarity, maybe recommend ensemble methods
TODO
  1. Publish and distribute results in an accessible format.
GitHub, zenodo, DockerHub, biorXiv, BMC
TODO
  1. Design the benchmark to enable future extensions.
We have that with the docker setup and having scripted everything
TODO: documentation on GitHub on how to reproduce the benchmarking (incl. extension)
  1. Follow reproducible research best practices, in particular by making all code and data publicly available.
Already covered with all previous points
About
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Readme
MIT license
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3 forks
Report repository
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