Literature DB >> 23220574

Nestly--a framework for running software with nested parameter choices and aggregating results.

Connor O McCoy1, Aaron Gallagher, Noah G Hoffman, Frederick A Matsen.   

Abstract

UNLABELLED: The execution of a software application or pipeline using various combinations of parameters and inputs is a common task in bioinformatics. In the absence of a specialized tool to organize, streamline and formalize this process, scientists must write frequently complex scripts to perform these tasks. We present nestly, a Python package to facilitate running tools with nested combinations of parameters and inputs. nestly provides three components. First, a module to build nested directory structures corresponding to choices of parameters. Second, the nestrun script to run a given command using each set of parameter choices. Third, the nestagg script to aggregate results of the individual runs into a CSV file, as well as support for more complex aggregation. We also include a module for easily specifying nested dependencies for the SCons build tool, enabling incremental builds. AVAILABILITY: Source, documentation and tutorial examples are available at http://github.com/fhcrc/nestly. nestly can be installed from the Python Package Index via pip; it is open source (MIT license).

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Mesh:

Year:  2012        PMID: 23220574      PMCID: PMC3562064          DOI: 10.1093/bioinformatics/bts696

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  6 in total

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2.  Bpipe: a tool for running and managing bioinformatics pipelines.

Authors:  Simon P Sadedin; Bernard Pope; Alicia Oshlack
Journal:  Bioinformatics       Date:  2012-04-12       Impact factor: 6.937

3.  Ruffus: a lightweight Python library for computational pipelines.

Authors:  Leo Goodstadt
Journal:  Bioinformatics       Date:  2010-09-16       Impact factor: 6.937

4.  Minimizing the average distance to a closest leaf in a phylogenetic tree.

Authors:  Frederick A Matsen; Aaron Gallagher; Connor O McCoy
Journal:  Syst Biol       Date:  2013-07-10       Impact factor: 15.683

5.  jModelTest 2: more models, new heuristics and parallel computing.

Authors:  Diego Darriba; Guillermo L Taboada; Ramón Doallo; David Posada
Journal:  Nat Methods       Date:  2012-07-30       Impact factor: 28.547

6.  Snakemake--a scalable bioinformatics workflow engine.

Authors:  Johannes Köster; Sven Rahmann
Journal:  Bioinformatics       Date:  2012-08-20       Impact factor: 6.937

  6 in total
  4 in total

Review 1.  Algorithms and design strategies towards automated glycoproteomics analysis.

Authors:  Han Hu; Kshitij Khatri; Joseph Zaia
Journal:  Mass Spectrom Rev       Date:  2016-01-04       Impact factor: 10.946

2.  Deep generative models for T cell receptor protein sequences.

Authors:  Kristian Davidsen; Branden J Olson; William S DeWitt; Jean Feng; Elias Harkins; Philip Bradley; Frederick A Matsen
Journal:  Elife       Date:  2019-09-05       Impact factor: 8.140

3.  NGSANE: a lightweight production informatics framework for high-throughput data analysis.

Authors:  Fabian A Buske; Hugh J French; Martin A Smith; Susan J Clark; Denis C Bauer
Journal:  Bioinformatics       Date:  2014-01-26       Impact factor: 6.937

4.  SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis.

Authors:  Masaomi Hatakeyama; Lennart Opitz; Giancarlo Russo; Weihong Qi; Ralph Schlapbach; Hubert Rehrauer
Journal:  BMC Bioinformatics       Date:  2016-06-02       Impact factor: 3.169

  4 in total

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