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CHURP: A Lightweight CLI Framework to Enable Novice Users to Analyze Sequencing Datasets in Parallel

Published: 28 July 2019 Publication History

Abstract

Progressive decreases in the cost of DNA sequencing have contributed to a decades-long exponential increase in the production of new sequencing datasets. The processing of these datasets has in turn led biology, a field that has traditionally relied on local "lab" servers to address its computational needs, to become increasingly reliant on High Performance Computing (HPC) resources. Though many operations on sequencing datasets are trivially parallelizable on multiple levels, the lack of an HPC tradition in biological research has hampered fully parallelized deployments.
Here we present a lightweight flexible framework for performing parallelized processing of raw gene expression data. The framework uses a Python3 based frontend for specifying analysis options, data paths, and reference datasets. This frontend sanitizes and resolves the options, providing verbose error checking before writing a human readable configuration file and basic scripts for batch submission. The submission scripts leverage the scheduler to implement a scatter-gather approach, submitting potentially hundreds of individual jobs via a job array, each small enough to take advantage of backfill in a high contention HPC environment. The gather component is handled through a script submitted with an "after-okay" dependency.

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      cover image ACM Other conferences
      PEARC '19: Practice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)
      July 2019
      775 pages
      ISBN:9781450372275
      DOI:10.1145/3332186
      • General Chair:
      • Tom Furlani
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 28 July 2019

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      • (2024)Multiomic analyses reveal new targets of polycomb repressor complex 2 in Schwann lineage cells and malignant peripheral nerve sheath tumorsNeuro-Oncology Advances10.1093/noajnl/vdae1886:1Online publication date: 9-Nov-2024
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