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Atropos: specific, sensitive, and speedy trimming of sequencing reads

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@beconstant @torstenseemann @jongsanders @pathogenomenick But there's a Github repo: https://t.co/UGaio0Ypgz from @jdidion and DOI https://t.co/mZOdbQiwZb & its already in bioconda & Docker. Nice!
@torstenseemann @jongsanders @pathogenomenick https://t.co/G4WinGgTDE
Atropos: specific, sensitive, and speedy trimming of sequencing reads - preprint by @jdidion​ @marcelm_ @NIHDirector https://t.co/KGOzDvqKxk https://t.co/57NEEsYsTK
152 days ago
Atropos: specific, sensitive, and speedy trimming of sequencing reads https://t.co/JxkX2T350t
Atropos: specific, sensitive, and speedy trimming of sequencing reads https://t.co/XYebtRtKPt
RT @jessicapolka: @BenSaunders Agree that momentum is awesome. But it's not the only choice for well-respected labs: https://t.co/kUfYqCHyxY
221 days ago
RT @jessicapolka: @BenSaunders Agree that momentum is awesome. But it's not the only choice for well-respected labs: https://t.co/kUfYqCHyxY
@BenSaunders Agree that momentum is awesome. But it's not the only choice for well-respected labs: https://t.co/kUfYqCHyxY
222 days ago
RT @David_McGaughey: .@jdidion I work a lot with public datasets, so this cut adapt fork with an automatic adapter finder looks useful: htt…
RT @David_McGaughey: .@jdidion I work a lot with public datasets, so this cut adapt fork with an automatic adapter finder looks useful: htt…
.@jdidion I work a lot with public datasets, so this cut adapt fork with an automatic adapter finder looks useful: https://t.co/mtXZn9Lq3v
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Supplemental Information

Memory usage of trimming tools on simulated datasets

Maximum memory usage, in MB, of jobs executed on our cluster for trimming tools run on simulated datasets with error rates of A) 0.2\%, B) 0.6\%, and C) 1.2\%. Note that this memory usage includes the overhead of the Singularity container and is thus an overestimate.

DOI: 10.7287/peerj.preprints.2452v4/supp-1

CPU Utilization of trimming tools

Average total CPU usage of each trimming tool run on A) simulated data, B) WGBS data, and C) mRNA-Seq data.

DOI: 10.7287/peerj.preprints.2452v4/supp-2

Mapping execution times

Execution time of A) bwa-meth on WGBS reads, and B) STAR on mRNA-Seq reads, for reads trimmed by each tool as well as the untrimmed reads.

DOI: 10.7287/peerj.preprints.2452v4/supp-3

Descriptions of software used in the benchmark workflow

DOI: 10.7287/peerj.preprints.2452v4/supp-4

Performance of trimming tools on desktop machine

Min/max execution time and average CPU usage for trimming of simulated datasets on a desktop with 4 parallel threads.

DOI: 10.7287/peerj.preprints.2452v4/supp-5

Performance of trimming tools on cluster node

Min/max execution time and average CPU usage for trimming of simulated datasets on a cluster node with 4, 8, or 16 parallel threads.

DOI: 10.7287/peerj.preprints.2452v4/supp-6

Memory usage of jobs run on cluster for trimming simulated datasets

DOI: 10.7287/peerj.preprints.2452v4/supp-7

Performance of trimming tools on WGBS dataset

Min/max execution time and average CPU usage for trimming of WGBS data on a cluster node with 4, 8, or 16 parallel threads.

DOI: 10.7287/peerj.preprints.2452v4/supp-8

Performance of trimming tools on RNA-Seq dataset

Min/max execution time and average CPU usage for trimming of mRNA-Seq data on a cluster node with 4, 8, or 16 parallel threads.

DOI: 10.7287/peerj.preprints.2452v4/supp-9

Additional Information

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

John P Didion conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Marcel Martin contributed reagents/materials/analysis tools, reviewed drafts of the paper.

Francis S Collins reviewed drafts of the paper.

Data Deposition

The following information was supplied regarding data availability:

GitHub: https://github.com/jdidion/atropos

DOI 10.5281/zenodo.154097

Funding

JPD and FSC are funded by the NIH intramural program. Additionally, JPD is funded by the American Diabetes Association (1-17-PDF-100). MM is supported by a grant from the Knut and Alice Wallenberg Foundation to the Wallenberg Advanced Bioinformatics Infrastructure. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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