Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

On the optimal trimming of high-throughput mRNA sequence data

View ORCID ProfileMatthew D. MacManes
doi: https://doi.org/10.1101/000422
Matthew D. MacManes
1University of New Hampshire. Durham, NH 03824
2Department of Molecular, Cellular & Biomedical Sciences Durham, NH 03824
3Hubbard Center for Genome Studies Durham, NH 03824
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Matthew D. MacManes
  • For correspondence: macmanes@gmail.com
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

The widespread and rapid adoption of high-throughput sequencing technologies has afforded researchers the opportunity to gain a deep understanding of genome level processes that underlie evolutionary change, and perhaps more importantly, the links between genotype and phenotype. In particular, researchers interested in functional biology and adaptation have used these technologies to sequence mRNA transcriptomes of specific tissues, which in turn are often compared to other tissues, or other individuals with different phenotypes. While these techniques are extremely powerful, careful attention to data quality is required. In particular, because high-throughput sequencing is more error-prone than traditional Sanger sequencing, quality trimming of sequence reads should be an important step in all data processing pipelines. While several software packages for quality trimming exist, no general guidelines for the specifics of trimming have been developed. Here, using empirically derived sequence data, I provide general recommendations regarding the optimal strength of trimming, specifically in mRNA-Seq studies. Although very aggressive quality trimming is common, this study suggests that a more gentle trimming, specifically of those nucleotides whose Phred score <2 or <5, is optimal for most studies across a wide variety of metrics.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY Unported 3.0 license.
Back to top
PreviousNext
Posted January 14, 2014.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
On the optimal trimming of high-throughput mRNA sequence data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
On the optimal trimming of high-throughput mRNA sequence data
Matthew D. MacManes
bioRxiv 000422; doi: https://doi.org/10.1101/000422
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
On the optimal trimming of high-throughput mRNA sequence data
Matthew D. MacManes
bioRxiv 000422; doi: https://doi.org/10.1101/000422

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4087)
  • Biochemistry (8762)
  • Bioengineering (6479)
  • Bioinformatics (23341)
  • Biophysics (11750)
  • Cancer Biology (9149)
  • Cell Biology (13248)
  • Clinical Trials (138)
  • Developmental Biology (7417)
  • Ecology (11369)
  • Epidemiology (2066)
  • Evolutionary Biology (15087)
  • Genetics (10399)
  • Genomics (14009)
  • Immunology (9121)
  • Microbiology (22040)
  • Molecular Biology (8779)
  • Neuroscience (47368)
  • Paleontology (350)
  • Pathology (1420)
  • Pharmacology and Toxicology (2482)
  • Physiology (3704)
  • Plant Biology (8050)
  • Scientific Communication and Education (1431)
  • Synthetic Biology (2208)
  • Systems Biology (6016)
  • Zoology (1249)