SeqWho: Reliable, rapid determination of sequence file identity using k-mer frequencies in Random Forest classifiers

Christopher Bennett, Micah Thornton, Chanhee Park, Gervaise Henry, Yun Zhang, Venkat Malladi, Daehwan Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Motivation: With the vast improvements in sequencing technologies and increased number of protocols, sequencing is being used to answer complex biological problems. Subsequently, analysis pipelines have become more time consuming and complicated, usually requiring highly extensive prevalidation steps. Here, we present SeqWho, a program designed to assess heuristically the quality of sequencing files and reliably classify the organism and protocol type by using Random Forest classifiers trained on biases native in k-mer frequencies and repeat sequence identities. Results: Using one of our primary models, we show that our method accurately and rapidly classifies human and mouse sequences from nine different sequencing libraries by species, library and both together, 98.32%, 97.86% and 96.38% of the time, respectively. Ultimately, we demonstrate that SeqWho is a powerful method for reliably validating the quality and identity of the sequencing files used in any pipeline.

Original languageEnglish (US)
Pages (from-to)1830-1837
Number of pages8
JournalBioinformatics
Volume38
Issue number7
DOIs
StatePublished - Apr 1 2022

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Fingerprint

Dive into the research topics of 'SeqWho: Reliable, rapid determination of sequence file identity using k-mer frequencies in Random Forest classifiers'. Together they form a unique fingerprint.

Cite this