Title

Accurate identification of polyadenylation sites from 3' end deep sequencing using a naive Bayes classifier

UMMS Affiliation

Program in Gene Function and Expression; Program in Molecular Medicine

Date

10-15-2013

Document Type

Article

Disciplines

Bioinformatics | Computational Biology

Abstract

MOTIVATION: 3' end processing is important for transcription termination, mRNA stability and regulation of gene expression. To identify 3' ends, most techniques use an oligo-dT primer to construct deep sequencing libraries. However, this approach can lead to identification of artifactual polyadenylation sites due to internal priming in homopolymeric stretches of adenines. Although heuristic filters have been applied in these cases, they typically result in a high proportion of both false-positive and -negative classifications. Therefore, there is a need to develop improved algorithms to better identify mis-priming events in oligo-dT primed sequences.

RESULTS: By analyzing sequence features flanking 3' ends derived from oligo-dT-based sequencing, we developed a naive Bayes classifier to classify them as true or false/internally primed. The resulting algorithm is highly accurate, outperforms previous heuristic filters and facilitates identification of novel polyadenylation sites.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rights and Permissions

Citation: Sheppard S, Lawson ND, Zhu LJ. Accurate identification of polyadenylation sites from 3' end deep sequencing using a naive Bayes classifier. Bioinformatics. 2013 Oct 15;29(20):2564-71. doi:10.1093/bioinformatics/btt446. Link to article on publisher's site

Comments

Erratum published to correct corresponding author details: Sheppard S, Lawson ND, Zhu LJ. Accurate identification of polyadenylation sites from 3' end deep sequencing using a naive Bayes classifier. Bioinformatics. 2014 Feb 15;30(4):596. doi: 10.1093/bioinformatics/btt714. Link to erratum on publisher's site

Related Resources

Link to Article in PubMed

PubMed ID

23962617