Accurate estimation of expression levels of homologous genes in RNA-seq experiments

Bogdan Paşaniuc*, Noah Zaitlen, Eran Halperin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Next generation high throughput sequencing (NGS) is poised to replace array based technologies as the experiment of choice for measuring RNA expression levels. Several groups have demonstrated the power of this new approach (RNA-seq), making significant and novel contributions and simultaneously proposing methodologies for the analysis of RNA-seq data. In a typical experiment, millions of short sequences (reads) are sampled from RNA extracts and mapped back to a reference genome. The number of reads mapping to each gene is used as proxy for its corresponding RNA concentration. A significant challenge in analyzing RNA expression of homologous genes is the large fraction of the reads that map to multiple locations in the reference genome. Currently, these reads are either dropped from the analysis, or a näive algorithm is used to estimate their underlying distribution. In this work, we present a rigorous alternative for handling the reads generated in an RNA-seq experiment within a probabilistic model for RNA-seq data; we develop maximum likelihood based methods for estimating the model parameters. In contrast to previous methods, our model takes into account the fact that the DNA of the sequenced individual is not a perfect copy of the reference sequence. We show with both simulated and real RNA-seq data that our new method improves the accuracy and power of RNA-seq experiments.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 14th Annual International Conference, RECOMB 2010, Proceedings
Pages397-409
Number of pages13
DOIs
StatePublished - 2010
Event14th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2010 - Lisbon, Portugal
Duration: 25 Apr 201028 Apr 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6044 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2010
Country/TerritoryPortugal
CityLisbon
Period25/04/1028/04/10

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