A New Strategy to Reduce Allelic Bias in RNA-Seq Readmapping

Abstract

Accurate estimation of expression levels from RNA-Seq data entails precise mapping of the sequence reads to a reference genome. Because the standard reference genome contains only one allele at any given locus, reads overlapping polymorphic loci that carry a non-reference allele are at least one mismatch away from the reference and hence, are less likely to be mapped. This bias in read mapping leads to inaccurate estimates of allele-specific expression (ASE). To address this read-mapping bias, we propose the construction of an enhanced reference genome that includes the alternative alleles at known polymorphic loci. We show that mapping to this enhanced reference reduced the read-mapping biases, leading to more reliable estimates of ASE. Experiments on simulated data show that the proposed strategy reduced the number of loci with mapping bias by 63% when compared with a previous approach that relies on masking the polymorphic loci and by 18% when compared with the standard approach that uses an unaltered reference. When we applied our strategy to actual RNA-Seq data, we found that it mapped up to 15% more reads than the previous approaches and identified many seemingly incorrect inferences made by them.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2012
Accession Number
ADA571156

Entities

People

  • Jaques Reifman
  • Nela Zavaljevski
  • Ravi V. Satya

Organizations

  • United States Army Medical Research and Development Command

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Acids
  • Application Software
  • Biotechnology
  • Chromosomes
  • Construction
  • Data Analysis
  • Data Sets
  • Department Of Defense
  • Gene Expression
  • Genetics
  • Genome
  • High Performance Computing
  • Human Genome
  • Identification
  • Nucleic Acids
  • Sequences
  • Standards

Fields of Study

  • Biology

Readers

  • Approximation Theory.
  • Molecular Genetics

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference