Accurate Estimation of Expression Levels of Homologous Genes in RNA-seq Experiments

  • Bogdan Paşaniuc
  • Noah Zaitlen
  • Eran Halperin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6044)


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 naïve 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cokus, S.J., Feng, S., Zhang, X., Chen, Z., Merriman, B., Haudenschild, C.D., Pradhan, S., Nelson, S.F., Pellegrini, M., Jacobsen, S.E.: Shotgun bisulphite sequencing of the arabidopsis genome reveals dna methylation patterning. Nature 452(7184), 215–219 (2008) (03 2008/03/13/print)Google Scholar
  2. 2.
    The ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the encode pilot project. Nature 447, 799–816 (2007)Google Scholar
  3. 3.
    The International HapMap Consortium. A second generation human haplotype map of over 3.1 million snps. Nature 449(7164), 851–861(2007) (10 2007/10/18/print)Google Scholar
  4. 4.
    Dohm, J.C., Lottaz, C., Borodina, T., Himmelbauer, H.: Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucl. Acids Res. 36(16), e105 (2008)Google Scholar
  5. 5.
    Halperin, E., Hazan, E.: Haplofreq: Estimating haplotype frequencies efficiently. Journal of Computational Biology 13(2), 481–500 (2006) (PMID: 16597253)Google Scholar
  6. 6.
    Hashimoto, T., de Hoon, M.J.L., Grimmond, S.M., Daub, C.O., Hayashizaki, Y., Faulkner, G.J.: Probabilistic resolution of multi-mapping reads in massively parallel sequencing data using MuMRescueLite. Bioinformatics 25(19), 2613–2614 (2009)CrossRefGoogle Scholar
  7. 7.
  8. 8.
  9. 9.
  10. 10.
  11. 11.
    Johnson, D.S., Mortazavi, A., Myers, R.M., Wold, B.: Genome-Wide Mapping of in Vivo Protein-DNA Interactions. Science (2007) 1141319Google Scholar
  12. 12.
    Li, H., Durbin, R.: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14), 1754–1760 (2009)CrossRefGoogle Scholar
  13. 13.
    Li, H., Ruan, J., Durbin, R.: Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Research 18(11), 1851–1858 (2008)CrossRefGoogle Scholar
  14. 14.
    Marioni, J.C., Mason, C.E., Mane, S.M., Stephens, M., Gilad, Y.: RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Research 18(9), 1509–1517 (2008)CrossRefGoogle Scholar
  15. 15.
    Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L., Wold, B.: Mapping and quantifying mammalian transcriptomes by rna-seq. Nat. Meth. 5(7), 621–628 (2008) (07 2008/07//print)Google Scholar
  16. 16.
    Schuster, S.C.: Next-generation sequencing transforms today’s biology. Nat. Meth. 5(1), 16–18 (2008) (01 2008/01//print)Google Scholar
  17. 17.
    Su, A.I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K.A., Block, D., Zhang, J., Soden, R., Hayakawa, M., Kreiman, G., Cooke, M.P., Walker, J.R., Hogenesch, J.B.: A gene atlas of the mouse and human protein-encoding transcriptomes. Proceedings of the National Academy of Sciences of the United States of America 101(16), 6062–6067 (2004)CrossRefGoogle Scholar
  18. 18.
    Wang, Z., Gerstein, M., Snyder, M.: Rna-seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10(1), 57–63 (2009) (01 2009/01//print)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Bogdan Paşaniuc
    • 1
  • Noah Zaitlen
    • 2
    • 3
  • Eran Halperin
    • 1
    • 2
    • 3
  1. 1.International Computer Science InstituteBerkeley
  2. 2.Molecular Microbiology and Biotechnology DepartmentTel-Aviv University 
  3. 3.The Blavatnik School of Computer ScienceTel-Aviv University 

Personalised recommendations