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Comparing reference-based RNA-Seq mapping methods for non-human primate data.

Publication ,  Journal Article
Benjamin, AM; Nichols, M; Burke, TW; Ginsburg, GS; Lucas, JE
Published in: BMC Genomics
July 7, 2014

BACKGROUND: The application of next-generation sequencing technology to gene expression quantification analysis, namely, RNA-Sequencing, has transformed the way in which gene expression studies are conducted and analyzed. These advances are of particular interest to researchers studying organisms with missing or incomplete genomes, as the need for knowledge of sequence information is overcome. De novo assembly methods have gained widespread acceptance in the RNA-Seq community for organisms with no true reference genome or transcriptome. While such methods have tremendous utility, computational cost is still a significant challenge for organisms with large and complex genomes. RESULTS: In this manuscript, we present a comparison of four reference-based mapping methods for non-human primate data. We utilize TopHat2 and GSNAP for mapping to the human genome, and Bowtie2 and Stampy for mapping to the human genome and transcriptome for a total of six mapping approaches. For each of these methods, we explore mapping rates and locations, number of detected genes, correlations between computed expression values, and the utility of the resulting data for differential expression analysis. CONCLUSIONS: We show that reference-based mapping methods indeed have utility in RNA-Seq analysis of mammalian data with no true reference, and the details of mapping methods should be carefully considered when doing so. Critical algorithm features include short seed sequences, the allowance of mismatches, and the allowance of gapped alignments in addition to splice junction gaps. Such features facilitate sensitive alignment of non-human primate RNA-Seq data to a human reference.

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Published In

BMC Genomics

DOI

EISSN

1471-2164

Publication Date

July 7, 2014

Volume

15

Issue

1

Start / End Page

570

Location

England

Related Subject Headings

  • Transcriptome
  • Sequence Analysis, RNA
  • Reference Standards
  • RNA
  • Papio
  • Male
  • High-Throughput Nucleotide Sequencing
  • Genome
  • Chromosome Mapping
  • Biological Evolution
 

Citation

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ICMJE
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Benjamin, A. M., Nichols, M., Burke, T. W., Ginsburg, G. S., & Lucas, J. E. (2014). Comparing reference-based RNA-Seq mapping methods for non-human primate data. BMC Genomics, 15(1), 570. https://doi.org/10.1186/1471-2164-15-570
Benjamin, Ashlee M., Marshall Nichols, Thomas W. Burke, Geoffrey S. Ginsburg, and Joseph E. Lucas. “Comparing reference-based RNA-Seq mapping methods for non-human primate data.BMC Genomics 15, no. 1 (July 7, 2014): 570. https://doi.org/10.1186/1471-2164-15-570.
Benjamin AM, Nichols M, Burke TW, Ginsburg GS, Lucas JE. Comparing reference-based RNA-Seq mapping methods for non-human primate data. BMC Genomics. 2014 Jul 7;15(1):570.
Benjamin, Ashlee M., et al. “Comparing reference-based RNA-Seq mapping methods for non-human primate data.BMC Genomics, vol. 15, no. 1, July 2014, p. 570. Pubmed, doi:10.1186/1471-2164-15-570.
Benjamin AM, Nichols M, Burke TW, Ginsburg GS, Lucas JE. Comparing reference-based RNA-Seq mapping methods for non-human primate data. BMC Genomics. 2014 Jul 7;15(1):570.
Journal cover image

Published In

BMC Genomics

DOI

EISSN

1471-2164

Publication Date

July 7, 2014

Volume

15

Issue

1

Start / End Page

570

Location

England

Related Subject Headings

  • Transcriptome
  • Sequence Analysis, RNA
  • Reference Standards
  • RNA
  • Papio
  • Male
  • High-Throughput Nucleotide Sequencing
  • Genome
  • Chromosome Mapping
  • Biological Evolution