Yanagi: Transcript Segment Library Construction for RNA-Seq Quantification

Authors Mohamed K. Gunady, Steffen Cornwell, Stephen M. Mount, Héctor Corrada Bravo



PDF
Thumbnail PDF

File

LIPIcs.WABI.2017.10.pdf
  • Filesize: 1.32 MB
  • 14 pages

Document Identifiers

Author Details

Mohamed K. Gunady
Steffen Cornwell
Stephen M. Mount
Héctor Corrada Bravo

Cite AsGet BibTex

Mohamed K. Gunady, Steffen Cornwell, Stephen M. Mount, and Héctor Corrada Bravo. Yanagi: Transcript Segment Library Construction for RNA-Seq Quantification. In 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 88, pp. 10:1-10:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/LIPIcs.WABI.2017.10

Abstract

Analysis of differential alternative splicing from RNA-seq data is complicated by the fact that many RNA-seq reads map to multiple transcripts, and that annotated transcripts from a given gene are often a small subset of many possible complete transcripts for that gene. Here we describe Yanagi, a tool which segments a transcriptome into disjoint regions to create a segments library from a complete transcriptome annotation that preserves all of its consecutive regions of a given length L while distinguishing annotated alternative splicing events in the transcriptome. In this paper, we formalize this concept of transcriptome segmentation and propose an efficient algorithm for generating segment libraries based on a length parameter dependent on specific RNA-Seq library construction. The resulting segment sequences can be used with pseudo-alignment tools to quantify expression at the segment level. We characterize the segment libraries for the reference transcriptomes of Drosophila melanogaster and Homo sapiens. Finally, we demonstrate the utility of quantification using a segment library based on an analysis of differential exon skipping in Drosophila melanogaster and Homo sapiens. The notion of transcript segmentation as introduced here and implemented in Yanagi will open the door for the application of lightweight, ultra-fast pseudo-alignment algorithms in a wide variety of analyses of transcription variation.
Keywords
  • RNA-Seq
  • Genome Sequencing
  • Kmer-based alignment
  • Transcriptome Quantification
  • Differential Alternative Splicing

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Simon Anders, Alejandro Reyes, and Wolfgang Huber. Detecting differential usage of exons from RNA-seq data. Genome research, 22(10):2008-2017, 2012. Google Scholar
  2. Nicolas L. Bray, Harold Pimentel, Páll Melsted, and Lior Pachter. Near-optimal probabilistic RNA-seq quantification. Nature biotechnology, 34(5):525-527, 2016. Google Scholar
  3. Brian J. Haas, Arthur L. Delcher, Stephen M. Mount, Jennifer R. Wortman, Roger K. Smith Jr., Linda I. Hannick, Rama Maiti, Catherine M. Ronning, Douglas B. Rusch, Christopher D. Town, et al. Improving the arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic acids research, 31(19):5654-5666, 2003. Google Scholar
  4. Steffen Heber, Max Alekseyev, Sing-Hoi Sze, Haixu Tang, and Pavel A. Pevzner. Splicing graphs and EST assembly problem. Bioinformatics, 18(suppl_1):S181, 2002. Google Scholar
  5. Daehwan Kim, Geo Pertea, Cole Trapnell, Harold Pimentel, Ryan Kelley, and Steven L. Salzberg. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome biology, 14(4):R36, 2013. Google Scholar
  6. Ben Langmead and Steven L. Salzberg. Fast gapped-read alignment with Bowtie 2. Nature methods, 9(4):357-359, 2012. Google Scholar
  7. Charity W. Law, Yunshun Chen, Wei Shi, and Gordon K. Smyth. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome biology, 15(2):R29, 2014. Google Scholar
  8. Bo Li and Colin N. Dewey. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC bioinformatics, 12(1):323, 2011. Google Scholar
  9. Rob Patro, Geet Duggal, Michael I. Love, Rafael A Irizarry, and Carl Kingsford. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods, 2017. Google Scholar
  10. Rob Patro, Stephen M. Mount, and Carl Kingsford. Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nature biotechnology, 32(5):462-464, May 2014. Google Scholar
  11. Gordon K. Smyth et al. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol, 3(1):3, 2004. Google Scholar
  12. Charlotte Soneson, Katarina L. Matthes, Malgorzata Nowicka, Charity W. Law, and Mark D. Robinson. Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage. Genome biology, 17(1):12, 2016. Google Scholar
  13. Avi Srivastava, Hirak Sarkar, Nitish Gupta, and Rob Patro. RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes. Bioinformatics, 32(12):i192, 2016. Google Scholar
  14. Mingxiang Teng, Michael I. Love, Carrie A. Davis, Sarah Djebali, Alexander Dobin, Brenton R. Graveley, Sheng Li, Christopher E. Mason, Sara Olson, Dmitri Pervouchine, et al. A benchmark for RNA-seq quantification pipelines. Genome biology, 17(1):74, 2016. Google Scholar
  15. Hagen Tilgner, Fereshteh Jahanbani, Tim Blauwkamp, Ali Moshrefi, Erich Jaeger, Feng Chen, Itamar Harel, Carlos D. Bustamante, Morten Rasmussen, and Michael P. Snyder. Comprehensive transcriptome analysis using synthetic long-read sequencing reveals molecular co-association of distant splicing events. Nature biotechnology, 33(7):736-742, 2015. Google Scholar
  16. Cole Trapnell, Lior Pachter, and Steven L. Salzberg. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25(9):1105-1111, 2009. Google Scholar
  17. Cole Trapnell, Brian A Williams, Geo Pertea, Ali Mortazavi, Gordon Kwan, Marijke J. Van Baren, Steven L. Salzberg, Barbara J. Wold, and Lior Pachter. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature biotechnology, 28(5):511-515, 2010. Google Scholar
  18. Jorge Vaquero-Garcia, Alejandro Barrera, Matthew R. Gazzara, Juan Gonzalez-Vallinas, Nicholas F. Lahens, John B. Hogenesch, Kristen W. Lynch, Yoseph Barash, and Juan Valcárcel. A new view of transcriptome complexity and regulation through the lens of local splicing variations. eLife, 5:e11752+, February 2016. Google Scholar
  19. S. Lawrence Zipursky, Woj M. Wojtowicz, and Daisuke Hattori. Got diversity? wiring the fly brain with dscam. Trends in biochemical sciences, 31(10):581-588, 2006. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail