SeqOthello: querying RNA-seq experiments at scale

Ye Yu, Jinpeng Liu, Xinan Liu, Yi Zhang, Eamonn Magner, Erik Lehnert, Chen Qian, Jinze Liu

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

We present SeqOthello, an ultra-fast and memory-efficient indexing structure to support arbitrary sequence query against large collections of RNA-seq experiments. It takes SeqOthello only 5 min and 19.1 GB memory to conduct a global survey of 11,658 fusion events against 10,113 TCGA Pan-Cancer RNA-seq datasets. The query recovers 92.7% of tier-1 fusions curated by TCGA Fusion Gene Database and reveals 270 novel occurrences, all of which are present as tumor-specific. By providing a reference-free, alignment-free, and parameter-free sequence search system, SeqOthello will enable large-scale integrative studies using sequence-level data, an undertaking not previously practicable for many individual labs.

Original languageEnglish
Article number167
JournalGenome Biology
Volume19
Issue number1
DOIs
StatePublished - Oct 19 2018

Bibliographical note

Publisher Copyright:
© 2018 The Author(s).

Keywords

  • Compression
  • Gene fusion
  • Othello
  • Pan-cancer
  • Query
  • RNA-seq
  • SeqOthello
  • TCGA

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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