A Novel Method to Detect Bias in Short Read NGS Data

Jamie Alnasir, Hugh Shanahan

Research output: Contribution to journalArticlepeer-review


Detecting sources of bias in transcriptomic data is essential to determine signals of Biological significance. We outline a novel method to detect sequence specific bias in short read Next Generation Sequencing data. This is based on determining intra-exon correlations between specific motifs. This requires a mild assumption that short reads sampled from specific regions from the same exon will be correlated with each other. This has been implemented on Apache Spark and and used to analyse two D. melanogaster eye-antennal disc data sets generated at the same laboratory. The wild type data set indicates a variation due to motif GC content that is more significant than that found due to exon GC content. There is a clear variation in the spread of correlations between the two data sets suggesting more variability in these data sets than one would expect.
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalJournal of Integrative Bioinformatics
Issue number3
Early online date23 Sept 2017
Publication statusPublished - 2017


  • NGS
  • BIAS
  • Spark
  • Hadoop

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