A Novel Method to Detect Bias in Short Read NGS Data

J Integr Bioinform. 2017 Sep 23;14(3):20170025. doi: 10.1515/jib-2017-0025.


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 used to analyse two D. melanogaster eye-antennal disc data sets generated at the same laboratory. The wild type data set in drosophila indicates a variation due to motif GC content that is more significant than that found due to exon GC content. The software is available online and could be applied for cross-experiment transcriptome data analysis in eukaryotes.

Keywords: RNA-Seq; bias; next-Generation sequencing; short reads; transcriptomics.

MeSH terms

  • Animals
  • Bias
  • Drosophila melanogaster / genetics
  • Exons / genetics
  • Gene Expression Profiling
  • High-Throughput Nucleotide Sequencing / methods*
  • High-Throughput Nucleotide Sequencing / standards*
  • Software*
  • Transcriptome / genetics