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A novel method to detect bias in short read NGS data
File(s)
A Novel Method to Detect Bias in Short Read NGS Data.pdf (1.42 MB)
Published version
Author(s)
Alnasir, Jamie
Shanahan, Hugh P
Type
Journal Article
Abstract
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.
Date Issued
2017-09-23
Date Acceptance
2017-08-10
Citation
Journal of Integrative Bioinformatics, 2017, 14 (3)
URI
http://hdl.handle.net/10044/1/76694
DOI
https://www.dx.doi.org/10.1515/jib-2017-0025
ISSN
1613-4516
Publisher
IMBio e.V.
Journal / Book Title
Journal of Integrative Bioinformatics
Volume
14
Issue
3
Copyright Statement
©2017 Jamie Alnasir et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/28941355
PII: /j/jib.2017.14.issue-3/jib-2017-0025/jib-2017-0025.xml
Subjects
RNA-Seq
bias
next-Generation sequencing
short reads
transcriptomics
Animals
Bias
Drosophila melanogaster
Exons
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Software
Transcriptome
Publication Status
Published
Coverage Spatial
Germany
Date Publish Online
2017-09-23
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