A graph-based algorithm for RNA-seq data normalization
Autoři:
Diem-Trang Tran aff001; Aditya Bhaskara aff001; Balagurunathan Kuberan aff002; Matthew Might aff004
Působiště autorů:
School of Computing, University of Utah, Salt Lake City, Utah, United States of America
aff001; Department of Medicinal Chemistry, University of Utah, Salt Lake City, Utah, United States of America
aff002; Department of Biology, University of Utah, Salt Lake City, Utah, United States of America
aff003; Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
aff004
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0227760
Souhrn
The use of RNA-sequencing has garnered much attention in recent years for characterizing and understanding various biological systems. However, it remains a major challenge to gain insights from a large number of RNA-seq experiments collectively, due to the normalization problem. Normalization has been challenging due to an inherent circularity, requiring that RNA-seq data be normalized before any pattern of differential (or non-differential) expression can be ascertained; meanwhile, the prior knowledge of non-differential transcripts is crucial to the normalization process. Some methods have successfully overcome this problem by the assumption that most transcripts are not differentially expressed. However, when RNA-seq profiles become more abundant and heterogeneous, this assumption fails to hold, leading to erroneous normalization. We present a normalization procedure that does not rely on this assumption, nor prior knowledge about the reference transcripts. This algorithm is based on a graph constructed from intrinsic correlations among RNA-seq transcripts and seeks to identify a set of densely connected vertices as references. Application of this algorithm on our synthesized validation data showed that it could recover the reference transcripts with high precision, thus resulting in high-quality normalization. On a realistic data set from the ENCODE project, this algorithm gave good results and could finish in a reasonable time. These preliminary results imply that we may be able to break the long persisting circularity problem in RNA-seq normalization.
Klíčová slova:
Algorithms – Clustering algorithms – Gene expression – Gene pool – RNA sequencing – Sequence alignment – Signal transduction – Transcriptome analysis
Zdroje
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