A graph-based algorithm for RNA-seq data normalization

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 b...

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Vydané v:PloS one Ročník 15; číslo 1; s. e0227760
Hlavní autori: Tran, Diem-Trang, Bhaskara, Aditya, Kuberan, Balagurunathan, Might, Matthew
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 24.01.2020
Public Library of Science (PLoS)
Predmet:
Algorithms
Apexes
Biology and Life Sciences
Circularity
Computational Biology - methods
Data Science - methods
Databases, Genetic - statistics & numerical data
Feasibility Studies
Gene sequencing
Genes
Hypotheses
Methods
Physical Sciences
Research and Analysis Methods
Ribonucleic acid
RNA
RNA-Seq - methods
RNA-Seq - statistics & numerical data
United States > US
Utah
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí: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.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0227760