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Cell Syst. 2019 Apr 24;8(4):352-357.e3. doi: 10.1016/j.cels.2019.03.004. Epub 2019 Apr 4.

exceRpt: A Comprehensive Analytic Platform for Extracellular RNA Profiling.

Author information

1
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
2
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
3
Bioinformatics Research Laboratory, Molecular and Human Genetics Department, Baylor College of Medicine, Houston, TX, USA.
4
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Department of Computer Science, Yale University, New Haven, CT, USA. Electronic address: mark@gersteinlab.org.

Abstract

Small RNA sequencing has been widely adopted to study the diversity of extracellular RNAs (exRNAs) in biofluids; however, the analysis of exRNA samples can be challenging: they are vulnerable to contamination and artifacts from different isolation techniques, present in lower concentrations than cellular RNA, and occasionally of exogenous origin. To address these challenges, we present exceRpt, the exRNA-processing toolkit of the NIH Extracellular RNA Communication Consortium (ERCC). exceRpt is structured as a cascade of filters and quantifications prioritized based on one's confidence in a given set of annotated RNAs. It generates quality control reports and abundance estimates for RNA biotypes. It is also capable of characterizing mappings to exogenous genomes, which, in turn, can be used to generate phylogenetic trees. exceRpt has been used to uniformly process all ∼3,500 exRNA-seq datasets in the public exRNA Atlas and is available from genboree.org and github.gersteinlab.org/exceRpt.

KEYWORDS:

RNA sequencing; RNA-seq; bioinformatics; bioinformatics tool; exRNAs; extracellular RNA; genomics; pipeline; transcriptome

PMID:
30956140
DOI:
10.1016/j.cels.2019.03.004
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