sRNAflow: A Tool for the Analysis of Small RNA-Seq Data

被引:1
作者
Zayakin, Pawel [1 ,2 ]
机构
[1] Latvian Biomed Res & Study Ctr, LV-1067 Riga, Latvia
[2] European Bioinformat Inst, EMBL EBI, Hinxton CB10 1SD, England
关键词
bioinformatics; small RNA; microbiome; non-coding RNA; biofluids; miRNA; isomiR; tRF; biomarker; cancer biology; MICRORNAS; CANCER; ANNOTATION; BIOMARKERS; ALIGNMENT; GENOMICS; SAMPLES; SEARCH; GENES;
D O I
10.3390/ncrna10010006
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The analysis of small RNA sequencing data across a range of biofluids is a significant research area, given the diversity of RNA types that hold potential diagnostic, prognostic, and predictive value. The intricate task of segregating the complex mixture of small RNAs from both human and other species, including bacteria, fungi, and viruses, poses one of the most formidable challenges in the analysis of small RNA sequencing data, currently lacking satisfactory solutions. This study introduces sRNAflow, a user-friendly bioinformatic tool with a web interface designed for the analysis of small RNAs obtained from biological fluids. Tailored to the unique requirements of such samples, the proposed pipeline addresses various challenges, including filtering potential RNAs from reagents and environment, classifying small RNA types, managing small RNA annotation overlap, conducting differential expression assays, analysing isomiRs, and presenting an approach to identify the sources of small RNAs within samples. sRNAflow also encompasses an alternative alignment-free analysis of RNA-seq data, featuring clustering and initial RNA source identification using BLAST. This comprehensive approach facilitates meaningful comparisons of results between different analytical methods.
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页数:16
相关论文
共 64 条
[31]  
Kitchen R., EXCERPT EXTRACELLULA
[32]  
Langmead B, 2012, NAT METHODS, V9, P357, DOI [10.1038/nmeth.1923, 10.1038/NMETH.1923]
[33]   Ultrafast and memory-efficient alignment of short DNA sequences to the human genome [J].
Langmead, Ben ;
Trapnell, Cole ;
Pop, Mihai ;
Salzberg, Steven L. .
GENOME BIOLOGY, 2009, 10 (03)
[34]   The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads [J].
Liao, Yang ;
Smyth, Gordon K. ;
Shi, Wei .
NUCLEIC ACIDS RESEARCH, 2019, 47 (08)
[35]   Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 [J].
Love, Michael I. ;
Huber, Wolfgang ;
Anders, Simon .
GENOME BIOLOGY, 2014, 15 (12)
[36]   tRNAscan-SE On-line: integrating search and context for analysis of transfer RNA genes [J].
Lowe, Todd M. ;
Chan, Patricia P. .
NUCLEIC ACIDS RESEARCH, 2016, 44 (W1) :W54-W57
[37]  
Martin M., 2011, EMBNET J, V17, DOI DOI 10.14806/EJ.17.1.200
[38]   Circulating microRNAs as stable blood-based markers for cancer detection [J].
Mitchell, Patrick S. ;
Parkin, Rachael K. ;
Kroh, Evan M. ;
Fritz, Brian R. ;
Wyman, Stacia K. ;
Pogosova-Agadjanyan, Era L. ;
Peterson, Amelia ;
Noteboom, Jennifer ;
O'Briant, Kathy C. ;
Allen, April ;
Lin, Daniel W. ;
Urban, Nicole ;
Drescher, Charles W. ;
Knudsen, Beatrice S. ;
Stirewalt, Derek L. ;
Gentleman, Robert ;
Vessella, Robert L. ;
Nelson, Peter S. ;
Martin, Daniel B. ;
Tewari, Muneesh .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2008, 105 (30) :10513-10518
[39]   sMETASeq: Combined Profiling of Microbiota and Host Small RNAs [J].
Mjelle, Robin ;
Aass, Kristin Roseth ;
Sjursen, Wenche ;
Hofsli, Eva ;
Saetrom, Pal L. .
ISCIENCE, 2020, 23 (05)
[40]   Interactive metagenomic visualization in a Web browser [J].
Ondov, Brian D. ;
Bergman, Nicholas H. ;
Phillippy, Adam M. .
BMC BIOINFORMATICS, 2011, 12