PRADA: pipeline for RNA sequencing data analysis.
CONCLUSIONS
Technological advances in high-throughput sequencing necessitate improved computational tools for processing and analyzing large-scale datasets in a systematic automated manner. For that purpose, we have developed PRADA (Pipeline for RNA-Sequencing Data Analysis), a flexible, modular and highly scalable software platform that provides many different types of information available by multifaceted analysis starting from raw paired-end RNA-seq data: gene expression levels, quality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic fusion variants, homology scores and fusion frame classification. PRADA uses a dual-mapping strategy that increases sensitivity and refines the analytical endpoints. PRADA has been used extensively and successfully in the glioblastoma and renal clear cell projects of The Cancer Genome Atlas program.


METHODS
http://sourceforge.net/projects/prada/


BACKGROUND
gadgetz@broadinstitute.org or rverhaak@mdanderson.org


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, The Eli and Edythe L. Broad Institute of Harvard University and MIT, Cambridge, MA 02142 and Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10015, USA.
Wandaliz
W
Torres-García
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, The Eli and Edythe L. Broad Institute of Harvard University and MIT, Cambridge, MA 02142 and Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10015, USA.
Siyuan
S
Zheng
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, The Eli and Edythe L. Broad Institute of Harvard University and MIT, Cambridge, MA 02142 and Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10015, USA.
Andrey
A
Sivachenko
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, The Eli and Edythe L. Broad Institute of Harvard University and MIT, Cambridge, MA 02142 and Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10015, USA.
Rahulsimham
R
Vegesna
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, The Eli and Edythe L. Broad Institute of Harvard University and MIT, Cambridge, MA 02142 and Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10015, USA.
Qianghu
Q
Wang
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, The Eli and Edythe L. Broad Institute of Harvard University and MIT, Cambridge, MA 02142 and Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10015, USA.
Rong
R
Yao
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, The Eli and Edythe L. Broad Institute of Harvard University and MIT, Cambridge, MA 02142 and Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10015, USA.
Michael F
MF
Berger
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, The Eli and Edythe L. Broad Institute of Harvard University and MIT, Cambridge, MA 02142 and Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10015, USA.
John N
JN
Weinstein
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, The Eli and Edythe L. Broad Institute of Harvard University and MIT, Cambridge, MA 02142 and Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10015, USA.
Gad
G
Getz
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, The Eli and Edythe L. Broad Institute of Harvard University and MIT, Cambridge, MA 02142 and Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10015, USA.
Roel G W
RG
Verhaak
D001483:Base Sequence; D050939:Gene Fusion; D015894:Genome, Human / Q000235:genetics; D006801:Humans; D009369:Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software; D013223:Statistics as Topic / Q000379:methods*
D012333:RNA, Messenger