Optimizing RNA-seq mapping with STAR

Methods in Molecular Biology

Volumn 1415, Issue , 2016, Pages 245-262

  Dobin, Alexander ^a   Gingeras, Thomas R ^a  

^a NONE

Author keywords

Reads mapping; RNA seq; Sequence alignment; Spliced alignment; STAR; Transcriptome

Indexed keywords

NUCLEOTIDE;

CHROMOSOME; EXON; GENE MAPPING; GENOME; MEMORY; RNA SEQUENCE; SEQUENCE ALIGNMENT; SOFTWARE; SPLICED TRANSCRIPTS ALIGNMENT TO A REFERENCE; TRANSCRIPTOMICS; ALGORITHM; BIOLOGY; COMPUTER INTERFACE; GENE EXPRESSION PROFILING; HUMAN; PROCEDURES; RNA SPLICING; SEQUENCE ANALYSIS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; HUMANS; RNA SPLICING; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, RNA; USER-COMPUTER INTERFACE;

EID: 84985018933     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4939-3572-7_13     Document Type: Chapter

References (5)

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2. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (2009) The sequence alignment/ map format and SAMtools. Bioinformatics 25: 2078–2079
3. Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12: 323
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5. Anders S, Pyl PT, Huber W (2014) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31: 166–169