Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation

Nature Biotechnology

Volumn 34, Issue 12, 2016, Pages 1287-1291

  Love, Michael I ^a,b   Hogenesch, John B ^c   Irizarry, Rafael A ^a,b  

^a DANA FARBER CANCER INSTITUTE   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA VISUALIZATION; RNA;

ABUNDANCE ESTIMATION; DATA VISUALIZATION TOOLS; FALSE POSITIVE; GC CONTENTS; ISOFORMS; RNA-SEQ DATUM; SEQUENCE FEATURES; TRUE POSITIVE;

SYSTEMATIC ERRORS;

MESSENGER RNA; RNA; TRANSCRIPTION FACTOR;

ARTICLE; BENCHMARKING; DNA BASE COMPOSITION; FALSE POSITIVE RESULT; LYMPHOBLASTOID CELL LINE; PRIORITY JOURNAL; RNA DEGRADATION; RNA SEQUENCE; RNA TRANSCRIPTION; SENSITIVITY ANALYSIS; SYSTEMATIC ERROR; ALGORITHM; ARTIFACT; BIOLOGICAL MODEL; COMPUTER SIMULATION; GENETICS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS; SOFTWARE; STATISTICAL MODEL;

ALGORITHMS; ARTIFACTS; BASE COMPOSITION; COMPUTER SIMULATION; MODELS, GENETIC; MODELS, STATISTICAL; REPRODUCIBILITY OF RESULTS; RNA; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS, RNA; SOFTWARE; TRANSCRIPTION FACTORS;

EID: 85003441754     PISSN: 10870156     EISSN: 15461696     Source Type: Journal    
DOI: 10.1038/nbt.3682     Document Type: Article

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