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A survey of best practices for RNA-seq data analysis | Genome Biology
Description:
RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.
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Keywords {馃攳}
pubmed, article, google, scholar, rnaseq, cas, data, central, analysis, expression, reads, gene, sequencing, genome, transcript, differential, genes, bioinformatics, methods, transcriptome, transcripts, rna, nat, number, quantification, expressed, read, res, mapping, samples, tools, functional, cell, reference, splicing, discovery, singlecell, isoform, nucleic, acids, experimental, quality, visualization, alternative, integration, mapped, replicates, small, genomics, biological,
Topics {鉁掞笍}
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Questions {鉂搣
- How does multiple testing correction work?
- RNA-seq differential expression studies: more sequence or more replication?
Schema {馃椇锔弣
WebPage:
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headline:A survey of best practices for RNA-seq data analysis
description:RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.
datePublished:2016-01-26T00:00:00Z
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Differential Expression Analysis
Reference Transcriptome
Transcript Identification
Transcript Discovery
Gene Transfer Format
Animal Genetics and Genomics
Human Genetics
Plant Genetics and Genomics
Microbial Genetics and Genomics
Bioinformatics
Evolutionary Biology
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headline:A survey of best practices for RNA-seq data analysis
description:RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.
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Differential Expression Analysis
Reference Transcriptome
Transcript Identification
Transcript Discovery
Gene Transfer Format
Animal Genetics and Genomics
Human Genetics
Plant Genetics and Genomics
Microbial Genetics and Genomics
Bioinformatics
Evolutionary Biology
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