A survey of best practices for RNA-seq data analysis | Genome Biology | Full Text
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.
Abstract 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.…
saved by
related reading
- GitHub - nf-core/rnaseq: RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.github.com
- rnaseq: Parametersnf-co.re
- GitHub - BioDepot/RNA-seq-lambda: Rapid RNA-seq pipeline using AWS lambda functions for alignmentgithub.com
- Signature-scoring methods developed for bulk samples are not adequate for cancer single-cell RNA sequencing data | eLifeelifesciences.org
- Identifying and mitigating batch effects in whole genome sequencing databmcbioinformatics.biomedcentral.com
- Studying bacterial transcriptomes using RNA-seq - PMCncbi.nlm.nih.gov
- Transcriptome assemblynbisweden.github.io
- Current best practices in single‐cell RNA‐seq analysis: a tutorial | Molecular Systems Biology | Springer Nature Linkembopress.org
- Quantifying the effect of experimental perturbations at single-cell resolutionnature.com
- Advancing regulatory variant effect prediction with AlphaGenomenature.com
- 10. Feature selection — Single-cell best practicessc-best-practices.org
- cncRNAs: Bi-functional RNAs with protein coding and non-coding functions - PMCncbi.nlm.nih.gov