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Bioinformatic Tools for RNASeq: A Curation

Curator: Stephen J. Williams, Ph.D. 

Note:  This will be an ongoing curation as new information and tools become available.

RNASeq is a powerful tool for the analysis of the transcriptome profile and has been used to determine the transcriptional changes occurring upon stimuli such as drug treatment or detecting transcript differences between biological sample cohorts such as tumor versus normal tissue.  Unlike its genomic companion, whole genome and whole exome sequencing, which analyzes the primary sequence of the genomic DNA, RNASeq analyzes the mRNA transcripts, thereby more closely resembling the ultimate translated proteome. In addition, RNASeq and transcriptome profiling can determine if splicing variants occur as well as determining the nonexomic sequences, such as miRNA and lncRNA species, all of which have shown pertinence in the etiology of many diseases, including cancer.

However, RNASeq, like other omic technologies, generates enormous big data sets, which requires multiple types of bioinformatic tools in order to correctly analyze the sequence reads, and to visualize and interpret the output data.  This post represents a curation by the RNA-Seq blog of such tools useful for RNASeq studies and lists and reviews published literature using these curated tools.

From the RNA-Seq Blog

List of RNA-Seq bioinformatics tools

Posted by: RNA-Seq Blog in Data Analysis, Web Tools September 16, 2015 6,251 Views

from: https://en.wiki2.org/wiki/List_of_RNA-Seq_bioinformatics_tools

• 1 Quality control and pre-processing data
  • 1.1 Quality control and filtering data
  • 1.2 Detection of chimeric reads
  • 1.3 Errors Correction
  • 1.4 Pre-processing data
• 2 Alignment Tools
  • 2.1 Short (Unspliced) aligners
  • 2.2 Spliced aligners
    • 2.2.1 Aligners based on known splice junctions (annotation-guided aligners)
    • 2.2.2 De novo Splice Aligners
      • 2.2.2.1 De novo Splice Aligners that also use annotation optionally
      • 2.2.2.2 Other Spliced Aligners
• 3 Normalization, Quantitative analysis and Differential Expression
  • 3.1 Multi-tool solutions
• 4 Workbench (analysis pipeline / integrated solutions)
  • 4.1 Commercial Solutions
  • 4.2 Open (free) Source Solutions
• 5 Alternative Splicing Analysis
  • 5.1 General Tools
  • 5.2 Intron Retention Analysis
• 6 Bias Correction
• 7 Fusion genes/chimeras/translocation finders/structural variations
• 8 Copy Number Variation identification
• 9 RNA-Seq simulators
• 10 Transcriptome assemblers
  • 10.1 Genome-Guided assemblers
  • 10.2 Genome-Independent (de novo) assemblers
    • 10.2.1 Assembly evaluation tools
• 11 Co-expression networks
• 12 miRNA prediction
• 13 Visualization tools
• 14 Functional, Network & Pathway Analysis Tools
• 15 Further annotation tools for RNA-Seq data
• 16 RNA-Seq Databases
• 17 Webinars and Presentations
• 18 References

A review of some of the literature using some of the aforementioned curated tools are discussed below:

A.   Tools Useful for Single Cell RNA-Seq Analysis

B.  Tools for RNA-Seq Analysis of the Sliceasome

C.  Tools Useful for RNA-Seq read assembly visualization

Other articles on RNA and Transcriptomics in this Open Access Journal Include:

NIH to Award Up to $12M to Fund DNA, RNA Sequencing Research: single-cell genomics, sample preparation, transcriptomics and epigenomics, and genome-wide functional analysis.

Single-cell Genomics: Directions in Computational and Systems Biology – Contributions of Prof. Aviv Regev @Broad Institute of MIT and Harvard, Cochair, the Human Cell Atlas Organizing Committee with Sarah Teichmann of the Wellcome Trust Sanger Institute

Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer

First challenge to make use of the new NCI Cloud Pilots – Somatic Mutation Challenge – RNA: Best algorithms for detecting all of the abnormal RNA molecules in a cancer cell

Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis