We have assigned each RNA-seq tool into categories by the computational task. The single-cell specific RNA-seq (scRNA-seq) tools are on the scRNA-seq collection page.

RNA-seq Pre-analysis Tools

Pre-analysis quality control of raw reads includes assessment of tolerable GC and k-mer contents, removal of sequence adaptors, PCR artifacts, and contaminations. The assessment of duplicates and sequencing errors. In addition, sequencing quality tends to decrease towards the 3' end of the reads; Thus, the reads must be trimmed to remove the low-quality ends

• Data Quality Assessment
  • Filtering (12 tools)
  • Trimming (19 tools)
  • Filtering and Trimming (13 tools)
  • Reporting/Visualization (17 tools)
  • Other Pre-analysis RNA-seq Tools (16 tools)

RNA-seq Core Analysis Tools

• Transcriptome Profiling
  • Read mapping or assembly
    If a reference genome or a transcriptome is available, we can map the reads to a reference. Otherwise, we need to assemble the reads into contigs that represent the transcripts of a gene.
    • De novo (reference free) transcriptome assembly
      Depending on the RNA-seq protocol, we can either lose the information on which strand reads originate or use a strand-specific RNA-seq protocol to retain the strand information.
      • Unstranded (4 tools)
      • Stranded (11 tools)
      • Quality Control (2 tools)
    • Mapping to a reference genome or transcriptome
      • Splice Aware (26 tools)
      • Splice unaware (8 tools)
      • Quality Control (15 tools)
• Expression Quantification
  • Union-exon Based (7 tools)
    This method counts a read to belong to a gene if its alignment has adequate overlap with a 'union-exon.' A 'union-exon' consists of all overlapping exons of a gene.
  • Transcript Based (40 tools)
    This method distributes reads between transcript isoforms of a gene.
  • Bacterial (4 tools)
    In bacteria, genes do not contain introns; Thus, the genes do not have alternatively spliced transcript isoforms.
• Differential Expression Analysis (DEA)
  • Pre-processing DEA (4 tools)
    To remove biases, normalization of data before DEA is a must to achieve reliable quantification of gene expression levels. For example, reads mapping on multiple locations can introduce bias. Many tools combine DEA analysis with a normalization step, but some tools solely perform normalization possibly combined diverse testing.
  • Parametric (42 tools)
    Parametric tests assume certain defined population distributions from which the data originates.
  • Non-parametric (13 tools)
    Non-parametric tests do not make any prior assumptions of population distributions.
  • Power analysis (11 tools)
    We should do a power analysis to find out the number of samples required to achieve the desired level of obtaining statistical significance in the differential expression (DE) analyses. Ideally, we should do a power analysis prior to sample collection. Note, that, e.g., data normalization and multiple hypothesis testing affect the power analysis.
• Functional Profiling
  • Enrichment Analysis (GSEA), annotation, other (76 tools)
    We perform gene set enrichment analysis to find out which genes are over-represented in an analysis set and thus potentially associated with a specific phenotype.
  • Comparison With Genome (2 tools)

RNA-seq Visualization and Other Analysis Tools

• Visualization
  • Web-Based (17 tools)
  • Stand-alone (22 tools)
• Other advanced analysis
  • Gene fusion (8 tools)
  • Pipelines (27 tools)
  • Other (64 tools)

Scientific Review articles of RNA-seq

Geistlinger L, Csaba G, Santarelli M, Ramos M, Schiffer L, Turaga N, Law C, Davis S, Carey V, Morgan M, Zimmer R, Waldron L. "Toward a Gold Standard for Benchmarking Gene Set Enrichment Analysis." Brief Bioinform. 2020 Feb 6:bbz158. doi:10.1093/bib/bbz158

Wang, Liguo et al. “Measure transcript integrity using RNA-seq data.” BMC bioinformatics vol. 17 58. 3 Feb. 2016, doi:10.1186/s12859-016-0922-z

Ji, Fei, and Ruslan I Sadreyev. “RNA-seq: Basic Bioinformatics Analysis.” Current protocols in molecular biology vol. 124,1 (2018): e68. doi:10.1002/cpmb.68

Chatterjee, Aniruddha et al. “A Guide for Designing and Analyzing RNA-Seq Data.” Methods in molecular biology (Clifton, N.J.) vol. 1783 (2018): 35-80. doi:10.1007/978-1-4939-7834-2_3

Zhang, Hong et al. “Transcriptome Sequencing: RNA-Seq.” Methods in molecular biology (Clifton, N.J.) vol. 1754 (2018): 15-27. doi:10.1007/978-1-4939-7717-8_2

Li, Xing et al. “Quality control of RNA-seq experiments.” Methods in molecular biology (Clifton, N.J.) vol. 1269 (2015): 137-46. doi:10.1007/978-1-4939-2291-8_8

Rodríguez-García, Antonio et al. “RNA-Seq-Based Comparative Transcriptomics: RNA Preparation and Bioinformatics.” Methods in molecular biology (Clifton, N.J.) vol. 1645 (2017): 59-72. doi:10.1007/978-1-4939-7183-1_5

Love, Michael I et al. “RNA-Seq workflow: gene-level exploratory analysis and differential expression.” F1000Research vol. 4 1070. 14 Oct. 2015, doi:10.12688/f1000research.7035.1

Marco-Puche, Guillermo et al. “RNA-Seq Perspectives to Improve Clinical Diagnosis.” Frontiers in genetics vol. 10 1152. 12 Nov. 2019, doi:10.3389/fgene.2019.01152

Liang, Hanquan, and Erliang Zeng. “RNA-Seq Experiment and Data Analysis.” Methods in molecular biology (Clifton, N.J.) vol. 1366 (2016): 99-114. doi:10.1007/978-1-4939-3127-9_9

Baruzzo G, Hayer KE, Kim EJ, Di Camillo B, FitzGerald GA, Grant GR. Simulation-based comprehensive benchmarking of RNA-seq aligners. Nat Methods. 2017;14(2):135–139. doi:10.1038/nmeth.4106

Han H, Men K. How does normalization impact RNA-seq disease diagnosis?. J Biomed Inform. 2018;85:80–92. doi:10.1016/j.jbi.2018.07.016

Todd, Erica V et al. “The power and promise of RNA-seq in ecology and evolution.” Molecular ecology vol. 25,6 (2016): 1224-41. doi:10.1111/mec.13526

Chen, Geng et al. “Characterizing and annotating the genome using RNA-seq data.” Science China. Life sciences vol. 60,2 (2017): 116-125. doi:10.1007/s11427-015-0349-4

Tarazona, Sonia et al. “Differential expression in RNA-seq: a matter of depth.” Genome research vol. 21,12 (2011): 2213-23. doi:10.1101/gr.124321.111

Arrigoni, Alberto et al. “Analysis RNA-seq and Noncoding RNA.” Methods in molecular biology (Clifton, N.J.) vol. 1480 (2016): 125-35. doi:10.1007/978-1-4939-6380-5_11