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638 Free RNA-seq Analysis Tools - Software and Resources

638 Free RNA-seq Analysis Tools - Software and Resources

Graph: RNA-seq analysis workflow

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

RNA-seq Core Analysis Tools

RNA-seq Visualization and Other Analysis 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

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