The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
Open accessAbstract
Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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Self-citationMapSplice: Accurate mapping of RNA-seq reads for splice junction discovery
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Cited by
HISAT: a fast spliced aligner with low memory requirements
Kim, D., Langmead, B., & Salzberg, S. L. (2015) · Nature Methods · doi:10.1038/nmeth.3317 · Cited by 24,112
Self-citationSCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Self-citationImproved vectors and genome-wide libraries for CRISPR screening
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Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
Soneson, C., Love, M. I., & Robinson, M. D. (2015) · F1000Research · doi:10.12688/f1000research.7563.1 · Cited by 3,929
Metadata
- Authors
- Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D. R., Pimentel, H., Salzberg, S. L., Rinn, J. L., & Pachter, L.
- Venue
- Nature Protocols
- Published
- 2012-03
- Volume
- 7
- Issue
- 3
- Pages
- 562-578
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W2100305481
- Cited by
- 12,116
- References
- 13
- FWCI
- 185.53
- Type
- journal article
- Language
- en
- Open access
- green
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