Table 2.
Widely used RNA-Seq software packages
| Primary category | Tool name | Notes |
|---|---|---|
| Splice-aware read alignment | GEM | Filtration-based approach to approximate string matching for alignment |
| GSNAP | Based on seed and extend alignment algorithm aware of complex variants | |
| MapSplice | Based on Burrows-Wheeler Transform (BWT) algorithm | |
| RUM | Integrates alignment tools Blat and Bowtie to increase accuracy | |
| STAR | Based on seed searching in an uncompressed suffix arrays followed by seed clustering and stitching procedure; fast but memory-intensive | |
| TopHat | Uses Bowtie, based on BWT, to align reads; resolves spliced reads using exons by split read mapping | |
| Transcript assembly and quantification | Cufflinks | Assembles transcripts to reference annotations or de novo and quantifies abundance |
| FluxCapacitor | Quantifies transcripts using reference annotations | |
| iReckon | Models novel isoforms and estimates their abundance | |
| Differential expression (DE) | BaySeq | Count-based approach using empirical Bayesian method to estimate posterior likelihoods |
| Cuffdiff2 | Isoform-based approach based on beta negative binomial distribution | |
| DESeq | Exon-based approach using the negative binomial model | |
| DEGSeq | Isoform-based approach using the Poisson model | |
| EdgeR | Count-based approach using empirical Bayes method based on the negative binomial model | |
| MISO | Isoform-based model using Bayes factors to estimate posterior probabilities | |
| Other tools | HCP | Normalizes expression data by inferring known and hidden factors with prior knowledge |
| PEER | Normalizes expression data by inferring known and hidden factors using a probabilistic estimation based on the Bayesian framework | |
| Matrix eQTL | Fast eQTL detection tool that uses linear models (linear regression or ANOVA) |
