RNA-Seq data processing and gene expression analysis

Below are two protocols for generation of gene- and transcript-level counts. Protocol 1 focuses on the classical alignment-based approach, whereas Protocol 2 uses recent advances in accelerated kmer-based 'pseudo-alignment'^{[1-3](#footnote1)} approaches for assigning reads to transcripts, which has some distinct advantages to alignment methods but rely on having a comprehensive and reliably annotated transcriptome:

• Protocol 1: Alignment-based approach
• Protocol 2: Estimated counts using graph-based or similar approaches

Bibliography

1. Patro, R., Mount, S. M., & Kingsford, C. (2014). Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nature biotechnology, 32(5), 462.
2. Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nature biotechnology, 34(5), 525.
3. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017). [Salmon provides fast and bias-aware quantification of transcript expression. Nature methods, 14(4), 417.

Here, this step depends on the protocol you followed in the previous step:

• Protocol 1: Alignment-based approach
• Protocol 2: Estimated counts using graph-based or similar approaches

Again, this depends on the protocol you chose from the begining:

• Protocol 1: Alignment-based approach
• Protocol 2: Estimated counts using graph-based or similar approaches