Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown

By Mihaela Pertea, Daehwan Kim, Geo Pertea, Jeffrey T. Leek, Steven L. Salzberg. Nature Protocols, 2016

5,669 citations recorded by the source corpus.

High-throughput sequencing of mRNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate and flexible software to reduce the raw read data to comprehensible results. HISAT (hierarchical indexing for spliced alignment of transcripts), StringTie and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol's execution time depends on the computing resources, but it typically takes under 45 min of computer time. HISAT, StringTie and Ballgown are available from http://ccb.jhu.edu/software.shtml.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d81719b712efae05a4fb1ccd5186c7a346428541

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Citations in the corpus

Genome graphs and the evolution of genome inference (2017, background): “One interesting approach to controlling index size is illustrated in HISAT 2 (Pertea et al. 2016); although its approach is not yet described in the literature, the tool is built around a collection of small graph self-indexes, rather than a single large index.”
Computational pan-genomics: status, promises and challenges (2016, methodology): “HISAT2(10) [94] implements an index structure that is also based on [131], but builds many small index structures that together cover the whole genome.”
High-resolution TADs reveal DNA sequences underlying genome organization in flies (2017, methodology): “0.4) (Pertea et al. 2016) and the reads were summarized per gene using featureCounts (v1.”
Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi (2017, methodology): “Interestingly, purely RNA-seq-based pipelines also yield the worst gene/transcript sensitivity and specificity for C. elegans.Comparing the results based on different transcript assemblers, we find that the results based on StringTie are better than those based on Cufflinks for A. thaliana and C. el”