Salmon is a wicked-fast program to produce a highly-accurate, transcript-level quantification estimates from RNA-seq data. Salmon achieves its accuracy and speed via a number of different innovations, including the use of selective-alignment (accurate but fast-to-compute proxies for traditional read alignments), and massively-parallel stochastic collapsed variational inference. The result is a versatile tool that fits nicely into many different pipelines. For example, you can choose to make use of our selective-alignment algorithm by providing Salmon with raw sequencing reads, or, if it is more convenient, you can provide Salmon with regular alignments (e.g. an unsorted BAM file with alignments to the transcriptome produced with your favorite aligner), and it will use the same wicked-fast, state-of-the-art inference algorithm to estimate transcript-level abundances for your experiment.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
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- Gafurov, Askar; Vinař, Tomáš; Brejová, Broňa: Probabilistic models of (k)-mer frequencies (extended abstract) (2021)
- Lim, David K.; Rashid, Naim U.; Ibrahim, Joseph G.: Model-based feature selection and clustering of RNA-seq data for unsupervised subtype discovery (2021)
- Rashid, Naim U.; Li, Quefeng; Yeh, Jen Jen; Ibrahim, Joseph G.: Modeling between-study heterogeneity for improved replicability in gene signature selection and clinical prediction (2020)
- Wolff, Alexander: Analysis of expression profile and gene variation via development of methods for next generation sequencing data (2018)
- Gunady, Mohamed K.; Cornwell, Steffen; Mount, Stephen M.; Bravo, Héctor Corrada: Yanagi: transcript segment library construction for RNA-seq quantification (2017)