[](http://bioconda.github.io/recipes/salmon/README.html)
**Try out alevin (salmon's single-cell processing module)! Get started with the [tutorial](https://combine-lab.github.io/alevin-tutorial/#blog)**
**Help guide the development of Salmon, [take our survey](https://docs.google.com/forms/d/e/1FAIpQLSeWhBNE_fA_0uVHvbAlAulDmfmowv7rAYla879DZpqCARyRTQ/viewform)**
### Pre-computed decoy transcriptomes
Although the precomputed decoys (<=v.14.2) are still compatible with the latest major release (v1.0.0). We highly recommend updating your index using the full genome, as it gives significantly higher accuracy, for more information please check our extensive benchmarking on comparing different alignment methods and their performance on RNA-seq quantification in the latest revised preprint [manuscript](https://www.biorxiv.org/content/10.1101/657874v2). tl;dr: fast is good but fast and accurate is better !
Please use the [tutorial](https://combine-lab.github.io/alevin-tutorial/2019/selective-alignment/) for a step-by-step guide on how to efficiently index the reference transcriptome and genome for accurate gentrome based RNA-seq quantification.
**Facing problems with Indexing ?, [Check if anyone else already had this problem in the issues section or fill the index generation request form](https://forms.gle/3baJc5SYrkSWb1z48)**
What is Salmon?
===============
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 *quasi-mapping* (accurate but fast-to-compute proxies for traditional read alignments), and
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 *quasi-mapping* 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 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.
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.
Give salmon a try! You can find the latest binary releases [here](https://github.com/COMBINE-lab/salmon/releases).
@@ -27,6 +34,9 @@ Documentation
The documentation for Salmon is available on [ReadTheDocs](http://readthedocs.org), check it out [here](http://salmon.readthedocs.org).
Salmon is, and will continue to be, [freely and actively supported on a best-effort basis](https://oceangenomics.com/about/#open).
If you need industrial-grade technical support, please consider the options at [oceangenomics.com/support](http://oceangenomics.com/support).
