# Salmon 1.8.0
[](http://salmon.readthedocs.org/en/latest)
[](http://bioconda.github.io/recipes/salmon/README.html)
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 *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.
Give salmon a try! You can find the latest binary releases
[here](https://github.com/COMBINE-lab/salmon/releases).
The current version number of the master branch of Salmon can be found
[**here**](http://combine-lab.github.io/salmon/version_info/latest)
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/contact](http://oceangenomics.com/contact).
Decoy sequences in transcriptomes
=================================
tl;dr: fast is good but fast and accurate is better!
[Alignment and mapping methodology influence transcript abundance
estimation](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02151-8),
and accounting for the [accounting for fragments of unexpected origin can
improve transcript
quantification](https://www.biorxiv.org/content/10.1101/2021.01.17.426996v1).
To this end, salmon provides the ability to index both the transcriptome as
well as decoy seuqence that can be considered during mapping and
quantification. The decoy sequence accounts for reads that might otherwise be
(spuriously) attributed to some annotated transcript. This
[tutorial](https://combine-lab.github.io/alevin-tutorial/2019/selective-alignment/)
provides a step-by-step guide on how to efficiently index the reference
transcriptome and genome to produce a decoy-aware index. Specifically, there
are 3 possible ways in which the salmon index can be created:
* cDNA-only index : salmon_index -
https://combine-lab.github.io/salmon/getting_started/. This method will
result in the smallest index and require the least resources to build, but
will be the most prone to possible spurious alignments.
* SA mashmap index: salmon_partial_sa_index - (regions of genome that have
high sequence similarity to the transcriptome) - Details can be found in
[this README](https://github.com/COMBINE-lab/SalmonTools/blob/master/README.md)
and using
[this script](https://raw.githubusercontent.com/COMBINE-lab/SalmonTools/master/scripts/generateDecoyTranscriptome.sh).
While running mashmap can require considerable resources, the resulting
decoy files are fairly small. This will result in an index bigger than the
cDNA-only index, but still mucch smaller than the full genome index below.
It will confer many, though not all, of the benefits of using the entire
genome as a decoy sequence.
* SAF genome index: salmon_sa_index - (the full genome is used as decoy) - The
tutorial for creating such an index can be found
[here](https://combine-lab.github.io/alevin-tutorial/2019/selective-alignment/).
This will result in the largest index, but likely does the best job in
avoiding spurious alignments to annotated transcripts.
**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)
**NOTE**:
If you are generating an index to be used for single-cell or single-nucleus
quantification with [alevin-fry](https://github.com/COMBINE-lab/alevin-fry),
then we recommend you consider building a spliced+intron (_splici_) reference.
This serves much of the purpose of a decoy-aware index when quantifying with
alevin-fry, while also providing the capability to attribute splicing status
to mapped fragments. More details about the _splici_ reference and the
Unspliced/Spliced/Ambiguous quantification mode it enables can be found
[here](https://combine-lab.github.io/alevin-fry-tutorials/2021/improving-txome-specificity/).
Activating the conda environment
================================
```console
bash
source /local/cluster/salmon-1.8.0/activate.sh
```
To use in `SGE_Batch` please add the source command above to your shell script
before any salmon commands.
Location and version:
```console
$ which salmon
/local/cluster/salmon-1.8.0/bin/salmon
$ salmon --version
salmon 1.8.0
```
help message:
```console
$ salmon --help
salmon v1.8.0
Usage: salmon -h|--help or
salmon -v|--version or
salmon -c|--cite or
salmon [--no-version-check] [-h | options]
Commands:
index : create a salmon index
quant : quantify a sample
alevin : single cell analysis
swim : perform super-secret operation
quantmerge : merge multiple quantifications into a single file
```
software ref:
research ref: