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https://github.com/COMBINE-lab/salmon
🐟 🍣 🍱 Highly-accurate & wicked fast transcript-level quantification from RNA-seq reads using selective alignment
https://github.com/COMBINE-lab/salmon
10x bioinformatics c-plus-plus gene-expression quantification quasi-mapping rna-seq rna-seq-quantification rnaseq sailfish salmon scrna-seq selective-alignment single-cell single-cell-rna-seq transcriptome
Last synced: 26 days ago
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🐟 🍣 🍱 Highly-accurate & wicked fast transcript-level quantification from RNA-seq reads using selective alignment
- Host: GitHub
- URL: https://github.com/COMBINE-lab/salmon
- Owner: COMBINE-lab
- License: gpl-3.0
- Created: 2015-03-19T22:37:41.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2024-03-15T20:02:53.000Z (9 months ago)
- Last Synced: 2024-04-14T18:13:12.076Z (8 months ago)
- Topics: 10x, bioinformatics, c-plus-plus, gene-expression, quantification, quasi-mapping, rna-seq, rna-seq-quantification, rnaseq, sailfish, salmon, scrna-seq, selective-alignment, single-cell, single-cell-rna-seq, transcriptome
- Language: C++
- Homepage: https://combine-lab.github.io/salmon
- Size: 209 MB
- Stars: 725
- Watchers: 39
- Forks: 156
- Open Issues: 324
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- Awesome-Bioinformatics-Benchmarks - Selective Alignment - bio.sourceforge.net/bowtie2/index.shtml) was considerably slower than all three of these approaches. However, in terms of accuracy, SA yielded the best results, followed by alignment to the genome (with subsequent transcriptomic projection) using STAR and SA (using carefully selected decoy sequences). Bowtie2 generally performed similarly to SA, but without the benefit of decoy sequences, seemed to admit more spurious mappings. Finally, lightweight mapping of sequencing reads to the transcriptome showed the lowest overall consistency with quantifications derived from the oracle alignments. Note: Both Selective Alignment and quasi-mapping are part of the [salmon](https://combine-lab.github.io/salmon/) codebase. (RNA-seq / Alignment/Quantification Methods)
README
[](http://salmon.readthedocs.org/en/latest)
[](http://bioconda.github.io/recipes/salmon/README.html)
**Try out the new [alevin-fry](https://alevin-fry.readthedocs.io/en/latest/) framework for single-cell analysis; tutorials can be found [here](https://combine-lab.github.io/alevin-fry-tutorials/)!**
**Help guide the development of Salmon, [take our survey](https://docs.google.com/forms/d/e/1FAIpQLSeWhBNE_fA_0uVHvbAlAulDmfmowv7rAYla879DZpqCARyRTQ/viewform)**
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/).
Chat live about Salmon
======================
You can chat with the Salmon developers and other users via Gitter (**Note**: Gitter is much less frequently monitored than GitHub, so if you have an important problem or question, please consider opening an issue here on GitHub)!
[](https://gitter.im/COMBINE-lab/salmon?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)