Salmon is a tool for wicked-fast transcript quantification from RNA-seq data. It requires a set of target transcripts (either from a reference or de-novo assembly) to quantify. All you need to run Salmon is a FASTA file containing your reference transcripts and a (set of) FASTA/FASTQ file(s) containing your reads. Optionally, Salmon can make use of pre-computed alignments (in the form of a SAM/BAM file) to the transcripts rather than the raw reads.

The quasi-mapping-based mode of Salmon runs in two phases; indexing and quantification. The indexing step is independent of the reads, and only need to be run one for a particular set of reference transcripts. The quantification step, obviously, is specific to the set of RNA-seq reads and is thus run more frequently. For a more complete description of all available options in Salmon, see below.

The alignment-based mode of Salmon does not require indexing. Rather, you can simply provide Salmon with a FASTA file of the transcripts and a SAM/BAM file containing the alignments you wish to use for quantification.

Using Salmon

As mentioned above, there are two “modes” of operation for Salmon. The first, requires you to build an index for the transcriptome, but then subsequently processes reads directly. The second mode simply requires you to provide a FASTA file of the transcriptome and a .sam or .bam file containing a set of alignments.


Read / alignment order

Salmon, like eXpress, uses a streaming inference method to perform transcript-level quantification. One of the fundamental assumptions of such inference methods is that observations (i.e. reads or alignments) are made “at random”. This means, for example, that alignments should not be sorted by target or position. If your reads or alignments do not appear in a random order with respect to the target transcripts, please randomize / shuffle them before performing quantification with Salmon.


Number of Threads

The number of threads that Salmon can effectively make use of depends upon the mode in which it is being run. In alignment-based mode, the main bottleneck is in parsing and decompressing the input BAM file. We make use of the Staden IO library for SAM/BAM/CRAM I/O (CRAM is, in theory, supported, but has not been thorougly tested). This means that multiple threads can be effectively used to aid in BAM decompression. However, we find that throwing more than a few threads at file decompression does not result in increased processing speed. Thus, alignment-based Salmon will only ever allocate up to 4 threads to file decompression, with the rest being allocated to quantification. If these threads are starved, they will sleep (the quantification threads do not busy wait), but there is a point beyond which allocating more threads will not speed up alignment-based quantification. We find that allocating 8 — 12 threads results in the maximum speed, threads allocated above this limit will likely spend most of their time idle / sleeping.

For quasi-mapping-based Salmon, the story is somewhat different. Generally, performance continues to improve as more threads are made available. This is because the determiniation of the potential mapping locations of each read is, generally, the slowest step in quasi-mapping-based quantification. Since this process is trivially parallelizable (and well-parallelized within Salmon), more threads generally equates to faster quantification. However, there may still be a limit to the return on invested threads. Specifically, writing to the mapping cache (see Misc below) is done via a single thread. With a huge number of quantification threads or in environments with a very slow disk, this may become the limiting step. If you’re certain that you have more than the required number of observations, or if you have reason to suspect that your disk is particularly slow on writes, then you can disable the mapping cache (--disableMappingCache), and potentially increase the parallelizability of quasi-mapping-based Salmon.

Quasi-mapping-based mode (including lightweight alignment)

One of the novel and innovative features of Salmon is its ability to accurately quantify transcripts using quasi-mappings. Quasi-mappings are mappings of reads to transcript positions that are computed without performing a base-to-base alignment of the read to the transcript. Quasi-mapping is typically much faster to compute than traditional (or full) alignments, and can sometimes provide superior accuracy by being more robust to errors in the read or genomic variation from the reference sequence.

Salmon currently supports two different methods for mapping reads to transcriptomes; (SMEM-based) lightweight-alignment and quasi-mapping. SMEM-based mapping is the original lightweight-alignment method used by Salmon, and quasi-mapping is a newer and considerably faster alternative. Both methods are currently exposed via the same quant command, but the methods require different indices so that SMEM-based mapping cannot be used with a quasi-mapping index and vice-versa.

If you want to use Salmon in quasi-mapping-based mode, then you first have to build an Salmon index for your transcriptome. Assume that transcripts.fa contains the set of transcripts you wish to quantify. First, you run the Salmon indexer:

> ./bin/salmon index -t transcripts.fa -i transcripts_index --type quasi -k 31

This will build the quasi-mapping-based index, using an auxiliary k-mer hash over k-mers of length 31. While quasi-mapping will make used of arbitrarily long matches between the query and reference, the k size selected here will act as the minimum acceptable length for a valid match. Thus, a smaller value of k may slightly improve sensitivty. We find that a k of 31 seems to work well for reads of 75bp or longer, but you might consider a smaller k if you plan to deal with shorter reads. Note that there is also a k parameter that can be passed to the quant command. However, this has no effect if one is using a quasi-mapping index, as the k value provided during the index building phase overrides any k provided during quantification in this case. Since quasi-mapping is the default index type in Salmon, you can actually leave off the --type quasi parameter when building the index. To build a lightweight-alignment (FMD-based) index instead, one would use the following command:

> ./bin/salmon index -t transcripts.fa -i transcripts_index --type fmd

Note that no value of k is given here. However, the SMEM-based mapping index makes use of a parameter k that is passed in during the quant phase (the default value is 19).

Then, you can quantify any set of reads (say, paired-end reads in files reads1.fq and reads2.fq) directly against this index using the Salmon quant command as follows:

> ./bin/salmon quant -i transcripts_index -l <LIBTYPE> -1 reads1.fq -2 reads2.fq -o transcripts_quant

If you are using single-end reads, then you pass them to Salmon with the -r flag like:

> ./bin/salmon quant -i transcripts_index -l <LIBTYPE> -r reads.fq -o transcripts_quant

This same quant command will work with either index (quasi-mapping or SMEM-based), and Salmon will automatically determine the type of index being read and perform the appropriate lightweight mapping accordingly.


Order of command-line parameters

The library type -l should be specified on the command line before the read files (i.e. the parameters to -1 and -2, or -r). This is because the contents of the library type flag is used to determine how the reads should be interpreted.

You can, of course, pass a number of options to control things such as the number of threads used or the different cutoffs used for counting reads. Just as with the alignment-based mode, after Salmon has finished running, there will be a directory called salmon_quant, that contains a file called quant.sf containing the quantification results.

Alignment-based mode

Say that you’ve prepared your alignments using your favorite aligner and the results are in the file aln.bam, and assume that the sequence of the transcriptome you want to quantify is in the file transcripts.fa. You would run Salmon as follows:

> ./bin/salmon quant -t transcripts.fa -l <LIBTYPE> -a aln.bam -o salmon_quant

The <LIBTYPE> parameter is described below and is shared between both modes of Salmon. After Salmon has finished running, there will be a directory called salmon_quant, that contains a file called quant.sf. This contains the quantification results for the run, and the columns it contains are similar to those of Sailfish (and self-explanatory where they differ).

For the full set of options that can be passed to Salmon in its alignment-based mode, and a description of each, run salmon quant --help-alignment.


Genomic vs. Transcriptomic alignments

Salmon expects that the alignment files provided are with respect to the transcripts given in the corresponding fasta file. That is, Salmon expects that the reads have been aligned directly to the transcriptome (like RSEM, eXpress, etc.) rather than to the genome (as does, e.g. Cufflinks). If you have reads that have already been aligned to the genome, there are currently 3 options for converting them for use with Salmon. First, you could convert the SAM/BAM file to a FAST{A/Q} file and then use the lightweight-alignment-based mode of Salmon described below. Second, given the converted FASTA{A/Q} file, you could re-align these converted reads directly to the transcripts with your favorite aligner and run Salmon in alignment-based mode as described above. Third, you could use a tool like sam-xlate to try and convert the genome-coordinate BAM files directly into transcript coordinates. This avoids the necessity of having to re-map the reads. However, we have very limited experience with this tool so far.

Multiple alignment files

If your alignments for the sample you want to quantify appear in multiple .bam/.sam files, then you can simply provide the Salmon -a parameter with a (space-separated) list of these files. Salmon will automatically read through these one after the other quantifying transcripts using the alignments contained therein. However, it is currently the case that these separate files must (1) all be of the same library type and (2) all be aligned with respect to the same reference (i.e. the @SQ records in the header sections must be identical).

Description of important options

Salmon exposes a number of useful optional command-line parameters to the user. The particularly important ones are explained here, but you can always run salmon quant -h to see them all.

-p / --numThreads

The number of threads that will be used for quasi-mapping, quantification, and bootstrapping / posterior sampling (if enabled). Salmon is designed to work well with many threads, so, if you have a sufficient number of processors, larger values here can speed up the run substantially.


Use the variational Bayesian EM algorithm rather than the “standard” EM algorithm to optimize abundance estimates. The details of the VBEM algorithm can be found in [2]_, and the details of the variant over fragment equivalence classes that we use can be found in [3]_. While both the standard EM and the VBEM produce accurate abundance estimates, those produced by the VBEM seem, generally, to be a bit more accurate. Further, the VBEM tends to converge after fewer iterations, so it may result in a shorter runtime; especially if you are computing many bootstrap samples.


Salmon has the ability to optionally compute bootstrapped abundance estimates. This is done by resampling (with replacement) from the counts assigned to the fragment equivalence classes, and then re-running the optimization procedure, either the EM or VBEM, for each such sample. The values of these different bootstraps allows us to assess technical variance in the main abundance estimates we produce. Such estimates can be useful for downstream (e.g. differential expression) tools that can make use of such uncertainty estimates. This option takes a positive integer that dictates the number of bootstrap samples to compute. The more samples computed, the better the estimates of varaiance, but the more computation (and time) required.


Just as with the bootstrap procedure above, this option produces samples that allow us to estimate the variance in abundance estimates. However, in this case the samples are generated using posterior Gibbs sampling over the fragment equivalence classes rather than bootstrapping. We are currently analyzing these different approaches to assess the potential trade-offs in time / accuracy. The --numBootstraps and --numGibbsSamples options are mutually exclusive (i.e. in a given run, you must set at most one of these options to a positive integer.)


Passing the --biasCorrect flag to Salmon will enable it to learn and correct for sequence-specific biases in the input data. Specifically, this model will attempt to correct for random hexamer priming bias, which results in the preferential sequencing of fragments starting with certain nucleotide motifs. By default, Salmon learns the sequence-specific bias parameters using 1,000,000 reads from the beginning of the input. If you wish to change the number of samples from which the model is learned, you can use the --numBiasSamples parameter. Note: This sequence-specific bias model is substantially different from the bias-correction methodology that was used in Salmon versions prior to 0.6.0 (and Sailfish versions prior to 0.9.0). This model specifically accounts for sequence-specific bias, and should not be prone to the over-fitting problem that was sometimes observed using the previous bias-correction methodology.


Passing the --useFSPD flag to Salmon will enable modeling of a position-specific fragment start distribution. This is meant to model non-uniform coverage biases that are sometimes present in RNA-seq data (e.g. 5’ or 3’ positional bias). Currently, a single global model is learned and applied to all transcripts, as there is typically not enough information to learn a separate model for each transcript. However, modeling the effect in this manner can still be helpful when there is a global bias in coverage. In the future, we will potentially be exploring more fine-grained positional bias models.

What’s this LIBTYPE?

Salmon, like sailfish, has the user provide a description of the type of sequencing library from which the reads come, and this contains information about e.g. the relative orientation of paired end reads. However, we’ve replaced the somewhat esoteric description of the library type with a simple set of strings; each of which represents a different type of read library. This new method of specifying the type of read library is being back-ported into Sailfish and will be available in the next release.

The library type string consists of three parts: the relative orientation of the reads, the strandedness of the library, and the directionality of the reads.

The first part of the library string (relative orientation) is only provided if the library is paired-end. The possible options are:

I = inward
O = outward
M = matching

The second part of the read library string specifies whether the protocol is stranded or unstranded; the options are:

S = stranded
U = unstranded

If the protocol is unstranded, then we’re done. The final part of the library string specifies the strand from which the read originates in a strand-specific protocol — it is only provided if the library is stranded (i.e. if the library format string is of the form S). The possible values are:

F = read 1 (or single-end read) comes from the forward strand
R = read 1 (or single-end read) comes from the reverse strand

An example of some library format strings and their interpretations are:

IU (an unstranded paired-end library where the reads face each other)
SF (a stranded single-end protocol where the reads come from the forward strand)
OSR (a stranded paired-end protocol where the reads face away from each other,
     read1 comes from reverse strand and read2 comes from the forward strand)


Strand Matching

Above, when it is said that the read “comes from” a strand, we mean that the read should align with / map to that strand. For example, for libraries having the OSR protocol as described above, we expect that read1 maps to the reverse strand, and read2 maps to the forward strand.

For more details on the library type, see Fragment Library Types.


For details of Salmon’s different output files and their formats see :ref: FileFormats.


Salmon deals with reading from compressed read files in the same way as sailfish — by using process substitution. Say in the lightweigh-alignment-based salmon example above, the reads were actually in the files reads1.fa.gz and reads2.fa.gz, then you’d run the following command to decompress the reads “on-the-fly”:

> ./bin/salmon quant -i transcripts_index -l <LIBTYPE> -1 <(gzcat reads1.fa.gz) -2 <(gzcat reads2.fa.gz) -o transcripts_quant

and the gzipped files will be decompressed via separate processes and the raw reads will be fed into salmon.

Finally, the purpose of making this software available is for people to use it and provide feedback. The pre-print describing this method is on bioRxiv. If you have something useful to report or just some interesting ideas or suggestions, please contact us ( and/or If you encounter any bugs, please file a detailed bug report at the Salmon GitHub repository.