Salmon
================

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.

.. note:: Read / alignment order

    Salmon, like eXpress [#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.

.. note:: 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 <http://sourceforge.net/projects/staden/files/io_lib/>`_ 
    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, when Salmon can begin
    to process fragments more quickly than they can be provided via the parser.
 
    
"""""""""""""""""""""""""""""""""""""""
Providing multiple read files to Salmon
"""""""""""""""""""""""""""""""""""""""

Often, a single library may be split into multiple FASTA/Q files.  Also, sometimes one may wish
to quantify multiple replicates or samples together, treating them as if they are one library.
Salmon allows the user to provide a *space-separated* list of read files to all of it's options
that expect input files (i.e. ``-r``, ``-1``, ``-2``).  When the input is paired-end reads, the
order of the files in the left and right lists must be the same.  There are a number of ways to
provide salmon with multiple read files, and treat these as a single library.  For the examples
below, assume we have two replicates ``lib_1`` and ``lib_2``.  The left and right reads for
``lib_1`` are ``lib_1_1.fq`` and ``lib_1_2.fq``, respectively.  The left and right reads for
``lib_2`` are ``lib_2_1.fq`` and ``lib_2_2.fq``, respectively.  The following are both valid
ways to input these reads to Salmon::

  > salmon quant -i index -l IU -1 lib_1_1.fq lib_2_1.fq -2 lib_1_2.fq lib_2_2.fq -o out

  > salmon quant -i index -l IU -1 <(cat lib_1_1.fq lib_2_1.fq) -2 <(cat lib_1_2.fq lib_2_2.fq) -o out

Similarly, both of these approaches can be adopted if the files are gzipped as well::

   > salmon quant -i index -l IU -1 lib_1_1.fq.gz lib_2_1.fq.gz -2 lib_1_2.fq.gz lib_2_2.fq.gz -o out

   > salmon quant -i index -l IU -1 <(gunzip -c lib_1_1.fq.gz lib_2_1.fq.gz) -2 <(gunzip -c lib_1_2.fq.gz lib_2_2.fq.gz) -o out

In each pair of commands, the first command lets Salmon natively parse the files, while the latter command
creates, on-the-fly, an input stream that consists of the concatenation of both files.  Both methods work, and
are acceptable ways to merge the files.  The latter method (i.e. process substitution) allows more complex
processing to be done to the reads in the substituted process before they are passed to Salmon as input, and thus,
in some situations, is more versatile.

    
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.  More details
about quasi-mappings, and how they are computed, can be found `here <http://bioinformatics.oxfordjournals.org/content/32/12/i192.full>`_.

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.

.. note:: 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``.

.. note:: 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 <https://github.com/mozack/ubu/wiki>`_
    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.

.. topic:: 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.

""""""""""""
``--dumpEq``
""""""""""""

If Salmon is passed the ``--dumpEq`` option, it will write a file in the auxiliary
directory, called ``eq_classes.txt`` that contains the equivalence classes and corresponding
counts that were computed during quasi-mapping.  The file has a format described in
:ref:`eq-class-file`.


"""""""""""""
``--fldMean``
"""""""""""""
*Note* : This option is only important when running Salmon with single-end reads.

Since the empirical fragment length distribution cannot be estimated
from the mappings of single-end reads, the ``--fldMean`` allows the
user to set the expected mean fragment lenth of the sequencing
library.  This value will affect the effective length correction, and
hence the estimated effective lengths of the transcripts and the TPMs.
The value passed to ``--fldMean`` will be used as the mean of the assumed
fragment length distribution (which is modeled as a truncated Gaussan with
a standard deviation given by ``--fldSD``).


"""""""""""
``--fldSD``
"""""""""""

*Note* : This option is only important when running Salmon with single-end reads.

Since the empirical fragment length distribution cannot be estimated
from the mappings of single-end reads, the ``--fldSD`` allows the user
to set the expected standard deviation of the fragment lenth
distribution of the sequencing library.  This value will affect the
effective length correction, and hence the estimated effective lengths
of the transcripts and the TPMs.  The value passed to ``--fldSD`` will
be used as the standard deviation of the assumed fragment length
distribution (which is modeled as a truncated Gaussan with a mean
given by ``--fldMean``).


""""""""""""""
``--useVBOpt``
""""""""""""""

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 [#salmon]_.  While both the standard EM and
the VBEM produce accurate abundance estimates, there are some
trade-offs between the approaches.  The EM algorithm tends to produce
sparser estimates (i.e. more transcripts estimated to have 0
abundance), while the VBEM, in part due to the prior, tends to
estimate non-zero abundance for more transcripts.  Conversely, the
prior used in the VBEM tends to have a regularizing effect, especially
for low abundance transcripts, that leads to more consistent estimates
of abundance at low expression levels.  We are currently working to
analyze and understand all the tradeoffs between these different optimization
approaches.  Also, the VBEM tends to converge after fewer iterations,
so it may result in a shorter runtime; especially if you are computing
many bootstrap samples.

The default prior used in the VB optimization is a *per-nucleotide* prior
of 0.001 per nucleotide.  This means that a transcript of length 1000 will
have a prior count of 1 fragment, while a transcript of length 500 will have
a prior count of 0.5 fragments, etc.  This behavior can be modified in two
ways.  First, the prior itself can be modified via Salmon's ``--vbPrior``
option.  The argument to this option is the value you wish to place as the
*per-nucleotide* prior.  Additonally, you can modify the behavior to use
a *per-transcript* rather than a *per-nucleotide* prior by passing the flag
``--perTranscriptPrior`` to Salmon.  In this case, whatever value is set
by ``--vbPrior`` will be used as the transcript-level prior, so that the
prior count is no longer dependent on the transcript length.  However,
the default behavior of a *per-nucleotide* prior is recommended when
using VB optimization.


"""""""""""""""""""
``--numBootstraps``
"""""""""""""""""""

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.

"""""""""""""""""""""
``--numGibbsSamples``
"""""""""""""""""""""

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.)

"""""""""""""""""""""
``--seqBias``
"""""""""""""""""""""

Passing the ``--seqBias`` 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. Salmon uses a variable-length Markov Model
(VLMM) to model the sequence specific biases at both the 5' and 3' end
of sequenced fragments. This methodology generally follows that of
Roberts et al. [#roberts]_, though some details of the VLMM differ.

*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.  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.

"""""""""""""""""""""
``--gcBias``
"""""""""""""""""""""

Passing the ``--gcBias`` flag to Salmon will enable it to learn and
correct for fragment-level GC biases in the input data.  Specifically,
this model will attempt to correct for biases in how likely a sequence
is to be observed based on its internal GC content.  This bias is
distinct from the primer biases learned with the ``--seqBias`` option.
Though these biases are distinct, they are not completely independent.
When both ``--seqBias`` and ``--gcBias`` are enabled, Salmon will
learn a conditional fragment-GC bias model.  By default, Salmon will
learn 3 different fragment-GC bias models based on the GC content of
the fragment start and end contexts, though this number of conditional
models can be changed with the (*hidden*) option
``--conditionalGCBins``.  Likewise, the number of distinct fragment GC
bins used to model the GC bias can be changed with the (*hidden*)
option ``--numGCBins``.

*Note* : In order to speed up the evaluation of the GC content of
arbitrary fragments, Salmon pre-computes and stores the cumulative GC
count for each transcript.  This requires an extra 4-bytes per
nucleotide.  While this extra memory usage should normally be minor,
it can nonetheless be controlled with the ``--gcSizeSamp`` option.
This option takes a positive integer argument *i*, such that Salmon
stores the values of the cumulative GC count only at every
*i*:sup:`th` nucleotide.  The cumulative GC count at values between
the sampled positions are recomputed on-the-fly when necessary.  Using
this option will reduce the memory required to store the GC
information by a factor of *i*, but will slow down the computation of
GC-fragment content by a factor of *i*/2.  Typically, the
``--gcSizeSamp`` can be left at its default value of 1, but larger
values can be chosen if necessary.

"""""""""""""""""""""
``--posBias``
"""""""""""""""""""""

Passing the ``--posBias`` 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 small and fixed number
of models are learned for different lenght classes of transcripts, as
is done in Roberts et al. [#roberts]_. *Note*: The positional bias
model is relatively new, and is still undergoing testing.  It replaces
the previous `--useFSPD` option, which is now deprecated.  This
feature should be considered as *experimental* in the current release.


"""""""""""""""""""
``--biasSpeedSamp``
"""""""""""""""""""

When evaluating the bias models (the GC-fragment model specifically),
Salmon must consider the probability of generating a fragment of every
possible length (with a non-trivial probability) from every position
on every transcript.  This results in a process that is quadratic in
the length of the transcriptome --- though each evaluation itself is
efficient and the process is highly parallelized.

It is possible to speed this process up by a multiplicative factor by
considering only every *i*:sup:`th` fragment length, and interploating
the intermediate results.  The ``--biasSpeedSamp`` option allows the
user to set this sampling factor.  Larger values speed up effective
length correction, but may decrease the fidelity of bias modeling.
However, reasonably small values (e.g. 10 or less) should have only a
minor effect on the computed effective lengths, and can considerably
speed up effective length correction on large transcriptomes.

""""""""""""""""""""""""
``--writeUnmappedNames``
""""""""""""""""""""""""

Passing the ``--writeUnmappedNames`` flag to Salmon will tell Salmon to
write out the names of reads (or mates in paired-end reads) that do not
map to the transcriptome.  When mapping paired-end reads, the entire
fragment (both ends of the pair) are identified by the name of the first
read (i.e. the read appearing in the ``_1`` file).  Each line of the umapped
reads file contains the name of the unmapped read followed by a simple flag
that designates *how* the read failed to map completely.  For single-end
reads, the only valid flag is ``u`` (unmapped).  However, for paired-end
reads, there are a number of different possibilities, outlined below:

::
   
   u   = The entire pair was unmapped. No mappings were found for either the left or right read.
   m1  = Left orphan (mappings were found for the left (i.e. first) read, but not the right).
   m2  = Right orphan (mappinds were found for the right read, but not the left).
   m12 = Left and right orphans. Both the left and right read mapped, but never to the same transcript. 

By reading through the file of unmapped reads and selecting the appropriate
sequences from the input FASTA/Q files, you can build an "unmapped" file that
can then be used to investigate why these reads may not have mapped
(e.g. poor quality, contamination, etc.).  Currently, this process must be
done independently, but future versions of Salmon may provide a script to
generate this unmapped FASTA/Q file from the unmapped file and the original
inputs.


"""""""""""""""""""
``--writeMappings``
"""""""""""""""""""

Passing the ``--writeMappings`` argument to Salmon will have an effect
only in mapping-based mode and *only when using a quasi-index*.  When
executed with the ``--writeMappings`` argument, Salmon will write out
the mapping information that it then processes to quantify transcript
abundances.  The mapping information will be written in a SAM
compatible format. If no options are provided to this argument, then
the output will be written to stdout (so that e.g. it can be piped to
samtools and directly converted into BAM format).  Otherwise, this 
argument can optionally be provided with a filename, and the mapping 
information will be written to that file.

.. note:: Compatible mappings

  The mapping information is computed and written *before* library
  type compatibility checks take place, thus the mapping file will
  contain information about all mappings of the reads considered by
  Salmon, even those that may later be filtered out due to
  incompatibility with the library type.
   
What's this ``LIBTYPE``?
------------------------

Salmon, 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.  As of version
0.7.0, Salmon also has the ability to automatically infer (i.e. guess)
the library type based on how the first few thousand reads map to the
transcriptome.  To allow Salmon to automatically infer the library
type, simply provide ``-l A`` or ``--libType A`` to Salmon.  Even if you
allow Salmon to infer the library type for you, you should still read
the section below, so that you can interpret how Salmon reports the
library type it discovers.

.. note:: Automatic library type detection in alignment-based mode

 The implementation of this feature involves opening the BAM
 file, peaking at the first record, and then closing it to
 determine if the library should be treated as single-end or
 paired-end.  Thus, *in alignment-based mode* automatic
 library type detection will not work with an input
 stream. If your input is a regular file, everything should
 work as expected; otherwise, you should provide the library
 type explicitly in alignment-based mode.
 
 Also the automatic library type detection is performed *on the
 basis of the alignments in the file*.  Thus, for example, if the
 upstream aligner has been told to perform strand-aware mapping
 (i.e. to ignore potential alignments that don't map in the
 expected manner), but the actual library is unstranded,
 automatic library type detection cannot detect this.  It will
 attempt to detect the library type that is most consistent *with
 the alignment that are provided*.

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)


.. note:: 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 :ref:`FragLibType`. 

Output
------

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

Misc
----

Salmon, in *quasi-mapping*-based mode, can accept reads from FASTA/Q
format files, or directly from gzipped FASTA/Q files (the ability to
accept compressed files directly is a feature of Salmon 0.7.0 and
higher).  If your reads are compressed in a different format, you can
still stream them directly to Salmon by using process substitution.
Say in the *quasi-mapping*-based Salmon example above, the reads were
actually in the files ``reads1.fa.bz2`` and ``reads2.fa.bz2``, then
you'd run the following command to decompress the reads "on-the-fly":

::

    > ./bin/salmon quant -i transcripts_index -l <LIBTYPE> -1 <(bunzip2 -c reads1.fa.gz) -2 <(bunzip2 -c reads2.fa.bz2) -o transcripts_quant

and the bzipped files will be decompressed via separate processes and
the raw reads will be fed into Salmon.  Actually, you can use this
same process even with gzip compressed reads (replacing ``bunzip2``
with ``gunzip`` or ``pigz -d``).  Depending on the number of threads
and the exact configuration, this may actually improve Salmon's
running time, since the reads are decompressed concurrently in a
separate process when you use process substitution.

**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 <http://biorxiv.org/content/early/2015/10/03/021592>`_.
If you have something useful to report or just some interesting ideas
or suggestions, please contact us (`rob.patro@cs.stonybrook.edu`
and/or `carlk@cs.cmu.edu`).  If you encounter any bugs, please file a
*detailed* bug report at the `Salmon GitHub repository <https://github.com/COMBINE-lab/salmon>`_.


References
----------

.. [#express] Roberts, Adam, and Lior Pachter. "Streaming fragment assignment for real-time analysis of sequencing experiments." Nature methods 10.1 (2013): 71-73.
   
.. [#roberts] Roberts, Adam, et al. "Improving RNA-Seq expression estimates by correcting for fragment bias." Genome biology 12.3 (2011): 1.

.. [#salmon] Patro, Rob, et al. "Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference." bioRxiv (2016).
