salmon/salmon_quant

Transcriptome

Quantification

Description

Salmon is a tool for wicked-fast transcript quantification from RNA-seq data. It can either make use of pre-computed alignments (in the form of a SAM/BAM file) to the transcripts rather than the raw reads, or can be run in the mapping-based mode.

Type

bash_script

License

GPL-3.0

Keywords

Transcriptome

Quantification

Links

Repository

Documentation

Contributors

Launch

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
lib_type: [ "A" ]  
output: "$id.$key.output.quant_output"  
quant_results: "$id.$key.quant_results.sf"  
mapping_sam: "$id.$key.mapping_sam.sam"  
aux_dir: "$id.$key.aux_dir.aux_info"  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/biobox.git \  
  -revision v0.3.0 \  
  -main-script target/nextflow/salmon/salmon_quant/main.nf \  
  -params-file params.yaml

Common input options

Name	Type & Properties	Description
--lib_type -l	string	Format string describing the library. The library type string consists of three parts: 1. Relative orientation of the reads: This part is only provided if the library is paired-end, The possible options are I = inward O = outward M = matching 2. Strandedness of the library: This part specifies whether the protocol is stranded or unstranded. The options are: S = stranded U = unstranded 3. Directionality of the reads: If the library is stranded, the final part of the library string is used to specify the strand from which the read originates. 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

Name

Type & Properties

--lib_type

-l

string

Mapping input options

Name	Type & Properties	Description
--index -i	file	Salmon index.
--unmated_reads -r	file multiple	List of files containing unmated reads of (e.g. single-end reads).
--mates1 -m1	file multiple	File containing the #1 mates.
--mates2 -m2	file multiple	File containing the #2 mates.

Name

Type & Properties

--index

-i

file

--unmated_reads

-r

file

multiple

--mates1

-m1

file

multiple

--mates2

-m2

file

multiple

Alignment input options

Name	Type & Properties	Description
--discard_orphans	boolean_true	Discard orphan alignments in the input [for alignment-based mode only]. If this flag is passed, then only paired alignments will be considered toward quantification estimates. The default behavior is to consider orphan alignments if no valid paired mappings exist.
--alignments -a	file multiple	Input alignment (BAM) file(s).
--eqclasses -e	file	input salmon weighted equivalence class file.
--targets -t	file	FASTA format file containing target transcripts.
--ont	boolean_true	Use alignment model for Oxford Nanopore long reads

Name

Type & Properties

--discard_orphans

boolean_true

--alignments

-a

file

multiple

--eqclasses

-e

file

--targets

-t

file

--ont

boolean_true

Output

Name	Type & Properties	Description
--output -o	file required output	Output quantification directory.
--quant_results	file output	Salmon quantification file.

Name

Type & Properties

--output

-o

file

required

output

--quant_results

file

output

Basic options

Name	Type & Properties	Description
--seq_bias	boolean_true	Perform sequence-specific bias correction.
--gc_bias	boolean_true	Perform fragment GC bias correction [beta for single-end reads].
--pos_bias	boolean_true	Perform positional bias correction.
--incompat_prior	double	Set the prior probability that an alignment that disagrees with the specified library type (--lib_type) results from the true fragment origin. Setting this to 0 specifies that alignments that disagree with the library type should be "impossible", while setting it to 1 says that alignments that disagree with the library type are no less likely than those that do.
--gene_map -g	file	File containing a mapping of transcripts to genes. If this file is provided salmon will output both quant.sf and quant.genes.sf files, where the latter contains aggregated gene-level abundance estimates. The transcript to gene mapping should be provided as either a GTF file, or a in a simple tab-delimited format where each line contains the name of a transcript and the gene to which it belongs separated by a tab. The extension of the file is used to determine how the file should be parsed. Files ending in '.gtf', '.gff' or '.gff3' are assumed to be in GTF format; files with any other extension are assumed to be in the simple format. In GTF / GFF format, the "transcript_id" is assumed to contain the transcript identifier and the "gene_id" is assumed to contain the corresponding gene identifier.
--aux_target_file	file	A file containing a list of "auxiliary" targets. These are valid targets (i.e., not decoys) to which fragments are allowed to map and be assigned, and which will be quantified, but for which auxiliary models like sequence-specific and fragment-GC bias correction should not be applied.
--meta	boolean_true	If you're using Salmon on a metagenomic dataset, consider setting this flag to disable parts of the abundance estimation model that make less sense for metagenomic data.
--score_exp	double	The factor by which sub-optimal alignment scores are downweighted to produce a probability. If the best alignment score for the current read is S, and the score for a particular alignment is w, then the probability will be computed porportional to exp( - scoreExp * (S-w) ).

Name

Type & Properties

--seq_bias

boolean_true

--gc_bias

boolean_true

--pos_bias

boolean_true

--incompat_prior

double

--gene_map

-g

file

--aux_target_file

file

--meta

boolean_true

--score_exp

double

Options specific to mapping mode

Name	Type & Properties	Description
--discard_orphans_quasi	boolean_true	[selective-alignment mode only] Discard orphan mappings in selective-alignment mode. If this flag is passed then only paired mappings will be considered toward quantification estimates. The default behavior is to consider orphan mappings if no valid paired mappings exist. This flag is independent of the option to write the orphaned mappings to file (--writeOrphanLinks).
--consensus_slack	double	[selective-alignment mode only] The amount of slack allowed in the selective-alignment filtering mechanism. If this is set to a fraction, X, greater than 0 (and in [0,1)), then uniMEM chains with scores below (100 * X)% of the best chain score for a read, and read pairs with a sum of chain scores below (100 * X)% of the best chain score for a read pair will be discounted as a mapping candidates. The default value of this option is 0.35.
--pre_merge_chain_sub_thresh	double	[selective-alignment mode only] The threshold of sub-optimal chains, compared to the best chain on a given target, that will be retained and passed to the next phase of mapping. Specifically, if the best chain for a read (or read-end in paired-end mode) to target t has score X_t, then all chains for this read with score >= X_t * preMergeChainSubThresh will be retained and passed to subsequent mapping phases. This value must be in the range [0, 1].
--post_merge_chain_sub_thresh	double	[selective-alignment mode only] The threshold of sub-optimal chains, compared to the best chain on a given target, that will be retained and passed to the next phase of mapping. This is different than post_merge_chain_sub_thresh, because this is applied to pairs of chains (from the ends of paired-end reads) after merging (i.e. after checking concordancy constraints etc.). Specifically, if the best chain pair to target t has score X_t, then all chain pairs for this read pair with score >= X_t * post_merge_chain_sub_thresh will be retained and passed to subsequent mapping phases. This value must be in the range [0, 1]. Note: This option is only meaningful for paired-end libraries, and is ignored for single-end libraries.
--orphan_chain_sub_thresh	double	[selective-alignment mode only] This threshold sets a global sub-optimality threshold for chains corresponding to orphan mappings. That is, if the merging procedure results in no concordant mappings then only orphan mappings with a chain score >= orphan_chain_sub_thresh * bestChainScore will be retained and passed to subsequent mapping phases. This value must be in the range [0, 1]. Note: This option is only meaningful for paired-end libraries, and is ignored for single-end libraries.
--min_score_fraction	double	[selective-alignment mode only] The fraction of the optimal possible alignment score that a mapping must achieve in order to be considered "valid" --- should be in (0,1]. Default 0.65
--mismatch_seed_skip	integer	[selective-alignment mode only] After a k-mer hit is extended to a uni-MEM, the uni-MEM extension can terminate for one of 3 reasons; the end of the read, the end of the unitig, or a mismatch. If the extension ends because of a mismatch, this is likely the result of a sequencing error. To avoid looking up many k-mers that will likely fail to be located in the index, the search procedure skips by a factor of mismatch_seed_skip until it either (1) finds another match or (2) is k-bases past the mismatch position. This value controls that skip length. A smaller value can increase sensitivity, while a larger value can speed up seeding.
--disable_chaining_heuristic	boolean_true	[selective-alignment mode only] By default, the heuristic of (Li 2018) is implemented, which terminates the chaining DP once a given number of valid backpointers are found. This speeds up the seed (MEM) chaining step, but may result in sub-optimal chains in complex situations (e.g. sequences with many repeats and overlapping repeats). Passing this flag will disable the chaining heuristic, and perform the full chaining dynamic program, guaranteeing the optimal chain is found in this step.
--decoy_threshold	double	[selective-alignment mode only] For an alignemnt to an annotated transcript to be considered invalid, it must have an alignment score < (decoy_threshold * bestDecoyScore). A value of 1.0 means that any alignment strictly worse than the best decoy alignment will be discarded. A smaller value will allow reads to be allocated to transcripts even if they strictly align better to the decoy sequence.
--ma	integer	[selective-alignment mode only] The value given to a match between read and reference nucleotides in an alignment.
--mp	integer	[selective-alignment mode only] The value given to a mis-match between read and reference nucleotides in an alignment.
--go	integer	[selective-alignment mode only] The value given to a gap opening in an alignment.
--ge	integer	[selective-alignment mode only] The value given to a gap extension in an alignment.
--bandwidth	integer	[selective-alignment mode only] The value used for the bandwidth passed to ksw2. A smaller bandwidth can make the alignment verification run more quickly, but could possibly miss valid alignments.
--allow_dovetail	boolean_true	[selective-alignment mode only] Allow dovetailing mappings.
--recover_orphans	boolean_true	[selective-alignment mode only] Attempt to recover the mates of orphaned reads. This uses edlib for orphan recovery, and so introduces some computational overhead, but it can improve sensitivity.
--mimicBT2	boolean_true	[selective-alignment mode only] Set flags to mimic parameters similar to Bowtie2 with --no-discordant and --no-mixed flags. This increases disallows dovetailing reads, and discards orphans. Note, this does not impose the very strict parameters assumed by RSEM+Bowtie2, like gapless alignments. For that behavior, use the --mimic_strictBT2 flag below.
--mimic_strictBT2	boolean_true	[selective-alignment mode only] Set flags to mimic the very strict parameters used by RSEM+Bowtie2. This increases --min_score_fraction to 0.8, disallows dovetailing reads, discards orphans, and disallows gaps in alignments.
--softclip	boolean_true	[selective-alignment mode only] Allos soft-clipping of reads during selective-alignment. If this option is provided, then regions at the beginning or end of the read can be withheld from alignment without any effect on the resulting score (i.e. neither adding nor removing from the score). This will drastically reduce the penalty if there are mismatches at the beginning or end of the read due to e.g. low-quality bases or adapters. NOTE: Even with soft-clipping enabled, the read must still achieve a score of at least min_score_fraction * maximum achievable score, where the maximum achievable score is computed based on the full (un-clipped) read length.
--softclip_overhangs	boolean_true	[selective-alignment mode only] Allow soft-clipping of reads that overhang the beginning or ends of the transcript. In this case, the overhaning section of the read will simply be unaligned, and will not contribute or detract from the alignment score. The default policy is to force an end-to-end alignment of the entire read, so that overhanings will result in some deletion of nucleotides from the read.
--full_length_alignment	boolean_true	[selective-alignment mode only] Perform selective alignment over the full length of the read, beginning from the (approximate) initial mapping location and using extension alignment. This is in contrast with the default behavior which is to only perform alignment between the MEMs in the optimal chain (and before the first and after the last MEM if applicable). The default strategy forces the MEMs to belong to the alignment, but has the benefit that it can discover indels prior to the first hit shared between the read and reference. Except in very rare circumstances, the default mode should be more accurate.
--hard_filter	boolean_true	[selective-alignment mode only] Instead of weighting mappings by their alignment score, this flag will discard any mappings with sub-optimal alignment score. The default option of soft-filtering (i.e. weighting mappings by their alignment score) usually yields slightly more accurate abundance estimates but this flag may be desirable if you want more accurate 'naive' equivalence classes, rather than range factorized equivalence classes.
--min_aln_prob	double	The minimum number of fragments that must be assigned to the transcriptome for quantification to proceed.
--write_mappings -z	boolean_true	If this option is provided, then the selective-alignment results will be written out in SAM-compatible format. By default, output will be directed to stdout, but an alternative file name can be provided instead.
--mapping_sam	file output	Path to file that should output the selective-alignment results in SAM-compatible format. This option must be provided while using --write_mappings
--write_qualities	boolean_true	This flag only has meaning if mappings are being written (with --write_mappings/-z). If this flag is provided, then the output SAM file will contain quality strings as well as read sequences. Note that this can greatly increase the size of the output file.
--hit_filter_policy	string	[selective-alignment mode only] Determines the policy by which hits are filtered in selective alignment. Filtering hits after chaining (the default) is more sensitive, but more computationally intensive, because it performs the chaining dynamic program for all hits. Filtering before chaining is faster, but some true hits may be missed. The options are BEFORE, AFTER, BOTH and NONE.

Name

Type & Properties

--discard_orphans_quasi

boolean_true

--consensus_slack

double

--pre_merge_chain_sub_thresh

double

--post_merge_chain_sub_thresh

double

--orphan_chain_sub_thresh

double

--min_score_fraction

double

--mismatch_seed_skip

integer

--disable_chaining_heuristic

boolean_true

--decoy_threshold

double

--ma

integer

--mp

integer

--go

integer

--ge

integer

--bandwidth

integer

--allow_dovetail

boolean_true

--recover_orphans

boolean_true

--mimicBT2

boolean_true

--mimic_strictBT2

boolean_true

--softclip

boolean_true

--softclip_overhangs

boolean_true

--full_length_alignment

boolean_true

--hard_filter

boolean_true

--min_aln_prob

double

--write_mappings

-z

boolean_true

--mapping_sam

file

output

--write_qualities

boolean_true

--hit_filter_policy

string

Advance options

Name	Type & Properties	Description
--alternative_init_mode	boolean_true	Use an alternative strategy (rather than simple interpolation between) the online and uniform abundance estimates to initialize the EM / VBEM algorithm.
--aux_dir	file output	The sub-directory of the quantification directory where auxiliary information e.g. bootstraps, bias parameters, etc. will be written.
--skip_quant	boolean_true	Skip performing the actual transcript quantification (including any Gibbs sampling or bootstrapping).
--dump_eq	boolean_true	Dump the simple equivalence class counts that were computed during mapping or alignment.
--dump_eq_weights -d	boolean_true	Dump conditional probabilities associated with transcripts when equivalence class information is being dumped to file. Note, this will dump the factorization that is actually used by salmon's offline phase for inference. If you are using range-factorized equivalence classes (the default) then the same transcript set may appear multiple times with different associated conditional probabilities.
--min_assigned_frags	integer	The minimum number of fragments that must be assigned to the transcriptome for quantification to proceed.
--reduce_GC_memory	boolean_true	If this option is selected, a more memory efficient (but slightly slower) representation is used to compute fragment GC content. Enabling this will reduce memory usage, but can also reduce speed. However, the results themselves will remain the same.
--bias_speed_samp	integer	The value at which the fragment length PMF is down-sampled when evaluating sequence-specific & GC fragment bias. Larger values speed up effective length correction, but may decrease the fidelity of bias modeling results.
--fld_max	integer	The maximum fragment length to consider when building the empirical distribution
--fld_mean	integer	The mean used in the fragment length distribution prior
--fld_SD	integer	The standard deviation used in the fragment length distribution prior
--forgetting_factor -f	double	The forgetting factor used in the online learning schedule. A smallervalue results in quicker learning, but higher variance and may be unstable. A larger value results in slower learning but may be more stable. Value should be in the interval (0.5, 1.0].
--init_uniform	boolean_true	Initialize the offline inference with uniform parameters, rather than seeding with online parameters.
--max_occs_per_hit	integer	When collecting "hits" (MEMs), hits having more than max_occs_per_hit occurrences won't be considered.
--max_read_occ	integer	Reads "mapping" to more than this many places won't be considered.
--no_length_correction	boolean_true	Entirely disables length correction when estimating the abundance of transcripts. This option can be used with protocols where one expects that fragments derive from their underlying targets without regard to that target's length (e.g. QuantSeq)
--no_effective_length_correction	boolean_true	Disables effective length correction when computing the probability that a fragment was generated from a transcript. If this flag is passed in,the fragment length distribution is not taken into account when computing this probability.
--no_single_frag_prob	boolean_true	Disables the estimation of an associated fragment length probability for single-end reads or for orphaned mappings in paired-end libraries. The default behavior is to consider the probability of all possible fragment lengths associated with the retained mapping. Enabling this flag (i.e. turning this default behavior off) will simply not attempt to estimate a fragment length probability in such cases.
--no_frag_length_dist	boolean_true	Don't consider concordance with the learned fragment length distribution when trying to determine the probability that a fragment has originated from a specified location. Normally, Fragments with unlikely lengths will be assigned a smaller relative probability than those with more likely lengths. When this flag is passed in, the observed fragment length has no effect on that fragment's a priori probability.
--no_bias_length_threshold	boolean_true	If this option is enabled, then no (lower) threshold will be set on how short bias correction can make effective lengths. This can increase the precision of bias correction, but harm robustness. The default correction applies a threshold.
--num_bias_samples	integer	Number of fragment mappings to use when learning the sequence-specific bias model.
--num_aux_model_samples	integer	The first <num_aux_model_samples> are used to train the auxiliary model parameters (e.g. fragment length distribution, bias, etc.). After ther first <num_aux_model_samples> observations the auxiliary model parameters will be assumed to have converged and will be fixed.
--num_pre_aux_model_samples	integer	The first <numPreAuxModelSamples> will have their assignment likelihoods and contributions to the transcript abundances computed without applying any auxiliary models. The purpose of ignoring the auxiliary models for the first <num_pre_aux_model_samples> observations is to avoid applying these models before their parameters have been learned sufficiently well.
--useEM	boolean_true	Use the traditional EM algorithm for optimization in the batch passes.
--useVBOpt	boolean_true	Use the Variational Bayesian EM [default]
--range_factorization_bins	integer	Factorizes the likelihood used in quantification by adopting a new notion of equivalence classes based on the conditional probabilities with which fragments are generated from different transcripts. This is a more fine-grained factorization than the normal rich equivalence classes. The default value (4) corresponds to the default used in Zakeri et al. 2017 (doi: 10.1093/bioinformatics/btx262), and larger values imply a more fine-grained factorization. If range factorization is enabled, a common value to select for this parameter is 4. A value of 0 signifies the use of basic rich equivalence classes.
--num_Gibbs_samples	integer	Number of Gibbs sampling rounds to perform.
--no_Gamma_draw	boolean_true	This switch will disable drawing transcript fractions from a Gamma distribution during Gibbs sampling. In this case the sampler does not account for shot-noise, but only assignment ambiguity
--num_bootstraps	integer	Number of bootstrap samples to generate. Note: This is mutually exclusive with Gibbs sampling.
--bootstrap_reproject	boolean_true	This switch will learn the parameter distribution from the bootstrapped counts for each sample, but will reproject those parameters onto the original equivalence class counts.
--thinning_factor	integer	Number of steps to discard for every sample kept from the Gibbs chain. The larger this number, the less chance that subsequent samples are auto-correlated, but the slower sampling becomes.
--quiet -q	boolean_true	Be quiet while doing quantification (don't write informative output to the console unless something goes wrong).
--per_transcript_prior	boolean_true	The prior (either the default or the argument provided via --vb_prior) will be interpreted as a transcript-level prior (i.e. each transcript will be given a prior read count of this value)
--per_nucleotide_prior	boolean_true	The prior (either the default or the argument provided via --vb_prior) will be interpreted as a nucleotide-level prior (i.e. each nucleotide will be given a prior read count of this value)
--sig_digits	integer	The number of significant digits to write when outputting the EffectiveLength and NumReads columns
--vb_prior	double	The prior that will be used in the VBEM algorithm. This is interpreted as a per-transcript prior, unless the --per_nucleotide_prior flag is also given. If the --per_nucleotide_prior flag is given, this is used as a nucleotide-level prior. If the default is used, it will be divided by 1000 before being used as a nucleotide-level prior, i.e. the default per-nucleotide prior will be 1e-5.
--write_orphan_links	boolean_true	Write the transcripts that are linked by orphaned reads.
--write_unmapped_names	boolean_true	Write the names of un-mapped reads to the file unmapped_names.txt in the auxiliary directory.

Name

Type & Properties

--alternative_init_mode

boolean_true

--aux_dir

file

output

--skip_quant

boolean_true

--dump_eq

boolean_true

--dump_eq_weights

-d

boolean_true

--min_assigned_frags

integer

--reduce_GC_memory

boolean_true

--bias_speed_samp

integer

--fld_max

integer

--fld_mean

integer

--fld_SD

integer

--forgetting_factor

-f

double

--init_uniform

boolean_true

--max_occs_per_hit

integer

--max_read_occ

integer

--no_length_correction

boolean_true

--no_effective_length_correction

boolean_true

--no_single_frag_prob

boolean_true

--no_frag_length_dist

boolean_true

--no_bias_length_threshold

boolean_true

--num_bias_samples

integer

--num_aux_model_samples

integer

--num_pre_aux_model_samples

integer

--useEM

boolean_true

--useVBOpt

boolean_true

--range_factorization_bins

integer

--num_Gibbs_samples

integer

--no_Gamma_draw

boolean_true

--num_bootstraps

integer

--bootstrap_reproject

boolean_true

--thinning_factor

integer

--quiet

-q

boolean_true

--per_transcript_prior

boolean_true

--per_nucleotide_prior

boolean_true

--sig_digits

integer

--vb_prior

double

--write_orphan_links

boolean_true

--write_unmapped_names

boolean_true

Alignment-specific options

Name	Type & Properties	Description
--no_error_model	boolean_true	Turn off the alignment error model, which takes into account the the observed frequency of different types of mismatches / indels when computing the likelihood of a given alignment. Turning this off can speed up alignment-based salmon, but can harm quantification accuracy.
--num_error_bins	integer	The number of bins into which to divide each read when learning and applying the error model. For example, a value of 10 would mean that effectively, a separate error model is leared and applied to each 10th of the read, while a value of 3 would mean that a separate error model is applied to the read beginning (first third), middle (second third) and end (final third).
--sample_out -s	boolean_true	Write a "postSample.bam" file in the output directory that will sample the input alignments according to the estimated transcript abundances. If you're going to perform downstream analysis of the alignments with tools which don't, themselves, take fragment assignment ambiguity into account, you should use this output.
--sample_unaligned -u	boolean_true	In addition to sampling the aligned reads, also write the un-aligned reads to "postSample.bam".
--gencode	boolean_true	This flag will expect the input transcript fasta to be in GENCODE format, and will split the transcript name at the first '\|' character. These reduced names will be used in the output and when looking for these transcripts in a gene to transcript GTF.
--mapping_cache_memory_limit	integer	If the file contained fewer than this many mapped reads, then just keep the data in memory for subsequent rounds of inference. Obviously, this value should not be too large if you wish to keep a low memory usage, but setting it large enough to accommodate all of the mapped read can substantially speed up inference on "small" files that contain only a few million reads.

Name

Type & Properties

--no_error_model

boolean_true

--num_error_bins

integer

--sample_out

-s

boolean_true

--sample_unaligned

-u

boolean_true

--gencode

boolean_true

--mapping_cache_memory_limit

integer