jackhmmer(1)

NAME
jackhmmer - iteratively search sequence(s) against a sequence database

SYNOPSIS
jackhmmer [options] seqfile seqdb

DESCRIPTION

jackhmmer iteratively searches each query sequence in seqfile against the target sequence(s) in seqdb. The first iteration is identical to a phmmer search. For the next iteration, a multiple alignment of the query together with all target sequences satisfying inclusion thresholds is assembled, a profile is constructed from this alignment (identical to using hmmbuild on the alignment), and profile search of the seqdb is done (identical to an hmmsearch with the profile).

The query seqfile may be '-' (a dash character), in which case the query sequences are read from a stdin pipe instead of from a file. The seqdb cannot be read from a stdin stream, because jackhmmer needs to do multiple passes over the database.

The output format is designed to be human-readable, but is often so voluminous that reading it is impractical, and parsing it is a pain. The --tblout and --domtblout options save output in simple tabular formats that are concise and easier to parse. The -o option allows redirecting the main output, including throwing it away in /dev/null.

OPTIONS

-h  Help; print a brief reminder of command line usage and all available options.

-N <n>
    Set the maximum number of iterations to <n>. The default is 5. If N=1, the result is equivalent to a phmmer search.

OPTIONS CONTROLLING OUTPUT
By default, output for each iteration appears on stdout in a somewhat human readable, somewhat parseable format. These options allow redirecting that output or saving additional kinds of output to files, including checkpoint files for each iteration.

-o <f>
    Direct the human-readable output to a file <f>.

-A <f>
    After the final iteration, save an annotated multiple alignment of all hits satisfying inclusion thresholds (also including the original query) to <f> in Stockholm format.

--tblout <f>
    After the final iteration, save a tabular summary of top sequence hits to <f> in a readily parseable, columnar, whitespace-delimited format.

--domtblout <f>
    After the final iteration, save a tabular summary of top domain hits to <f> in a readily parseable, columnar, whitespace-delimited format.

--chkhmm prefix
    At the start of each iteration, checkpoint the query HMM, saving it to a file named prefix-n.hmm where n is the iteration number (from 1..N).

--chkali prefix
    At the end of each iteration, checkpoint an alignment of all domains satisfying inclusion thresholds (e.g. what will become the query HMM for the next iteration), saving it to a file named prefix-n.sto in Stockholm format, where n is the iteration number (from 1..N).

--acc
    Use accessions instead of names in the main output, where available for profiles and/or sequences.

--noali
    Omit the alignment section from the main output. This can greatly reduce the output volume.

--notextw
    Unlimit the length of each line in the main output. The default is a limit of 120 characters per line, which helps in displaying the output cleanly on terminals and in editors, but can truncate target profile description lines.

--textw <n>
    Set the main output's line length limit to <n> characters per line. The default is 120.

OPTIONS CONTROLLING SINGLE SEQUENCE SCORING (FIRST ITERATION)
By default, the first iteration uses a search model constructed from a single query sequence. This model is constructed using a standard 20x20 substitution matrix for residue probabilities, and two additional parameters for position-independent gap open and gap extend probabilities. These options allow the default single-sequence scoring parameters to be changed.

--popen <x>
    Set the gap open probability for a single sequence query model to <x>. The default is 0.02. <x> must be >= 0 and < 0.5.

--pextend <x>
    Set the gap extend probability for a single sequence query model to <x>. The default is 0.4. <x> must be >= 0 and < 1.0.

--mx <s>
    Obtain residue alignment probabilities from the built-in substitution matrix named <s>. Several standard matrices are built-in, and do not need to be read from files. The matrix name <s> can be PAM30, PAM70, PAM120, PAM240, BLOSUM45, BLOSUM50, BLOSUM62, BLOSUM80, or BLOSUM90. Only one of the --mx and --mxfile options may be used.

--mxfile mxfile
    Obtain residue alignment probabilities from the substitution matrix in file mxfile. The default score matrix is BLOSUM62 (this matrix is internal to HMMER and does not have to be available as a file). The format of a substitution matrix mxfile is the standard format accepted by BLAST, FASTA, and other sequence analysis software. See ftp.ncbi.nlm.nih.gov/blast/matrices/ for example files. (The only exception: we require matrices to be square, so for DNA, use files like NCBI's NUC.4.4, not NUC.4.2.)

OPTIONS CONTROLLING REPORTING THRESHOLDS
Reporting thresholds control which hits are reported in output files (the main output, --tblout, and --domtblout). In each iteration, sequence hits and domain hits are ranked by statistical significance (E-value) and output is generated in two sections called per-target and per-domain output. In per-target output, by default, all sequence hits with an E-value <= 10 are reported. In the per-domain output, for each target that has passed per-target reporting thresholds, all domains satisfying per-domain reporting thresholds are reported. By default, these are domains with conditional E-values of <= 10. The following options allow you to change the default E-value reporting thresholds, or to use bit score thresholds instead.

-E <x>
    Report sequences with E-values <= <x> in per-sequence output. The default is 10.0.

-T <x>
    Use a bit score threshold for per-sequence output instead of an E-value threshold (any setting of -E is ignored). Report sequences with a bit score of >= <x>. By default this option is unset.

-Z <x>
    Declare the total size of the database to be <x> sequences, for purposes of E-value calculation. Normally E-values are calculated relative to the size of the database you actually searched (e.g. the number of sequences in target_seqdb). In some cases (for instance, if you've split your target sequence database into multiple files for parallelization of your search), you may know better what the actual size of your search space is.

--domE <x>
    Report domains with conditional E-values <= <x> in per-domain output, in addition to the top-scoring domain per significant sequence hit. The default is 10.0.

--domT <x>
    Use a bit score threshold for per-domain output instead of an E-value threshold (any setting of --domT is ignored). Report domains with a bit score of >= <x> in per-domain output, in addition to the top-scoring domain per significant sequence hit. By default this option is unset.

--domZ <x>
    Declare the number of significant sequences to be <x> sequences, for purposes of conditional E-value calculation for additional domain significance. Normally conditional E-values are calculated relative to the number of sequences passing per-sequence reporting threshold.

OPTIONS CONTROLLING INCLUSION THRESHOLDS
Inclusion thresholds control which hits are included in the multiple alignment and profile constructed for the next search iteration. By default, a sequence must have a per-sequence E-value of <= 0.001 (see -E option) to be included, and any additional domains in it besides the top-scoring one must have a conditional E-value of <= 0.001 (see --domE option). The difference between reporting thresholds and inclusion thresholds is that inclusion thresholds control which hits actually get used in the next iteration (or the final output multiple alignment if the -A option is used), whereas reporting thresholds control what you see in output. Reporting thresholds are generally more loose so you can see borderline hits in the top of the noise that might be of interest.

--incE <x>
    Include sequences with E-values <= <x> in subsequent iteration or final alignment output by -A. The default is 0.001.

--incT <x>
    Use a bit score threshold for per-sequence inclusion instead of an E-value threshold (any setting of --incE is ignored). Include sequences with a bit score of >= <x>. By default this option is unset.

--incdomE <x>
    Include domains with conditional E-values <= <x> in subsequent iteration or final alignment output by -A, in addition to the top-scoring domain per significant sequence hit. The default is 0.001.

--incdomT <x>
    Use a bit score threshold for per-domain inclusion instead of an E-value threshold (any setting of --incT is ignored). Include domains with a bit score of >= <x>. By default this option is unset.

OPTIONS CONTROLLING ACCELERATION HEURISTICS
HMMER3 searches are accelerated in a three-step filter pipeline: the MSV filter, the Viterbi filter, and the Forward filter. The first filter is the fastest and most approximate; the last is the full Forward scoring algorithm, slowest but most accurate. There is also a bias filter step between MSV and Viterbi. Targets that pass all the steps in the acceleration pipeline are then subjected to postprocessing -- domain identification and scoring using the Forward/Backward algorithm. Essentially the only free parameters that control HMMER's heuristic filters are the P-value thresholds controlling the expected fraction of nonhomologous sequences that pass the filters. Setting the default thresholds higher will pass a higher proportion of nonhomologous sequence, increasing sensitivity at the expense of speed; conversely, setting lower P-value thresholds will pass a smaller proportion, decreasing sensitivity and increasing speed. Setting a filter's P-value threshold to 1.0 means it will passing all sequences, and effectively disables the filter. Changing filter thresholds only removes or includes targets from consideration; changing filter thresholds does not alter bit scores, E-values, or alignments, all of which are determined solely in postprocessing.

--max
    Maximum sensitivity. Turn off all filters, including the bias filter, and run full Forward/Backward postprocessing on every target. This increases sensitivity slightly, at a large cost in speed.

--F1 <x>
    First filter threshold; set the P-value threshold for the MSV filter step. The default is 0.02, meaning that roughly 2% of the highest scoring nonhomologous targets are expected to pass the filter.

--F2 <x>
    Second filter threshold; set the P-value threshold for the Viterbi filter step. The default is 0.001.

--F3 <x>
    Third filter threshold; set the P-value threshold for the Forward filter step. The default is 1e-5.

--nobias
    Turn off the bias filter. This increases sensitivity somewhat, but can come at a high cost in speed, especially if the query has biased residue composition (such as a repetitive sequence region, or if it is a membrane protein with large regions of hydrophobicity). Without the bias filter, too many sequences may pass the filter with biased queries, leading to slower than expected performance as the computationally intensive Forward/Backward algorithms shoulder an abnormally heavy load.

OPTIONS CONTROLLING PROFILE CONSTRUCTION (LATER ITERATIONS)
jackhmmer always includes your original query sequence in the alignment result at every iteration, and consensus positions are always defined by that query sequence. That is, a jackhmmer profile is always the same length as your original query, at every iteration. Therefore jackhmmer gives you less control over profile construction than hmmbuild does; it does not have the --fast, or --hand, or --symfrac options. The only profile construction option available in jackhmmer is --fragthresh:

--fragthresh <x>
    We only want to count terminal gaps as deletions if the aligned sequence is known to be full-length, not if it is a fragment (for instance, because only part of it was sequenced). HMMER uses a simple rule to infer fragments: if the sequence length L is less than or equal to a fraction <x> times the alignment length in columns, then the sequence is handled as a fragment. The default is 0.5. Setting --fragthresh 0 will define no (nonempty) sequence as a fragment; you might want to do this if you know you've got a carefully curated alignment of full-length sequences. Setting --fragthresh 1 will define all sequences as fragments; you might want to do this if you know your alignment is entirely composed of fragments, such as translated short reads in metagenomic shotgun data.

OPTIONS CONTROLLING RELATIVE WEIGHTS
Whenever a profile is built from a multiple alignment, HMMER uses an ad hoc sequence weighting algorithm to downweight closely related sequences and upweight distantly related ones. This has the effect of making models less biased by uneven phylogenetic representation. For example, two identical sequences would typically each receive half the weight that one sequence would (and this is why jackhmmer isn't concerned about always including your original query sequence in each iteration's alignment, even if it finds it again in the database you're searching). These options control which algorithm gets used.

--wpb
    Use the Henikoff position-based sequence weighting scheme [Henikoff and Henikoff, J. Mol. Biol. 243:574, 1994]. This is the default.

--wgsc
    Use the Gerstein/Sonnhammer/Chothia weighting algorithm [Gerstein et al, J. Mol. Biol. 235:1067, 1994].

--wblosum
    Use the same clustering scheme that was used to weight data in calculating BLOSUM subsitution matrices [Henikoff and Henikoff, Proc. Natl. Acad. Sci 89:10915, 1992]. Sequences are single-linkage clustered at an identity threshold (default 0.62; see --wid) and within each cluster of c sequences, each sequence gets relative weight 1/c.

--wnone
    No relative weights. All sequences are assigned uniform weight.

--wid <x>
    Sets the identity threshold used by single-linkage clustering when using --wblosum. Invalid with any other weighting scheme. Default is 0.62.

OPTIONS CONTROLLING EFFECTIVE SEQUENCE NUMBER
After relative weights are determined, they are normalized to sum to a total effective sequence number, eff_nseq. This number may be the actual number of sequences in the alignment, but it is almost always smaller than that. The default entropy weighting method (--eent) reduces the effective sequence number to reduce the information content (relative entropy, or average expected score on true homologs) per consensus position. The target relative entropy is controlled by a two-parameter function, where the two parameters are settable with --ere and --esigma.

--eent
    Adjust effective sequence number to achieve a specific relative entropy per position (see --ere). This is the default.

--eclust
    Set effective sequence number to the number of single-linkage clusters at a specific identity threshold (see --eid). This option is not recommended; it's for experiments evaluating how much better --eent is.

--enone
    Turn off effective sequence number determination and just use the actual number of sequences. One reason you might want to do this is to try to maximize the relative entropy/position of your model, which may be useful for short models.

--eset <x>
    Explicitly set the effective sequence number for all models to <x>.

--ere <x>
    Set the minimum relative entropy/position target to <x>. Requires --eent. Default depends on the sequence alphabet; for protein sequences, it is 0.59 bits/position.

--esigma <x>
    Sets the minimum relative entropy contributed by an entire model alignment, over its whole length. This has the effect of making short models have higher relative entropy per position than --ere alone would give. The default is 45.0 bits.

--eid <x>
    Sets the fractional pairwise identity cutoff used by single linkage clustering with the --eclust option. The default is 0.62.

OPTIONS CONTROLLING PRIORS
In profile construction, by default, weighted counts are converted to mean posterior probability parameter estimates using mixture Dirichlet priors. Default mixture Dirichlet prior parameters for protein models and for nucleic acid (RNA and DNA) models are built in. The following options allow you to override the default priors.

--pnone
    Don't use any priors. Probability parameters will simply be the observed frequencies, after relative sequence weighting.

--plaplace
    Use a Laplace +1 prior in place of the default mixture Dirichlet prior.

OPTIONS CONTROLLING E-VALUE CALIBRATION
Estimating the location parameters for the expected score distributions for MSV filter scores, Viterbi filter scores, and Forward scores requires three short random sequence simulations.

--EmL <n>
    Sets the sequence length in simulation that estimates the location parameter mu for MSV filter E-values. Default is 200.

--EmN <n>
    Sets the number of sequences in simulation that estimates the location parameter mu for MSV filter E-values. Default is 200.

--EvL <n>
    Sets the sequence length in simulation that estimates the location parameter mu for Viterbi filter E-values. Default is 200.

--EvN <n>
    Sets the number of sequences in simulation that estimates the location parameter mu for Viterbi filter E-values. Default is 200.

--EfL <n>
    Sets the sequence length in simulation that estimates the location parameter tau for Forward E-values. Default is 100.

--EfN <n>
    Sets the number of sequences in simulation that estimates the location parameter tau for Forward E-values. Default is 200.

--Eft <x>
    Sets the tail mass fraction to fit in the simulation that estimates the location parameter tau for Forward evalues. Default is 0.04.

OTHER OPTIONS

--nonull2
    Turn off the null2 score corrections for biased composition.

-Z <x>
    Assert that the total number of targets in your searches is <x>, for the purposes of per-sequence E-value calculations, rather than the actual number of targets seen.

--domZ <x>
    Assert that the total number of targets in your searches is <x>, for the purposes of per-domain conditional E-value calculations, rather than the number of targets that passed the reporting thresholds.

--seed <n>
    Seed the random number generator with <n>, an integer >= 0. If <n> is >0, any stochastic simulations will be reproducible; the same command will give the same results. If <n> is 0, the random number generator is seeded arbitrarily, and stochastic simulations will vary from run to run of the same command. The default seed is 42.

--qformat <s>
    Assert that input query seqfile is in format <s>, bypassing format autodetection. Common choices for <s> include: fasta, embl, genbank. Alignment formats also work; common choices include: stockholm, a2m, afa, psiblast, clustal, phylip. For more information, and for codes for some less common formats, see main documentation. The string <s> is case-insensitive (fasta or FASTA both work).

--tformat <s>
    Assert that the input target sequence seqdb is in format <s>. See --qformat above for accepted choices for <s>.

--cpu <n>
    Set the number of parallel worker threads to <n>. On multicore machines, the default is 2. You can also control this number by setting an environment variable, HMMER_NCPU. There is also a master thread, so the actual number of threads that HMMER spawns is <n>+1. This option is not available if HMMER was compiled with POSIX threads support turned off.

--stall
    For debugging the MPI master/worker version: pause after start, to enable the developer to attach debuggers to the running master and worker(s) processes. Send SIGCONT signal to release the pause. (Under gdb: (gdb) signal SIGCONT) (Only available if optional MPI support was enabled at compile-time.)

--mpi
    Run under MPI control with master/worker parallelization (using mpirun, for example, or equivalent). Only available if optional MPI support was enabled at compile-time.

SEE ALSO
See hmmer(1) for a master man page with a list of all the individual man pages for programs in the HMMER package.

For complete documentation, see the user guide that came with your HMMER distribution (Userguide.pdf); or see the HMMER web page (http://hmmer.org/).

COPYRIGHT
Copyright (C) 2019 Howard Hughes Medical Institute.
Freely distributed under the BSD open source license.
For additional information on copyright and licensing, see the file called COPYRIGHT in your HMMER source distribution, or see the HMMER web page (http://hmmer.org/).

AUTHOR
http://eddylab.org