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hmmscan(1) HMMER Manual hmmscan(1)

hmmscan - search sequence(s) against a profile database

hmmscan [options] hmmdb seqfile

hmmscan is used to search protein sequences against collections of protein profiles. For each sequence in seqfile, use that query sequence to search the target database of profiles in hmmdb, and output ranked lists of the profiles with the most significant matches to the sequence.

The seqfile may contain more than one query sequence. Each will be searched in turn against hmmdb.

The hmmdb needs to be press'ed using hmmpress before it can be searched with hmmscan. This creates four binary files, suffixed .h3{fimp}.

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 hmmdb cannot be read from a stdin stream, because it needs to have those four auxiliary binary files generated by hmmpress.

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.

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

Direct the main human-readable output to a file <f> instead of the default stdout.

Save a simple tabular (space-delimited) file summarizing the per-target output, with one data line per homologous target model found.

Save a simple tabular (space-delimited) file summarizing the per-domain output, with one data line per homologous domain detected in a query sequence for each homologous model.

Save an especially succinct tabular (space-delimited) file summarizing the per-target output, with one data line per homologous target model found.

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

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

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.

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

Reporting thresholds control which hits are reported in output files (the main output, --tblout, and --domtblout).

In the per-target output, report target profiles with an E-value of <= <x>. The default is 10.0, meaning that on average, about 10 false positives will be reported per query, so you can see the top of the noise and decide for yourself if it's really noise.

Instead of thresholding per-profile output on E-value, instead report target profiles with a bit score of >= <x>.

In the per-domain output, for target profiles that have already satisfied the per-profile reporting threshold, report individual domains with a conditional E-value of <= <x>. The default is 10.0. A conditional E-value means the expected number of additional false positive domains in the smaller search space of those comparisons that already satisfied the per-profile reporting threshold (and thus must have at least one homologous domain already).

Instead of thresholding per-domain output on E-value, instead report domains with a bit score of >= <x>.

Inclusion thresholds are stricter than reporting thresholds. Inclusion thresholds control which hits are considered to be reliable enough to be included in an output alignment or a subsequent search round. In hmmscan, which does not have any alignment output (like hmmsearch or phmmer) nor any iterative search steps (like jackhmmer), inclusion thresholds have little effect. They only affect what domains get marked as significant (!) or questionable (?) in domain output.

Use an E-value of <= <x> as the per-target inclusion threshold. The default is 0.01, meaning that on average, about 1 false positive would be expected in every 100 searches with different query sequences.

Instead of using E-values for setting the inclusion threshold, instead use a bit score of >= <x> as the per-target inclusion threshold. It would be unusual to use bit score thresholds with hmmscan, because you don't expect a single score threshold to work for different profiles; different profiles have slightly different expected score distributions.

Use a conditional E-value of <= <x> as the per-domain inclusion threshold, in targets that have already satisfied the overall per-target inclusion threshold. The default is 0.01.

Instead of using E-values, instead use a bit score of >= <x> as the per-domain inclusion threshold. As with --incT above, it would be unusual to use a single bit score threshold in hmmscan.

Curated profile databases may define specific bit score thresholds for each profile, superseding any thresholding based on statistical significance alone.

To use these options, the profile must contain the appropriate (GA, TC, and/or NC) optional score threshold annotation; this is picked up by hmmbuild from Stockholm format alignment files. Each thresholding option has two scores: the per-sequence threshold <x1> and the per-domain threshold <x2>. These act as if -T <x1> --incT <x1> --domT <x2> --incdomT <x2> has been applied specifically using each model's curated thresholds.

Use the GA (gathering) bit scores in the model to set per-sequence (GA1) and per-domain (GA2) reporting and inclusion thresholds. GA thresholds are generally considered to be the reliable curated thresholds defining family membership; for example, in Pfam, these thresholds define what gets included in Pfam Full alignments based on searches with Pfam Seed models.

Use the NC (noise cutoff) bit score thresholds in the model to set per-sequence (NC1) and per-domain (NC2) reporting and inclusion thresholds. NC thresholds are generally considered to be the score of the highest-scoring known false positive.

Use the NC (trusted cutoff) bit score thresholds in the model to set per-sequence (TC1) and per-domain (TC2) reporting and inclusion thresholds. TC thresholds are generally considered to be the score of the lowest-scoring known true positive that is above all known false positives.

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. 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.

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.

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

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.

Set the P-value threshold for the Viterbi filter step. The default is 0.001.

Set the P-value threshold for the Forward filter step. The default is 1e-5.

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.

Turn off the null2 score corrections for biased composition.

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.

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.

Set the random number seed to <n>. Some steps in postprocessing require Monte Carlo simulation. The default is to use a fixed seed (42), so that results are exactly reproducible. Any other positive integer will give different (but also reproducible) results. A choice of 0 uses an arbitrarily chosen seed.

Assert that input 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).

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.

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.)

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 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 (C) 2020 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/).

http://eddylab.org

Nov 2020 HMMER 3.3.2