SYNOPSIScmemit [options] <cmfile>
The cmemit program samples (emits) sequences from the covariance model(s) in <cmfile>, and writes them to output. Sampling sequences may be useful for a variety of purposes, including creating synthetic true positives for benchmarks or tests.
The default is to sample ten unaligned sequence from each CM. Alternatively, with the -c option, you can emit a single majority-rule consensus sequence; or with the -a option, you can emit an alignment.
The <cmfile> may contain a library of CMs, in which case each CM will be used in turn.
<cmfile> may be '-' (dash), which means reading this input from stdin rather than a file.
For models with zero basepairs, sequences are sampled from the profile HMM filter instead of the CM. However, since these models will be nearly identical (unless special options were used in cmbuild to prevent this), using the HMM instead of the CM will not change the output in a significant way, unless the -l option is used. With -l, the HMM will be configured for equiprobable model begin and end positions, while the CM will not. You can force cmemit to always sample from the CM with the --nohmmonly option.
Help; print a brief reminder of command line usage and available
- -o <f>
Save the synthetic sequences to file
rather than writing them to stdout.
- -N <n>
sequences. The default value for
Write the generated sequences in unaligned format (FASTA). This is
the default behavior.
Write the generated sequences in an aligned format (STOCKHOLM) with
consensus structure annotation rather than FASTA. Other output formats
are possible with the
Predict a single majority-rule consensus sequence instead of sampling
sequences from the CM's probability distribution. Highly conserved
residues (base paired residues that score higher than 3.0 bits, or
single stranded residues that score higher than 1.0 bits) are shown in
upper case; others are shown in lower case.
- -e <n>
Embed the CM emitted sequences in a larger randomly generated
sequence of length
generated from an HMM that was trained on real genomic sequences with
various GC contents (the same HMM used by
You can use the
option to generate 25% A, C, G, and U sequence instead.
The CM emitted sequence will begin at a random position within the larger
sequence and will be included in its entirety unless the
options are used.
is used in combination with
the CM emitted sequence will always begin at position 1 of the larger
sequence and will be truncated 5'. When used in combination
the CM emitted sequence will always end at position
of the larger sequence and will be truncated 3'.
Configure the CMs into local mode before emitting sequences. By
default the model will be in global mode. In local mode, large
insertions and deletions are more common than in global mode.
OPTIONS FOR TRUNCATING EMITTED SEQUENCES
Truncate all emitted sequences at a randomly chosen start position
by only outputting residues beginning at
A different start point is randomly chosen for each sequence.
Truncate all emitted sequences at a randomly chosen end position
by only outputting residues up to position
A different end point is randomly chosen for each sequence.
- --a5p <n>
In combination with the
option, truncate the emitted alignment at a randomly chosen start
by only outputting alignment columns for positions after match state
must be an integer between 0 and the consensus length of the model
(which can be determined using the
program. As a special case, using 0 as
will result in a randomly chosen start position.
- --a3p <n>
In combination with the
option, truncate the emitted alignment at a randomly chosen end
by only outputting alignment columns for positions before match state
must be an integer between 1 and the consensus length of the model
(which can be determined using the
program). As a special case, using 0 as
will result in a randomly chosen end position.
- --seed <n>
Seed the random number generator with
an integer >= 0. If
is nonzero, stochastic sampling of sequences will be reproducible; the same
command will give the same results.
is 0, the random number generator is seeded arbitrarily, and
stochastic samplings will vary from run to run of the same command.
The default seed is 0.
generate the larger sequences as 25% each A, C, G and U.
Specify that the emitted sequences be output as RNA sequences. This is true by default.
Specify that the emitted sequences be output as DNA sequences. By default,
the output alphabet is RNA.
- --idx <n>
Specify that the emitted sequences be named starting with
- --outformat <s>
specify the output alignment format as
Acceptable formats are: Pfam, AFA, A2M, Clustal, and Phylip.
AFA is aligned fasta. Only Pfam and Stockholm alignment formats will
include consensus structure annotation.
- --tfile <f>
Dump tabular sequence parsetrees (tracebacks) for each
emitted sequence to file
Primarily useful for debugging.
- --exp <x>
Exponentiate the emission and transition probabilities of the CM by
and then renormalize those distributions before emitting
sequences. This option changes the CM probability distribution of
parsetrees relative to default. With
less than 1.0 the emitted sequences will tend to have
lower bit scores upon alignment to the CM.
With <x> greater than 1.0, the emitted sequences will tend
to have higher bit scores upon alignment to
the CM. This bit score difference will increase as <x> moves
further away from 1.0 in either direction.
If <x> equals 1.0, this option has no effect relative to default.
This option is useful for generating sequences that are either
more difficult (
< 1.0) or easier (
> 1.0) for the CM to
distinguish as homologous from background, random sequence.
Emit from the filter profile HMM instead of the CM.
Never emit from the filter profile HMM, always use the CM, even for
models with zero basepairs.
Copyright (C) 2014 Howard Hughes Medical Institute. Freely distributed under the GNU General Public License (GPLv3).
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