Provided by: hmmer_3.1b2-2_amd64 
NAME
hmmsim - collect score distributions on random sequences
SYNOPSIS
hmmsim [options] <hmmfile>
DESCRIPTION
The hmmsim program generates random sequences, scores them with the model(s) in <hmmfile>, and outputs
various sorts of histograms, plots, and fitted distributions for the resulting scores.
hmmsim is not a mainstream part of the HMMER package. Most users would have no reason to use it. It is
used to develop and test the statistical methods used to determine P-values and E-values in HMMER3. For
example, it was used to generate most of the results in a 2008 paper on H3's local alignment statistics
(PLoS Comp Bio 4:e1000069, 2008; http://www.ploscompbiol.org/doi/pcbi.1000069).
Because it is a research testbed, you should not expect it to be as robust as other programs in the
package. For example, options may interact in weird ways; we haven't tested nor tried to anticipate all
different possible combinations.
The main task is to fit a maximum likelihood Gumbel distribution to Viterbi scores or an maximum
likelihood exponential tail to high-scoring Forward scores, and to test that these fitted distributions
obey the conjecture that lambda ~ log_2 for both the Viterbi Gumbel and the Forward exponential tail.
The output is a table of numbers, one row for each model. Four different parametric fits to the score
data are tested: (1) maximum likelihood fits to both location (mu/tau) and slope (lambda) parameters; (2)
assuming lambda=log_2, maximum likelihood fit to the location parameter only; (3) same but assuming an
edge-corrected lambda, using current procedures in H3 [Eddy, 2008]; and (4) using both parameters
determined by H3's current procedures. The standard simple, quick and dirty statistic for goodness-of-fit
is 'E@10', the calculated E-value of the 10th ranked top hit, which we expect to be about 10.
In detail, the columns of the output are:
name Name of the model.
tailp Fraction of the highest scores used to fit the distribution. For Viterbi, MSV, and Hybrid scores,
this defaults to 1.0 (a Gumbel distribution is fitted to all the data). For Forward scores, this
defaults to 0.02 (an exponential tail is fitted to the highest 2% scores).
mu/tau Location parameter for the maximum likelihood fit to the data.
lambda Slope parameter for the maximum likelihood fit to the data.
E@10 The E-value calculated for the 10th ranked high score ('E@10') using the ML mu/tau and lambda. By
definition, this expected to be about 10, if E-value estimation were accurate.
mufix Location parameter, for a maximum likelihood fit with a known (fixed) slope parameter lambda of
log_2 (0.693).
E@10fix
The E-value calculated for the 10th ranked score using mufix and the expected lambda = log_2 =
0.693.
mufix2 Location parameter, for a maximum likelihood fit with an edge-effect-corrected lambda.
E@10fix2
The E-value calculated for the 10th ranked score using mufix2 and the edge-effect-corrected
lambda.
pmu Location parameter as determined by H3's estimation procedures.
plambda
Slope parameter as determined by H3's estimation procedures.
pE@10 The E-value calculated for the 10th ranked score using pmu, plambda.
At the end of this table, one more line is printed, starting with # and summarizing the overall CPU time
used by the simulations.
Some of the optional output files are in xmgrace xy format. xmgrace is powerful and freely available
graph-plotting software.
MISCELLANEOUS OPTIONS
-h Help; print a brief reminder of command line usage and all available options.
-a Collect expected Viterbi alignment length statistics from each simulated sequence. This only works
with Viterbi scores (the default; see --vit). Two additional fields are printed in the output
table for each model: the mean length of Viterbi alignments, and the standard deviation.
-v (Verbose). Print the scores too, one score per line.
-L <n> Set the length of the randomly sampled (nonhomologous) sequences to <n>. The default is 100.
-N <n> Set the number of randomly sampled sequences to <n>. The default is 1000.
--mpi Run in MPI parallel mode, under mpirun. It is parallelized at the level of sending one profile at
a time to an MPI worker process, so parallelization only helps if you have more than one profile
in the <hmmfile>, and you want to have at least as many profiles as MPI worker processes. (Only
available if optional MPI support was enabled at compile-time.)
OPTIONS CONTROLLING OUTPUT
-o <f> Save the main output table to a file <f> rather than sending it to stdout.
--afile <f>
When collecting Viterbi alignment statistics (the -a option), for each sampled sequence, output
two fields per line to a file <f>: the length of the optimal alignment, and the Viterbi bit score.
Requires that the -a option is also used.
--efile <f>
Output a rank vs. E-value plot in XMGRACE xy format to file <f>. The x-axis is the rank of this
sequence, from highest score to lowest; the y-axis is the E-value calculated for this sequence. E-
values are calculated using H3's default procedures (i.e. the pmu, plambda parameters in the
output table). You expect a rough match between rank and E-value if E-values are accurately
estimated.
--ffile <f>
Output a "filter power" file to <f>: for each model, a line with three fields: model name, number
of sequences passing the P-value threshold, and fraction of sequences passing the P-value
threshold. See --pthresh for setting the P-value threshold, which defaults to 0.02 (the default
MSV filter threshold in H3). The P-values are as determined by H3's default procedures (the
pmu,plambda parameters in the output table). If all is well, you expect to see filter power equal
to the predicted P-value setting of the threshold.
--pfile <f>
Output cumulative survival plots (P(S>x)) to file <f> in XMGRACE xy format. There are three plots:
(1) the observed score distribution; (2) the maximum likelihood fitted distribution; (3) a maximum
likelihood fit to the location parameter (mu/tau) while
assuming lambda=log_2.
--xfile <f>
Output the bit scores as a binary array of double-precision floats (8 bytes per score) to file
<f>. Programs like Easel's esl-histplot can read such binary files. This is useful when
generating extremely large sample sizes.
OPTIONS CONTROLLING MODEL CONFIGURATION (MODE)
H3 only uses multihit local alignment ( --fs mode), and this is where we believe the statistical fits.
Unihit local alignment scores (Smith/Waterman; --sw mode) also obey our statistical conjectures. Glocal
alignment statistics (either multihit or unihit) are still not adequately understood nor adequately
fitted.
--fs Collect multihit local alignment scores. This is the default. alignment as 'fragment search
mode'.
--sw Collect unihit local alignment scores. The H3 J state is disabled. alignment as 'Smith/Waterman
search mode'.
--ls Collect multihit glocal alignment scores. In glocal (global/local) alignment, the entire model
must align, to a subsequence of the target. The H3 local entry/exit transition probabilities are
disabled. 'ls' comes from HMMER2's historical terminology for multihit local alignment as 'local
search mode'.
--s Collect unihit glocal alignment scores. Both the H3 J state and local entry/exit transition
probabilities are disabled. 's' comes from HMMER2's historical terminology for unihit glocal
alignment.
OPTIONS CONTROLLING SCORING ALGORITHM
--vit Collect Viterbi maximum likelihood alignment scores. This is the default.
--fwd Collect Forward log-odds likelihood scores, summed over alignment ensemble.
--hyb Collect 'Hybrid' scores, as described in papers by Yu and Hwa (for instance, Bioinformatics
18:864, 2002). These involve calculating a Forward matrix and taking the maximum cell value. The
number itself is statistically somewhat unmotivated, but the distribution is expected be a well-
behaved extreme value distribution (Gumbel).
--msv Collect MSV (multiple ungapped segment Viterbi) scores, using H3's main acceleration heuristic.
--fast For any of the above options, use H3's optimized production implementation (using SIMD
vectorization). The default is to use the implementations sacrifice a small amount of numerical
precision. This can introduce confounding noise into statistical simulations and fits, so when one
gets super-concerned about exact details, it's better to be able to factor that source of noise
out.
OPTIONS CONTROLLING FITTED TAIL MASSES FOR FORWARD
In some experiments, it was useful to fit Forward scores to a range of different tail masses, rather than
just one. These options provide a mechanism for fitting an evenly-spaced range of different tail masses.
For each different tail mass, a line is generated in the output.
--tmin <x>
Set the lower bound on the tail mass distribution. (The default is 0.02 for the default single
tail mass.)
--tmax <x>
Set the upper bound on the tail mass distribution. (The default is 0.02 for the default single
tail mass.)
--tpoints <n>
Set the number of tail masses to sample, starting from --tmin and ending at --tmax. (The default
is 1, for the default 0.02 single tail mass.)
--tlinear
Sample a range of tail masses with uniform linear spacing. The default is to use uniform
logarithmic spacing.
OPTIONS CONTROLLING H3 PARAMETER ESTIMATION METHODS
H3 uses three short random sequence simulations to estimating the location parameters for the expected
score distributions for MSV scores, Viterbi scores, and Forward scores. These options allow these
simulations to be modified.
--EmL <n>
Sets the sequence length in simulation that estimates the location parameter mu for MSV E-values.
Default is 200.
--EmN <n>
Sets the number of sequences in simulation that estimates the location parameter mu for MSV E-
values. Default is 200.
--EvL <n>
Sets the sequence length in simulation that estimates the location parameter mu for Viterbi E-
values. Default is 200.
--EvN <n>
Sets the number of sequences in simulation that estimates the location parameter mu for Viterbi 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.
DEBUGGING OPTIONS
--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.)
--seed <n>
Set the random number seed to <n>. The default is 0, which makes the random number generator use
an arbitrary seed, so that different runs of hmmsim will almost certainly generate a different
statistical sample. For debugging, it is useful to force reproducible results, by fixing a random
number seed.
EXPERIMENTAL OPTIONS
These options were used in a small variety of different exploratory experiments.
--bgflat
Set the background residue distribution to a uniform distribution, both for purposes of the null
model used in calculating scores, and for generating the random sequences. The default is to use a
standard amino acid background frequency distribution.
--bgcomp
Set the background residue distribution to the mean composition of the profile. This was used in
exploring some of the effects of biased composition.
--x-no-lengthmodel
Turn the H3 target sequence length model off. Set the self-transitions for N,C,J and the null
model to 350/351 instead; this emulates HMMER2. Not a good idea in general. This was used to
demonstrate one of the main H2 vs. H3 differences.
--nu <x>
Set the nu parameter for the MSV algorithm -- the expected number of ungapped local alignments per
target sequence. The default is 2.0, corresponding to a E->J transition probability of 0.5. This
was used to test whether varying nu has significant effect on result (it doesn't seem to, within
reason). This option only works if --msv is selected (it only affects MSV), and it will not work
with --fast (because the optimized implementations are hardwired to assume nu=2.0).
--pthresh <x>
Set the filter P-value threshold to use in generating filter power files with --ffile. The
default is 0.02 (which would be appropriate for testing MSV scores, since this is the default MSV
filter threshold in H3's acceleration pipeline.) Other appropriate choices (matching defaults in
the acceleration pipeline) would be 0.001 for Viterbi, and 1e-5 for Forward.
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 ().
COPYRIGHT
Copyright (C) 2015 Howard Hughes Medical Institute.
Freely distributed under the GNU General Public License (GPLv3).
For additional information on copyright and licensing, see the file called COPYRIGHT in your HMMER source
distribution, or see the HMMER web page ().
AUTHOR
Eddy/Rivas Laboratory
Janelia Farm Research Campus
19700 Helix Drive
Ashburn VA 20147 USA
http://eddylab.org