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PROGRAM:

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.

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