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NAME

       hmmsim - collect profile 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 and 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.

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

              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.

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.  "fs" comes from
              HMMER2's historical terminology for multihit local alignment  as  'fragment  search
              mode'.

       --sw   Collect unihit local alignment scores. The H3 J state is disabled.  "sw" comes from
              HMMER2's historical terminology  for  unihit  local  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 "generic" implementation  (slow  and
              non-vectorized).   The  optimized  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 (http://hmmer.org/).

COPYRIGHT

       Copyright (C) 2018 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