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hmmsim(1) HMMER Manual hmmsim(1) 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 sta- tistical 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 ro- bust as other programs in the package. For example, options may inter- act in weird ways; we haven't tested nor tried to anticipate all dif- ferent possible combinations. The main task is to fit a maximum likelihood Gumbel distribution to Viterbi scores or an maximum likelihood exponential tail to high-scor- ing 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 differ- ent parametric fits to the score data are tested: (1) maximum likeli- hood 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 de- fault; see --vit). Two additional fields are printed in the output table for each model: the mean length of Viterbi align- ments, 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 de- fault 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 esti- mated. --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 distri- bution; (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 lo- cal alignment as 'fragment search mode'. --sw Collect unihit local alignment scores. The H3 J state is dis- abled. "sw" comes from HMMER2's historical terminology for uni- hit local alignment as 'Smith/Waterman search mode'. --ls Collect multihit glocal alignment scores. In glocal (global/lo- cal) 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 align- ment ensemble. --hyb Collect 'Hybrid' scores, as described in papers by Yu and Hwa (for instance, Bioinformatics 18:864, 2002). These involve cal- culating 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 distri- bution (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 im- plementation (using SIMD vectorization). The default is to use the "generic" implementation (slow and non-vectorized). The op- timized 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 ex- act 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 loca- tion parameters for the expected score distributions for MSV scores, Viterbi scores, and Forward scores. These options allow these simula- tions to be modified. --EmL _n_ Sets the sequence length in simulation that estimates the loca- tion 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 loca- tion 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 loca- tion 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 esti- mates 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 mas- ter and worker(s) processes. Send SIGCONT signal to release the pause. (Under gdb: (gdb) signal SIGCONT) (Only available if op- tional 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 dif- ferent 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 ex- periments. --bgflat Set the background residue distribution to a uniform distribu- tion, 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-tran- sitions 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 num- ber of ungapped local alignments per target sequence. The de- fault 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 op- tion only works if --msv is selected (it only affects MSV), and it will not work with --fast (because the optimized implementa- tions 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 ap- propriate choices (matching defaults in the acceleration pipe- line) 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 HM- MER distribution (Userguide.pdf); or see the HMMER web page (http://hm- mer.org/). COPYRIGHT Copyright (C) 2019 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 HMMER 3.3 Nov 2019 hmmsim(1)
NAME | SYNOPSIS | DESCRIPTION | OPTIONS | OPTIONS CONTROLLING OUTPUT | OPTIONS CONTROLLING MODEL CONFIGURATION (MODE) | OPTIONS CONTROLLING SCORING ALGORITHM | OPTIONS CONTROLLING FITTED TAIL MASSES FOR FORWARD | OPTIONS CONTROLLING H3 PARAMETER ESTIMATION METHODS | DEBUGGING OPTIONS | EXPERIMENTAL OPTIONS | SEE ALSO | COPYRIGHT | AUTHOR
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