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<!-- XML Authors: Corinne Maufrais -->
<!-- 'Biological Software and Databases' Group, Institut Pasteur, Paris. -->
<!-- Distributed under LGPLv2 License. Please refer to the COPYING.LIB document. -->
<program>
<head>
<name>hmmsim</name>
<xi:include xmlns:xi="http://www.w3.org/2001/XInclude" href="Entities/hmmer_package.xml"/>
<doc>
<title>HMMSIM</title>
<description>
<text lang="en">
Collect profile HMM score distributions on random sequences
</text>
</description>
</doc>
<category>hmm:simulation</category>
<command>hmmsim</command>
</head>
<parameters>
<parameter ismandatory="1" issimple="1">
<name>hmmfile</name>
<prompt lang="en">HMM file</prompt>
<type>
<datatype>
<class>HmmProfile</class>
<superclass>AbstractText</superclass>
</datatype>
</type>
<format>
<code proglang="perl">" $value"</code>
<code proglang="python">" "+str(value)</code>
</format>
<argpos>30</argpos>
</parameter>
<paragraph>
<name>generalOptions</name>
<prompt lang="en">General options</prompt>
<argpos>1</argpos>
<parameters>
<parameter>
<name>aln</name>
<prompt lang="en">Obtain alignment length statistics (-a)</prompt>
<type>
<datatype>
<class>Boolean</class>
</datatype>
</type>
<precond>
<code proglang="perl">$altSco eq '--vit'</code>
<code proglang="python">altSco == '--vit'</code>
</precond>
<vdef>
<value>0</value>
</vdef>
<format>
<code proglang="perl">($value) ? " -a" : ""</code>
<code proglang="python">( "" , " -a" )[ value ]</code>
</format>
<comment>
<text lang="en">Collect expected Viterbi alignment length statistics from each simulated sequence.
This only works with Viterbi scores (the default; see --vit). Two additional fields are
printed in the output table for each model: the mean length of Viterbi alignments,
and the standard deviation.</text>
</comment>
</parameter>
<parameter>
<name>verbose</name>
<prompt lang="en">Verbose: print scores (-v)</prompt>
<type>
<datatype>
<class>Boolean</class>
</datatype>
</type>
<vdef>
<value>0</value>
</vdef>
<format>
<code proglang="perl">($value) ? " -v" : ""</code>
<code proglang="python">( "" , " -v" )[ value ]</code>
</format>
</parameter>
<parameter>
<name>Length</name>
<prompt lang="en">Length of random target sequences (-L)</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>100</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef ) ? " -L $value" : ""</code>
<code proglang="python">( "" , " -L " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<ctrl>
<message>
<text lang="en">value > 0</text>
</message>
<code proglang="perl">$value > 0</code>
<code proglang="python">value > 0</code>
</ctrl>
</parameter>
<parameter>
<name>number</name>
<prompt lang="en">Number of random target sequences (-N)</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>1000</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef) ? " -N $value" : ""</code>
<code proglang="python">( "" , " -N " + str(value) )[ value is not None and value !=vdef]</code>
</format>
<ctrl>
<message>
<text lang="en">value > 0</text>
</message>
<code proglang="perl">$value > 0</code>
<code proglang="python">value > 0</code>
</ctrl>
</parameter>
</parameters>
</paragraph>
<paragraph>
<name>AdvancedOptions</name>
<prompt lang="en">Advanced options</prompt>
<argpos>1</argpos>
<parameters>
<parameter>
<name>altAln</name>
<prompt lang="en">Alternative alignment styles</prompt>
<type>
<datatype>
<class>Choice</class>
</datatype>
</type>
<vdef>
<value>--fs</value>
</vdef>
<vlist>
<velem>
<value>--fs</value>
<label>multihit local alignment (fs)</label>
</velem>
<velem>
<value>--sw</value>
<label>unihit local alignment (sw)</label>
</velem>
<velem>
<value>--ls</value>
<label>multihit glocal alignment (ls)</label>
</velem>
</vlist>
<!--<velem> 'Failed to parse command line: Abbreviated option "-s" is ambiguous'
<value>-s</value>
<label>unihit glocal alignment (s)</label>
</velem>
-->
<format>
<code proglang="perl">(defined $value and $value ne $vdef) ? " $value" : ""</code>
<code proglang="python">( "" , " " + str(value) )[ value is not None and value != vdef ]</code>
</format>
<comment>
<text lang="en">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.</text>
<text lang="en">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'.</text>
<text lang="en">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'.</text>
<text lang="en">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'.</text>
<text lang="en">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.</text>
</comment>
</parameter>
<parameter>
<name>altSco</name>
<prompt lang="en">Option controlling scoring algorithm</prompt>
<type>
<datatype>
<class>Choice</class>
</datatype>
</type>
<vdef>
<value>--vit</value>
</vdef>
<vlist>
<velem>
<value>--vit</value>
<label>Score sequences with the Viterbi algorithm (vit)</label>
</velem>
<velem>
<value>--fwd</value>
<label>Score sequences with the Forward algorithm (fwd)</label>
</velem>
<velem>
<value>--hyd</value>
<label>Score sequences with the Hybrid algorithm (hyd)</label>
</velem>
<velem>
<value>--msv</value>
<label>Score sequences with the MSV algorithm (msv)</label>
</velem>
<velem>
<value>--fast</value>
<label>Use the optimized versions of the above (fast)</label>
</velem>
</vlist>
<format>
<code proglang="perl">(defined $value and $value ne $vdef) ? " $value" : ""</code>
<code proglang="python">( "" , " " + str(value) )[ value is not None and value != vdef ]</code>
</format>
<comment>
<text lang="en">vit: Collect Viterbi maximum likelihood alignment scores. This is the default.</text>
<text lang="en">fwd: Collect Forward log-odds likelihood scores, summed over alignment ensemble.</text>
<text lang="en">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).</text>
<text lang="en">msv: Collect MSV (multiple ungapped segment Viterbi) scores, using H3's main acceleration
heuristic.</text>
<text lang="en">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.</text>
</comment>
</parameter>
</parameters>
</paragraph>
<paragraph>
<name>controlMasse</name>
<prompt lang="en">Controlling range of fitted tail masses</prompt>
<argpos>1</argpos>
<parameters>
<parameter>
<name>tmin</name>
<prompt lang="en">Set lower bound tail mass for fwd,island (--tmin)</prompt>
<type>
<datatype>
<class>Float</class>
</datatype>
</type>
<vdef>
<value>0.02</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef) ? " --tmin $value" : ""</code>
<code proglang="python">( "" , " --tmin " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">Set the lower bound on the tail mass distribution. (The default is 0.02 for the default
single tail mass.)</text>
</comment>
</parameter>
<parameter>
<name>tmax</name>
<prompt lang="en">Set upper bound tail mass for fwd,island (--tmax)</prompt>
<type>
<datatype>
<class>Float</class>
</datatype>
</type>
<vdef>
<value>0.02</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef) ? " --tmax $value" : ""</code>
<code proglang="python">( "" , " --tmax " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">Set the upper bound on the tail mass distribution. (The default is 0.02 for the default
single tail mass.)</text>
</comment>
</parameter>
<parameter>
<name>tpoints</name>
<prompt lang="en">Set number of tail probs to try (--tpoints)</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>1</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef) ? " --tpoints $value" : ""</code>
<code proglang="python">( "" , " --tpoints " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">Set the number of tail masses to sample, starting from --tmin and ending at --tmax.
The default is 1, for the default 0.02 single tail mass.</text>
</comment>
</parameter>
<parameter>
<name>tlinear</name>
<prompt lang="en">Use linear not log spacing of tail probs (--tlinear)</prompt>
<type>
<datatype>
<class>Boolean</class>
</datatype>
</type>
<vdef>
<value>0</value>
</vdef>
<format>
<code proglang="perl">($value) ? " --tlinear" : ""</code>
<code proglang="python">( "" , " --tlinear" )[ value ]</code>
</format>
<comment>
<text lang="en">Sample a range of tail masses with uniform linear spacing. The default is to use
uniform logarithmic spacing.</text>
</comment>
</parameter>
</parameters>
</paragraph>
<paragraph>
<name>ECalibration</name>
<prompt lang="en">Options controlling h3 parameter estimation methods</prompt>
<argpos>1</argpos>
<comment>
<text lang="en">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.</text>
</comment>
<parameters>
<parameter>
<name>EmL</name>
<prompt lang="en">Lengt of sequences for MSV Gumbel mu fit (EmL)</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>200</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value!=$vdef) ? " --EmL $value" : ""</code>
<code proglang="python">( "" , " --EmL " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">Sets the sequence length in simulation that estimates the location parameter mu
for MSV E-values. Default is 200. Enter a value > 0. </text>
</comment>
<ctrl>
<message>
<text lang="en">Enter a value > 0 </text>
</message>
<code proglang="perl">$value > 0 </code>
<code proglang="python">value > 0 </code>
</ctrl>
</parameter>
<parameter>
<name>EmN</name>
<prompt lang="en">Number of sequences for MSV Gumbel mu fit (EmN)</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>200</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value!=$vdef) ? " --EmN $value" : ""</code>
<code proglang="python">( "" , " --EmN " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">Sets the number of sequences in simulation that estimates the location parameter
mu for MSV E-values. Default is 200. Enter a value > 0.</text>
</comment>
<ctrl>
<message>
<text lang="en">Enter a value > 0 </text>
</message>
<code proglang="perl">$value > 0 </code>
<code proglang="python">value > 0 </code>
</ctrl>
</parameter>
<parameter>
<name>EvL</name>
<prompt lang="en">Lengt of sequences for Viterbi Gumbel mu fit (EvL)</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>200</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value!=$vdef) ? " --EvL $value" : ""</code>
<code proglang="python">( "" , " --EvL " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">Sets the sequence length in simulation that estimates the location parameter mu
for Viterbi E-values. Default is 200. Enter a value > 0</text>
</comment>
<ctrl>
<message>
<text lang="en">Enter a value > 0 </text>
</message>
<code proglang="perl">$value > 0 </code>
<code proglang="python">value > 0 </code>
</ctrl>
</parameter>
<parameter>
<name>EvN</name>
<prompt lang="en">Number of sequences for Viterbi Gumbel mu fit (EvN)</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>200</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef) ? " --EvN $value" : ""</code>
<code proglang="python">( "" , " --EvN " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">Sets the number of sequences in simulation that estimates the location parameter
mu for Viterbi E-values. Default is 200. Enter a value > 0.</text>
</comment>
<ctrl>
<message>
<text lang="en">Enter a value > 0 </text>
</message>
<code proglang="perl">$value > 0 </code>
<code proglang="python">value > 0 </code>
</ctrl>
</parameter>
<parameter>
<name>EfL</name>
<prompt lang="en">Lengt of sequences for Forward exp tail tau fit (EfL)</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>100</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef) ? " --EfL $value" : ""</code>
<code proglang="python">( "" , " --EfL " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">Sets the sequence length in simulation that estimates the location parameter tau
for Forward E-values. Default is 100. Enter a value > 0</text>
</comment>
<ctrl>
<message>
<text lang="en">Enter a value > 0 </text>
</message>
<code proglang="perl">$value > 0 </code>
<code proglang="python">value > 0 </code>
</ctrl>
</parameter>
<parameter>
<name>EfN</name>
<prompt lang="en">Number of sequences for Forward exp tail tau fit (EfN)</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>200</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef) ? " --EfN $value" : ""</code>
<code proglang="python">( "" , " --EfN " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">Sets the number of sequences in simulation that estimates the location parameter
tau for Forward E-values. Default is 200. Enter a value > 0</text>
</comment>
<ctrl>
<message>
<text lang="en">Enter a value > 0 </text>
</message>
<code proglang="perl">$value > 0 </code>
<code proglang="python">value > 0 </code>
</ctrl>
</parameter>
<parameter>
<name>Eft</name>
<prompt lang="en">Tail mass for Forward exponential tail tau fit (Eft)</prompt>
<type>
<datatype>
<class>Float</class>
</datatype>
</type>
<vdef>
<value>0.04</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef) ? " --Eft $value" : ""</code>
<code proglang="python">( "" , " --Eft " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">Sets the tail mass fraction to fit in the simulation that estimates the location parameter
tau for Forward evalues. Default is 0.04. Enter a value > 0 and < 1</text>
</comment>
<ctrl>
<message>
<text lang="en">Enter a value > 0 and < 1</text>
</message>
<code proglang="perl">$value > 0 and $value < 1</code>
<code proglang="python">value > 0 and value < 1</code>
</ctrl>
</parameter>
</parameters>
</paragraph>
<paragraph>
<name>debugg</name>
<prompt lang="en">Debugging options</prompt>
<argpos>1</argpos>
<parameters>
<parameter>
<name>stall</name>
<prompt lang="en">Arrest after start: for debugging MPI under gdb (--stall)</prompt>
<type>
<datatype>
<class>Boolean</class>
</datatype>
</type>
<vdef>
<value>0</value>
</vdef>
<format>
<code proglang="perl">($value) ? " --stall" : ""</code>
<code proglang="python">( "" , " --stall" )[ value ]</code>
</format>
<comment>
<text lang="en">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.)</text>
</comment>
</parameter>
<parameter>
<name>seed</name>
<prompt lang="en">Set random number seed (--seed)</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>0</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef) ? " --seed $value" : ""</code>
<code proglang="python">( "" , " --seed " + str(value))[ value is not None and value != vdef ]</code>
</format>
<comment>
<text lang="en">Set the random number seed. The default is 0, which makes the random
number generator use an arbitrary seed, so that different runs of hmmsim will
almost certainly generate a different statistical sample. For debugging, it is useful
to force reproducible results, by fixing a random number seed.</text>
</comment>
</parameter>
</parameters>
</paragraph>
<paragraph>
<name>expert</name>
<prompt lang="en">Experiments options</prompt>
<argpos>1</argpos>
<comment>
<text lang="en">These options were used in a small variety of different exploratory experiments.</text>
</comment>
<parameters>
<parameter>
<name>bgflat</name>
<prompt lang="en">Set uniform background frequencies (--bgflat)</prompt>
<type>
<datatype>
<class>Boolean</class>
</datatype>
</type>
<vdef>
<value>0</value>
</vdef>
<format>
<code proglang="perl">($value) ? " --bgflat" : ""</code>
<code proglang="python">( "" , " --bgflat" )[ value ]</code>
</format>
<comment>
<text lang="en">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.</text>
</comment>
</parameter>
<parameter>
<name>bgcomp</name>
<prompt lang="en">Set bg frequencies to model's average composition (--bgcomp)</prompt>
<type>
<datatype>
<class>Boolean</class>
</datatype>
</type>
<vdef>
<value>0</value>
</vdef>
<format>
<code proglang="perl">($value) ? " --bgcomp" : ""</code>
<code proglang="python">( "" , " --bgcomp" )[ value ]</code>
</format>
<comment>
<text lang="en">Set the background residue distribution to the mean composition of the profile. This
was used in exploring some of the effects of biased composition.</text>
</comment>
</parameter>
<parameter>
<name>lengthmode</name>
<prompt lang="en">Turn the H3 length model off (--x-no-lengthmode)</prompt>
<type>
<datatype>
<class>Boolean</class>
</datatype>
</type>
<vdef>
<value>0</value>
</vdef>
<format>
<code proglang="perl">($value) ? " --x-no-lengthmode" : ""</code>
<code proglang="python">( "" , " --x-no-lengthmode " )[value ]</code>
</format>
<comment>
<text lang="en">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.</text>
</comment>
</parameter>
<parameter>
<name>nu</name>
<prompt lang="en">Set nu parameter (expected HSPs) for GMSV (--nu)</prompt>
<type>
<datatype>
<class>Float</class>
</datatype>
</type>
<precond>
<code proglang="perl">$altSco eq '--msv'</code>
<code proglang="python">altSco == '--msv'</code>
</precond>
<vdef>
<value>2.0</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef) ? " --nu $value" : ""</code>
<code proglang="python">( "" , " --nu " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">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).</text>
</comment>
</parameter>
<parameter>
<name>pthresh</name>
<prompt lang="en">Set P-value threshold for --ffile (--pthresh)</prompt>
<type>
<datatype>
<class>Float</class>
</datatype>
</type>
<precond>
<code proglang="perl">defined $ffile</code>
<code proglang="python">ffile is not None</code>
</precond>
<vdef>
<value>0.02</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef) ? " --pthresh $value" : ""</code>
<code proglang="python">( "" , " --pthresh " + str(value) )[ value is not None and value !=vdef ]</code>
</format>
<comment>
<text lang="en">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.</text>
</comment>
</parameter>
</parameters>
</paragraph>
<paragraph>
<name>output_options</name>
<prompt lang="en">Output options</prompt>
<argpos>1</argpos>
<parameters>
<parameter>
<name>save</name>
<prompt lang="en">Direct summary output to file, not stdout. (-o)</prompt>
<type>
<datatype>
<class>Filename</class>
</datatype>
</type>
<format>
<code proglang="perl">(defined $value)? " -o $value" : ""</code>
<code proglang="python">( "" , " -o " + str(value) )[ value is not None ]</code>
</format>
<argpos>1</argpos>
</parameter>
<parameter isout="1">
<name>save_out</name>
<prompt lang="en">Direct summary output to file.</prompt>
<type>
<datatype>
<class>Report</class>
</datatype>
</type>
<precond>
<code proglang="perl">defined $save</code>
<code proglang="python">save is not None</code>
</precond>
<filenames>
<code proglang="perl">"$save"</code>
<code proglang="python">str( save )</code>
</filenames>
<argpos>1</argpos>
</parameter>
<parameter>
<name>afile</name>
<prompt lang="en">Output alignment lengths to file (--afile)</prompt>
<type>
<datatype>
<class>Filename</class>
</datatype>
</type>
<precond>
<code proglang="perl">$aln and $altSco eq '--vit'</code>
<code proglang="python">aln and altSco == '--vit'</code>
</precond>
<format>
<code proglang="perl">(defined $value)? " --afile $value" : ""</code>
<code proglang="python">( "" , " --afile " + str(value) )[ value is not None ]</code>
</format>
<argpos>1</argpos>
<comment>
<text lang="en">When collecting Viterbi alignment statistics (the -a option), for each sampled sequence,
output two fields per line to a file: the length of the optimal alignment,
and the Viterbi bit score. Requires that the -a option is also used.</text>
</comment>
</parameter>
<parameter isout="1">
<name>afile_out</name>
<prompt lang="en">Output alignment lengths</prompt>
<type>
<datatype>
<class>Report</class>
</datatype>
</type>
<precond>
<code proglang="perl">defined $afile</code>
<code proglang="python">afile is not None</code>
</precond>
<filenames>
<code proglang="perl">"$afile"</code>
<code proglang="python">str( afile )</code>
</filenames>
<argpos>1</argpos>
</parameter>
<parameter>
<name>efile</name>
<prompt lang="en">Output E vs. E plots to file in xy format (--efile)</prompt>
<type>
<datatype>
<class>Filename</class>
</datatype>
</type>
<format>
<code proglang="perl">(defined $value)? " --efile $value" : ""</code>
<code proglang="python">( "" , " --efile " + str(value) )[ value is not None ]</code>
</format>
<argpos>1</argpos>
<comment>
<text lang="en">Output a rank versus. E-value plot in XMGRACE xy format to file. 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.</text>
</comment>
</parameter>
<parameter isout="1">
<name>efile_out</name>
<prompt lang="en">Output E vs. E plots to file in xy format</prompt>
<type>
<datatype>
<class>Report</class>
</datatype>
</type>
<precond>
<code proglang="perl">defined $efile</code>
<code proglang="python">efile is not None</code>
</precond>
<filenames>
<code proglang="perl">"$efile"</code>
<code proglang="python">str( efile )</code>
</filenames>
<argpos>1</argpos>
</parameter>
<parameter>
<name>ffile</name>
<prompt lang="en">Output filter fraction: sequences passing P thresh (--ffile)</prompt>
<type>
<datatype>
<class>Filename</class>
</datatype>
</type>
<format>
<code proglang="perl">(defined $value)? " --ffile $value" : ""</code>
<code proglang="python">( "" , " --ffile " + str(value) )[ value is not None ]</code>
</format>
<argpos>1</argpos>
<comment>
<text lang="en">Output a 'filter power' file: 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.</text>
</comment>
</parameter>
<parameter isout="1">
<name>ffile_out</name>
<prompt lang="en">Output filter fraction: sequences passing P thresh</prompt>
<type>
<datatype>
<class>Report</class>
</datatype>
</type>
<precond>
<code proglang="perl">defined $ffile</code>
<code proglang="python">ffile is not None</code>
</precond>
<filenames>
<code proglang="perl">"$ffile"</code>
<code proglang="python">str( ffile )</code>
</filenames>
<argpos>1</argpos>
</parameter>
<parameter>
<name>pfile</name>
<prompt lang="en">Output cumulative survival plots (--pfile)</prompt>
<type>
<datatype>
<class>Filename</class>
</datatype>
</type>
<format>
<code proglang="perl">(defined $value)? " --pfile $value" : ""</code>
<code proglang="python">( "" , " --pfile " + str(value) )[ value is not None ]</code>
</format>
<argpos>1</argpos>
<comment>
<text lang="en">Output cumulative survival plots (P(S>x)) to file 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.</text>
</comment>
</parameter>
<parameter isout="1">
<name>pfile_out</name>
<prompt lang="en">Output cumulative survival plots</prompt>
<type>
<datatype>
<class>Report</class>
</datatype>
</type>
<precond>
<code proglang="perl">defined $pfile</code>
<code proglang="python">pfile is not None</code>
</precond>
<filenames>
<code proglang="perl">"$pfile"</code>
<code proglang="python">str( pfile )</code>
</filenames>
<argpos>1</argpos>
</parameter>
<parameter>
<name>xfile</name>
<prompt lang="en">Output bitscores as binary double vector to file (--xfile)</prompt>
<type>
<datatype>
<class>Filename</class>
</datatype>
</type>
<format>
<code proglang="perl">(defined $value)? " --xfile $value" : ""</code>
<code proglang="python">( "" , " --xfile " + str(value) )[ value is not None ]</code>
</format>
<argpos>1</argpos>
<comment>
<text lang="en">Output the bit scores as a binary array of double-precision floats (8 bytes per score)
to file. Programs like Easel's esl-histplot can read such binary files. This is
useful when generating extremely large sample sizes.</text>
</comment>
</parameter>
<parameter isout="1">
<name>xfile_out</name>
<prompt lang="en">Output bitscores as binary double vector to file</prompt>
<type>
<datatype>
<class>BitScores</class>
<superclass>Binary</superclass>
</datatype>
</type>
<precond>
<code proglang="perl">defined $xfile</code>
<code proglang="python">xfile is not None</code>
</precond>
<filenames>
<code proglang="perl">"$xfile"</code>
<code proglang="python">str( xfile )</code>
</filenames>
<argpos>1</argpos>
</parameter>
</parameters>
</paragraph>
</parameters>
</program>
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