Provided by: raxml_8.2.12+dfsg-4_amd64 
NAME
raxmlHPC - Randomized Axelerated Maximum Likelihood
SYNOPSIS
raxmlHPC
[-SSE3|-AVX|-PTHREADS|-PTHREADS-SSE3|-PTHREADS-AVX|-HYBRID|-HYBRID-SSE3|HYBRID-AVX] -s
sequenceFileName -n outputFileName -m substitutionModel [-a weightFileName] [-A
secondaryStructureSubstModel] [-b bootstrapRandomNumberSeed] [-B wcCriterionThreshold] [-c
numberOfCategories] [-C] [-d] [-D] [-e likelihoodEpsilon] [-E excludeFileName] [-f
a|A|b|B|c|C|d|D|e|E|F|g|G|h|H|i|I|j|J|k|m|n|N|o|p|P|q|r|R|s|S|t|T|u|v|V|w|W|x|y] [-F] [-g
groupingFileName] [-G placementThreshold] [-h] [-H] [-i initialRearrangementSetting] [-I
autoFC|autoMR|autoMRE|autoMRE_IGN] [-j] [-J MR|MR_DROP|MRE|STRICT|STRICT_DROP|T_<PERCENT>]
[-k] [-K] [-L MR|MRE|T_<PERCENT>] [-M] [-o outGroupName1[,outGroupName2[,...]]][-O] [-p
parsimonyRandomSeed] [-P proteinModel] [-q multipleModelFileName] [-r
binaryConstraintTree] [-R binaryModelParamFile] [-S secondaryStructureFile] [-t
userStartingTree] [-T numberOfThreads] [-u] [-U] [-v] [-V] [-w outputDirectory] [-W
slidingWindowSize] [-x rapidBootstrapRandomNumberSeed] [-X] [-y] [-Y
quartetGroupingFileName|ancestralSequenceCandidatesFileName] [-z multipleTreesFile] [-#|-N
numberOfRuns|autoFC|autoMR|autoMRE|autoMRE_IGN]
[--mesquite][--silent][--no-seq-check][--no-bfgs]
[--asc-corr=stamatakis|felsenstein|lewis] [--flag-check][--auto-prot=ml|bic|aic|aicc]
[--epa-keep-placements=number][--epa-accumulated-threshold=threshold]
[--epa-prob-threshold=threshold] [--JC69][--K80][--HKY85] [--bootstop-perms=number]
[--quartets-without-replacement] [---without-replacement] [--print-identical-sequences]
OPTIONS
-a Specify a column weight file name to assign individual weights to each column of
the alignment. Those weights must be integers separated by any type and number of
whitespaces whithin a separate file, see file "example_weights" for an example.
-A Specify one of the secondary structure substitution models implemented in RAxML.
The same nomenclature as in the PHASE manual is used, available models: S6A, S6B,
S6C, S6D, S6E, S7A, S7B, S7C, S7D, S7E, S7F, S16, S16A, S16B
DEFAULT: 16-state GTR model (S16)
-b Specify an integer number (random seed) and turn on bootstrapping
DEFAULT: OFF
-B specify a floating point number between 0.0 and 1.0 that will be used as cutoff
threshold for the MR-based bootstopping criteria. The recommended setting is 0.03.
DEFAULT: 0.03 (recommended empirically determined setting)
-c Specify number of distinct rate catgories for RAxML when model of rate
heterogeneity is set to CAT Individual per-site rates are categorized into
numberOfCategories rate categories to accelerate computations.
DEFAULT: 25
-C Enable verbose output for the "-L" and "-f i" options. This will produce more, as
well as more verbose output files
DEFAULT: OFF
-d start ML optimization from random starting tree
DEFAULT: OFF
-D ML search convergence criterion. This will break off ML searches if the relative
Robinson-Foulds distance between the trees obtained from two consecutive lazy SPR
cycles is smaller or equal to 1%. Usage recommended for very large datasets in
terms of taxa. On trees with more than 500 taxa this will yield execution time
improvements of approximately 50% While yielding only slightly worse trees.
DEFAULT: OFF
-e set model optimization precision in log likelihood units for final optimization of
tree topology
DEFAULT: 0.1
for models not using proportion of invariant sites estimate
0.001 for models using proportion of invariant sites estimate
-E specify an exclude file name, that contains a specification of alignment positions
you wish to exclude. Format is similar to Nexus, the file shall contain entries
like "100-200 300-400", to exclude a single column write, e.g., "100-100", if you
use a mixed model, an appropriately adapted model file will be written.
-f select algorithm:
-f a: rapid Bootstrap analysis and search for best-scoring ML tree in one program
run
-f A: compute marginal ancestral states on a ROOTED reference tree provided with
"-t"
-f b: draw bipartition information on a tree provided with "-t" based on multiple
trees (e.g., from a bootstrap) in a file specified by "-z"
-f B: optimize br-len scaler and other model parameters (GTR, alpha, etc.) on a
tree provided with "-t". The tree needs to contain branch lengths. The branch
lengths will not be optimized, just scaled by a single common value.
-f c: check if the alignment can be properly read by RAxML -f C: ancestral sequence
test for Jiajie, users will also need to provide a list of taxon names via -Y
separated by whitespaces -f d: new rapid hill-climbing
DEFAULT: ON
-f D: rapid hill-climbing with RELL bootstraps
-f e: optimize model+branch lengths for given input tree under GAMMA/GAMMAI only
-f E: execute very fast experimental tree search, at present only for testing
-f F: execute fast experimental tree search, at present only for testing
-f g: compute per site log Likelihoods for one or more trees passed via
-z and write them to a file that can be read by CONSEL. The model parameters will
be estimated on the first tree only!
-f G: compute per site log Likelihoods for one or more trees passed via "-z" and
write them to a file that can be read by CONSEL. The model parameters will be
re-estimated for each tree
-f h: compute log likelihood test (SH-test) between best tree passed via "-t" and a
bunch of other trees passed via "-z" The model parameters will be estimated on the
first tree only!
-f H: compute log likelihood test (SH-test) between best tree passed via "-t" and a
bunch of other trees passed via "-z" The model parameters will be re-estimated for
each tree
-f i: calculate IC and TC scores (Salichos and Rokas 2013) on a tree provided with
"-t" based on multiple trees (e.g., from a bootstrap) in a file specified by "-z"
-f I: a simple tree rooting algorithm for unrooted trees. It roots the tree by
rooting it at the branch that best balances the subtree lengths (sum over branches
in the subtrees) of the left and right subtree. A branch with an optimal balance
does not always exist! You need to specify the tree you want to root via "-t".
-f j: generate a bunch of bootstrapped alignment files from an original alignemnt
file. You need to specify a seed with "-b" and the number of replicates with "-#"
-f J: Compute SH-like support values on a given tree passed via "-t".
-f k: Fix long branch lengths in partitioned data sets with missing data using the
branch length stealing algorithm. This option only works in conjunction with "-t",
"-M", and "-q". It will print out a tree with shorter branch lengths, but having
the same likelihood score.
-f m: compare bipartitions between two bunches of trees passed via "-t" and "-z"
respectively. This will return the Pearson correlation between all bipartitions
found in the two tree files. A file called
RAxML_bipartitionFrequencies.outpuFileName will be printed that contains the
pair-wise bipartition frequencies of the two sets
-f n: compute the log likelihood score of all trees contained in a tree file
provided by "-z" under GAMMA or GAMMA+P-Invar. The model parameters will be
estimated on the first tree only!
-f N: compute the log likelihood score of all trees contained in a tree file
provided by "-z" under GAMMA or GAMMA+P-Invar. The model parameters will be
re-estimated for each tree
-f o: old and slower rapid hill-climbing without heuristic cutoff
-f p: perform pure stepwise MP addition of new sequences to an incomplete starting
tree and exit
-f P: perform a phylogenetic placement of sub trees specified in a file passed via
"-z" into a given reference tree in which these subtrees are contained that is
passed via "-t" using the evolutionary placement algorithm.
-f q: fast quartet calculator
-f r: compute pairwise Robinson-Foulds (RF) distances between all pairs of trees in
a tree file passed via "-z" if the trees have node labales represented as integer
support values the program will also compute two flavors of the weighted
Robinson-Foulds (WRF) distance
-f R: compute all pairwise Robinson-Foulds (RF) distances between a large reference
tree passed via "-t" and many smaller trees (that must have a subset of the taxa of
the large tree) passed via "-z". This option is intended for checking the
plausibility of very large phylogenies that can not be inspected visually any more.
-f s: split up a multi-gene partitioned alignment into the respective subalignments
-f S: compute site-specific placement bias using a leave one out test inspired by
the evolutionary placement algorithm
-f t: do randomized tree searches on one fixed starting tree
-f T: do final thorough optimization of ML tree from rapid bootstrap search in
stand-alone mode
-f u: execute morphological weight calibration using maximum likelihood, this will
return a weight vector. you need to provide a morphological alignment and a
reference tree via "-t"
-f v: classify a bunch of environmental sequences into a reference tree using
thorough read insertions you will need to start RAxML with a non-comprehensive
reference tree and an alignment containing all sequences (reference + query)
-f V: classify a bunch of environmental sequences into a reference tree using
thorough read insertions you will need to start RAxML with a non-comprehensive
reference tree and an alignment containing all sequences (reference + query)
WARNING: this is a test implementation for more efficient handling of
multi-gene/whole-genome datasets!
-f w: compute ELW test on a bunch of trees passed via "-z". The model parameters
will be estimated on the first tree only!
-f W: compute ELW test on a bunch of trees passed via "-z". The model parameters
will be re-estimated for each tree
-f x: compute pair-wise ML distances, ML model parameters will be estimated on an
MP starting tree or a user-defined tree passed via "-t", only allowed for
GAMMA-based models of rate heterogeneity
-f y: classify a bunch of environmental sequences into a reference tree using
parsimony you will need to start RAxML with a non-comprehensive reference tree and
an alignment containing all sequences (reference + query)
DEFAULT for -f: new rapid hill climbing
-F enable ML tree searches under CAT model for very large trees without switching to
GAMMA in the end (saves memory). This option can also be used with the GAMMA
models in order to avoid the thorough optimization of the best-scoring ML tree in
the end.
DEFAULT: OFF
-g specify the file name of a multifurcating constraint tree this tree does not need
to be comprehensive, i.e. must not contain all taxa
-G enable the ML-based evolutionary placement algorithm heuristics by specifiyng a
threshold value (fraction of insertion branches to be evaluated using slow
insertions under ML).
-h Display this help message.
-H Disable pattern compression.
DEFAULT: ON
-i Initial rearrangement setting for the subsequent application of topological changes
phase
-I a posteriori bootstopping analysis. Use:
-I autoFC for the frequency-based criterion
-I autoMR for the majority-rule consensus tree criterion
-I autoMRE for the extended majority-rule consensus tree criterion
-I autoMRE_IGN for metrics similar to MRE, but include bipartitions under the
threshold whether they are compatible or not. This emulates MRE but is faster to
compute.
You also need to pass a tree file containg several bootstrap replicates via "-z"
-j Specifies that intermediate tree files shall be written to file during the standard
ML and BS tree searches.
DEFAULT: OFF
-J Compute majority rule consensus tree with "-J MR" or extended majority rule
consensus tree with "-J MRE" or strict consensus tree with "-J STRICT". For a
custom consensus threshold >= 50%, specify T_<NUM>, where 100 >= NUM >= 50.
Options "-J STRICT_DROP" and "-J MR_DROP" will execute an algorithm that identifies
dropsets which contain rogue taxa as proposed by Pattengale et al. in the paper
"Uncovering hidden phylogenetic consensus". You will also need to provide a tree
file containing several UNROOTED trees via "-z"
-k Specifies that bootstrapped trees should be printed with branch lengths. The
bootstraps will run a bit longer, because model parameters will be optimized at the
end of each run under GAMMA or GAMMA+P-Invar respectively.
DEFAULT: OFF
-K Specify one of the multi-state substitution models (max 32 states) implemented in
RAxML. Available models are: ORDERED, MK, GTR
DEFAULT: GTR model
-L Compute consensus trees labelled by IC supports and the overall TC value as
proposed in Salichos and Rokas 2013. Compute a majority rule consensus tree with
"-L MR" or an extended majority rule consensus tree with "-L MRE". For a custom
consensus threshold >= 50%, specify "-L T_<NUM>", where 100 >= NUM >= 50. You will
of course also need to provide a tree file containing several UNROOTED trees via
"-z"!
-m Model of Binary (Morphological), Nucleotide, Multi-State, or Amino Acid
Substitution:
BINARY:
"-m BINCAT[X]"
: Optimization of site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be evaluated
automatically under BINGAMMA, depending on the tree search option. With the
optional "X" appendix you can specify a ML estimate of base frequencies.
"-m BINCATI[X]"
: Optimization of site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be evaluated
automatically under BINGAMMAI, depending on the tree search option. With the
optional "X" appendix you can specify a ML estimate of base frequencies.
"-m ASC_BINCAT[X]"
: Optimization of site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be evaluated
automatically under BINGAMMA, depending on the tree search option. With the
optional "X" appendix you can specify a ML estimate of base frequencies. The ASC
prefix willl correct the likelihood for ascertainment bias.
"-m BINGAMMA[X]"
: GAMMA model of rate heterogeneity (alpha parameter will be estimated).
With the optional "X" appendix you can specify a ML estimate of base frequencies.
"-m ASC_BINGAMMA[X]" : GAMMA model of rate heterogeneity (alpha parameter will be
estimated).
The ASC prefix willl correct the likelihood for ascertainment bias. With the
optional "X" appendix you can specify a ML estimate of base frequencies.
"-m BINGAMMAI[X]"
: Same as BINGAMMA, but with estimate of proportion of invariable sites.
With the optional "X" appendix you can specify a ML estimate of base frequencies.
NUCLEOTIDES:
"-m GTRCAT[X]"
: GTR + Optimization of substitution rates + Optimization of site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be
evaluated under GTRGAMMA, depending on the tree search option. With the optional
"X" appendix you can specify a ML estimate of base frequencies.
"-m GTRCATI[X]"
: GTR + Optimization of substitution rates + Optimization of site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be
evaluated under GTRGAMMAI, depending on the tree search option. With the optional
"X" appendix you can specify a ML estimate of base frequencies.
"-m ASC_GTRCAT[X]"
: GTR + Optimization of substitution rates + Optimization of site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be
evaluated under GTRGAMMA, depending on the tree search option. With the optional
"X" appendix you can specify a ML estimate of base frequencies. The ASC prefix
willl correct the likelihood for ascertainment bias.
"-m GTRGAMMA[X]"
: GTR + Optimization of substitution rates + GAMMA model of rate
heterogeneity (alpha parameter will be estimated).
With the optional "X" appendix you can specify a ML estimate of base frequencies.
"-m ASC_GTRGAMMA[X]" : GTR + Optimization of substitution rates + GAMMA model of rate
heterogeneity (alpha parameter will be estimated). The ASC prefix willl correct
the likelihood for ascertainment bias. With the optional "X" appendix you can
specify a ML estimate of base frequencies.
"-m GTRGAMMAI[X]"
: Same as GTRGAMMA, but with estimate of proportion of invariable sites.
With the optional "X" appendix you can specify a ML estimate of base frequencies.
MULTI-STATE:
"-m MULTICAT[X]"
: Optimization of site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be evaluated
automatically under MULTIGAMMA, depending on the tree search option. With the
optional "X" appendix you can specify a ML estimate of base frequencies.
"-m MULTICATI[X]"
: Optimization of site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be evaluated
automatically under MULTIGAMMAI, depending on the tree search option. With the
optional "X" appendix you can specify a ML estimate of base frequencies.
"-m ASC_MULTICAT[X]"
: Optimization of site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be evaluated
automatically under MULTIGAMMA, depending on the tree search option. With the
optional "X" appendix you can specify a ML estimate of base frequencies. The ASC
prefix willl correct the likelihood for ascertainment bias.
"-m MULTIGAMMA[X]"
: GAMMA model of rate heterogeneity (alpha parameter will be estimated).
With the optional "X" appendix you can specify a ML estimate of base frequencies.
"-m ASC_MULTIGAMMA[X]" : GAMMA model of rate heterogeneity (alpha parameter will be
estimated).
The ASC prefix willl correct the likelihood for ascertainment bias. With the
optional "X" appendix you can specify a ML estimate of base frequencies.
"-m MULTIGAMMAI[X]"
: Same as MULTIGAMMA, but with estimate of proportion of invariable sites.
With the optional "X" appendix you can specify a ML estimate of base frequencies.
You can use up to 32 distinct character states to encode multi-state regions, they
must be used in the following order: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E,
F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V i.e., if you have 6 distinct
character states you would use 0, 1, 2, 3, 4, 5 to encode these. The substitution
model for the multi-state regions can be selected via the "-K" option
AMINO ACIDS:
"-m PROTCATmatrixName[F|X]"
: specified AA matrix + Optimization of substitution rates + Optimization of
site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be
evaluated automatically under PROTGAMMAmatrixName[F|X], depending on the tree
search option. With the optional "X" appendix you can specify a ML estimate of
base frequencies.
"-m PROTCATImatrixName[F|X]"
: specified AA matrix + Optimization of substitution rates + Optimization of
site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be
evaluated automatically under PROTGAMMAImatrixName[F|X], depending on the tree
search option. With the optional "X" appendix you can specify a ML estimate of
base frequencies.
"-m ASC_PROTCATmatrixName[F|X]"
: specified AA matrix + Optimization of substitution rates + Optimization of
site-specific
evolutionary rates which are categorized into numberOfCategories distinct
rate categories for greater computational efficiency. Final tree might be
evaluated automatically under PROTGAMMAmatrixName[F|X], depending on the tree
search option. With the optional "X" appendix you can specify a ML estimate of
base frequencies. The ASC prefix willl correct the likelihood for ascertainment
bias.
"-m PROTGAMMAmatrixName[F|X]"
: specified AA matrix + Optimization of substitution rates + GAMMA model of rate
heterogeneity (alpha parameter will be estimated).
With the optional "X" appendix you can specify a ML estimate of base frequencies.
"-m ASC_PROTGAMMAmatrixName[F|X]" : specified AA matrix + Optimization of substitution
rates + GAMMA model of rate
heterogeneity (alpha parameter will be estimated). The ASC prefix willl correct
the likelihood for ascertainment bias. With the optional "X" appendix you can
specify a ML estimate of base frequencies.
"-m PROTGAMMAImatrixName[F|X]"
: Same as PROTGAMMAmatrixName[F|X], but with estimate of proportion of invariable
sites.
With the optional "X" appendix you can specify a ML estimate of base frequencies.
Available AA substitution models: DAYHOFF, DCMUT, JTT, MTREV, WAG, RTREV, CPREV,
VT, BLOSUM62, MTMAM, LG, MTART, MTZOA, PMB, HIVB, HIVW, JTTDCMUT, FLU, STMTREV,
DUMMY, DUMMY2, AUTO, LG4M, LG4X, PROT_FILE, GTR_UNLINKED, GTR With the optional "F"
appendix you can specify if you want to use empirical base frequencies. AUTOF and
AUTOX are not supported any more, if you specify AUTO it will test prot subst.
models with and without empirical base frequencies now! Please note that for
partitioned models you can in addition specify the per-gene AA model in the
partition file (see manual for details). Also note that if you estimate AA GTR
parameters on a partitioned dataset, they will be linked (estimated jointly) across
all partitions to avoid over-parametrization
-M Switch on estimation of individual per-partition branch lengths. Only has effect
when used in combination with "-q" Branch lengths for individual partitions will be
printed to separate files A weighted average of the branch lengths is computed by
using the respective partition lengths
DEFAULT: OFF
-n Specifies the name of the output file.
-o Specify the name of a single outgroup or a comma-separated list of outgroups, eg
"-o Rat" or "-o Rat,Mouse", in case that multiple outgroups are not monophyletic
the first name in the list will be selected as outgroup, don't leave spaces between
taxon names!
-O Disable check for completely undetermined sequence in alignment. The program will
not exit with an error message when "-O" is specified.
DEFAULT: check enabled
-p Specify a random number seed for the parsimony inferences. This allows you to
reproduce your results and will help me debug the program.
-P Specify the file name of a user-defined AA (Protein) substitution model. This file
must contain 420 entries, the first 400 being the AA substitution rates (this must
be a symmetric matrix) and the last 20 are the empirical base frequencies
-q Specify the file name which contains the assignment of models to alignment
partitions for multiple models of substitution. For the syntax of this file please
consult the manual.
-r Specify the file name of a binary constraint tree. this tree does not need to be
comprehensive, i.e. must not contain all taxa
-R Specify the file name of a binary model parameter file that has previously been
generated with RAxML using the -f e tree evaluation option. The file name should
be: RAxML_binaryModelParameters.runID
-s Specify the name of the alignment data file in PHYLIP format
-S Specify the name of a secondary structure file. The file can contain "." for
alignment columns that do not form part of a stem and characters "()<>[]{}" to
define stem regions and pseudoknots
-t Specify a user starting tree file name in Newick format
-T PTHREADS VERSION ONLY! Specify the number of threads you want to run. Make sure to
set "-T" to at most the number of CPUs you have on your machine, otherwise, there
will be a huge performance decrease!
-u use the median for the discrete approximation of the GAMMA model of rate
heterogeneity
DEFAULT: OFF
-U Try to save memory by using SEV-based implementation for gap columns on large gappy
alignments The technique is described here:
http://www.biomedcentral.com/1471-2105/12/470 This will only work for DNA and/or
PROTEIN data and only with the SSE3 or AVX-vextorized version of the code.
-v Display version information
-V Disable rate heterogeneity among sites model and use one without rate heterogeneity
instead. Only works if you specify the CAT model of rate heterogeneity.
DEFAULT: use rate heterogeneity
-w FULL (!) path to the directory into which RAxML shall write its output files
DEFAULT: current directory
-W Sliding window size for leave-one-out site-specific placement bias algorithm only
effective when used in combination with "-f S"
DEFAULT: 100 sites
-x Specify an integer number (random seed) and turn on rapid bootstrapping CAUTION:
unlike in version 7.0.4 RAxML will conduct rapid BS replicates under the model of
rate heterogeneity you specified via "-m" and not by default under CAT
-X Same as the "-y" option below, however the parsimony search is more superficial.
RAxML will only do a randomized stepwise addition order parsimony tree
reconstruction without performing any additional SPRs. This may be helpful for
very broad whole-genome datasets, since this can generate topologically more
different starting trees.
DEFAULT: OFF
-y If you want to only compute a parsimony starting tree with RAxML specify "-y", the
program will exit after computation of the starting tree
DEFAULT: OFF
-Y Pass a quartet grouping file name defining four groups from which to draw quartets
The file input format must contain 4 groups in the following form: (Chicken, Human,
Loach), (Cow, Carp), (Mouse, Rat, Seal), (Whale, Frog); Only works in combination
with -f q !
-z Specify the file name of a file containing multiple trees e.g. from a bootstrap
that shall be used to draw bipartition values onto a tree provided with "-t", It
can also be used to compute per site log likelihoods in combination with "-f g" and
to read a bunch of trees for a couple of other options ("-f h", "-f m", "-f n").
-#|-N Specify the number of alternative runs on distinct starting trees In combination
with the "-b" option, this will invoke a multiple boostrap analysis Note that "-N"
has been added as an alternative since "-#" sometimes caused problems with certain
MPI job submission systems, since "-#" is often used to start comments. If you
want to use the bootstopping criteria specify "-# autoMR" or "-# autoMRE" or "-#
autoMRE_IGN" for the majority-rule tree based criteria (see -I option) or "-#
autoFC" for the frequency-based criterion. Bootstopping will only work in
combination with "-x" or "-b"
DEFAULT: 1 single analysis
--mesquite Print output files that can be parsed by Mesquite.
DEFAULT: Off
--silent Disables printout of warnings related to identical sequences and entirely
undetermined sites in the alignment
DEFAULT: Off
--no-seq-check Disables checking the input MSA for identical sequences and entirely
undetermined sites.
Enabling this option may save time, in particular for large phylogenomic
alignments. Before using this, make sure to check the alignment using the "-f c"
option!
DEFAULT: Off
--no-bfgs Disables automatic usage of BFGS method to optimize GTR rates on unpartitioned
DNA datasets
DEFAULT: BFGS on
--asc-corr Allows to specify the type of ascertainment bias correction you wish to use.
There are 3
types available: --asc-corr=lewis: the standard correction by Paul Lewis
--asc-corr=felsenstein: a correction introduced by Joe Felsenstein that allows to
explicitely specify
the number of invariable sites (if known) one wants to correct for.
--asc-corr=stamatakis: a correction introduced by myself that allows to explicitely
specify
the number of invariable sites for each character (if known) one wants to correct
for.
--flag-check When using this option, RAxML will only check if all command line flags
specifed are available and then exit
with a message listing all invalid command line flags or with a message stating
that all flags are valid.
--auto-prot=ml|bic|aic|aicc When using automatic protein model selection you can chose the
criterion for selecting these models.
RAxML will test all available prot subst. models except for LG4M, LG4X and
GTR-based models, with and without empirical base frequencies. You can chose
between ML score based selection and the BIC, AIC, and AICc criteria.
DEFAULT: ml
--epa-keep-placements=number specify the number of potential placements you want to keep
for each read in the EPA algorithm.
Note that, the actual values printed will also depend on the settings for
--epa-prob-threshold=threshold !
DEFAULT: 7
--epa-prob-threshold=threshold specify a percent threshold for including potential
placements of a read depending on the
maximum placement weight for this read. If you set this value to 0.01 placements
that have a placement weight of 1 per cent of the maximum placement will still be
printed to file if the setting of --epa-keep-placements allows for it
DEFAULT: 0.01
--epa-accumulated-threshold=threshold specify an accumulated likelihood weight threshold
for which different placements of read are printed
to file. Placements for a read will be printed until the sum of their placement
weights has reached the threshold value. Note that, this option can neither be
used in combination with --epa-prob-threshold nor with --epa-keep-placements!
--JC69 specify that all DNA partitions will evolve under the Jukes-Cantor model, this
overrides all other model specifications for DNA partitions.
DEFAULT: Off
--K80 specify that all DNA partitions will evolve under the K80 model, this overrides all
other model specifications for DNA partitions.
DEFAULT: Off
--HKY85 specify that all DNA partitions will evolve under the HKY85 model, this overrides
all other model specifications for DNA partitions.
DEFAULT: Off
--bootstop-perms=number specify the number of permutations to be conducted for the
bootstopping/bootstrap convergence test.
The allowed minimum number is 100!
DEFAULT: 100
--quartets-without-replacement specify that quartets are randomly subsampled, but without
replacement.
DEFAULT: random sampling with replacements
--print-identical-sequences specify that RAxML shall automatically generate a .reduced
alignment with all
undetermined columns removed, but without removing exactly identical sequences
DEFAULT: identical sequences will also be removed in the .reduced file
See also
Please also consult the RAxML-manual.
Please report bugs via the RAxML google group! Please send us all input files, the exact
invocation, details of the HW and operating system, as well as all error messages printed
to screen.
AUTHOR
This manpage was written by Andreas Tille for the Debian distribution and can be used for
any other usage of the program.
The code itself was written by Alexandros Stamatakis. With greatly appreciated code
contributions by:
Andre Aberer (HITS)
Simon Berger (HITS)
Alexey Kozlov (HITS)
Kassian Kobert (HITS)
David Dao (KIT and HITS)
Sarah Lutteropp (KIT and HITS)
Nick Pattengale (Sandia)
Wayne Pfeiffer (SDSC)
Akifumi S. Tanabe (NRIFS)
Charlie Taylor (UF)