Provided by: raxml_8.2.12+dfsg-1_amd64 bug

NAME

       Use - Randomized Axelerated Maximum Likelihood

DESCRIPTION

       Use raxml with PTHREADS support

       This is RAxML version 8.2.12 released by Alexandros Stamatakis on May 2018.

       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)

       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.

       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]

       -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

       This is RAxML version 8.2.12 released by Alexandros Stamatakis on May 2018.

       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)