trusty (1) raxmlHPC.1.gz

Provided by: raxml_7.2.8-2_amd64 bug

NAME

       raxmlHPC - Randomized Axelerated Maximum Likelihood

DESCRIPTION

       This is RAxML version 7.2.8 released by Alexandros Stamatakis in October 2010.

       With  greatly  appreciated code contributions by: Andre Aberer (TUM) Simon Berger (TUM) John Cazes (TACC)
       Michael Ott (TUM) Nick Pattengale (UNM) Wayne Pfeiffer (SDSC) Akifumi S. Tanabe (Univ. Tsukuba)

       Please also consult the RAxML-manual

       To report bugs send an email to stamatak@cs.tum.edu Please send me all input files, the exact invocation,
       details of the HW and operating system, as well as all error messages printed to screen.

       raxmlHPC[-SSE3|-PTHREADS|-PTHREADS-SSE3|-HYBRID|-HYBRID-SSE3]

       -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|b|c|d|e|E|F|g|h|i|I|j|J|m|n|o|p|r|R|s|S|t|u|U|v|w|x|y]    [-F]    [-g
              groupingFileName]     [-G     placementThreshold]     [-h]     [-H     placementThreshold]     [-i
              initialRearrangementSetting]       [-I       autoFC|autoMR|autoMRE|autoMRE_IGN]      [-j]      [-J
              MR|MR_DROP|MRE|STRICT|STRICT_DROP] [-k] [-K]  [-M]  [-o  outGroupName1[,outGroupName2[,...]]]  [-O
              checkPointInterval]  [-p  parsimonyRandomSeed]  [-P  proteinModel]  [-q multipleModelFileName] [-r
              binaryConstraintTree] [-R binaryModelParamFile] [-S secondaryStructureFile] [-t  userStartingTree]
              [-T    numberOfThreads]    [-U]    [-v]    [-w   outputDirectory]   [-W   slidingWindowSize]   [-x
              rapidBootstrapRandomNumberSeed]      [-y]      [-Y]      [-z       multipleTreesFile]       [-#|-N
              numberOfRuns|autoFC|autoMR|autoMRE|autoMRE_IGN]

       -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 modelOfEvolution  is  set  to  GTRCAT  or
              GTRMIX  Individual  per-site  rates  are  categorized  into  numberOfCategories rate categories to
              accelerate computations.

              DEFAULT: 25

       -C     Conduct  model  parameter  optimization  on  gappy,   partitioned   multi-gene   alignments   with
              per-partition  branch  length  estimates  (-M  enabled)  using the fast method with pointer meshes
              described in: Stamatakis and Ott: "Efficient computation of the phylogenetic  likelihood  function
              on  multi-gene  alignments  and  multi-core  processors"  WARNING:  We can not conduct useful tree
              searches using this method yet! Does not work with Pthreads version.

       -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
              under MIX/MIXI or GAMMA/GAMMAI

       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 appropriatly 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  b":
              draw bipartition information on a tree provided with "-t" based on multiple trees

              (e.g., from a bootstrap) in a file specifed by "-z"

              "-f c": check if the alignment can be properly read by RAxML "-f d": new rapid hill-climbing

              DEFAULT: ON

              "-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 ore more
              trees passed via

              "-z" and write them to a file that can be read by CONSEL

              "-f h": compute log likelihood test (SH-test) between best tree passed via "-t"

              and a bunch of other trees passed via "-z"

              "-f i": EXPERIMENTAL do not use for real tree inferences: conducts a single cycle of fast lazy SPR
              moves

              on a given input tree, to be used in combination with -C and -M

              "-f I": EXPERIMENTAL do not use for real tree inferences: conducts a single cycle of thorough lazy
              SPR moves

              on a given input tree, to be used in combination with -C and -M

              "-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  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

              "-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  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 rogue taxa using new  statistical  method  based  on  the  evolutionary  placement
              algorithm

              WARNING: this is experimental code

              "-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 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 U": execute morphological wieght calibration  using  parsimony,  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 the slow
              heuristics without dynamic alignment

              you will need to start RAxML with a non-comprehensive reference tree and an  alignment  containing
              all sequences (reference + query)

              "-f  w":  compute  ELW  test  on  a  bunch  of  trees passed via "-z" "-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 the fast
              heuristics without dynamic alignment

              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     enable  the  MP-based  evolutionary placement algorithm heuristics by specifiyng a threshold value
              (fraction of insertion branches to be evaluated using slow insertions under ML).

       -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".  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

       -m     Model of Binary (Morphological), Nucleotide, Multi-State, or Amino Acid Substitution:

              BINARY:

       "-m BINCAT"
              : 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

       "-m BINCATI"
              : 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

       "-m BINGAMMA"
              : GAMMA model of rate

              heterogeneity (alpha parameter will be estimated)

       "-m BINGAMMAI"
              : Same as BINGAMMA, but with estimate of proportion of invariable sites

              NUCLEOTIDES:

       "-m GTRCAT"
              : 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

       "-m GTRCAT_FLOAT"
              : Same as above but uses single-precision floating point arithemtics instead of double-precision

              Usage  only  recommened  for testing, the code will run slower, but can save almost 50% of memory.
              If you have problems with phylogenomic datasets and large memory requirements you may  give  it  a
              shot.  Keep in mind that numerical stability seems to be okay but needs further testing.

       "-m GTRCATI"
              : 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

       "-m GTRGAMMA"
              : GTR + Optimization of substitution rates + GAMMA model of rate

              heterogeneity (alpha parameter will be estimated)

              "-m  GTRGAMMA_FLOAT"  :  Same  as  GTRGAMMA,  but  also  with  single-precision  arithmetics, same
              cautionary notes as for

              GTRCAT_FLOAT apply.

       "-m GTRGAMMAI"
              : Same as GTRGAMMA, but with estimate of proportion of invariable sites

              MULTI-STATE:

       "-m MULTICAT"
              : 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

       "-m MULTICATI"
              : 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

       "-m MULTIGAMMA"
              : GAMMA model of rate heterogeneity (alpha parameter will be estimated)

       "-m MULTIGAMMAI"
              : Same as MULTIGAMMA, but with estimate of proportion of invariable sites

              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]"
              : 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], depending on the tree search option

       "-m PROTCATmatrixName[F]_FLOAT"
              : PROTCAT with single precision arithmetics, same cautionary notes as for GTRCAT_FLOAT apply

       "-m PROTCATImatrixName[F]"
              : 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], depending on the tree search option

       "-m PROTGAMMAmatrixName[F]"
              : specified AA matrix + Optimization of substitution rates + GAMMA model of rate

              heterogeneity (alpha parameter will be estimated)

              "-m PROTGAMMAmatrixName[F]_FLOAT" : PROTGAMMA with single precision arithmetics,  same  cautionary
              notes    as    for    GTRCAT_FLOAT    apply    "-m   PROTGAMMAImatrixName[F]"        :   Same   as
              PROTGAMMAmatrixName[F], but with estimate of proportion of invariable sites

              Available AA substitution models: DAYHOFF, DCMUT, JTT, MTREV, WAG,  RTREV,  CPREV,  VT,  BLOSUM62,
              MTMAM,  LG,  MTART,  MTZOA, PMB, HIVB, HIVW, JTTDCMUT, FLU, GTR With the optional "F" appendix you
              can specify if you want to use empirical base frequencies Please note that for  mixed  models  you
              can  in  addition  specify the per-gene AA model in the mixed model 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 outgrpoup 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     Enable  checkpointing using the dmtcp library available at http://dmtcp.sourceforge.net/ This only
              works if you call the program by preceded by the command "dmtcp_checkpoint" and if you  compile  a
              dedicated  binary  using the appropriate Makefile.  With "-O" you can specify the interval between
              checkpoints in seconds.

              DEFAULT: 3600.0 seconds

       -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     Try to save memory by using SEV-based implementation for gap columns  on  large  gappy  alignments
              WARNING: this will only work for DNA under GTRGAMMA and is still in an experimental state.

       -v     Display version information

       -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

       -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     Do  a  more thorough parsimony tree search using a parsimony ratchet and exit.  specify the number
              of ratchet searches via "-#" or "-N" This has just been implemented for completeness, if you  want
              a fast MP implementation use TNT

              DEFAULT: OFF

       -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

       This is RAxML version 7.2.8 released by Alexandros Stamatakis in October 2010.

       With greatly appreciated code contributions by: Andre Aberer (TUM) Simon Berger (TUM) John  Cazes  (TACC)
       Michael Ott (TUM) Nick Pattengale (UNM) Wayne Pfeiffer (SDSC) Akifumi S. Tanabe (Univ. Tsukuba)