Provided by: phast_1.5+dfsg-1_amd64

**NAME**

phastCons - Identify conserved elements or produce conservation scores, given

**SYNOPSIS**

The alignment file can be in any of several file formats (see--msa-format). The phylogenetic models must be in the .mod format produced by the phyloFit program.

**DESCRIPTION**

Identify conserved elements or produce conservation scores, given a multiple alignment and a phylo-HMM. By default, a phylo-HMM consisting of two states is assumed: a "conserved" state and a "non-conserved" state. Separate phylogenetic models can be specified for these two states, e.g., phastCons myfile.ss cons.mod,noncons.mod > scores.wig or a single model can be given for the non-conserved state, e.g., phastCons myfile.ss--rho0.5 noncons.mod > scores.wig in which case the model for the conserved state will be obtained by multiplying all branch lengths by the scaling parameter rho (0 < rho < 1). If the--rhooption is not used, rho will be set to its default value of 0.3. By default, the phylogenetic models will be left unaltered, but if the--estimate-treesoption is used, e.g., phastCons myfile.ss init.mod--estimate-treesnewtree > scores.wig then the phylogenetic models for the two states will be estimated from the data, and the given tree model (there must be only one in this case) will be used for initialization only. It is also possible to estimate only the scale factor--rho, using the--estimate-rhooption. The transition probabilities for the HMM can either be specified at the command line or estimated from the data using an EM algorithm. To specify them at the command line, use either the--transitionsoption or the--target-coverageand--expected-lengthoptions. The recommended method is to use--target-coverageand--expected-length, e.g., phastCons--target-coverage0.25--expected-length12 myfile.ss cons.mod,noncons.mod > scores.wigTheprogramproducestwomaintypesofoutput.The primary output, sent to stdout in fixed-step WIG format (http://genome.ucsc.edu/goldenPath/help/wiggle.html), is a set of base-by-base conservation scores. The score at each base is equal to the posterior probability that that base was "generated" by the conserved state of the phylo-HMM. The scores are reported in the coordinate frame of a designated reference sequence (see--refidx), which is by default the first sequence in the alignment. They can be suppressed with the--no-post-probsoption. The secondary type of output, activated with the--most-conserved(aka--viterbi) option, is a set of discrete conserved elements. These elements are output in either BED or GFF format, also in the coordinate system of the reference sequence (see--most-conserved). They can be assigned log-odds scores using the--scoreoption. Other uses are also supported, but will not be described in detail here. For example, it is possible to produce conservation scores and conserved elements using a k-state phylo-HMM of the kind described by Felsenstein and Churchill (1996) (see--FC), and it is possible to produce a "coding potential" score instead of a conservation score (see--coding-potential). It is also possible to give the program a custom HMM and to specify any subset of its states to use for prediction (see--hmmand--states). See the phastCons HOWTO for additional details.

**EXAMPLE**

1. Given phylogenetic models for conserved and nonconserved regions and HMM transition parameters, compute a set of conservation scores. phastCons--transitions0.01,0.01 mydata.ss cons.mod,noncons.mod > scores.wig 2. Similar to (1), but define the conserved model as a scaled version of the nonconserved model, with rho=0.4 as the scaling parameter. Also predict conserved elements as well as conservation scores, and assign log-odds scores to predictions. phastCons--transitions0.01,0.01--most-conservedmostcons.bed--score--rho0.4 mydata.ss noncons.mod > scores.wig (if output file were "mostcons.gff," then output would be in GFF instead of BED format) 3. This time, estimate the parameter rho from the data. Suppress both the scores and the conserved elements. Specify the transition probabilities using--target-coverageand--expected-lengthinstead of--transitions. phastCons--target-coverage0.25--expected-length12--estimate-rhonewtree--no-post-probsmydata.ss noncons.mod 4. This time estimate all free parameters of the tree models. phastCons--target-coverage0.25--expected-length12--estimate-treesnewtree--no-post-probsmydata.ss noncons.mod 5. Estimate the state-transition parameters but not the tree models. Output the conservation scores but not the conserved elements. phastCons mydata.ss cons.mod,noncons.mod > scores.wig 6. Estimate just the expected-length parameter and also estimate rho. phastCons--target-coverage0.25--estimate-rhonewtree mydata.ss noncons.mod > scores.wig

**OPTIONS**

Treemodels--rho,-R<rho> Set the *scale* (overall evolutionary rate) of the model for the conserved state to be <rho> times that of the model for the non-conserved state (0 < <rho> < 1; default 0.3). If used with--estimate-treesor--estimate-rho, the specified value will be used for initialization only (rho will be estimated). This option is ignored if two tree models are given.--estimate-trees,-T<fname_root> Estimate free parameters of tree models and write new models to <fname_root>.cons.mod and <fname_root>.noncons.mod.--estimate-rho,-O<fname_root> Like--estimate-trees, but estimate only the parameter rho.--gc,-G<val> (Optionally use with--estimate-treesor--estimate-rho) Assume a background nucleotide distribution consistent with the given average G+C content (0 < <val> < 1) when estimating tree models. (The frequencies of G and C will be set to <val>/2 and the frequencies of A and T will be set to (1-<val>)/2.) This option overrides the default behavior of estimating the background distribution from the data (if--estimate-trees) or obtaining them from the input model (if--estimate-rho).--nrates,-k<nrates> | <nrates_conserved,nrates_nonconserved> (Optionally use with a discrete-gamma model and--estimate-trees) Assume the specified number of rate categories, instead of the number given in the *.mod file. The shape parameter 'alpha' will be as given in the *.mod file. In the case of the default two-state HMM, two values can be specified, for the numbers of rates for the conserved and the nonconserved states, resp.State-transitionparameters--transitions,-t[~]<mu>,<nu> Fix the transition probabilities of the two-state HMM as specified, rather than estimating them by maximum likelihood. Alternatively, if first character of argument is '~', estimate parameters, but initialize to specified values. The argument <mu> is the probability of transitioning from the conserved to the non-conserved state, and <nu> is the probability of the reverse transition. The probabilities of self transitions are thus 1-<mu> and 1-<nu> and the expected lengths of conserved and nonconserved elements are 1/<mu> and 1/<nu>, respectively.--target-coverage,-C<gamma> (Alternative to--transitions) Constrain transition parameters such that the expected fraction of sites in conserved elements is <gamma> (0 < <gamma> < 1). This is a *prior* rather than *posterior* expectation and assumes stationarity of the state-transition process. Adding this constraint causes the ratio mu/nu to be fixed at (1-<gamma>)/<gamma>. If used with--expected-length, the transition probabilities will be completely fixed; otherwise the expected-length parameter <omega> will be estimated by maximum likelihood.--expected-length,-E[~]<omega> {--expected-lengths also allowed, for backward compatibility} (For use with--target-coverage, alternative to--transitions) Set transition probabilities such that the expected length of a conserved element is <omega>. Specifically, the parameter mu is set to 1/<omega>. If preceded by '~', <omega> will be estimated, but will be initialized to the specified value.Input/output--msa-format,-iPHYLIP|FASTA|MPM|SS|MAF Alignment file format. Default is to guess format based on file contents. Note that the msa_view program can be used to convert between formats.--viterbi[alternatively--most-conserved],-V<fname> Predict discrete elements using the Viterbi algorithm and write to specified file. Output is in BED format, unless <fname> has suffix ".gff", in which case output is in GFF.--score,-s(Optionally use with--viterbi) Assign a log-odds score to each prediction.--lnl,-L<fname> Compute total log likelihood using the forward algorithm and write to specified file.--no-post-probs,-nSuppress output of posterior probabilities. Useful if only discrete elements or likelihood is of interest.--log,-g<log_fname> (Optionally use when estimating free parameters) Write log of optimization procedure to specified file.--refidx,-r<refseq_idx> Use coordinate frame of specified sequence in output. Default value is 1, first sequence in alignment; 0 indicates coordinate frame of entire multiple alignment.--seqname,-N<name> (Optionally use with--viterbi) Use specified string for 'seqname' (GFF) or 'chrom' field in output file. Default is obtained from input file name (double filename root, e.g., "chr22" if input file is "chr22.35.ss").--idpref,-P<name> (Optionally use with--viterbi) Use specified string as prefix of generated ids in output file. Can be used to ensure ids are unique. Default is obtained from input file name (single filename root, e.g., "chr22.35" if input file is "chr22.35.ss").--quiet,-qProceed quietly (without updates to stderr).--help,-hPrint this help message. (Indels) [experimental]--indels,-IExpand HMM state space to model indels as described in Siepel & Haussler (2004).--max-micro-indel,-Y<length> (Optionally use with--indels) Maximum length of an alignment gap to be considered a "micro-indel" and therefore addressed by the indel model. Gaps longer than this threshold will be treated as missing data. Default value is 20.--indel-params,-D[~]<alpha_0,beta_0,tau_0,alpha_1,beta_1,tau_1> (Optionally use with--indelsand default two-state HMM) Fix the indel parameters at (alpha_0, beta_0, tau_0) for the conserved state and at (alpha_1, beta_1, tau_1) for the non-conserved state, rather than estimating them by maximum likelihood. Alternatively, if first character of argument is '~', estimate parameters, but initialize with specified values. Alpha_j is the rate of insertion events per substitution per site in state j (typically ~0.05), beta_j is the rate of deletion events per substitution per site in state j (typically ~0.05), and tau_j is approximately the inverse of the expected indel length in state j (typically 0.2-0.5).--indels-only,-JLike--indelsbut force the use of a single-state HMM. This option allows the effect of the indel model in isolation to be observed. Implies--no-post-probs. Use with--lnl. (Felsenstein/Churchill model) [rarely used]--FC,-X(Alternative to--hmm; specify only one *.mod file with this option) Use an HMM with a state for every rate category in the given phylogenetic model, and transition probabilities defined by an autocorrelation parameter lambda (as described by Felsenstein and Churchill, 1996). A rate constant for each state (rate category) will be multiplied by the branch lengths of the phylogenetic model, to create a "scaled" version of the model for that state. If the phylogenetic model was estimated using Yang's discrete gamma method (-koption to phyloFit), then the rate constants will be defined according to the estimated shape parameter 'alpha', as described by Yang (1994). Otherwise, a nonparameteric model of rate variation must have been used (-Koption to phyloFit), and the rate constants will be as defined (explicitly) in the *.mod file. By default, the parameter lambda will be estimated by maximum likelihood (see--lambda).--lambda,-l[~]<lambda> (Optionally use with--FC) Fix lambda at the specified value rather than estimating it by maximum likelihood. Alternatively, if first character is '~', estimate but initialize at specified value. Allowable range is 0-1. With k rate categories, the transition probability between state i and state j will be lambda * I(i == j) + (1-lambda)/k, where I is the indicator function. Thus, lambda = 0 implies no autocorrelation and lambda = 1 implies perfect autocorrelation. (Coding potential) [experimental]--coding-potential,-pUse parameter settings that cause output to be interpretable as a coding potential score. By default, a simplified version of exoniphy's phylo-HMM is used, with a noncoding (background) state, a conserved non-coding (CNS) state, and states for the three codon positions. This option implies--catmap"NCATS=4; CNS 1; CDS 2-4"--hmm<default-HMM-file>--statesCDS--reflect-strandbackground,CNS and a set of default *.mod files (all of which can be overridden). This option can be used with or without--indels.--extrapolate,-e<phylog.nh> | default Extrapolate to a larger set of species based on the given phylogeny (Newick-format). The trees in the given tree models (*.mod files) must be subtrees of the larger phylogeny. For each tree model M, a copy will be created of the larger phylogeny, then scaled such that the total branch length of the subtree corresponding to M's tree equals the total branch length of M's tree; this new version will then be used in place of M's tree. (Any species name present in this tree but not in the data will be ignored.) If the string "default" is given instead of a filename, then a phylogeny for 25 vertebrate species, estimated from sequence data for Target 1 (CFTR) of the NISC Comparative Sequencing Program (Thomas et al., 2003), will be assumed.--alias,-A<alias_def> Alias names in input alignment according to given definition, e.g., "hg17=human; mm5=mouse; rn3=rat". Useful with default *.mod files, e.g., with--coding-potential. (Default models use generic common names such as "human", "mouse", and "rat". This option allows a mapping to be established between the leaves of trees in these files and the sequences of an alignment that uses an alternative naming convention.)CustomHMMs[rarelyused]--hmm,-H<hmm_fname> Name of HMM file explicitly defining the probabilities of all state transitions. States in the file must correspond in number and order to phylogenetic models in <mod_fname_list>. Expected file format is as produced by 'hmm_train.'--catmap,-c<fname>|<string> (Optionally use with--hmm) Mapping of feature types to category numbers. Can give either a filename or an "inline" description of a simple category map, e.g.,--catmap"NCATS = 3 ; CDS 1-3".--states,-S<state_list> States of interest in the phylo-HMM, specified by number (indexing starts with 0), or if--catmap, by category name. Default value is 1. Choosing--states"0,1,2" will cause output of the sum of the posterior probabilities for states 0, 1, and 2, and/or of regions in which the Viterbi path coincides with (any of) states 0, 1, or 2 (see--viterbi).--reflect-strand,-U<pivot_states> (Optionally use with--hmm) Given an HMM describing the forward strand, create a larger HMM that allows for features on both strands by "reflecting" the original HMM about the specified "pivot" states. The new HMM will be used for prediction on both strands. States can be specified by number (indexing starts with 0), or if--catmap, by category name.Missingdata[rarelyused]--require-informative,-M<states> Require "informative" columns (i.e., columns with more than two non-missing-data characters, excluding sequences specified by--not-informative) in specified HMM states, to help eliminate false positive predictions. States can be specified by number (indexing starts with 0) or, if--catmapis used, by category name. Non-informative columns will be given emission probabilities of zero. By default, this option is active, with <states> equal to the set of states of interest for prediction (as specified by--states). Use "none" to disable completely.--not-informative,-F<list> Do not consider the specified sequences (listed by name) when deciding whether a column is informative. This option may be useful when sequences are present that are very close to the reference sequence and thus do not contribute much in the way of phylogenetic information. E.g., one might use "--not-informative chimp" with a human-referenced multiple alignment including chimp sequence, to avoid false-positive predictions based only on human/chimp alignments (can be a problem, e.g., with--coding-potential).--ignore-missing,-z(For use when estimating transition probabilities) Ignore regions of missing data in all sequences but the reference sequence (excluding sequences specified by--not-informative) when estimating transition probabilities. Can help avoid too-low estimates of <mu> and <nu> or too-high estimates of <lambda>. Warning: this option should not be used with--viterbibecause coordinates in output will be unrecognizable.

**SEE** **ALSO**

J. Felsenstein and G. Churchill. 1996. A hidden Markov model approach to variation among sites in rate of evolution. Mol. Biol. Evol., 13:93-104. A. Siepel, G. Bejerano, J. S. Pedersen, et al. 2005. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. (in press) A. Siepel and D. Haussler. 2004. Computational identification of evolutionarily conserved exons. Proc. 8th Annual Int'l Conf. on Research in Computational Biology (RECOMB '04), pp. 177-186. J. Thomas et al. 2003. Comparative analyses of multi-species sequences from targeted genomic regions. Nature 424:788-793. Z. Yang. 1994. Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. J. Mol. Evol., 39:306-314.