int main(int argc, char *argv[]) {
char *msa_fname = NULL, *alph = "ACGT";
msa_format_type input_format = UNKNOWN_FORMAT;
char c;
int opt_idx, seed=-1;
String *optstr;
List *tmplist = NULL;
struct phyloFit_struct *pf;
FILE *infile;
struct option long_opts[] = {
{"msa", 1, 0, 'm'},
{"tree", 1, 0, 't'},
{"subst-mod", 1, 0, 's'},
{"msa-format", 1, 0, 'i'},
{"nrates", 1, 0, 'k'},
{"alpha", 1, 0, 'a'},
{"features", 1, 0, 'g'},
{"catmap", 1, 0, 'c'},
{"log", 1, 0, 'l'},
{"out-root", 1, 0, 'o'},
{"EM", 0, 0, 'E'},
{"error", 1, 0, 'e'},
{"precision", 1, 0, 'p'},
{"do-cats", 1, 0, 'C'},
{"non-overlapping", 0, 0, 'V'},
{"markov", 0, 0, 'N'},
{"reverse-groups", 1, 0, 'R'},
{"init-model", 1, 0, 'M'},
{"init-random", 0, 0, 'r'},
{"init-parsimony", 0, 0, 'y'},
{"print-parsimony", 1, 0, 'Y'},
{"lnl", 0, 0, 'L'},
{"scale-only", 0, 0, 'B'},
{"scale-subtree", 1, 0, 'S'},
{"estimate-freqs", 0, 0, 'F'},
{"sym-freqs", 0, 0, 'W'},
{"no-freqs", 0, 0, 'f'},
{"no-rates", 0, 0, 'n'},
{"no-opt", 1, 0, 'O'},
{"min-informative", 1, 0, 'I'},
{"gaps-as-bases", 0, 0, 'G'},
{"quiet", 0, 0, 'q'},
{"help", 0, 0, 'h'},
{"windows", 1, 0, 'w'},
{"windows-explicit", 1, 0, 'v'},
{"ancestor", 1, 0, 'A'},
{"post-probs", 0, 0, 'P'},
{"expected-subs", 0, 0, 'X'},
{"expected-total-subs", 0, 0, 'Z'},
{"expected-subs-col", 0, 0, 'J'},
{"column-probs", 0, 0, 'U'},
{"rate-constants", 1, 0, 'K'},
{"ignore-branches", 1, 0, 'b'},
{"clock", 0, 0, 'z'},
{"alt-model", 1, 0, 'd'},
{"label-branches", 1, 0, 0},
{"label-subtree", 1, 0, 0},
{"selection", 1, 0, 0},
{"bound", 1, 0, 'u'},
{"seed", 1, 0, 'D'},
{0, 0, 0, 0}
};
// NOTE: remaining shortcuts left: HjQx
pf = phyloFit_struct_new(0);
while ((c = (char)getopt_long(argc, argv, "m:t:s:g:c:C:i:o:k:a:l:w:v:M:p:A:I:K:S:b:d:O:u:Y:e:D:GVENRqLPXZUBFfnrzhWyJ", long_opts, &opt_idx)) != -1) {
switch(c) {
case 'm':
msa_fname = optarg;
break;
case 't':
if (optarg[0] == '(') /* in this case, assume topology given
at command line */
pf->tree = tr_new_from_string(optarg);
else
pf->tree = tr_new_from_file(phast_fopen(optarg, "r"));
break;
case 's':
pf->subst_mod = tm_get_subst_mod_type(optarg);
if (pf->subst_mod == UNDEF_MOD)
die("ERROR: illegal substitution model. Type \"phyloFit -h\" for usage.\n");
break;
case 'g':
pf->gff = gff_read_set(phast_fopen(optarg, "r"));
break;
case 'c':
pf->cm = cm_new_string_or_file(optarg);
break;
case 'C':
pf->cats_to_do_str = get_arg_list(optarg);
break;
case 'V':
pf->nonoverlapping = TRUE;
break;
case 'o':
pf->output_fname_root = optarg;
break;
case 'k':
pf->nratecats = get_arg_int_bounds(optarg, 0, INFTY);
break;
case 'a':
pf->alpha = get_arg_dbl(optarg);
break;
case 'R':
pf->reverse_group_tag = optarg;
break;
case 'i':
input_format = msa_str_to_format(optarg);
if (input_format == UNKNOWN_FORMAT)
die("ERROR: unrecognized alignment format. Type 'phyloFit -h' for usage.\n");
break;
case 'l':
if (!strcmp(optarg, "-"))
pf->logf = stderr;
else pf->logf = phast_fopen(optarg, "w+");
break;
case 'N':
pf->use_conditionals = 1;
break;
case 'w':
tmplist = get_arg_list(optarg);
if (lst_size(tmplist) != 2 ||
str_as_int(lst_get_ptr(tmplist, 0), &(pf->window_size)) != 0 ||
str_as_int(lst_get_ptr(tmplist, 1), &(pf->window_shift)) != 0)
die("ERROR: illegal arguments to --windows.\n");
lst_free_strings(tmplist);
lst_free(tmplist);
break;
case 'v':
tmplist = get_arg_list(optarg);
if (lst_size(tmplist) % 2 != 0)
die("ERROR: argument to --windows-explicit must be a list of even length.\n");
pf->window_coords = str_list_as_int(tmplist);
lst_free(tmplist);
break;
case 'E':
pf->use_em = TRUE;
break;
case 'e':
pf->error_fname=optarg;
break;
case 'p':
if (!strcmp(optarg, "LOW")) pf->precision = OPT_LOW_PREC;
else if (!strcmp(optarg, "MED")) pf->precision = OPT_MED_PREC;
else if (!strcmp(optarg, "HIGH")) pf->precision = OPT_HIGH_PREC;
else if (!strcmp(optarg, "VERY_HIGH")) pf->precision = OPT_VERY_HIGH_PREC;
else die("ERROR: --precision must be LOW, MED, or HIGH.\n\n");
break;
case 'M':
pf->input_mod = tm_new_from_file(phast_fopen(optarg, "r"), 1);
break;
case 'r':
pf->random_init = TRUE;
break;
case 'y':
pf->init_parsimony = TRUE;
break;
case 'Y':
pf->init_parsimony = TRUE;
pf->parsimony_cost_fname = optarg;
pf->parsimony_only=TRUE;
break;
case 'L':
pf->likelihood_only = TRUE;
break;
case 'q':
pf->quiet = TRUE;
break;
case 'P':
pf->do_bases = TRUE;
break;
case 'X':
pf->do_expected_nsubst = TRUE;
break;
case 'Z':
pf->do_expected_nsubst_tot = TRUE;
break;
case 'J':
pf->do_expected_nsubst_col = TRUE;
break;
case 'U':
pf->likelihood_only = TRUE; /* force -L */
pf->nsites_threshold = 0; /* also force this; typical use is
with small number of tuples, no
tuple_idx */
pf->do_column_probs = TRUE;
break;
case 'A':
pf->root_seqname = optarg;
break;
case 'I':
pf->nsites_threshold = get_arg_int(optarg);
break;
case 'G':
pf->gaps_as_bases = TRUE;
alph = "ACGT-";
break;
case 'B':
pf->estimate_scale_only = TRUE;
break;
case 'S':
pf->subtree_name = optarg;
break;
case 'F':
pf->estimate_backgd = TRUE;
break;
case 'W':
pf->estimate_backgd = TRUE;
pf->symfreq = TRUE;
break;
case 'f':
pf->no_freqs = TRUE;
break;
case 'n':
pf->no_rates = TRUE;
break;
case 'K':
tmplist = get_arg_list(optarg);
pf->rate_consts = str_list_as_dbl(tmplist);
pf->nratecats = lst_size(pf->rate_consts);
pf->use_em = 1;
lst_free_strings(tmplist); lst_free(tmplist);
break;
case 'b':
pf->ignore_branches = get_arg_list(optarg);
break;
case 'z':
pf->assume_clock = TRUE;
break;
case 'O':
if (pf->nooptstr == NULL)
pf->nooptstr = str_new_charstr(optarg);
else die("ERROR: no-opt argument can only be used once! parameters can be comma-separated list.");
break;
case 'd':
if (pf->alt_mod_str == NULL) {
pf->alt_mod_str = lst_new_ptr(1);
}
optstr = str_new_charstr(optarg);
lst_push_ptr(pf->alt_mod_str, optstr);
break;
case 0:
if (strcmp(long_opts[opt_idx].name, "label-branches") == 0 ||
strcmp(long_opts[opt_idx].name, "label-subtree") == 0) {
optstr = str_new_charstr(optarg);
if (pf->label_str == NULL) {
pf->label_str = lst_new_ptr(3);
pf->label_type = lst_new_int(3);
}
lst_push_ptr(pf->label_str, optstr);
lst_push_int(pf->label_type,
strcmp(long_opts[opt_idx].name, "label-branches") == 0 ?
BRANCH_TYPE : SUBTREE_TYPE);
}
else if (strcmp(long_opts[opt_idx].name, "selection") == 0) {
pf->selection = get_arg_dbl(optarg);
pf->use_selection = TRUE;
}
else {
die("ERROR: unknown option. Type 'phyloFit -h' for usage.\n");
}
break;
case 'u':
if (pf->bound_arg == NULL)
pf->bound_arg = lst_new_ptr(1);
optstr = str_new_charstr(optarg);
lst_push_ptr(pf->bound_arg, optstr);
break;
case 'D':
seed = get_arg_int_bounds(optarg, 1, INFTY);
break;
case 'h':
printf("%s", HELP);
exit(0);
case '?':
die("ERROR: illegal argument. Type 'phyloFit -h' for usage.\n");
}
}
set_seed(seed);
if (msa_fname == NULL) {
if (optind >= argc)
die("ERROR: missing alignment filename. Type 'phyloFit -h' for usage.\n");
msa_fname = argv[optind];
pf->msa_fname = msa_fname;
}
infile = phast_fopen(msa_fname, "r");
if (input_format == UNKNOWN_FORMAT)
input_format = msa_format_for_content(infile, 1);
if (pf->nonoverlapping && (pf->use_conditionals || pf->gff != NULL ||
pf->cats_to_do_str || input_format == SS))
die("ERROR: cannot use --non-overlapping with --markov, --features,\n--msa-format SS, or --do-cats.\n");
/* read alignment */
if (!pf->quiet) fprintf(stderr, "Reading alignment from %s ...\n", msa_fname);
if (input_format == MAF) {
pf->msa = maf_read(infile, NULL,
tm_order(pf->subst_mod) + 1,
NULL, pf->gff, pf->cm,
pf->nonoverlapping ? tm_order(pf->subst_mod) + 1 : -1,
FALSE, pf->reverse_group_tag, NO_STRIP, FALSE);
if (pf->gaps_as_bases)
msa_reset_alphabet(pf->msa, alph);
}
else
pf->msa = msa_new_from_file_define_format(infile,
input_format, alph);
/* set up for categories */
/* first label sites, if necessary */
pf->label_categories = (input_format != MAF);
run_phyloFit(pf);
if (pf->logf != NULL && pf->logf != stderr && pf->logf != stdout)
phast_fclose(pf->logf);
if (!pf->quiet) fprintf(stderr, "Done.\n");
sfree(pf);
return 0;
}
int main(int argc, char* argv[]) {
FILE* F;
MSA *msa;
int *msa_gap_patterns = NULL;
HMM *hmm = NULL;
TreeNode *tree = NULL;
int i, input_format = SS, msa_idx, quiet_mode = FALSE,
ncats, nmsas, ncats_unspooled, indel_nseqs = -1;
String *msa_fname, *gff_fname;
List *gff_fname_list = NULL, *msa_fname_list = NULL,
*msa_length_list = NULL, *model_indels_str = NULL;
Matrix *traincounts = NULL;
Vector *begcounts = NULL, *statecounts = NULL;
CategoryMap *cm = NULL;
char c;
GapPatternMap *gpm = NULL;
GFF_Set *gff;
char *reverse_groups_tag = NULL;
while ((c = getopt(argc, argv, "i:g:c:m:M:R:I:n:t:P:G:qh")) != -1) {
switch(c) {
case 'i':
input_format = msa_str_to_format(optarg);
if (input_format == -1)
die("ERROR: bad alignment format.\n");
break;
case 'g':
gff_fname_list = get_arg_list(optarg);
break;
case 'c':
cm = cm_new_string_or_file(optarg);
break;
case 'm':
msa_fname_list = get_arg_list(optarg);
break;
case 'M':
msa_length_list = str_list_as_int(get_arg_list(optarg));
break;
case 'R':
reverse_groups_tag = optarg;
break;
case 'I':
model_indels_str = get_arg_list(optarg);
break;
case 'n':
indel_nseqs = get_arg_int(optarg);
break;
case 't':
if (optarg[0] == '(') /* in this case, assume topology given
at command line */
tree = tr_new_from_string(optarg);
else
tree = tr_new_from_file(phast_fopen(optarg, "r"));
break;
case 'q':
quiet_mode = TRUE;
break;
case 'h':
print_usage();
exit(0);
case '?':
die("ERROR: unrecognized option.\n\nType 'hmm_train -h' for usage.\n");
}
}
if (msa_fname_list == NULL)
die("ERROR: -m required. Type 'hmm_train -h' for usage.\n");
if (gff_fname_list == NULL)
die("ERROR: -g required in training mode. Type 'hmm_train -h' for usage.\n");
if (msa_length_list != NULL && msa_fname_list != NULL)
die("ERROR: -m and -M are mutually exclusive. Type 'hmm_train -h' for usage.\n");
if (model_indels_str != NULL && tree == NULL)
die("ERROR: -I requires -t. Type 'hmm_train -h' for usage.\n");
if (cm == NULL)
die("ERROR: category map required.\n");
set_seed(-1);
ncats = cm->ncats + 1;
ncats_unspooled = cm->unspooler != NULL ? cm->unspooler->nstates_unspooled :
ncats;
nmsas = (msa_length_list != NULL ? lst_size(msa_length_list) :
lst_size(msa_fname_list));
if (model_indels_str != NULL) {
if (tree == NULL)
die("ERROR: tree is NULL\n"); /*FIXME: indel_ncats broken */
gpm = gp_create_gapcats(cm, model_indels_str, tree, FALSE);
ncats = cm->ncats + 1; /* numbers will change */
ncats_unspooled = cm->unspooler == NULL ? ncats :
cm->unspooler->nstates_unspooled;
}
/* allocate memory for storage of "training paths" */
traincounts = mat_new(ncats_unspooled, ncats_unspooled);
statecounts = vec_new(ncats_unspooled);
begcounts = vec_new(ncats_unspooled);
mat_zero(traincounts);
vec_zero(statecounts);
vec_zero(begcounts);
/* create skeleton of new HMM. */
hmm = hmm_new_nstates(ncats_unspooled, 0, 0);
/* Main loop: consider each MSA in turn */
for (msa_idx = 0; msa_idx < nmsas; msa_idx++) {
if (msa_fname_list != NULL) {
msa_fname = (String*)lst_get_ptr(msa_fname_list, msa_idx);
F = phast_fopen(msa_fname->chars, "r");
if (!quiet_mode)
fprintf(stderr, "Reading alignment from %s ...\n",
F == stdin ? "stdin" : msa_fname->chars);
msa = msa_new_from_file(F, NULL);
phast_fclose(F);
}
else { /* only lengths of alignments specified */
msa = msa_new(NULL, NULL, 0, lst_get_int(msa_length_list, msa_idx), NULL);
/* just a shell in this case */
}
gff_fname = (String*)lst_get_ptr(gff_fname_list, msa_idx);
if (!quiet_mode)
fprintf(stderr, "Reading annotations from %s ...\n", gff_fname->chars);
gff = gff_read_set(phast_fopen(gff_fname->chars, "r"));
/* convert GFF to coordinate frame of alignment */
if (msa_length_list == NULL) {
if (!quiet_mode)
fprintf(stderr, "Mapping annotations to alignment ...\n");
msa_map_gff_coords(msa, gff, 1, 0, 0); /* assume seq 1 is ref */
}
if (model_indels_str != NULL) {
if (!quiet_mode)
fprintf(stderr, "Obtaining gap patterns ...\n");
msa_gap_patterns = smalloc(msa->length * sizeof(int));
gp_set_phylo_patterns(gpm, msa_gap_patterns, msa);
}
/* at this point, we don't actually need the alignment anymore;
if using ordered suff stats (likely with large data sets),
can free them now, to avoid running out of memory */
if (msa->ss != NULL) {
ss_free(msa->ss);
msa->ss = NULL;
}
if (reverse_groups_tag != NULL) {
if (!quiet_mode)
fprintf(stderr, "Reverse complementing features on negative strand (group by '%s') ...\n",
reverse_groups_tag);
/* we don't need to reverse complement the whole alignment --
just the gff and possibly the gap pattern array (pass a
NULL msa) */
gff_group(gff, reverse_groups_tag);
msa_reverse_compl_feats(NULL, gff, msa_gap_patterns);
}
if (!quiet_mode)
fprintf(stderr, "Labeling sites by category ...\n");
msa_label_categories(msa, gff, cm);
gff_free_set(gff);
if (model_indels_str != NULL) {
if (!quiet_mode)
fprintf(stderr, "Remapping categories according to gap patterns ...\n");
if (indel_nseqs > 0 && indel_nseqs != msa->nseqs) {
/* in this case, we'll simply reassign non-trivial gap
patterns randomly. This will achieve the desired
effect with minimal coding, as long as the number of
sites is not too small (the indel model is probably
useless anyway if the number is small) */
int pat, newpat;
int npatterns = 4 * indel_nseqs - 5;
int complex_allowed[cm->ncats+1];
List *no_complex_names, *no_complex_nums;
if (!quiet_mode)
fprintf(stderr, "(target number of sequences: %d)\n", indel_nseqs);
/* set up index indicating by cat no. whether complex gaps
are allowed */
for (i = 0; i < ncats; i++) complex_allowed[i] = 1;
no_complex_names = lst_new_ptr(10);
str_split(str_new_charstr(NO_COMPLEX), ",", no_complex_names);
no_complex_nums = cm_get_category_list(cm, no_complex_names, 1);
for (i = 0; i < lst_size(no_complex_nums); i++)
complex_allowed[lst_get_int(no_complex_nums, i)] = 0;
lst_free(no_complex_nums);
lst_free_strings(no_complex_names);
lst_free(no_complex_names);
/* now reassign all non-null numbers */
for (i = 0; i < msa->length; ) {
if ((pat = msa_gap_patterns[i]) != 0) {
if (complex_allowed[msa->categories[i]])
newpat = 1 + ((double)npatterns * unif_rand());
/* random number in interval [1, npatterns] */
else
newpat = 1 + ((double)(npatterns-1) * unif_rand());
/* random number in interval [1,npatterns-1]
(excludes complex gap pattern) */
for (; i < msa->length && msa_gap_patterns[i] == pat; i++)
msa_gap_patterns[i] = newpat; /* change for whole sequence */
}
else i++;
}
}
/* obtain gapped category number for each site */
for (i = 0; i < msa->length; i++)
if (gpm->cat_x_pattern_to_gapcat[msa->categories[i]] != NULL)
msa->categories[i] = gpm->cat_x_pattern_to_gapcat[msa->categories[i]][msa_gap_patterns[i]];
}
if (!quiet_mode)
fprintf(stderr, "Unspooling categories ...\n");
cm_spooled_to_unspooled(cm, msa->categories, msa->length);
if (!quiet_mode)
fprintf(stderr, "Collecting training data ...\n");
hmm_train_update_counts(traincounts, statecounts, begcounts,
msa->categories, msa->length,
ncats_unspooled);
if (msa_gap_patterns != NULL) sfree(msa_gap_patterns);
msa_free(msa);
}
/* now train HMM, using cumulative data */
hmm_train_from_counts(hmm, traincounts, NULL, statecounts, NULL,
begcounts, NULL);
/* if modeling indels, adjust begin transitions so probability is
distributed among different "gap pattern" states that all
correspond to the same ungapped state (category); this helps
avoid problems that occur when training on a few large sequences
(e.g., whole chromosomes) and then testing on many shorter ones */
if (model_indels_str != NULL) {
double tprob[gpm->ncats];
int nst[gpm->ncats]; /* total prob and number of states per
spooled, ungapped category */
for (i = 0; i < gpm->ncats; i++) tprob[i] = nst[i] = 0;
for (i = 0; i < hmm->nstates; i++) {
if (vec_get(hmm->begin_transitions, i) > 0)
/* have to go from unspooled space to spooled space, then to
ungapped space (HMM states correspond to unspooled,
gapped categories). Note that states with nonzero begin
probs shouldn't be conditioned on other states. */
tprob[gpm->gapcat_to_cat[cm_unspooled_to_spooled_cat(cm, i)]] +=
vec_get(hmm->begin_transitions, i);
nst[gpm->gapcat_to_cat[cm_unspooled_to_spooled_cat(cm, i)]]++;
}
for (i = 0; i < hmm->nstates; i++)
if (tprob[gpm->gapcat_to_cat[cm_unspooled_to_spooled_cat(cm, i)]] > 0)
vec_set(hmm->begin_transitions, i,
tprob[gpm->gapcat_to_cat[cm_unspooled_to_spooled_cat(cm, i)]] /
nst[gpm->gapcat_to_cat[cm_unspooled_to_spooled_cat(cm, i)]]);
/* (uniform prior) */
}
/* write trained HMM */
hmm_print(stdout, hmm);
if (!quiet_mode) fprintf(stderr, "Done.\n");
return 0;
}
int main(int argc, char *argv[]) {
TreeNode *tree = NULL;
TreeModel *backgd_mod = NULL;
int i, j,
size = DEFAULT_SIZE, meme_mode = 0, profile_mode = 0,
nrestarts = 10, npseudocounts = 5, nsamples = -1,
nmostprevalent = -1, tuple_size = -1, nbest = -1, sample_parms = 0,
nmotifs = DEFAULT_NUMBER, nseqs = -1, do_html = 0, do_bed = 0,
suppress_stdout = 0;
List *msa_name_list = NULL, *pos_examples = NULL, *init_list = NULL, *tmpl;
List *msas, *motifs;
SeqSet *seqset = NULL;
PooledMSA *pmsa = NULL;
msa_format_type msa_format = UNKNOWN_FORMAT;
Vector *backgd_mnmod = NULL;
Hashtable *hash=NULL;
String *output_prefix = str_new_charstr("phastm.");
double *has_motif = NULL;
double prior = PRIOR;
char c;
GFF_Set *bedfeats = NULL;
while ((c = getopt(argc, argv, "t:i:b:sk:md:pn:I:R:P:w:c:SB:o:HDxh")) != -1) {
switch (c) {
case 't':
tree = tr_new_from_file(phast_fopen(optarg, "r"));
break;
case 'i':
msa_format = msa_str_to_format(optarg);
if (msa_format == UNKNOWN_FORMAT)
die("ERROR: bad input format.\n");
break;
case 'b':
backgd_mod = tm_new_from_file(phast_fopen(optarg, "r"), 1);
break;
case 's':
break;
case 'k':
size = get_arg_int(optarg);
break;
case 'm':
meme_mode = 1;
break;
case 'd':
pos_examples = get_arg_list(optarg);
break;
case 'p':
profile_mode = 1;
break;
case 'n':
nrestarts = get_arg_int(optarg);
break;
case 'I':
init_list = get_arg_list(optarg);
break;
case 'P':
tmpl = str_list_as_int(get_arg_list(optarg));
if (lst_size(tmpl) != 2) die("ERROR: bad argument to -P.\n");
nmostprevalent = lst_get_int(tmpl, 0);
tuple_size = lst_get_int(tmpl, 1);
if (!(nmostprevalent > 0 && tuple_size > 0))
die("ERROR: bad argument nmostprevalent=%i tuple_size=%i\n",
nmostprevalent, tuple_size);
lst_free(tmpl);
break;
case 'R':
tmpl = str_list_as_int(get_arg_list(optarg));
if (lst_size(tmpl) != 2) die("ERROR: bad argument to -R.\n");
nsamples = lst_get_int(tmpl, 0);
tuple_size = lst_get_int(tmpl, 1);
if (!(nsamples > 0 && tuple_size > 0))
die("ERROR nsamples=%i tuple_sizse=%i\n", nsamples, tuple_size);
lst_free(tmpl);
break;
case 'c':
npseudocounts = get_arg_int(optarg);
break;
case 'w':
nbest = get_arg_int(optarg);
break;
case 'S':
sample_parms = 1;
break;
case 'B':
nmotifs = get_arg_int(optarg);
break;
case 'o':
str_free(output_prefix);
output_prefix = str_new_charstr(optarg);
str_append_char(output_prefix, '.');
break;
case 'H':
do_html = 1;
break;
case 'D':
do_bed = 1;
break;
case 'x':
suppress_stdout = 1;
break;
case 'h':
usage(argv[0]);
case '?':
die("Bad argument. Try '%s -h'.\n", argv[0]);
}
}
if (optind != argc - 1)
die("ERROR: List of alignment files required. Try '%s -h'.\n", argv[0]);
if ((nsamples > 0 && nmostprevalent > 0) ||
(nsamples > 0 && init_list != NULL) ||
(nmostprevalent > 0 && init_list != NULL))
die("ERROR: -I, -P, and -R are mutually exclusive.");
set_seed(-1);
msa_name_list = get_arg_list(argv[optind]);
if (backgd_mod != NULL && tree == NULL) tree = backgd_mod->tree;
if (tree == NULL && !meme_mode && !profile_mode)
die("ERROR: Must specify -t, -m, or -p.\n");
if ((init_list != NULL || nsamples > 0 || nmostprevalent > 0) &&
!sample_parms)
nrestarts = 1;
if (pos_examples != NULL) {
hash = hsh_new(lst_size(pos_examples));
for (i = 0; i < lst_size(pos_examples); i++)
hsh_put_int(hash, ((String*)lst_get_ptr(pos_examples, i))->chars, 1);
has_motif = smalloc(lst_size(msa_name_list) * sizeof(double));
}
/* open all MSAs */
msas = lst_new_ptr(lst_size(msa_name_list));
fprintf(stderr, "Reading alignment(s) ...\n");
for (i = 0, j = 0; i < lst_size(msa_name_list); i++) {
String *name = lst_get_ptr(msa_name_list, i);
FILE *mfile = phast_fopen(name->chars, "r");
msa_format_type temp_format;
MSA *msa;
if (msa_format == UNKNOWN_FORMAT)
temp_format = msa_format_for_content(mfile, 1);
else temp_format = msa_format;
msa = msa_new_from_file_define_format(mfile, temp_format, NULL);
phast_fclose(mfile);
if (nseqs == -1) nseqs = msa->nseqs;
if (!meme_mode &&
(msa->length - msa_num_gapped_cols(msa, STRIP_ANY_GAPS, -1, -1) < 300 ||
msa->nseqs != nseqs)) {
fprintf(stderr, "WARNING: ignoring alignment '%s' -- too few informative sites.\n", name->chars);
msa_free(msa);
continue;
}
if (msa_alph_has_lowercase(msa)) msa_toupper(msa);
msa_remove_N_from_alph(msa); /* Ns can be a problem */
lst_push_ptr(msas, msa);
if (has_motif != NULL) {
int k, hm = (hsh_get_int(hash, name->chars) == 1);
if (meme_mode) { /* here need to record at individ seq level */
has_motif = srealloc(has_motif,
(j + msa->nseqs + 1) * sizeof(double)); /* FIXME */
for (k = 0; k < msa->nseqs; k++) has_motif[j++] = hm;
}
else has_motif[j++] = hm;
}
}
if (!meme_mode) {
fprintf(stderr, "Extracting and pooling sufficient statistics ...\n");
pmsa = ss_pooled_from_msas(msas, 1, size, NULL, 0);
msa_remove_N_from_alph(pmsa->pooled_msa);
}
/* obtain individual sequences, if necessary */
if (nmostprevalent > 0 || nsamples > 0 || meme_mode) {
if (meme_mode) fprintf(stderr, "Converting to individual sequences ...\n");
else fprintf(stderr, "Obtaining reference sequences for pre-processing ...\n");
seqset = mtf_get_seqset(msas, meme_mode ? -1 : 1, 10 * size);
/* for now, assume 1st seq is reference */
msa_remove_N_from_alph(seqset->set);
}
if (nmostprevalent > 0) {
fprintf(stderr, "Obtaining %d most prevalent %d-tuples ...\n",
nmostprevalent, tuple_size);
init_list = lst_new_ptr(nmostprevalent);
mtf_get_common_ntuples(seqset, init_list, tuple_size, nmostprevalent);
}
else if (nsamples > 0) {
fprintf(stderr, "Sampling %d %d-tuples ...\n", nsamples, tuple_size);
init_list = lst_new_ptr(nsamples);
mtf_sample_ntuples(seqset, init_list, tuple_size, nsamples);
}
/* in meme_mode, backgd model can be specified as eq freqs in a .mod file */
if (meme_mode && backgd_mod != NULL && has_motif == NULL)
backgd_mnmod = backgd_mod->backgd_freqs;
/* estimate background model, if necessary */
else if (backgd_mod == NULL && (!meme_mode || has_motif == NULL)) {
fprintf(stderr, "Fitting background model%s ...\n",
has_motif == NULL ? "" : " (for use in initialization)");
/* if discriminative, be clear
backgd isn't really part of the
estimation procedure */
if (meme_mode) {
backgd_mnmod = vec_new(strlen(seqset->set->alphabet));
mtf_estim_backgd_mn(seqset, backgd_mnmod);
}
else {
backgd_mod = tm_new(tr_create_copy(tree), NULL, NULL, F81,
pmsa->pooled_msa->alphabet, 1, 0, NULL, -1);
tm_fit(backgd_mod, pmsa->pooled_msa,
tm_params_init(backgd_mod, .1, 5, 0),
-1, OPT_MED_PREC, NULL, 0, NULL);
}
}
/* select subset of init strings, if necessary */
if (nbest > 0 && init_list != NULL) {
fprintf(stderr, "Winnowing candidate start strings ...\n");
tmpl = lst_new_ptr(nbest);
mtf_winnow_starts(meme_mode ? (void*)seqset : (void*)pmsa,
init_list, nbest, tmpl, !meme_mode, size, tree,
meme_mode ? (void*)backgd_mnmod : (void*)backgd_mod,
has_motif);
lst_free(init_list);
init_list = tmpl;
}
/* Now find motifs */
motifs = mtf_find(meme_mode ? (void*)seqset : (void*)pmsa,
!meme_mode, size, nmotifs, tree,
meme_mode ? (void*)backgd_mnmod : (void*)backgd_mod,
has_motif, prior, nrestarts, init_list, sample_parms,
npseudocounts);
fprintf(stderr, "\n\n");
if (do_bed)
bedfeats = gff_new_set_init("phast_motif", "0.1b");
/* generate output */
for (i = 0; i < lst_size(motifs); i++) {
Motif *m = lst_get_ptr(motifs, i);
if (!suppress_stdout) {
if (lst_size(motifs) > 1)
printf("\n**********\nMOTIF #%d\n**********\n\n", i+1);
mtf_print(stdout, m);
}
if (do_html) {
String *fname = str_dup(output_prefix);
str_append_int(fname, i+1);
str_append_charstr(fname, ".html");
mtf_print_html(phast_fopen(fname->chars, "w+"), m);
str_free(fname);
}
if (do_bed)
mtf_add_features(m, bedfeats);
}
if (do_html) {
String *fname = str_dup(output_prefix);
str_append_charstr(fname, "index.html");
mtf_print_summary_html(phast_fopen(fname->chars, "w+"),
motifs, output_prefix);
str_free(fname);
}
if (do_bed) {
String *fname = str_dup(output_prefix);
str_append_charstr(fname, "bed");
gff_print_bed(phast_fopen(fname->chars, "w+"),
bedfeats, FALSE);
str_free(fname);
}
return 0;
}
