/*************************************************************************
* Converts the motif frequency matrix into a odds matrix: taken from old ama-scan.c
*************************************************************************/
void convert_to_odds_matrix(MOTIF_T* motif, ARRAY_T* bg_freqs){
const int asize = alph_size(get_motif_alph(motif), ALPH_SIZE);
int motif_position_index,alph_index;
MATRIX_T *freqs;
freqs = get_motif_freqs(motif);
const int num_motif_positions = get_num_rows(freqs);
for (alph_index=0;alph_index<asize;++alph_index){
double bg_likelihood = get_array_item(alph_index, bg_freqs);
for (motif_position_index=0;motif_position_index<num_motif_positions;++motif_position_index){
freqs->rows[motif_position_index]->items[alph_index] /= bg_likelihood;
}
}
}
/*************************************************************************
* Copies the motif frequency matrix and converts it into a odds matrix
*************************************************************************/
MATRIX_T* create_odds_matrix(MOTIF_T *motif, ARRAY_T* bg_freqs){
const int asize = alph_size(get_motif_alph(motif), ALPH_SIZE);
int pos, aidx;
MATRIX_T *odds;
odds = duplicate_matrix(get_motif_freqs(motif));
const int num_pos = get_num_rows(odds);
for (aidx = 0; aidx < asize; ++aidx) {
double bg_likelihood = get_array_item(aidx, bg_freqs);
for (pos = 0; pos < num_pos; ++pos) {
odds->rows[pos]->items[aidx] /= bg_likelihood;
}
}
return odds;
}
/*************************************************************************
* Output JSON data for a motif
*************************************************************************/
static void output_motif_json(JSONWR_T* json, MOTIF_STATS_T* stats,
SITE_COUNTS_T* counts) {
//vars
MOTIF_T *motif;
MATRIX_T *freqs;
int i, j, mlen, asize, end;
motif = stats->motif;
freqs = get_motif_freqs(motif);
asize = alph_size(get_motif_alph(motif), ALPH_SIZE);
jsonwr_start_object_value(json);
jsonwr_lng_prop(json, "db", stats->db->id);
jsonwr_str_prop(json, "id", get_motif_id(motif));
if (*(get_motif_id2(motif))) {
jsonwr_str_prop(json, "alt", get_motif_id2(motif));
}
mlen = get_motif_length(motif);
jsonwr_lng_prop(json, "len", mlen);
jsonwr_dbl_prop(json, "motif_evalue", get_motif_evalue(motif));
jsonwr_dbl_prop(json, "motif_nsites", get_motif_nsites(motif));
if (get_motif_url(motif) && *get_motif_url(motif)) {
jsonwr_str_prop(json, "url", get_motif_url(motif));
}
jsonwr_property(json, "pwm");
jsonwr_start_array_value(json);
for (i = 0; i < mlen; i++) {
jsonwr_start_array_value(json);
for (j = 0; j < asize; j++) {
jsonwr_dbl_value(json, get_matrix_cell(i, j, freqs));
}
jsonwr_end_array_value(json);
}
jsonwr_end_array_value(json);
jsonwr_lng_prop(json, "bin_width", stats->central_window+1);
jsonwr_dbl_prop(json, "bin_sites", stats->central_sites);
jsonwr_lng_prop(json, "total_sites", counts->total_sites);
jsonwr_dbl_prop(json, "log_pvalue", stats->log_adj_pvalue);
jsonwr_dbl_prop(json, "max_prob", stats->max_prob);
jsonwr_property(json, "sites");
jsonwr_start_array_value(json);
end = counts->allocated - (mlen - 1);
for (i = (mlen - 1); i < end; i += 2) {
jsonwr_dbl_value(json, counts->sites[i]);
}
jsonwr_end_array_value(json);
jsonwr_end_object_value(json);
}
/*************************************************************************
* Calculate the odds score for each motif-sized window at each
* site in the sequence using the given nucleotide frequencies.
*
* This function is a lightweight version based on the one contained in
* motiph-scoring. Several calculations that are unnecessary for gomo
* have been removed in order to speed up the process
*************************************************************************/
static double score_sequence(
SEQ_T *seq, // sequence to scan (IN)
MOTIF_T *motif, // motif already converted to odds values (IN)
PSSM_T *m_pssm, // motif pssm (IN)
MATRIX_T *m_odds, // motif odds (IN)
int method, // method used for scoring (IN)
double threshold, // Threshold to use in TOTAL_HITS mode with a PWM
ARRAY_T *bg_freqs //background model
)
{
assert(seq != NULL);
assert(motif != NULL);
assert((method == TOTAL_HITS && m_pssm) || (method != TOTAL_HITS && m_odds));
char* raw_seq = get_raw_sequence(seq);
int seq_length = get_seq_length(seq);
// Get the pv lookup table
ARRAY_T* pv_lookup = NULL;
if (NULL != m_pssm) {
pv_lookup = m_pssm->pv;
assert(get_array_length(pv_lookup) > 0);
}
// Prepare storage for the string representing the portion
// of the reference sequence within the window.
char* window_seq = (char *) mm_malloc(sizeof(char) * (get_motif_length(motif) + 1));
window_seq[get_motif_length(motif)] = '\0';
int max_index = seq_length - get_motif_length(motif);
if (max_index < 0) max_index = 0;
const int asize = alph_size(get_motif_alph(motif), ALPH_SIZE);
double* odds = (double*) mm_malloc(sizeof(double)*max_index);
double* scaled_log_odds = (double*) mm_malloc(sizeof(double)*max_index);
// For each site in the sequence
int seq_index;
for (seq_index = 0; seq_index < max_index; seq_index++) {
double odd = 1.0;
scaled_log_odds[seq_index] = 0;
// For each site in the motif window
int motif_position;
for (motif_position = 0; motif_position < get_motif_length(motif); motif_position++) {
char c = raw_seq[seq_index + motif_position];
window_seq[motif_position] = c;
// Check for gaps at this site
if(c == '-' || c == '.') {
break;
}
// Check for ambiguity codes at this site
//TODO: This next call is very expensive - it takes up approx. 10% of a
// programme's running time. It should be fixed up somehow.
int aindex = alph_index(get_motif_alph(motif), c);
if (aindex > asize) {
break;
}
if (method == TOTAL_HITS) {
//If we're in this mode, then we're using LOG ODDS.
//scaled_log_odds[seq_index] += get_matrix_cell(motif_position, aindex, get_motif_freqs(motif));
scaled_log_odds[seq_index] += get_matrix_cell(motif_position, aindex, m_pssm->matrix);
} else {
odd *= get_matrix_cell(motif_position, aindex, m_odds);
}
}
odds[seq_index] = odd;
}
// return odds as requested (MAX or AVG scoring)
double requested_odds = 0.0;
if (method == AVG_ODDS){
for (seq_index = 0; seq_index < max_index; seq_index++) {
requested_odds += odds[seq_index];
}
requested_odds /= max_index + 1; // Divide by 0 if max_index==0
} else if (method == MAX_ODDS){
for (seq_index = 0; seq_index < max_index; seq_index++) {
if (odds[seq_index] > requested_odds){
requested_odds = odds[seq_index];
}
}
} else if (method == SUM_ODDS) {
for (seq_index = 0; seq_index < max_index; seq_index++) {
requested_odds += odds[seq_index];
}
} else if (method == TOTAL_HITS) {
for (seq_index = 0; seq_index < max_index; seq_index++) {
if (scaled_log_odds[seq_index] >= (double)get_array_length(pv_lookup)) {
scaled_log_odds[seq_index] = (double)(get_array_length(pv_lookup) - 1);
}
double pvalue = get_array_item((int) scaled_log_odds[seq_index], pv_lookup);
//Figure out how to calculate the p-value of a hit
//fprintf(stderr, "m: %s pv_l len: %i scaled_log_odds: %g seq index: %i pvalue: %g\n",
// get_motif_id(motif), get_array_length(pv_lookup), scaled_log_odds[seq_index], seq_index, pvalue);
if (pvalue < threshold) {
requested_odds++; //Add another hit.
}
if (verbosity > HIGHER_VERBOSE) {
fprintf(stderr, "Window Data: %s\t%s\t%i\t%g\t%g\t%g\n",
get_seq_name(seq), get_motif_id(motif), seq_index, scaled_log_odds[seq_index], pvalue, threshold);
}
}
}
myfree(odds);
myfree(scaled_log_odds);
myfree(window_seq);
return requested_odds;
}
/**************************************************************************
* Dump sequence matches sorted by the name of the sequence.
*
* Outputs Columns:
* 1) Trimmed lowercase sequence with uppercase matches.
* 2) Position of the secondary match within the whole sequence.
* 3) Sequence fragment that the primary matched.
* 4) Strand of the primary match (+|-)
* 5) Sequence fragment that the secondary matched.
* 6) Strand of the secondary match (+|-)
* 7) Is the primary match on the same strand as the secondary (s|o)
* 8) Is the secondary match downstream or upstream (d|u)
* 9) The gap between the primary and secondary matches
* 10) The name of the sequence
* 11) The p-value of the bin containing the match (adjusted for # of bins)
* ---if the FASTA input file sequence names are in Genome Browser format:
* 12-14) Position of primary match in BED coordinates
* 15) Position of primary match in Genome Browser coordinates
* 16-18) Position of secondary match in BED coordinates
* 19) Position of secondary match in Genome Browser coordinates
*
* If you wish to sort based on the gap column:
* Sort individual output:
* sort -n -k 9,9 -o seqs_primary_secondary.txt seqs_primary_secondary.txt
* Or sort all outputs:
* for f in seqs_*.txt; do sort -n -k 9,9 -o $f $f; done
* Or to get just locations of primary motif in BED coordinates
* where the secondary is on the opposite strand, upstream with a gap of 118bp:
* awk '$7=="o" && $8=="u" && $9==118 {print $12"\t"$13"\t"$14;}' seqs_primary_secondary.txt
*
**************************************************************************/
static void dump_sequence_matches(FILE *out, int margin, int bin,
double sigthresh, BOOLEAN_T sig_only, RBTREE_T *sequences,
MOTIF_T *primary_motif, SECONDARY_MOTIF_T *secondary_motif,
ARRAY_T **matches) {
RBNODE_T *node;
SEQUENCE_T *sequence;
int idx, seqlen, i, j, start, end, secondary, secondary_pos, primary_len, secondary_len, distance;
BOOLEAN_T primary_rc, secondary_rc, downstream;
char *buffer, *seq, *primary_match, *secondary_match;
ARRAY_T *secondary_array;
ALPH_T *alph;
// get the alphabet
alph = get_motif_alph(primary_motif);
// allocate a buffer for copying the trimmed sequence into and modify it
seqlen = margin * 2 + get_motif_trimmed_length(primary_motif);
buffer = (char*)mm_malloc(sizeof(char) * (seqlen + 1));
// get the lengths of the motifs
primary_len = get_motif_trimmed_length(primary_motif);
secondary_len = get_motif_trimmed_length(secondary_motif->motif);
// allocate some strings for storing the matches
primary_match = (char*)mm_malloc(sizeof(char) * (primary_len + 1));
secondary_match = (char*)mm_malloc(sizeof(char) * (secondary_len + 1));
// add null byte at the end of the match strings
primary_match[primary_len] = '\0';
secondary_match[secondary_len] = '\0';
// iterate over all the sequences
for (node = rbtree_first(sequences); node != NULL; node = rbtree_next(node)) {
sequence = (SEQUENCE_T*)rbtree_value(node);
primary_rc = get_array_item(0, sequence->primary_matches) < 0;
//secondary = matches[sequence->index];
secondary_array = matches[sequence->index];
if (! secondary_array) continue;
int n_secondary_matches = get_array_length(secondary_array);
for (idx=0; idx<n_secondary_matches; idx++) {
secondary = get_array_item(idx, secondary_array);
secondary_rc = secondary < 0;
secondary_pos = abs(secondary);
// calculate the distance
if (secondary_pos <= margin) {
distance = margin - secondary_pos - secondary_len + 1;
downstream = primary_rc;
} else {
distance = secondary_pos - margin - primary_len - 1;
downstream = !primary_rc;
}
// copy the trimmed sequence
seq = sequence->data;
for (i = 0; i < seqlen; ++i) {
buffer[i] = (alph_is_case_insensitive(alph) ? tolower(seq[i]) : seq[i]);
}
buffer[seqlen] = '\0';
// uppercase primary
start = margin;
end = margin + primary_len;
for (i = start, j = 0; i < end; ++i, ++j) {
buffer[i] = (alph_is_case_insensitive(alph) ? toupper(buffer[i]) : buffer[i]);
primary_match[j] = buffer[i];
}
// uppercase secondary
// note orign was one, subtract 1 to make origin zero as required for arrays
start = secondary_pos -1;
end = start + secondary_len;
for (i = start, j = 0; i < end; ++i, ++j) {
buffer[i] = (alph_is_case_insensitive(alph) ? toupper(buffer[i]) : buffer[i]);
secondary_match[j] = buffer[i];
}
// get the p-value of the seconndary match
SPACING_T *spacings;
if (secondary_rc == primary_rc) {
spacings = downstream ? secondary_motif->spacings+(SAME+RIGHT) : secondary_motif->spacings+(SAME+LEFT);
} else {
spacings = downstream ? secondary_motif->spacings+(OPPO+RIGHT) : secondary_motif->spacings+(OPPO+LEFT);
}
double p_value = spacings->pvalue[distance/bin];
// skip match if not significant and only reporting significant matches
if (sig_only && (p_value > sigthresh)) continue;
// output line to file
fprintf(out, "%s %3d %s %s %s %s %s %s %3d %s %.1e",
buffer,
secondary_pos,
primary_match,
(primary_rc ? "-" : "+"),
secondary_match,
(secondary_rc ? "-" : "+"),
(secondary_rc == primary_rc ? "s" : "o"),
(downstream ? "d" : "u"),
distance,
sequence->name,
p_value
);
// Parse the sequence name to see if we can get genomic coordinates
// and print additional columns with primary and secondary matches
// in both BED and Genome Browser coordinates.
char *chr_name;
size_t chr_name_len;
int start_pos, end_pos;
if (parse_genomic_coordinates_helper(
sequence->name,
&chr_name,
&chr_name_len,
&start_pos,
&end_pos))
{
// Get the start and end of the primary match in
// 0-relative, half-open genomic coordinates.
int p_start = start_pos + fabs(get_array_item(0, sequence->primary_matches)) - 1;
int p_end = p_start + primary_len;
// Get the start and end of the secondary match in
// 0-relative, half-open genomic coordinates.
int s_start, s_end;
if ( (!primary_rc && downstream) || (primary_rc && !downstream) ) {
s_start = p_end + distance;
s_end = s_start + secondary_len;
} else {
s_end = p_start - distance;
s_start = s_end - secondary_len;
}
fprintf(out, " %s %d %d %s:%d-%d",
chr_name, p_start, p_end, chr_name, p_start+1, p_end);
fprintf(out, " %s %d %d %s:%d-%d\n",
chr_name, s_start, s_end, chr_name, s_start+1, s_end);
} else {
fprintf(out, "\n");
}
} // secondary match
} // primary match
free(buffer);
free(primary_match);
free(secondary_match);
}
/*************************************************************************
* Entry point for ama
*************************************************************************/
int main(int argc, char **argv) {
AMA_OPTIONS_T options;
ARRAYLST_T *motifs;
clock_t c0, c1; // measuring cpu_time
MOTIF_AND_PSSM_T *combo;
CISML_T *cisml;
PATTERN_T** patterns;
PATTERN_T *pattern;
FILE *fasta_file, *text_output, *cisml_output;
int i, seq_loading_num, seq_counter, unique_seqs, seq_len, scan_len, x1, x2, y1, y2;
char *seq_name, *path;
bool need_postprocessing, created;
SEQ_T *sequence;
RBTREE_T *seq_ids;
RBNODE_T *seq_node;
double *logcumback;
ALPH_T *alph;
// process the command
process_command_line(argc, argv, &options);
// load DNA motifs
motifs = load_motifs(&options);
// get the alphabet
if (arraylst_size(motifs) > 0) {
combo = (MOTIF_AND_PSSM_T*)arraylst_get(0, motifs);
alph = alph_hold(get_motif_alph(combo->motif));
} else {
alph = alph_dna();
}
// pick columns for GC operations
x1 = -1; x2 = -1; y1 = -1; y2 = -1;
if (alph_size_core(alph) == 4 && alph_size_pairs(alph) == 2) {
x1 = 0; // A
x2 = alph_complement(alph, x1); // T
y1 = (x2 == 1 ? 2 : 1); // C
y2 = alph_complement(alph, y1); // G
assert(x1 != x2 && y1 != y2 && x1 != y1 && x2 != y2 && x1 != y2 && x2 != y1);
}
// record starting time
c0 = clock();
// Create cisml data structure for recording results
cisml = allocate_cisml(PROGRAM_NAME, options.command_line, options.motif_filename, options.fasta_filename);
set_cisml_background_file(cisml, options.bg_filename);
// make a CISML pattern to hold scores for each motif
for (i = 0; i < arraylst_size(motifs); i++) {
combo = (MOTIF_AND_PSSM_T*)arraylst_get(i, motifs);
add_cisml_pattern(cisml, allocate_pattern(get_motif_id(combo->motif), ""));
}
// Open the FASTA file for reading.
fasta_file = NULL;
if (!open_file(options.fasta_filename, "r", false, "FASTA", "sequences", &fasta_file)) {
die("Couldn't open the file %s.\n", options.fasta_filename);
}
if (verbosity >= NORMAL_VERBOSE) {
if (options.last == 0) {
fprintf(stderr, "Using entire sequence\n");
} else {
fprintf(stderr, "Limiting sequence to last %d positions.\n", options.last);
}
}
//
// Read in all sequences and score with all motifs
//
seq_loading_num = 0; // keeps track on the number of sequences read in total
seq_counter = 0; // holds the index to the seq in the pattern
unique_seqs = 0; // keeps track on the number of unique sequences
need_postprocessing = false;
sequence = NULL;
logcumback = NULL;
seq_ids = rbtree_create(rbtree_strcasecmp,rbtree_strcpy,free,rbtree_intcpy,free);
while (read_one_fasta(alph, fasta_file, options.max_seq_length, &sequence)) {
++seq_loading_num;
seq_name = get_seq_name(sequence);
seq_len = get_seq_length(sequence);
scan_len = (options.last != 0 ? options.last : seq_len);
// red-black trees are only required if duplicates should be combined
if (options.combine_duplicates){
//lookup seq id and create new entry if required, return sequence index
seq_node = rbtree_lookup(seq_ids, get_seq_name(sequence), true, &created);
if (created) { // assign it a loading number
rbtree_set(seq_ids, seq_node, &unique_seqs);
seq_counter = unique_seqs;
++unique_seqs;
} else {
seq_counter = *((int*)rbnode_get(seq_node));
}
}
//
// Set up sequence-dependent background model and compute
// log cumulative probability of sequence.
// This needs the sequence in raw format.
//
if (options.sdbg_order >= 0)
logcumback = log_cumulative_background(alph, options.sdbg_order, sequence);
// Index the sequence, throwing away the raw format and ambiguous characters
index_sequence(sequence, alph, SEQ_NOAMBIG);
// Get the GC content of the sequence if binning p-values by GC
// and store it in the sequence object.
if (options.num_gc_bins > 1) {
ARRAY_T *freqs = get_sequence_freqs(sequence, alph);
set_total_gc_sequence(sequence, get_array_item(y1, freqs) + get_array_item(y2, freqs)); // f(C) + f(G)
free_array(freqs); // clean up
} else {
set_total_gc_sequence(sequence, -1); // flag ignore
}
// Scan with motifs.
for (i = 0; i < arraylst_size(motifs); i++) {
pattern = get_cisml_patterns(cisml)[i];
combo = (MOTIF_AND_PSSM_T*)arraylst_get(i, motifs);
if (verbosity >= HIGHER_VERBOSE) {
fprintf(stderr, "Scanning %s sequence with length %d "
"abbreviated to %d with motif %s with length %d.\n",
seq_name, seq_len, scan_len,
get_motif_id(combo->motif), get_motif_length(combo->motif));
}
SCANNED_SEQUENCE_T* scanned_seq = NULL;
if (!options.combine_duplicates || get_pattern_num_scanned_sequences(pattern) <= seq_counter) {
// Create a scanned_sequence record and save it in the pattern.
scanned_seq = allocate_scanned_sequence(seq_name, seq_name, pattern);
set_scanned_sequence_length(scanned_seq, scan_len);
} else {
// get existing sequence record
scanned_seq = get_pattern_scanned_sequences(pattern)[seq_counter];
set_scanned_sequence_length(scanned_seq, max(scan_len, get_scanned_sequence_length(scanned_seq)));
}
// check if scanned component of sequence has sufficient length for the motif
if (scan_len < get_motif_length(combo->motif)) {
// set score to zero and p-value to 1 if not set yet
if(!has_scanned_sequence_score(scanned_seq)){
set_scanned_sequence_score(scanned_seq, 0.0);
}
if(options.pvalues && !has_scanned_sequence_pvalue(scanned_seq)){
set_scanned_sequence_pvalue(scanned_seq, 1.0);
}
add_scanned_sequence_scanned_position(scanned_seq);
if (get_scanned_sequence_num_scanned_positions(scanned_seq) > 0L) {
need_postprocessing = true;
}
if (verbosity >= HIGH_VERBOSE) {
fprintf(stderr, "%s too short for motif %s. Score set to 0.\n",
seq_name, get_motif_id(combo->motif));
}
} else {
// scan the sequence using average/maximum motif affinity
ama_sequence_scan(alph, sequence, logcumback, combo->pssm_pair,
options.scoring, options.pvalues, options.last, scanned_seq,
&need_postprocessing);
}
} // All motifs scanned
free_seq(sequence);
if (options.sdbg_order >= 0) myfree(logcumback);
} // read sequences
fclose(fasta_file);
if (verbosity >= HIGH_VERBOSE) fprintf(stderr, "(%d) sequences read in.\n", seq_loading_num);
if (verbosity >= NORMAL_VERBOSE) fprintf(stderr, "Finished \n");
// if any sequence identifier was multiple times in the sequence set then
// postprocess of the data is required
if (need_postprocessing || options.normalize_scores) {
post_process(cisml, motifs, options.normalize_scores);
}
// output results
if (options.output_format == DIRECTORY_FORMAT) {
if (create_output_directory(options.out_dir, options.clobber, verbosity > QUIET_VERBOSE)) {
// only warn in higher verbose modes
fprintf(stderr, "failed to create output directory `%s' or already exists\n", options.out_dir);
exit(1);
}
path = make_path_to_file(options.out_dir, text_filename);
//FIXME check for errors: MEME doesn't either and we at least know we have a good directory
text_output = fopen(path, "w");
free(path);
path = make_path_to_file(options.out_dir, cisml_filename);
//FIXME check for errors
cisml_output = fopen(path, "w");
free(path);
print_cisml(cisml_output, cisml, true, NULL, false);
print_score(cisml, text_output);
fclose(cisml_output);
fclose(text_output);
} else if (options.output_format == GFF_FORMAT) {
print_score(cisml, stdout);
} else if (options.output_format == CISML_FORMAT) {
print_cisml(stdout, cisml, true, NULL, false);
} else {
die("Output format invalid!\n");
}
//
// Clean up.
//
rbtree_destroy(seq_ids);
arraylst_destroy(motif_and_pssm_destroy, motifs);
free_cisml(cisml);
rbtree_destroy(options.selected_motifs);
alph_release(alph);
// measure time
if (verbosity >= NORMAL_VERBOSE) { // starting time
c1 = clock();
fprintf(stderr, "cycles (CPU); %ld cycles\n", (long) c1);
fprintf(stderr, "elapsed CPU time: %f seconds\n", (float) (c1-c0) / CLOCKS_PER_SEC);
}
return 0;
}
/*************************************************************************
* Build a completely connected HMM.
*************************************************************************/
void build_complete_hmm
(ARRAY_T* background,
int spacer_states,
MOTIF_T *motifs,
int nmotifs,
MATRIX_T *transp_freq,
MATRIX_T *spacer_ave,
BOOLEAN_T fim,
MHMM_T **the_hmm)
{
ALPH_T alph;
int motif_states; // Total length of the motifs.
int num_spacers; // Total number of spacer states.
int num_states; // Total number of states in the model.
int i_motif; // Index of the current "from" motif.
int j_motif; // Index of the current "to" motif.
int i_position; // Index within the current motif or spacer.
int i_state = 0; // Index of the current state.
assert(nmotifs > 0);
alph = get_motif_alph(motifs);// get the alphabet from the first motif
// Count the width of the motifs.
for (motif_states = 0, i_motif = 0; i_motif < nmotifs; i_motif++)
motif_states += get_motif_length(motif_at(motifs, i_motif));
// Count the spacer states adjacent to begin and end.
num_spacers = nmotifs * 2;
// Add the spacer states between motifs.
num_spacers += nmotifs * nmotifs;
// Total states = motifs + spacer_states + begin/end
num_states = motif_states + (num_spacers * spacer_states) + 2;
// Allocate the model.
*the_hmm = allocate_mhmm(alph, num_states);
// Record that this is a completely connected model.
(*the_hmm)->type = COMPLETE_HMM;
// Record the number of motifs in the model.
(*the_hmm)->num_motifs = nmotifs;
// Record the number of states in the model.
(*the_hmm)->num_states = num_states;
(*the_hmm)->num_spacers = ((nmotifs + 1) * (nmotifs + 1)) - 1;
(*the_hmm)->spacer_states = spacer_states;
// Put the background distribution into the model.
copy_array(background, (*the_hmm)->background);
// Build the begin state.
build_complete_state(
START_STATE,
i_state,
alph,
0, // expected length
NULL, // Emissions.
0, // Number of sites.
NON_MOTIF_INDEX,
NON_MOTIF_POSITION,
nmotifs,
0, // previous motif
0, // next motif
transp_freq,
spacer_states,
num_spacers,
motifs,
&((*the_hmm)->states[i_state]));
i_state++;
int from_motif_state, to_motif_state;
// Build the spacer states. No transitions from the end state.
for (i_motif = 0; i_motif <= nmotifs; i_motif++) {
// No transitions to the start state.
for (j_motif = 1; j_motif <= nmotifs+1; j_motif++) {
// No transitions from start to end.
if ((i_motif == 0) && (j_motif == nmotifs+1))
continue;
// Allow multi-state spacers.
for (i_position = 0; i_position < spacer_states; i_position++, i_state++) {
build_complete_state(
SPACER_STATE,
i_state,
alph,
get_matrix_cell(i_motif, j_motif, spacer_ave),
background,
SPACER_NUMSITES,
NON_MOTIF_INDEX,
i_position,
nmotifs,
i_motif,
j_motif,
transp_freq,
spacer_states,
num_spacers,
motifs,
&((*the_hmm)->states[i_state]));
}
}
}
// Build the motif states.
for (i_motif = 0; i_motif < nmotifs; i_motif++) {
MOTIF_T *this_motif = motif_at(motifs, i_motif);
STATE_T state;
for (i_position = 0; i_position < get_motif_length(this_motif); i_position++, i_state++) {
if (i_position == 0) {
state = START_MOTIF_STATE;
} else if (i_position == (get_motif_length(this_motif) - 1)) {
state = END_MOTIF_STATE;
} else {
state = MID_MOTIF_STATE;
}
build_complete_state(
MID_MOTIF_STATE,
i_state,
alph,
0, // Expected spacer length.
get_matrix_row(i_position, get_motif_freqs(this_motif)),
get_motif_nsites(this_motif),
i_motif,
i_position,
nmotifs,
0, // Previous motif index.
0, // Next motif index.
transp_freq,
spacer_states,
num_spacers,
motifs,
&((*the_hmm)->states[i_state]));
}
}
// Build the end state.
build_complete_state(
END_STATE,
i_state,
alph,
0, // Expected spacer length.
NULL, // Emissions
0, // Number of sites.
NON_MOTIF_INDEX,
NON_MOTIF_POSITION,
nmotifs,
0, // Previous motif index.
0, // Next motif index.
transp_freq,
spacer_states,
num_spacers,
motifs,
&((*the_hmm)->states[i_state]));
i_state++;
// Convert spacers to FIMs if requested.
if (fim) {
convert_to_fims(*the_hmm);
}
// Fill in the transition matrix.
build_transition_matrix(*the_hmm);
}
/*************************************************************************
* Build a linear HMM.
*************************************************************************/
void build_linear_hmm
(ARRAY_T* background,
ORDER_T* order_spacing,
int spacer_states,
RBTREE_T* motifs, // motifs with key as in order_spacing
BOOLEAN_T fim,
MHMM_T** the_hmm)
{
ALPH_T alph;
int model_length; // Total number of states in the model.
int i_state; // Index of the current state.
int i_order; // Index within the order and spacing.
int i_position; // Index within the current motif or spacer.
int motif_i; // motif key in order spacing
MOTIF_T *motif; // motif
RBNODE_T *node;
alph = get_motif_alph((MOTIF_T*)rbtree_value(rbtree_first(motifs)));
// Calculate the total length of the model.
model_length = 2; // start and end state
for (i_order = 0; i_order < get_order_occurs(order_spacing); i_order++) {
motif_i = get_order_motif(order_spacing, i_order);
motif = (MOTIF_T*)rbtree_get(motifs, &motif_i);
model_length += get_motif_length(motif);
}
model_length += (get_order_occurs(order_spacing) + 1) * spacer_states;
// Allocate the model.
*the_hmm = allocate_mhmm(alph, model_length);
check_sq_matrix((*the_hmm)->trans, model_length);
// Record that this is a linear model.
(*the_hmm)->type = LINEAR_HMM;
// Record the number of motifs in the model.
// It doesn't want the distinct count
(*the_hmm)->num_motifs = get_order_occurs(order_spacing);
// Record the number of states in the model.
(*the_hmm)->num_states = model_length;
(*the_hmm)->num_spacers = get_order_occurs(order_spacing) + 1;
(*the_hmm)->spacer_states = spacer_states;
// Put the background distribution into the model.
copy_array(background, (*the_hmm)->background);
// Begin the model with a non-emitting state.
i_state = 0;
check_sq_matrix((*the_hmm)->trans, (*the_hmm)->num_states);
build_linear_state(
alph,
START_STATE,
i_state,
get_spacer_length(order_spacing, 0),
NULL, // Emissions.
0, // Number of sites.
NON_MOTIF_INDEX,
NON_MOTIF_POSITION, // position within state (not relevant to start state)
NULL, // no motif
&((*the_hmm)->states[i_state]));
++i_state;
// Build the first spacer.
for (i_position = 0; i_position < spacer_states; i_position++, i_state++) {
check_sq_matrix((*the_hmm)->trans, (*the_hmm)->num_states);
build_linear_state(
alph,
SPACER_STATE,
i_state,
get_spacer_length(order_spacing, 0),
background,
SPACER_NUMSITES,
NON_MOTIF_INDEX,
i_position, // position within spacer
NULL, // no motif
&((*the_hmm)->states[i_state]));
}
// Build each motif and subsequent spacer.
for (i_order = 0; i_order < get_order_occurs(order_spacing); i_order++) {
STATE_T state;
int spacer_len;
motif_i = get_order_motif(order_spacing, i_order);
motif = (MOTIF_T*)rbtree_get(motifs, &motif_i);
// Build the motif.
for (i_position = 0; i_position < get_motif_length(motif); i_position++, i_state++) {
if (i_position == 0) {
state = START_MOTIF_STATE;
spacer_len = get_spacer_length(order_spacing, i_order);
} else if (i_position == (get_motif_length(motif) - 1)) {
state = END_MOTIF_STATE;
spacer_len = get_spacer_length(order_spacing, i_order+1);
} else {
state = MID_MOTIF_STATE;
spacer_len = 0;
}
check_sq_matrix((*the_hmm)->trans, (*the_hmm)->num_states);
build_linear_state(
alph,
state,
i_state,
spacer_len, // Expected spacer length.
get_matrix_row(i_position, get_motif_freqs(motif)),
get_motif_nsites(motif),
i_order,
i_position, // position within motif (middle)
motif,
&((*the_hmm)->states[i_state]));
}
// Build the following spacer.
for (i_position = 0; i_position < spacer_states; i_position++, i_state++) {
check_sq_matrix((*the_hmm)->trans, (*the_hmm)->num_states);
build_linear_state(
alph,
SPACER_STATE,
i_state,
get_spacer_length(order_spacing, i_order+1),
background,
SPACER_NUMSITES,
NON_MOTIF_INDEX,
i_position, // position within spacer
NULL, // no motif
&((*the_hmm)->states[i_state]));
}
}
check_sq_matrix((*the_hmm)->trans, (*the_hmm)->num_states);
// Finish up the model with a non-emitting end state.
build_linear_state(
alph,
END_STATE,
i_state,
get_spacer_length(order_spacing, i_order),
NULL, // Emissions.
0, // Number of sites.
NON_MOTIF_INDEX,
NON_MOTIF_POSITION, // position within state (not relevant to end state)
NULL, // no motif
&((*the_hmm)->states[i_state]));
++i_state;
assert(i_state == model_length);
check_sq_matrix((*the_hmm)->trans, (*the_hmm)->num_states);
// Convert spacers to FIMs if requested.
if (fim) {
convert_to_fims(*the_hmm);
}
// Fill in the transition matrix.
build_transition_matrix(*the_hmm);
}
/*************************************************************************
* Build a star topology HMM.
*************************************************************************/
void build_star_hmm
(ARRAY_T* background,
int spacer_states,
MOTIF_T* motifs,
int nmotifs,
BOOLEAN_T fim,
MHMM_T** the_hmm)
{
ALPH_T alph;
int motif_states; /* Total length of the motifs. */
int num_spacers; /* Total number of spacer states. */
int num_states; /* Total number of states in the model. */
int i_motif; /* Index of the current "from" motif. */
int i_position; /* Index within the current motif or spacer. */
int i_state = 0; /* Index of the current state. */
alph = get_motif_alph(motif_at(motifs, 0));
/* Count the width of the motifs. */
for (motif_states = 0, i_motif = 0; i_motif < nmotifs; i_motif++)
motif_states += get_motif_length(motif_at(motifs, i_motif));
// Only 1 spacer.
num_spacers = 1;
/* Total states = motifs + spacer_states + begin/end */
num_states = motif_states + (num_spacers * spacer_states) + 2;
/* fprintf(stderr, "motif_states=%d num_spacers=%d num_states=%d\n",
motif_states, num_spacers, num_states); */
/* Allocate the model. */
*the_hmm = allocate_mhmm(alph, num_states);
/* Record that this is a star model. */
(*the_hmm)->type = STAR_HMM;
/* Record the number of motifs in the model. */
(*the_hmm)->num_motifs = nmotifs;
/* Record the number of states in the model. */
(*the_hmm)->num_states = num_states;
(*the_hmm)->num_spacers = 1;
(*the_hmm)->spacer_states = spacer_states;
// Put the background distribution into the model.
copy_array(background, (*the_hmm)->background);
/* Build the begin state. */
build_star_state(
alph,
START_STATE,
i_state,
0, // expected length
NULL,
0, // Number of sites.
NON_MOTIF_INDEX,
NON_MOTIF_POSITION,
nmotifs,
spacer_states,
motifs,
&((*the_hmm)->states[i_state])
);
i_state++;
// Build the spacer state (state 0). Allow multi-state spacers.
for (i_position = 0; i_position < spacer_states; i_position++) {
build_star_state(
alph,
SPACER_STATE,
i_state,
DEFAULT_SPACER_LENGTH,
background,
SPACER_NUMSITES,
NON_MOTIF_INDEX,
i_position,
nmotifs,
spacer_states,
motifs,
&((*the_hmm)->states[i_state])
);
i_state++;
}
/* Build the motif states. */
for (i_motif = 0; i_motif < nmotifs; i_motif++) {
MOTIF_T *this_motif = motif_at(motifs, i_motif);
assert(get_motif_length(this_motif) > 1);
i_position = 0;
build_star_state(
alph,
START_MOTIF_STATE,
i_state,
0, // Expected spacer length.
get_matrix_row(i_position, get_motif_freqs(this_motif)),
get_motif_nsites(this_motif),
i_motif,
i_position,
nmotifs,
spacer_states,
motifs,
&((*the_hmm)->states[i_state])
);
i_state++;
for (i_position = 1; i_position < get_motif_length(this_motif) - 1; i_position++) {
build_star_state(
alph,
MID_MOTIF_STATE,
i_state,
0, // Expected spacer length.
get_matrix_row(i_position, get_motif_freqs(this_motif)),
get_motif_nsites(this_motif),
i_motif,
i_position,
nmotifs,
spacer_states,
motifs,
&((*the_hmm)->states[i_state])
);
i_state++;
}
build_star_state(
alph,
END_MOTIF_STATE,
i_state,
0, // Expected spacer length.
get_matrix_row(i_position, get_motif_freqs(this_motif)),
get_motif_nsites(this_motif),
i_motif,
i_position,
nmotifs,
spacer_states,
motifs,
&((*the_hmm)->states[i_state])
);
i_state++;
}
/* Build the end state. */
build_star_state(
alph,
END_STATE,
i_state,
0, // Expected spacer length.
NULL, // Emissions
0, // Number of sites.
NON_MOTIF_INDEX,
NON_MOTIF_POSITION,
nmotifs,
spacer_states,
motifs,
&((*the_hmm)->states[i_state])
);
i_state++;
/* Convert spacers to FIMs if requested. */
if (fim) {
convert_to_fims(*the_hmm);
}
/* Fill in the transition matrix. */
build_transition_matrix(*the_hmm);
} // build_star_hmm