/**************************************************************************
* Prepare a sequence for recognition by
* - making sure it is uppercase,
* - making sure it doesn't contain illegal characters,
* - adding flanking Xs to match START/END states, and
* - converting it to an integer format
* - computing cumulative GC counts
*
* In the integer form, each character in the sequence is replaced by
* the index of that character in the alphabet array. Thus, if the
* alphabet is 'ACGT', every occurence of the letter 'G' in the
* sequence will be represented by the index 2.
**************************************************************************/
void prepare_sequence
(SEQ_T* sequence, ALPH_T alph)
{
int i_seq; // Index in the sequence.
int badchar; // Number of characters converted.
char wildcard; // Wildcard character
wildcard = alph_wildcard(alph);
badchar = 0;
for (i_seq = 0; i_seq < get_seq_length(sequence); i_seq++) {
// Make sure the sequence is uppercase.
if (islower((int)(sequence->sequence)[i_seq])) {
(sequence->sequence)[i_seq] = toupper((int)(sequence->sequence)[i_seq]);
}
// Convert non-alphabetic characters to ambiguous.
if (alph_index(alph, (sequence->sequence)[i_seq]) == -1) {
fprintf(stderr, "%c -> %c\n", (sequence->sequence)[i_seq], wildcard);
(sequence->sequence)[i_seq] = wildcard;
badchar++;
}
}
// Tell the user about the conversions.
if (badchar) {
fprintf(stderr, "Warning: converted %d non-alphabetic ", badchar);
fprintf(stderr, "characters to %c in sequence %s.\n", wildcard,
get_seq_name(sequence));
}
// Add flanking X's.
add_flanking_xs(sequence, alph);
// Make the integer sequence.
sequence->intseq = (int *)mm_malloc(sizeof(int) * get_seq_length(sequence));
for (i_seq = 0; i_seq < get_seq_length(sequence); i_seq++) {
(sequence->intseq)[i_seq]
= alph_index(alph, (sequence->sequence)[i_seq]);
}
//
// Get cumulative GC counts.
//
if (alph == DNA_ALPH) {
int len = get_seq_length(sequence);
char c = (sequence->sequence)[0]; // first character
sequence->gc = (int *)mm_malloc(sizeof(int) * get_seq_length(sequence));
// set count at first position
(sequence->gc)[0] = (c == 'G' || c == 'C') ? 1 : 0;
// set cumulative counts at rest of postitions
for (i_seq = 1; i_seq < len; i_seq++) {
c = (sequence->sequence)[i_seq];
(sequence->gc)[i_seq] = (c == 'G' || c == 'C') ?
(sequence->gc)[i_seq-1] + 1 : (sequence->gc)[i_seq-1];
}
}
}
/**********************************************************************
shuffle_sequence()
shuffle a given sequences based on their content
**********************************************************************/
void shuffle_sequence(
SEQ_T* seq, /* original sequence IN */
unsigned int seed, /* seed IN */
SEQ_T** target /* target sequence OUT */
){
my_srand(seed);
assert(*target==NULL);
// reset target if not null
if (*target != NULL){
free_seq(*target);
}
*target = allocate_seq(get_seq_name(seq),"shuffled",get_seq_offset(seq),get_raw_sequence(seq));
char *raw = get_raw_sequence(*target);
/* copy original in temp string */
char* tmp = (char*)mm_calloc(get_seq_length(seq)+1,sizeof(char));
strcpy(tmp,get_raw_sequence(seq));
tmp[get_seq_length(seq)]='\0';
int i,j;
char *ss;
char *dd;
for(j=0,i=get_seq_length(seq);i>0;i--){
// Pick a random number in the range:
int pick = rand() % i;
raw[j++] = tmp[pick];
// "shift" routine here eliminates the "picked" base from the _src string:
// dd starts at the picked position: ss is one beyond that:
for( dd = tmp+pick , ss = dd + 1 ; *dd ; *dd++=*ss++ );
}
myfree(tmp);
}
/****************************************************************************
* Create a new alignment with any sequence that contains nothing but
* gap ('-') characters removed. Returns the new alignment. Does not
* change the old alignment.
* If there are no all-gap sequences, the returned alignment is the
* same object as the original alignment.
****************************************************************************/
static ALIGNMENT_T* remove_allgap_sequences(ALIGNMENT_T* alignment)
{
ALIGNMENT_T* new_alignment;
int i_aln;
int l_aln = get_num_aligned_sequences(alignment);
STRING_LIST_T* keeper_seqs = new_string_list();
// Identify the all-gap sequences.
for (i_aln=0; i_aln<l_aln; i_aln++) {
SEQ_T* sequence = get_alignment_sequence(i_aln, alignment);
int i_seq;
int l_seq = get_seq_length(sequence);
// Add sequence to keepers if it contains a non-gap.
for (i_seq=0; i_seq<l_seq; i_seq++) {
if (get_seq_char(i_seq, sequence) != '-') { // not gap?
add_string(get_seq_name(sequence), keeper_seqs); // non-gap: keeper
break;
}
}
}
// Remove any sequences not in keeper list.
if (get_num_strings(keeper_seqs) < l_aln) {
new_alignment = remove_alignment_seqs(keeper_seqs, alignment);
free_string_list(keeper_seqs);
} else {
new_alignment = alignment;
}
return(new_alignment);
} // remove_allgap_sequences
void CompTool::search(int argc, char** argv){
if(argc < 3) {cout << "File not enough" << endl; exit(1);}
const string seq1_file = argv[1];
const string seq2_file = argv[2];
const string seq1_name = basename(seq1_file);
const string seq2_name = basename(seq2_file);
// Options
int kmer_size = 15;
int slide_letters = 1;
int bwt_interval = 1;
int max_num_matches = 1000000000;
bool search_forward = true;
bool search_reverse = true;
if(argc > 3){
for(int i = 3; i < argc; i++){
if (argv[i][1] == 'k') kmer_size = atoi(argv[++i]);
else if(argv[i][1] == 'l') slide_letters = atoi(argv[++i]);
else if(argv[i][1] == 'i') bwt_interval = atoi(argv[++i]);
else if(argv[i][1] == 'm') max_num_matches = atoi(argv[++i]);
else if(argv[i][1] == 'f') search_reverse = false;
else if(argv[i][1] == 'r') search_forward = false;
}
}
seq1_size_ = get_seq_length(seq1_file) + 1;
seq2_size_ = get_seq_length(seq2_file) + 1;
seq1_ = read_fasta_and_create_int8_t_array(seq1_file, seq1_size_);
seq2_ = read_fasta_and_create_int8_t_array(seq2_file, seq2_size_);
int* SA = create_SA(seq1_file, seq1_size_);
BWT bwt(seq1_, SA, seq1_size_, num_char_, bwt_interval);
if(search_forward) search_forward_matches(seq1_name, seq2_name, SA, bwt, kmer_size, slide_letters, max_num_matches);
if(search_reverse) search_reverse_matches(seq1_name, seq2_name, SA, bwt, kmer_size, slide_letters, max_num_matches);
delete SA;
}
/*************************************************************************
* Read all the sequences into an array of SEQ_T
*************************************************************************/
static void read_sequences(ALPH_T alph, char *seq_file_name,
SEQ_T ***sequences, int *seq_num) {
const int max_sequence = 32768; // unlikely to be this big
int i, seq_len, move;
FILE * seq_fh = fopen(seq_file_name, "r");
if (!seq_fh) die("failed to open sequence file `%s'", seq_file_name);
read_many_fastas(alph, seq_fh, max_sequence, seq_num, sequences);
if (fclose(seq_fh) != 0) die("failed to close sequence file\n");
seq_len = get_seq_length((*sequences)[0]);
move = 0;
for (i = 1; i < *seq_num; i++) {
if (seq_len == get_seq_length((*sequences)[i])) {
if (move > 0) (*sequences)[i-move] = (*sequences)[i];
} else {
fprintf(stderr, "Skipping sequence %s as its length (%d) does not "
"match the first sequence (%d).\n", get_seq_name((*sequences)[i]),
get_seq_length((*sequences)[i]), seq_len);
move++;
}
}
*seq_num -= move;
for (i--; i >= *seq_num; i--) (*sequences)[i] = NULL;
}
/***************************************************************************
* Get the maximum sequence length from a set of sequences.
***************************************************************************/
int get_max_seq_length
(int num_seqs,
SEQ_T** sequences)
{
int max_length;
int this_length;
int i_seq;
max_length = 0;
for (i_seq = 0; i_seq < num_seqs; i_seq++) {
this_length = get_seq_length(sequences[i_seq]);
if (this_length > max_length) {
max_length = this_length;
}
}
return(max_length);
}
static double * log_cumulative_background(ALPH_T *alph, const int sdbg_order, SEQ_T *sequence) {
BGCALC_T *calc;
ARRAY_T *cp;
double *logcumback;
const char *raw_seq;
int i;
if (sdbg_order < 0) die("No such thing as a negative background order");
logcumback = mm_malloc(sizeof(double) * (get_seq_length(sequence)+1));
raw_seq = get_raw_sequence(sequence);
calc = NULL;
// calculate background model
calculate_markov_model(alph, sdbg_order, 1.0, false, raw_seq, &calc);
cp = calculate_markov_model(alph, sdbg_order, 1.0, false, NULL, &calc);
// add x-tuples to model
extend_markov_model(alph, true, SUM_FREQS, cp);
// normalize for each prefix (convert to conditional probability)
for (i = 0; i < get_array_length(cp); i += alph_size_wild(alph)) {
normalize_subarray(i, alph_size_core(alph), 0, cp);
set_array_item(i + alph_wild(alph), 1.0, cp);
}
calculate_log_cumulative_background(alph, true, sdbg_order, cp, raw_seq, logcumback);
free_array(cp);
return logcumback;
}
/*************************************************************************
* 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;
}
/*************************************************************************
* 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;
}
/*************************************************************************
* 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.
* Scores sequence with up to two motifs.
*************************************************************************/
static double score_sequence(
ALPH_T* alph, // alphabet (IN)
SEQ_T* seq, // sequence to scan (IN)
double *logcumback, // cumulative bkg probability of sequence (IN)
PSSM_PAIR_T *pssm_pair, // pos and neg pssms (IN)
SCORING_EN method, // method used for scoring (IN)
int last, // score only last <n> or score all if <n>
// is zero (IN)
BOOLEAN_T* isFeasible // FLAG indicated if there is at least one position
// where the motif could be matched against (OUT)
)
{
PSSM_T *pos_pssm, *neg_pssm, *pssm;
int strands, seq_length, w, n, asize, strand, start, N_scored, s_pos, m_pos;
double max_odds, sum_odds, requested_odds, odds, adjust, log_p;
int8_t *isequence, *iseq;
assert(pssm_pair != NULL);
assert(seq != NULL);
asize = alph_size_core(alph);
pos_pssm = pssm_pair->pos_pssm;
assert(pos_pssm != NULL);
neg_pssm = pssm_pair->neg_pssm;
strands = neg_pssm ? 2 : 1;
isequence = get_isequence(seq);
seq_length = get_seq_length(seq);
w = get_num_rows(pos_pssm->matrix);
n = seq_length - w + 1;
if (verbosity >= DUMP_VERBOSE) {
fprintf(stderr, "Debug strands: %d seq_length: %d w: %d n: %d.\n",
strands, seq_length, w, n);
}
// Dependent on the "last" parameter, change the starting point
if (last > 0 && last < seq_length) {
start = seq_length - last;
N_scored = strands * (last - w + 1); // number of sites scored
} else {
start = 0;
N_scored = strands * n; // number of sites scored
}
// For each motif (positive and reverse complement)
max_odds = 0.0;
sum_odds = 0.0;
if (verbosity >= HIGHER_VERBOSE) {
fprintf(stderr, "Starting scan at position %d .\n", start);
}
for (strand = 0; strand < strands; strand++) { // pos (and negative) motif
pssm = (strand == 0 ? pos_pssm : neg_pssm); // choose +/- motif
// For each site in the sequence
for (s_pos = start; s_pos < n; s_pos++) {
odds = 1.0;
// For each position in the motif window
for (m_pos = 0, iseq = isequence+s_pos; m_pos < w; m_pos++, iseq++) {
if (*iseq == -1) {
N_scored--;
odds = 0;
break;
}
// multiple odds by value in appropriate motif cell
odds *= get_matrix_cell(m_pos, *iseq, pssm->matrix);
}
// Apply sequence-dependent background model.
if (logcumback) {
log_p = logcumback[s_pos+w] - logcumback[s_pos]; // log Pr(x | background)
//printf("log_p:: %g motif_pos %d\n", log_p, m_pos);
adjust = exp(w*log(1/4.0) - log_p); // Pr(x | uniform) / Pr(x | background)
odds *= adjust;
}
// Add odds to growing sum.
sum_odds += odds; // sum of odds
if (odds > max_odds) max_odds = odds; // max of odds
} // site
} // strand
if (verbosity >= HIGHER_VERBOSE) {
fprintf(stderr, "Scored %d positions with the sum odds %f and the "
"max odds %f.\n", N_scored, sum_odds, max_odds);
}
// has there been anything matched at all?
if (N_scored == 0) {
if (verbosity >= NORMAL_VERBOSE) {
fprintf(stderr,"Sequence \'%s\' offers no location to match "
"the motif against (sequence length too short?)\n",
get_seq_name(seq));
}
*isFeasible = false;
return 0.0;
// return odds as requested (MAX or AVG scoring)
} else if (method == AVG_ODDS) {
return sum_odds / N_scored; // mean
} else if (method == MAX_ODDS) {
return max_odds; // maximum
} else if (method == SUM_ODDS) {
return sum_odds; // sum
} else {
die("Unknown scoring method");
// should not get here... but the compiler will complain if I don't handle this case
*isFeasible = false;
return 0.0;
}
} // score_sequence
/*************************************************************************
* Entry point for ama
*************************************************************************/
int main(int argc, char *argv[]) {
int max_seq_length = MAX_SEQ;
STRING_LIST_T* selected_motifs = NULL;
double pseudocount = 0.01;
int output_format = CISML_FORMAT;
program_name = "ama";
int scoring = AVG_ODDS;
BOOLEAN_T pvalues = FALSE;
BOOLEAN_T normalize_scores = FALSE;
BOOLEAN_T combine_duplicates = FALSE;
int num_gc_bins = 1;
int sdbg_order = -1; // don't use sequence background
BOOLEAN_T scan_both_strands = TRUE;
ARRAY_T* pos_bg_freqs = NULL;
ARRAY_T* rev_bg_freqs = NULL;
clock_t c0, c1; /* measuring cpu_time */
CISML_T *cisml;
char * out_dir = NULL;
BOOLEAN_T clobber = FALSE;
int i;
int last = 0;
ALPH_T alph = INVALID_ALPH;
/**********************************************
* COMMAND LINE PROCESSING
**********************************************/
const int num_options = 16;
cmdoption const motif_scan_options[] = {
{ "max-seq-length", REQUIRED_VALUE },
{ "motif", REQUIRED_VALUE },
{ "motif-pseudo", REQUIRED_VALUE },
{ "rma", NO_VALUE },
{ "pvalues", NO_VALUE },
{ "sdbg", REQUIRED_VALUE },
{ "norc", NO_VALUE },
{ "cs", NO_VALUE },
{ "o-format", REQUIRED_VALUE },
{ "o", REQUIRED_VALUE },
{ "oc", REQUIRED_VALUE },
{ "scoring", REQUIRED_VALUE },
{ "verbosity", REQUIRED_VALUE },
{ "gcbins", REQUIRED_VALUE },
{ "last", REQUIRED_VALUE },
{ "version", NO_VALUE }
};
int option_index = 0;
// Define the usage message.
char usage[] = "USAGE: ama [options] <motif file> <sequence file> [<background file>]\n"
"\n"
" Options:\n"
" --sdbg <order>\t\t\tUse Markov background model of\n"
" \t\t\t\t\torder <order> derived from the sequence\n"
" \t\t\t\t\tto compute its likelihood ratios.\n"
" \t\t\t\t\tOverrides --pvalues, --gcbins and --rma;\n"
" \t\t\t\t\t<background file> is required unless\n"
" \t\t\t\t\t--sdbg is given.\n"
" --motif <id>\t\t\tUse only the motif identified by <id>.\n"
" \t\t\t\t\tThis option may be repeated.\n"
" --motif-pseudo <float>\t\tThe value <float> times the background\n"
" \t\t\t\t\tfrequency is added to the count of each\n"
" \t\t\t\t\tletter when creating the likelihood \n"
" \t\t\t\t\tratio matrix (default: %g).\n"
" --norc\t\t\t\tDisables the scanning of the reverse\n"
" \t\t\t\t\tcomplement strand.\n"
" --scoring [avg-odds|max-odds]\tIndicates whether the average or \n"
" \t\t\t\t\tthe maximum odds should be calculated\n"
" \t\t\t\t\t(default: avg-odds)\n"
" --rma\t\t\t\tScale motif scores to the range 0-1.\n"
" \t\t\t\t\t(Relative Motif Affinity).\n"
" \t\t\t\t\tMotif scores are scaled by the maximum\n"
" \t\t\t\t\tscore achievable by that PWM. (default:\n"
" \t\t\t\t\tmotif scores are not normalized)\n"
" --pvalues\t\t\t\tPrint p-value of avg-odds score in cisml\n"
" \t\t\t\t\toutput. Ignored for max-odds scoring.\n"
" \t\t\t\t\t(default: p-values are not printed)\n"
" --gcbins <bins>\t\t\tCompensate p-values for GC content of\n"
" \t\t\t\t\teach sequence using given number of \n"
" \t\t\t\t\tGC range bins. Recommended bins: 41.\n"
" \t\t\t\t\t(default: p-values are based on\n"
" \t\t\t\t\tfrequencies in background file)\n"
" --cs\t\t\t\tEnable combining sequences with same\n"
" \t\t\t\t\tidentifier by taking the average score\n"
" \t\t\t\t\tand the Sidac corrected p-value.\n"
" --o-format [gff|cisml]\t\tOutput file format (default: cisml)\n"
" \t\t\t\t\tignored if --o or --oc option used\n"
" --o <directory>\t\t\tOutput all available formats to\n"
" \t\t\t\t\t<directory>; give up if <directory>\n"
" \t\t\t\t\texists\n"
" --oc <directory>\t\t\tOutput all available formats to\n"
" \t\t\t\t\t<directory>; if <directory> exists\n"
" \t\t\t\t\toverwrite contents\n"
" --verbosity [1|2|3|4]\t\tControls amount of screen output\n"
" \t\t\t\t\t(default: %d)\n"
" --max-seq-length <int>\t\tSet the maximum length allowed for \n"
" \t\t\t\t\tinput sequences. (default: %d)\n"
" --last <int>\t\t\tUse only scores of (up to) last <n>\n"
" \t\t\t\t\tsequence positions to compute AMA.\n"
" --version \t\t\tPrint version and exit.\n"
"\n";
// Parse the command line.
if (simple_setopt(argc, argv, num_options, motif_scan_options) != NO_ERROR) {
die("Error processing command line options: option name too long.\n");
}
BOOLEAN_T setoutputformat = FALSE;
BOOLEAN_T setoutputdirectory = FALSE;
while (TRUE) {
int c = 0;
char* option_name = NULL;
char* option_value = NULL;
const char * message = NULL;
// Read the next option, and break if we're done.
c = simple_getopt(&option_name, &option_value, &option_index);
if (c == 0) {
break;
} else if (c < 0) {
(void) simple_getopterror(&message);
die("Error processing command line options (%s).\n", message);
} else if (strcmp(option_name, "max-seq-length") == 0) {
max_seq_length = atoi(option_value);
} else if (strcmp(option_name, "norc") == 0) {
scan_both_strands = FALSE;
} else if (strcmp(option_name, "cs") == 0) {
combine_duplicates = TRUE;
} else if (strcmp(option_name, "motif") == 0) {
if (selected_motifs == NULL) {
selected_motifs = new_string_list();
}
add_string(option_value, selected_motifs);
} else if (strcmp(option_name, "motif-pseudo") == 0) {
pseudocount = atof(option_value);
} else if (strcmp(option_name, "o-format") == 0) {
if (setoutputdirectory) {
if (verbosity >= NORMAL_VERBOSE)
fprintf(stderr, "output directory specified, ignoring --o-format\n");
} else {
setoutputformat = TRUE;
if (strcmp(option_value, "gff") == 0)
output_format = GFF_FORMAT;
else if (strcmp(option_value, "cisml") == 0)
output_format = CISML_FORMAT;
else {
if (verbosity >= NORMAL_VERBOSE)
fprintf(stderr, "Output format not known. Using standard instead (cisML).\n");
output_format = CISML_FORMAT;
}
}
} else if (strcmp(option_name, "o") == 0 || strcmp(option_name, "oc") == 0) {
setoutputdirectory = TRUE;
if (setoutputformat) {
if (verbosity >= NORMAL_VERBOSE)
fprintf(stderr, "output directory specified, ignoring --o-format\n");
}
clobber = strcmp(option_name, "oc") == 0;
out_dir = (char*) malloc (sizeof(char)*(strlen(option_value)+1));
strcpy(out_dir, option_value);
output_format = DIRECTORY_FORMAT;
} else if (strcmp(option_name, "verbosity") == 0) {
verbosity = atoi(option_value);
} else if (strcmp(option_name, "scoring") == 0) {
if (strcmp(option_value, "max-odds") == 0)
scoring = MAX_ODDS;
else if (strcmp(option_value, "avg-odds") == 0)
scoring = AVG_ODDS;
else if (strcmp(option_value, "sum-odds") == 0)
scoring = SUM_ODDS;
else
die("Specified scoring scheme not known.\n", message);
} else if (strcmp(option_name, "pvalues") == 0) {
pvalues = TRUE;
} else if (strcmp(option_name, "rma") == 0) {
normalize_scores = TRUE;
fprintf(stderr, "Normalizing motif scores using RMA method.\n");
} else if (strcmp(option_name, "gcbins") == 0) {
num_gc_bins = atoi(option_value);
pvalues = TRUE;
if (num_gc_bins <= 1) die("Number of bins in --gcbins must be greater than 1.\n", message);
} else if (strcmp(option_name, "sdbg") == 0) {
sdbg_order = atoi(option_value); // >=0 means use sequence bkg
}
else if (strcmp(option_name, "last") == 0) {
int i = 0;
if (option_value[0] == '-') ++i;
while (option_value[i] != '\0') {
if (!isdigit(option_value[i])) {
die("Specified parameter 'last' contains non-numeric characters.\n");
}
++i;
}
last = atoi(option_value);
if (errno != 0) {
die("Specified parameter 'last' could not be parsed as a number as:\n%s\n",strerror(errno));
}
if (last < 0) {
die("Specified parameter 'last' had negative value (%d) when only postive or zero values are allowed \n", last);
}
}
else if (strcmp(option_name, "version") == 0) {
fprintf(stdout, VERSION "\n");
exit(EXIT_SUCCESS);
}
}
// --sdbg overrides --pvalues and --gcbins and --rma
int req_args = 3;
if (sdbg_order >= 0) {
pvalues = FALSE;
normalize_scores = FALSE;
num_gc_bins = 1;
req_args = 2;
}
// Check all required arguments given
if (sdbg_order >= 0 && argc > option_index + req_args) {
die("<background file> cannot be given together with --sdbg.\n");
} else if (argc != option_index + req_args) {
fprintf(stderr, usage, pseudocount, verbosity, max_seq_length);
exit(EXIT_FAILURE);
}
// Get required arguments.
char* motif_filename = argv[option_index];
option_index++;
char* fasta_filename = argv[option_index];
option_index++;
char* bg_filename;
if (req_args == 3) { // required unless --sdbg given
bg_filename = argv[option_index];
option_index++;
} else {
bg_filename = "--uniform--"; // So PSSMs will use uniform background;
// we can multiply them out later.
}
// measure time
c0 = clock();
// Set up hash tables for computing reverse complement if doing --sdbg
if (sdbg_order >= 0) setup_hash_alph(DNAB);
// Create cisml data structure for recording results
cisml = allocate_cisml(program_name, motif_filename, fasta_filename);
set_cisml_background_file(cisml, bg_filename);
/**********************************************
* Read the motifs and background model.
**********************************************/
int num_motifs = 0;
MREAD_T *mread;
ARRAYLST_T *motifs;
PSSM_PAIR_T** pssm_pairs; // note pssm_pairs is an array of pointers
//this reads any meme file, xml, txt and html
mread = mread_create(motif_filename, OPEN_MFILE);
mread_set_bg_source(mread, bg_filename);
mread_set_pseudocount(mread, pseudocount);
motifs = mread_load(mread, NULL);
alph = mread_get_alphabet(mread);
pos_bg_freqs = mread_get_background(mread);
mread_destroy(mread);
num_motifs = arraylst_size(motifs);
// allocate memory for PSSM pairs
pssm_pairs = (PSSM_PAIR_T**)mm_malloc(sizeof(PSSM_PAIR_T*) * num_motifs);
if (verbosity >= NORMAL_VERBOSE)
fprintf(stderr, "Number of motifs in file %d.\n", num_motifs);
// make a CISML pattern to hold scores for each motif
PATTERN_T** patterns = NULL;
Resize(patterns, num_motifs, PATTERN_T*);
int motif_index;
for (motif_index = 0; motif_index < num_motifs; motif_index++) {
MOTIF_T* motif = (MOTIF_T*)arraylst_get(motif_index, motifs);
patterns[motif_index] = allocate_pattern(get_motif_id(motif), "");
add_cisml_pattern(cisml, patterns[motif_index]);
}
// make reverse complement motifs and background frequencies.
if (scan_both_strands == TRUE) {
add_reverse_complements(motifs);
assert(arraylst_size(motifs) == (2 * num_motifs));
rev_bg_freqs = allocate_array(get_array_length(pos_bg_freqs));
complement_dna_freqs(pos_bg_freqs, rev_bg_freqs);
}
/**************************************************************
* Convert motif matrices into log-odds matrices.
* Scale them.
* Compute the lookup tables for the PDF of scaled log-odds scores.
**************************************************************/
int ns = scan_both_strands ? 2 : 1; // number of strands
for (motif_index = 0; motif_index < num_motifs; motif_index++) {
MOTIF_T *motif, *motif_rc;
motif = (MOTIF_T*)arraylst_get(motif_index*ns, motifs);
if (scan_both_strands)
motif_rc = (MOTIF_T*)arraylst_get(motif_index*ns + 1, motifs);
else
motif_rc = NULL;
/*
* Note: If scanning both strands, we complement the motif frequencies
* but not the background frequencies so the motif looks the same.
* However, the given frequencies are used in computing the p-values
* since they represent the frequencies on the negative strands.
* (If we instead were to complement the input sequence, keeping the
* the motif fixed, we would need to use the complemented frequencies
* in computing the p-values. Is that any clearer?)
*/
double range = 300; // 100 is not very good; 1000 is great but too slow
PSSM_T* pos_pssm =
build_motif_pssm(
motif,
pos_bg_freqs,
pos_bg_freqs,
NULL, // Priors not used
0.0L, // alpha not used
range,
num_gc_bins,
TRUE
);
PSSM_T* neg_pssm = (scan_both_strands ?
build_motif_pssm(
motif_rc,
rev_bg_freqs,
pos_bg_freqs,
NULL, // Priors not used
0.0L, // alpha not used
range,
num_gc_bins,
TRUE
)
: NULL
);
pssm_pairs[motif_index] = create_pssm_pair(pos_pssm, neg_pssm);
}
// Open the FASTA file for reading.
FILE* fasta_file = NULL;
if (open_file(fasta_filename, "r", FALSE, "FASTA", "sequences", &fasta_file) == 0) {
die("Couldn't open the file %s.\n", fasta_filename);
}
if (verbosity >= NORMAL_VERBOSE) {
if (last == 0) {
fprintf(stderr, "Using entire sequence\n");
} else {
fprintf(stderr, "Limiting sequence to last %d positions.\n", last);
}
}
/**************************************************************
* Read in all sequences and score with all motifs
**************************************************************/
int seq_loading_num = 0; // keeps track on the number of sequences read in total
int seq_counter = 0; // holds the index to the seq in the pattern
int unique_seqs = 0; // keeps track on the number of unique sequences
BOOLEAN_T need_postprocessing = FALSE;
SEQ_T* sequence = NULL;
RBTREE_T* seq_ids = rbtree_create(rbtree_strcasecmp,NULL,free,rbtree_intcpy,free);
RBNODE_T* seq_node;
BOOLEAN_T created;
while (read_one_fasta(alph, fasta_file, max_seq_length, &sequence)) {
++seq_loading_num;
created = FALSE;
char* seq_name = get_seq_name(sequence);
int seq_len = get_seq_length(sequence);
int scan_len;
if (last != 0) {
scan_len = last;
} else {
scan_len = seq_len;
}
// red-black trees are only required if duplicates should be combined
if (combine_duplicates){
//lookup seq id and create new entry if required, return sequence index
char *tmp_id = mm_malloc(strlen(seq_name)+1); // required copy for rb-tree
strncpy(tmp_id,seq_name,strlen(seq_name)+1);
seq_node = rbtree_lookup(seq_ids, tmp_id, 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.
//
double *logcumback = NULL; // array of log cumulative probs.
if (sdbg_order >= 0) {
Resize(logcumback, seq_len+1, double);
char* raw_seq = get_raw_sequence(sequence);
BOOLEAN rc = FALSE;
double *a_cp = get_markov_from_sequence(raw_seq, alph_string(alph), rc, sdbg_order, 0);
log_cum_back(raw_seq, a_cp, sdbg_order, logcumback);
myfree(a_cp);
}
// Get the GC content of the sequence if binning p-values by GC
// and store it in the sequence object.
if (num_gc_bins > 1) {
ARRAY_T *freqs = get_sequence_freqs(sequence, alph);
set_total_gc_sequence(sequence,
get_array_item(1,freqs) + get_array_item(2,freqs)); // f(C) + f(G)
free_array(freqs); // clean up
} else {
set_total_gc_sequence(sequence, -1); // flag ignore
}
/**************************************************************
* Process all motifs.
**************************************************************/
int ns = scan_both_strands ? 2 : 1;
for (motif_index = 0; motif_index < num_motifs; motif_index++) {
PATTERN_T *pattern = patterns[motif_index];
MOTIF_T* motif = (MOTIF_T*)arraylst_get(ns*motif_index, motifs);
char* motif_id = (scan_both_strands ? get_motif_st_id(motif) : get_motif_id(motif));
if (verbosity >= HIGH_VERBOSE) {
fprintf(stderr, "Using motif %s of width %d.\n", motif_id, get_motif_length(motif));
}
if ((selected_motifs == NULL) || (have_string(get_motif_id(motif), selected_motifs) == TRUE)) {
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, motif_id, get_motif_length(motif));
}
SCANNED_SEQUENCE_T* scanned_seq = NULL;
if (!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(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(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, motif_id);
} else {
// scan the sequence using average/maximum motif affinity
ama_sequence_scan(alph, sequence, logcumback, pssm_pairs[motif_index], scoring,
pvalues, last, scanned_seq, &need_postprocessing);
}
} else {
if (verbosity >= HIGH_VERBOSE) fprintf(stderr, "Skipping motif %s.\n", motif_id);
}
} // All motifs parsed
free_seq(sequence);
if (sdbg_order >= 0) myfree(logcumback);
} // read sequences
void ramen_scan_sequences() {
FILE* seq_file = NULL;
MOTIF_T* motif = NULL;
MOTIF_T* rev_motif = NULL;
SEQ_T* sequence = NULL;
SCANNED_SEQUENCE_T* scanned_seq = NULL;
PATTERN_T* pattern;
int i;
int j;
SEQ_T** seq_list;
int num_seqs;
int seq_len;
//For the bdb_bg mode:
ARRAY_T* seq_bg_freqs;
double atcontent;
double roundatcontent;
double avg_seq_length = 0;
//Open the file.
if (open_file(args.sequence_filename, "r", FALSE, "FASTA", "sequences", &seq_file) == 0) {
fprintf(stderr, "Couldn't open the file %s.\n", args.sequence_filename);
ramen_terminate(1);
}
//Start reading in the sequences
read_many_fastas(ramen_alph, seq_file, MAX_SEQ_LENGTH, &num_seqs, &seq_list);
seq_ids = new_string_list();
seq_fscores = allocate_array(num_seqs);
//Allocate the required space for results
results = malloc(sizeof(double*) * motifs.num);
for (i=0;i<motifs.num;i++) {
results[i] = malloc(sizeof(double)*num_seqs);
}
for (j=0;j<num_seqs;j++) {
fprintf(stderr, "\rScanning %i of %i sequences...", j+1, num_seqs);
//copy the pointer into our current object for clarity
sequence = seq_list[j];
//Read the fluorescence data from the description field.
add_string(get_seq_name(sequence),seq_ids);
seq_len = get_seq_length(sequence);
set_array_item(j,atof(get_seq_description(sequence)),seq_fscores);
//Scan with each motif.
for (i=0;i<motifs.num;i++) {
int motifindex = i*2;
results[i][j] = ramen_sequence_scan(sequence, motif_at(motifs.motifs, motifindex),
motif_at(motifs.motifs, motifindex+1),
NULL, NULL, //No need to pass PSSM.
AVG_ODDS, 0, TRUE, 0, motifs.bg_freqs);
if (TRUE == args.linreg_normalise) {
int k;
double maxscore = 1;
motif = motif_at(motifs.motifs,motifindex);
for (k=0;k<get_motif_length(motif);k++) {
double maxprob = 0;
if (maxprob < get_matrix_cell(k, alph_index(ramen_alph, 'A'), get_motif_freqs(motif)))
maxprob = get_matrix_cell(k, alph_index(ramen_alph, 'A'), get_motif_freqs(motif));
if (maxprob < get_matrix_cell(k, alph_index(ramen_alph, 'C'), get_motif_freqs(motif)))
maxprob = get_matrix_cell(k, alph_index(ramen_alph, 'C'), get_motif_freqs(motif));
if (maxprob < get_matrix_cell(k, alph_index(ramen_alph, 'G'), get_motif_freqs(motif)))
maxprob = get_matrix_cell(k, alph_index(ramen_alph, 'G'), get_motif_freqs(motif));
if (maxprob < get_matrix_cell(k, alph_index(ramen_alph, 'T'), get_motif_freqs(motif)))
maxprob = get_matrix_cell(k, alph_index(ramen_alph, 'T'), get_motif_freqs(motif));
maxscore *= maxprob;
}
results[i][j] /= maxscore;
}
}
}
}
/*************************************************************************
* 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.
* Scores sequence with up to two motifs.
*************************************************************************/
double score_sequence(
SEQ_T* seq, // sequence to scan (IN)
double *logcumback, // cumulative bkg probability of sequence (IN)
PSSM_PAIR_T* pssm_pair, // pos and neg pssms (IN)
int method, // method used for scoring (IN)
int last, //score only last <n> or
//score all if <n> is zero (IN)
BOOLEAN_T* isFeasible // FLAG indicated if there is at least one position
// where the motif could be matched against (OUT)
)
{
assert(pssm_pair != NULL);
assert(seq != NULL);
PSSM_T* pos_pssm = pssm_pair->pos_pssm;
assert(pos_pssm != NULL);
PSSM_T* neg_pssm = pssm_pair->neg_pssm;
int n_motifs = neg_pssm ? 2 : 1;
char* raw_seq = get_raw_sequence(seq);
int seq_length = get_seq_length(seq);
int w = get_num_rows(pos_pssm->matrix);
int n = seq_length - w + 1;
if (verbosity >= DUMP_VERBOSE) {
fprintf(stderr, "Debug n_motifs: %d seq_length: %d w: %d n: %d.\n", n_motifs, seq_length, w, n);
}
// Get alphabet;
char* alphabet = get_alphabet(FALSE);
int alph_size = get_alph_size(ALPH_SIZE);
// Dependent on the "last" parameter, change the starting point
int start;
int N_scored;
if (last > 0 && last < seq_length) {
start = seq_length - last;
N_scored = n_motifs * (last - w + 1); // number of sites scored
} else {
start = 0;
N_scored = n_motifs * n; // number of sites scored
}
// For each motif (positive and reverse complement)
double max_odds = 0.0;
double sum_odds = 0.0;
double requested_odds = 0.0;
int i;
if (verbosity >= HIGHER_VERBOSE) {
fprintf(stderr, "Starting scan at position %d .\n", start);
}
for (i=0; i<n_motifs; i++) { // pos (and negative) motif
PSSM_T* pssm = (i==0 ? pos_pssm : neg_pssm); // choose +/- motif
// For each site in the sequence
int seq_index;
for (seq_index = start; seq_index < n; seq_index++) { // site
double odds = 1.0;
// For each position in the motif window
int motif_position;
for (motif_position = 0; motif_position < w; motif_position++) { // column
int i_site = seq_index + motif_position;
char c = raw_seq[i_site];
// Check for gaps at this site
if (c == '-' || c == '.') { N_scored--; odds = 0; break; }
// Check for ambiguity codes at this site
int alph_index = alphabet_index(c, alphabet);
if (alph_index >= alph_size || alph_index < 0) { N_scored--; odds = 0; break; }
// multiple odds by value in appropriate motif cell
odds *= get_matrix_cell(motif_position, alph_index, pssm->matrix);
} // column
//
// Apply sequence-dependent background model.
//
if (logcumback) {
int i_site = seq_index;
double log_p = logcumback[i_site+w] - logcumback[i_site]; // log Pr(x | background)
//printf("log_p:: %g motif_pos %d\n", log_p, motif_position);
double adjust = exp(w*log(1/4.0) - log_p); // Pr(x | uniform) / Pr(x | background)
odds *= adjust;
}
// Add odds to growing sum.
sum_odds += odds; // sum of odds
if (odds > max_odds) max_odds = odds; // max of odds
} // site
} // motif
if (verbosity >= HIGHER_VERBOSE) {
fprintf(stderr, "Scored %d positions with the sum odds %f and the max odds %f.\n", N_scored, sum_odds, max_odds);
}
// has there been anything matched at all?
if (N_scored == 0){
if (verbosity >= NORMAL_VERBOSE) {
fprintf(stderr,"Sequence \'%s\' offers no location to match the motif against (sequence length too short?)\n",get_seq_name(seq));
}
*isFeasible = FALSE;
return 0.0;
// return odds as requested (MAX or AVG scoring)
} else if (method == AVG_ODDS) {
requested_odds = sum_odds / N_scored; // mean
} else if (method == MAX_ODDS) {
requested_odds = max_odds; // maximum
} else if (method == SUM_ODDS) {
requested_odds = sum_odds ; // sum
}
return(requested_odds);
} // score_sequence
/*************************************************************************
* Entry point for centrimo
*************************************************************************/
int main(int argc, char *argv[]) {
CENTRIMO_OPTIONS_T options;
SEQ_SITES_T seq_sites;
SITE_COUNTS_T counts;
int seqN, motifN, seqlen, db_i, motif_i, i;
double log_pvalue_thresh;
SEQ_T** sequences = NULL;
ARRAY_T* bg_freqs = NULL;
ARRAYLST_T *stats_list;
MOTIF_DB_T **dbs, *db;
MREAD_T *mread;
MOTIF_STATS_T *stats;
MOTIF_T *motif, *rev_motif;
PSSM_T *pos_pssm, *rev_pssm;
char *sites_path, *desc;
FILE *sites_file;
HTMLWR_T *html;
JSONWR_T *json;
// COMMAND LINE PROCESSING
process_command_line(argc, argv, &options);
// load the sequences
read_sequences(options.alphabet, options.seq_source, &sequences, &seqN);
seqlen = (seqN ? get_seq_length(sequences[0]) : 0);
// calculate a sequence background (unless other background is given)
if (!options.bg_source) {
bg_freqs = calc_bg_from_fastas(options.alphabet, seqN, sequences);
}
// load the motifs
motifN = 0;
dbs = mm_malloc(sizeof(MOTIF_DB_T*) * arraylst_size(options.motif_sources));
for (i = 0; i < arraylst_size(options.motif_sources); i++) {
char* db_source;
db_source = (char*)arraylst_get(i, options.motif_sources);
dbs[i] = read_motifs(i, db_source, options.bg_source, &bg_freqs,
options.pseudocount, options.selected_motifs, options.alphabet);
motifN += arraylst_size(dbs[i]->motifs);
}
log_pvalue_thresh = log(options.evalue_thresh) - log(motifN);
// Setup some things for double strand scanning
if (options.scan_both_strands == TRUE) {
// Set up hash tables for computing reverse complement
setup_hash_alph(DNAB);
setalph(0);
// Correct background by averaging on freq. for both strands.
average_freq_with_complement(options.alphabet, bg_freqs);
normalize_subarray(0, alph_size(options.alphabet, ALPH_SIZE), 0.0, bg_freqs);
calc_ambigs(options.alphabet, FALSE, bg_freqs);
}
// Create output directory
if (create_output_directory(options.output_dirname, options.allow_clobber,
(verbosity >= NORMAL_VERBOSE))) {
die("Couldn't create output directory %s.\n", options.output_dirname);
}
// open output files
sites_path = make_path_to_file(options.output_dirname, SITES_FILENAME);
sites_file = fopen(sites_path, "w");
free(sites_path);
// setup html monolith writer
json = NULL;
if ((html = htmlwr_create(get_meme_etc_dir(), TEMPLATE_FILENAME))) {
htmlwr_set_dest_name(html, options.output_dirname, HTML_FILENAME);
htmlwr_replace(html, "centrimo_data.js", "data");
json = htmlwr_output(html);
if (json == NULL) die("Template does not contain data section.\n");
} else {
DEBUG_MSG(QUIET_VERBOSE, "Failed to open html template file.\n");
}
if (json) {
// output some top level variables
jsonwr_str_prop(json, "version", VERSION);
jsonwr_str_prop(json, "revision", REVISION);
jsonwr_str_prop(json, "release", ARCHIVE_DATE);
jsonwr_str_array_prop(json, "cmd", argv, argc);
jsonwr_property(json, "options");
jsonwr_start_object_value(json);
jsonwr_dbl_prop(json, "motif-pseudo", options.pseudocount);
jsonwr_dbl_prop(json, "score", options.score_thresh);
jsonwr_dbl_prop(json, "ethresh", options.evalue_thresh);
jsonwr_lng_prop(json, "maxbin", options.max_window+1);
jsonwr_bool_prop(json, "norc", !options.scan_both_strands);
jsonwr_bool_prop(json, "noflip", options.no_flip);
jsonwr_end_object_value(json);
// output the description
desc = prepare_description(&options);
if (desc) {
jsonwr_str_prop(json, "job_description", desc);
free(desc);
}
// output size metrics
jsonwr_lng_prop(json, "seqlen", seqlen);
jsonwr_lng_prop(json, "tested", motifN);
// output the fasta db
jsonwr_property(json, "sequence_db");
jsonwr_start_object_value(json);
jsonwr_str_prop(json, "source", options.seq_source);
jsonwr_lng_prop(json, "count", seqN);
jsonwr_end_object_value(json);
// output the motif dbs
jsonwr_property(json, "motif_dbs");
jsonwr_start_array_value(json);
for (db_i = 0; db_i < arraylst_size(options.motif_sources); db_i++) {
db = dbs[db_i];
jsonwr_start_object_value(json);
jsonwr_str_prop(json, "source", db->source);
jsonwr_lng_prop(json, "count", arraylst_size(db->motifs));
jsonwr_end_object_value(json);
}
jsonwr_end_array_value(json);
// start the motif array
jsonwr_property(json, "motifs");
jsonwr_start_array_value(json);
}
/**************************************************************
* Tally the positions of the best sites for each of the
* selected motifs.
**************************************************************/
// prepare the sequence sites
memset(&seq_sites, 0, sizeof(SEQ_SITES_T));
// prepare the site counts
counts.allocated = ((2 * seqlen) - 1);
counts.sites = mm_malloc(sizeof(double) * counts.allocated);
// prepare the motifs stats list
stats_list = arraylst_create();
// prepare the other vars
motif = NULL; pos_pssm = NULL; rev_motif = NULL; rev_pssm = NULL;
for (db_i = 0; db_i < arraylst_size(options.motif_sources); db_i++) {
db = dbs[db_i];
for (motif_i = 0; motif_i < arraylst_size(db->motifs); motif_i++) {
motif = (MOTIF_T *) arraylst_get(motif_i, db->motifs);
DEBUG_FMT(NORMAL_VERBOSE, "Using motif %s of width %d.\n",
get_motif_id(motif), get_motif_length(motif));
// reset the counts
for (i = 0; i < counts.allocated; i++) counts.sites[i] = 0;
counts.total_sites = 0;
// create the pssm
pos_pssm = make_pssm(bg_freqs, motif);
// If required, do the same for the reverse complement motif.
if (options.scan_both_strands) {
rev_motif = dup_rc_motif(motif);
rev_pssm = make_pssm(bg_freqs, rev_motif);
}
// scan the sequences
for (i = 0; i < seqN; i++)
score_sequence(&options, sequences[i], pos_pssm, rev_pssm,
&seq_sites, &counts);
// DEBUG check that the sum of the sites is close to the site count
double sum_check = 0, sum_diff;
for (i = 0; i < counts.allocated; i++) sum_check += counts.sites[i];
sum_diff = counts.total_sites - sum_check;
if (sum_diff < 0) sum_diff = -sum_diff;
if (sum_diff > 0.1) {
fprintf(stderr, "Warning: site counts don't sum to accurate value! "
"%g != %ld", sum_check, counts.total_sites);
}
// output the plain text site counts
output_site_counts(sites_file, seqlen, db, motif, &counts);
// compute the best central window
stats = compute_stats(options.max_window, seqlen, db, motif, &counts);
// check if it passes the threshold
if (json && stats->log_adj_pvalue <= log_pvalue_thresh) {
output_motif_json(json, stats, &counts);
arraylst_add(stats, stats_list);
} else {
free(stats);
}
// Free memory associated with this motif.
free_pssm(pos_pssm);
free_pssm(rev_pssm);
destroy_motif(rev_motif);
}
}
if (json) jsonwr_end_array_value(json);
// finish writing sites
fclose(sites_file);
// finish writing html file
if (html) {
if (htmlwr_output(html) != NULL) {
die("Found another JSON replacement!\n");
}
htmlwr_destroy(html);
}
// write text file
output_centrimo_text(&options, motifN, stats_list);
// Clean up.
for (i = 0; i < seqN; ++i) {
free_seq(sequences[i]);
}
free(sequences);
for (i = 0; i < arraylst_size(options.motif_sources); i++) {
free_db(dbs[i]);
}
free(dbs);
free_array(bg_freqs);
free(counts.sites);
free(seq_sites.sites);
arraylst_destroy(free, stats_list);
cleanup_options(&options);
return 0;
}
/*************************************************************************
* Calculate the log-odds score for each possible motif site in the
* sequence and record the sites of the best. Apply a count to each
* best site and increment the total site count.
*************************************************************************/
static void score_sequence(
CENTRIMO_OPTIONS_T *options,
SEQ_T* sequence,
PSSM_T* pssm,
PSSM_T* rev_pssm,
SEQ_SITES_T* seq_sites,
SITE_COUNTS_T* counts
)
{
char *raw_seq, *seg;
int i, L, w, pos;
double score;
double count;
SEQ_SITE_T *site;
// check we got passed stuff
assert(options != NULL);
assert(sequence != NULL);
assert(pssm != NULL);
assert(seq_sites != NULL);
assert(counts != NULL);
// make Mac OS compiler happy.
score = -BIG;
// Score and record each possible motif site in the sequence
raw_seq = get_raw_sequence(sequence);
L = get_seq_length(sequence);
w = pssm->w;
// Reset the sequence stats structure
seq_sites->best = -BIG;
seq_sites->used = 0;
// Read and score each position in the sequence.
for (i = 0; i < L - w + 1; i++) {
seg = raw_seq+i;
// Score and record forward strand
if (score_motif_site(options->alphabet, seg, pssm, &score))
track_site(seq_sites, score, i, '+');
// Score and record reverse strand if appropriate.
if (rev_pssm && score_motif_site(options->alphabet, seg, rev_pssm, &score))
track_site(seq_sites, score, i, '-');
}
// Record the position of best site, averaging ties
// and using position in RC of sequence if site on reverse strand
// unless no_flip is true.
if (seq_sites->used && seq_sites->best >= options->score_thresh) {
// add 1/n_ties to each tied position's count,
// averaging rather than random choice
count = (double)1.0 / (double)seq_sites->used;
for (i = 0; i < seq_sites->used; i++) {
site = seq_sites->sites+i;
if (options->no_flip || site->strand == '+') {
//pos = 2 * (site->start + w/2 - 1/2); // a motif of width 1 can have sites at the first index
pos = 2 * site->start + w - 1; // a motif of width 1 can have sites at the first index
} else {
//pos = 2 * (L - (site->start + w/2) - 1; // a motif of width 1 can have sites at the first index
pos = 2 * (L - site->start) - w - 1;
}
//record the count
counts->sites[pos] += count;
}
counts->total_sites++;
}
}
