/* @brief For an alignment column
* - generate cons_type [SProfile] for SNP entries;
* - generate [LProfile] for LP entries i.e. Dx or Ix.
* - Compute lamda values [lamda_S/L], and raw probabilities in [p_S/L]
*/
void pileup_profiling (pileup_profile_t& profile_info, const col_t& col,
const std::vector<jeb_t>& jeb, const GlobalParam& gParam) {
int num_entry = col.entries.size();
bool is_ins_col = (col.ref_pos % 2 == 1) ? true : false;
for (int i = 0; i < num_entry; ++ i) {
if (col.entries[i].cons_type.at(0) == 'D'
|| col.entries[i].cons_type.at(0) == 'I') {
add_to_profile (profile_info.LProfile, col.entries[i].cons_type);
} else {
if (is_ins_col) add_to_profile (profile_info.LProfile, "d");
else {
add_to_profile (profile_info.LProfile, "i");
add_to_profile (profile_info.SProfile, col.entries[i].cons_type);
}
}
if (jeb[col.entries[i].eb_index].lp_sum != 0) { //
//double tmp = (jeb[col.entries[i].eb_index].lp_err + 1.0)/
// (jeb[col.entries[i].eb_index].lp_sum + 1.0);
//tmp = (tmp == 1.0) ? 0.5 : tmp;
int idx = col.entries[i].eb_index;
// constant prior
//double tmp = (jeb[idx].lp_err + gParam.bkinfo.get_prior_L())/
// (jeb[idx].lp_sum + gParam.bkinfo.average_L());
// weighted prior
//double tmp = (jeb[idx].lp_err * gParam.bkinfo.average_L() +
// jeb[idx].lp_sum * gParam.bkinfo.get_prior_L())/
// (2 * jeb[idx].lp_sum * gParam.bkinfo.average_L());
//lamda_lp += std::min(tmp, gParam.lenpoly_pe); // over calling
double pe = get_pe (jeb[idx].lp_err, jeb[idx].lp_sum,
1.0 * gParam.bkinfo.get_prior_L()/ gParam.bkinfo.average_L());
profile_info.lamda_L += pe; // under calling
profile_info.p_L.push_back(pe);
}
if (! is_ins_col && col.entries[i].cons_type.at(0) != 'D'
&& jeb[col.entries[i].eb_index].snp_sum != 0) { // SC
//double tmp = (jeb[col.entries[i].eb_index].snp_err + 1.0)/
// (jeb[col.entries[i].eb_index].snp_sum + 1.0);
//tmp = (tmp == 1.0) ? 0.5 : tmp;
int idx = col.entries[i].eb_index;
//double tmp = (jeb[idx].snp_err + gParam.bkinfo.get_prior_S())/
// (jeb[idx].snp_sum + gParam.bkinfo.average_S());
// weighted piror
//double tmp = (jeb[idx].snp_err * gParam.bkinfo.average_S() +
// jeb[idx].snp_sum * gParam.bkinfo.get_prior_S())/
// (2 * jeb[idx].snp_sum * gParam.bkinfo.average_S());
//lamda_snp += gParam.pe; // uniform
//lamda_snp += std::min(gParam.pe, tmp); // over-calling
double pe = get_pe (jeb[idx].snp_err, jeb[idx].snp_sum,
1.0 * gParam.bkinfo.get_prior_S()/ gParam.bkinfo.average_S());
profile_info.lamda_S += pe;
profile_info.p_S.push_back(pe);
}
}// for (int i = 0
} // pileup_profiling
int main(int argc, char** argv) {
//structs used for timing
struct timeval tval_before, tval_after, tval_result;
gettimeofday(&tval_before, NULL);
FILE * fp;
int option = 0;
//parse arguments
while((option = getopt(argc, argv, "k:d:t:")) != -1) {
switch(option) {
case 'k':
k = atoi(optarg);
break;
case 'd':
d = atoi(optarg);
break;
case 't':
t = atoi(optarg);
break;
default:
printf("Usage: motif_finder [-k motif_size] [-d wild_cards] [-t max_time] input_file");
break;
}
}
if((fp = fopen(argv[argc-1], "r")) == NULL) {
printf("FILE: \"%s\" DOESN'T EXIST\n", argv[argc - 1]);
exit(1); //Failed to open file.
}
//count how big our buffer needs to be (every other line)
char line[80];
int totalSequences = 0;
while(fgets(line, 80, fp) != NULL) {
if(line[0] == '>') {
totalSequences++;
}
}
rewind(fp);//reset fp to start of file
//read in the sequences. account for multi-line sequences
char* sequences[totalSequences];
int sequenceNum = 0;
int sequenceLength = 0;
int beginningOfSequence = 1;
while(fgets(line, 80, fp) != NULL) {
//sequence header
if(line[0] == '>') {
sscanf(line, ">Sequence%d length %d", &sequenceNum, &sequenceLength);
sequenceNum--; //keep 0 indexed
//note: sequenceLength is multiplied by 2, because it seemed that some of the "lengths" were off in the file
char* temp = malloc((sequenceLength*2)*sizeof(char));
if(temp == NULL) {
return -1;
}
sequences[sequenceNum] = temp;
beginningOfSequence = 1;
//read the line (if its the start of a sequence)
} else if(beginningOfSequence) {
strcpy(sequences[sequenceNum], line);
beginningOfSequence = 0;
//otherwise append it to the current sequences
} else {
strcat(sequences[sequenceNum], line);
}
//clear line (to be safe)
memset(line, 0, 80*sizeof(char));
}
fclose(fp);
//determine frequency
int a_count = 0, c_count = 0, g_count = 0, t_count = 0;
double total_count = 0; //to force double division later for probabilities
for(int i = 0; i < totalSequences; i++) {
for(int j = 0; j < strlen(sequences[i]); j++) {
switch(sequences[i][j]) {
case 'A':
a_count++;
total_count++;
break;
case 'C':
c_count++;
total_count++;
break;
case 'G':
g_count++;
total_count++;
break;
case 'T':
t_count++;
total_count++;
break;
}
}
}
//only calculate this once
double a_freq = a_count/total_count;
double c_freq = c_count/total_count;
double g_freq = g_count/total_count;
double t_freq = t_count/total_count;
double freqs[4];
freqs[0] = a_freq;
freqs[1] = c_freq;
freqs[2] = g_freq;
freqs[3] = t_freq;
//initialize our profile matrix
int* profile[4];
for(int i = 0; i < 4; i++) {
profile[i] = (int*)malloc((k+1)*sizeof(int));//slot 0 will be the total count
memset(profile[i], 0, (k+1)*sizeof(int));
}
//pick random starting locations
int startIndices[totalSequences];
srand(time(NULL));
for(int i = 0; i < totalSequences; i++) {
startIndices[i] = rand() % (strlen(sequences[i])-k);
}
generate_profile(startIndices, profile, sequences, totalSequences);
//kind of hacky to avoid errors when d == 0
int* dontCares = malloc(((d==0)?1:d)*sizeof(int));
int* bestDontCares = malloc(((d==0)?1:d)*sizeof(int));
//grab the inital score
double logScore = profile_score(profile, totalSequences, freqs, dontCares);
double bestLogScore = logScore;
//more timing
gettimeofday(&tval_after, NULL);
timersub(&tval_after, &tval_before, &tval_result);
//proceed until we're out of time
while(tval_result.tv_sec < t) {
//for each sequence
for(int i = 0; i < totalSequences; i++) {
int oldStartIndex = startIndices[i];
int currBestStart = oldStartIndex;
//try each starting position
remove_from_profile(i, startIndices, profile, sequences);
for(int j = 0; j < strlen(sequences[i])-k; j++) {
if(j != oldStartIndex) {
startIndices[i] = j;
add_to_profile(i, startIndices, profile, sequences);
logScore = profile_score(profile, totalSequences, freqs, dontCares);
//keep track of the best score so far
if(logScore > bestLogScore) {
currBestStart = j;
bestLogScore = logScore;
memcpy(bestDontCares, dontCares, d*sizeof(int));
}
remove_from_profile(i, startIndices, profile, sequences);
}
}
//reset profile with the best new starting index for sequences i
startIndices[i] = currBestStart;
add_to_profile(i, startIndices, profile, sequences);
//check if we're out of time yet
gettimeofday(&tval_after, NULL);
timersub(&tval_after, &tval_before, &tval_result);
if(tval_result.tv_sec >= t) {
break;
}
}
}
//done with algorithm, now just print stuff
//figure out the motif
char* motif = (char*)malloc((k+1)*sizeof(char));
if(motif == NULL) {
return -1;
}
for(int i = 0; i < k; i++) {
int biggestChar = 0;
motif[i] = 'A';
if(isValInArray(i, bestDontCares)) {
motif[i] = '*';
continue;
}
if(profile[1][i+1] > profile[biggestChar][i+1]) {
biggestChar = 1;
motif[i] = 'C';
}
if(profile[2][i+1] > profile[biggestChar][i+1]) {
biggestChar = 2;
motif[i] = 'G';
}
if(profile[3][i+1] > profile[biggestChar][i+1]) {
biggestChar = 3;
motif[i] = 'T';
}
}
//print results
motif[k] = '\0';
printf("Best motif of length %d with %d don't cares is %s\n", k, d, motif);
printf("Log likelihood is %lf\n", bestLogScore);
printf("Loci of the best motif are here:\n");
for(int i = 0; i < totalSequences; i++) {
printf("%d\n", startIndices[i]);
free(sequences[i]);
}
//clean up heap
for(int i = 0; i < 4; i++) {
free(profile[i]);
}
free(dontCares);
free(bestDontCares);
free(motif);
return 0;
}