GCBias::GCBias(const char* ref_filename, PosTable& foreground_position_table,
pos_t median_frag_len,
sequencing_bias* seqbias[2],
const char* task_name)
{
faidx_t* ref_file = fai_load(ref_filename);
if (!ref_file) {
Logger::abort("Can't open fasta file '%s'.", ref_filename);
}
std::vector<ReadPos> foreground_positions;
const size_t max_dump = 10000000;
foreground_position_table.dump(foreground_positions, max_dump);
std::sort(foreground_positions.begin(), foreground_positions.end(), ReadPosSeqnameCmp());
Logger::push_task(task_name, foreground_positions.size());
LoggerTask& task = Logger::get_task(task_name);
typedef std::pair<float, float> WeightedGC;
std::vector<WeightedGC> foreground_gc, background_gc;
int seqlen = 0;
SeqName curr_seqname;
char* seq = NULL;
twobitseq tbseq;
twobitseq tbseqrc;
rng_t rng;
pos_t L = seqbias[0] ? seqbias[0]->getL() : 0;
std::vector<ReadPos>::iterator i;
for (i = foreground_positions.begin(); i != foreground_positions.end(); ++i) {
if (i->seqname != curr_seqname) {
free(seq);
seq = faidx_fetch_seq(ref_file, i->seqname.get().c_str(), 0, INT_MAX, &seqlen);
Logger::debug("read sequence %s.", i->seqname.get().c_str());
if (seq == NULL) {
Logger::warn("warning: reference sequence not found, skipping.");
}
else {
for (char* c = seq; *c; c++) *c = tolower(*c);
tbseq = seq;
tbseqrc = tbseq;
tbseqrc.revcomp();
}
curr_seqname = i->seqname;
}
if (seq == NULL || (pos_t) tbseq.size() < median_frag_len) continue;
// fragments with many copies tend to have too much weight when training
// leading to somewhat less than stable results.
if (i->count > 4) continue;
// sample background position
boost::random::uniform_int_distribution<pos_t> random_uniform(
i->start + L, i->end - median_frag_len);
pos_t pos = random_uniform(rng);
float gc = (float) gc_count(seq + pos, median_frag_len) / median_frag_len;
float sb = seqbias[0] ?
seqbias[0]->get_bias(tbseq, pos - L) *
seqbias[1]->get_bias(tbseqrc, seqlen - pos - 1 - L) : 1.0;
background_gc.push_back(WeightedGC(gc, 1.0 / sb));
// sample foreground position
if (i->strand == 0) {
if (i->pos >= i->start && i->pos + median_frag_len - 1 <= i->end) {
float sb = seqbias[0] ?
seqbias[0]->get_bias(tbseq, i->pos - L) *
seqbias[1]->get_bias(tbseqrc, seqlen - i->pos - 1 - L) : 1.0;
foreground_gc.push_back(
WeightedGC((float) gc_count(seq + i->pos, median_frag_len) / median_frag_len,
1.0 / sb));
}
} else {
if (i->pos - median_frag_len >= i->start && i->pos <= i->end) {
float sb = seqbias[0] ?
seqbias[0]->get_bias(tbseq, i->pos - median_frag_len - L) *
seqbias[1]->get_bias(tbseqrc, seqlen - i->pos - median_frag_len - 1 - L) : 1.0;
foreground_gc.push_back(
WeightedGC((float) gc_count(seq + i->pos - median_frag_len, median_frag_len) / median_frag_len,
1.0 /sb));
}
}
task.inc();
}
free(seq);
fai_destroy(ref_file);
#if 0
FILE* out = fopen("gcbias.tsv", "w");
fprintf(out, "group\tgc\tweight\n");
BOOST_FOREACH (WeightedGC& value, foreground_gc) {
fprintf(out, "foreground\t%f\t%f\n", (double) value.first, (double) value.second);
}
void sequencing_bias::build(const char* ref_fn,
PosTable& T,
size_t max_reads,
pos_t L, pos_t R,
const char* task_name,
double complexity_penalty)
{
Logger::push_task(task_name);
clear();
const size_t min_positions = 1000;
if (T.size() < min_positions) return;
this->ref_fn = ref_fn;
this->L = L;
this->R = R;
const size_t max_dump = 10000000;
std::vector<ReadPos> S;
S.reserve(max_dump);
T.dump(S, max_dump);
/* sort by tid (so we can load one chromosome at a time) */
random_shuffle(S.begin(), S.end());
sort(S.begin(), S.end(), ReadPosSeqnameCmp());
//sort(S.begin(), S.end(), ReadPosCountCmp());
//sort(S.begin(), S.begin() + max_reads, ReadPosSeqnameCmp());
/* sample foreground and background kmer frequencies */
ref_f = fai_load(ref_fn);
if (ref_f == NULL) {
Logger::abort("Can't open fasta file '%s'.", ref_fn);
}
std::deque<twobitseq*> foreground_seqs;
std::deque<twobitseq*> background_seqs;
/* background sampling */
int bg_samples = 1; // make this many samples for each read
int bg_sample_num; // keep track of the number of samples made
pos_t bg_pos;
int seqlen = 0;
SeqName curr_seqname;
char* seq = NULL;
char* local_seq;
local_seq = new char[ L + R + 2 ];
local_seq[L+R+1] = '\0';
std::vector<ReadPos>::iterator i;
for (i = S.begin(); i != S.end() && i != S.begin() + max_reads; ++i) {
/* Load/switch sequences (chromosomes) as they are encountered in the
* read stream. The idea here is to avoid thrashing by loading a large
* sequence, but also avoid overloading memory by only loading one
* chromosome at a time. */
if (i->seqname != curr_seqname) {
if (seq) free(seq);
seq = faidx_fetch_seq(ref_f, i->seqname.get().c_str(), 0, INT_MAX, &seqlen);
Logger::debug("read sequence %s.", i->seqname.get().c_str());
if (seq == NULL) {
Logger::warn("warning: reference sequence not found, skipping.");
}
else {
for (char* c = seq; *c; c++) *c = tolower(*c);
}
curr_seqname = i->seqname;
}
if (seq == NULL) continue;
/* add a foreground sequence */
if (i->strand == strand_neg) {
if (i->pos < R || i->pos >= seqlen - L) continue;
memcpy(local_seq, seq + i->pos - R, (L+1+R)*sizeof(char));
seqrc(local_seq, L+1+R);
}
else {
if (i->pos < L || i->pos >= seqlen - R) continue;
memcpy(local_seq, seq + (i->pos - L), (L+1+R)*sizeof(char));
}
if (strchr(local_seq, 'n') != NULL) continue;
foreground_seqs.push_back(new twobitseq(local_seq));
/* add a background sequence */
/* adjust the current read position randomly, and sample */
for (bg_sample_num = 0; bg_sample_num < bg_samples;) {
random_uniform_int.param(
boost::random::uniform_int_distribution<pos_t>::param_type(i->start, i->end));
bg_pos = random_uniform_int(rng);
if (i->strand == strand_neg) {
if (bg_pos < R || bg_pos >= seqlen - L) continue;
memcpy(local_seq, seq + bg_pos - R, (L+1+R)*sizeof(char));
seqrc(local_seq, L+1+R);
}
else {
if (bg_pos < L || bg_pos >= seqlen - R) continue;
memcpy(local_seq, seq + (bg_pos-L), (L+1+R)*sizeof(char));
}
if (strchr(local_seq, 'n') != NULL) continue;
background_seqs.push_back(new twobitseq(local_seq));
bg_sample_num++;
}
}
size_t max_parents = 4;
size_t max_distance = 10;
/* A bit of a hack: if we are training on very few reads (a couple thousand,
* as a opposed to tens of thousands), we tend to end up with too sparse of
* a model. */
if (foreground_seqs.size() < 10000) complexity_penalty = 0.25;
M = new motif(background_seqs,
foreground_seqs,
L + 1 + R,
max_parents,
max_distance,
complexity_penalty,
task_name);
std::deque<twobitseq*>::iterator seqit;
for (seqit = background_seqs.begin(); seqit != background_seqs.end(); seqit++) {
delete *seqit;
}
for (seqit = foreground_seqs.begin(); seqit != foreground_seqs.end(); seqit++) {
delete *seqit;
}
free(seq);
delete [] local_seq;
Logger::pop_task(task_name);
}
