// Test CpG sites in this read for methylation
void calculate_methylation_for_read(const ModelMap& model_map,
const Fast5Map& name_map,
const faidx_t* fai,
const bam_hdr_t* hdr,
const bam1_t* record,
size_t read_idx,
const OutputHandles& handles)
{
// Load a squiggle read for the mapped read
std::string read_name = bam_get_qname(record);
std::string fast5_path = name_map.get_path(read_name);
SquiggleRead sr(read_name, fast5_path);
// An output map from reference positions to scored CpG sites
std::map<int, ScoredSite> site_score_map;
for(size_t strand_idx = 0; strand_idx < NUM_STRANDS; ++strand_idx) {
std::vector<double> site_scores;
std::vector<int> site_starts;
std::vector<int> site_ends;
std::vector<int> site_count;
// replace the baked-in pore model with the methylation model
// (including unmethylated kmers) for this strand
std::string curr_model = sr.pore_model[strand_idx].name;
std::string methyl_model = curr_model + ".ecoli_er2925.pcr_MSssI.timp.021216.alphabet_cpg.model";
auto model_iter = model_map.find(methyl_model);
if(model_iter != model_map.end()) {
sr.pore_model[strand_idx].update_states( model_iter->second );
} else {
fprintf(stderr, "Error, methylated model %s not found\n", methyl_model.c_str());
exit(EXIT_FAILURE);
}
size_t k = sr.pore_model[strand_idx].k;
// Align in event space using the new model
EventAlignmentParameters params;
params.sr = &sr;
params.fai = fai;
params.hdr = hdr;
params.record = record;
params.strand_idx = strand_idx;
params.read_idx = read_idx;
params.alphabet = mtest_alphabet;
std::vector<EventAlignment> alignment_output = align_read_to_ref(params);
if(alignment_output.empty())
continue;
std::string contig = alignment_output.front().ref_name.c_str();
// Convert the EventAlignment to a map between reference positions and events
std::vector<AlignedPair> event_aligned_pairs;
for(size_t i = 0; i < alignment_output.size(); ++i) {
AlignedPair ap = { alignment_output[i].ref_position,
alignment_output[i].event_idx };
event_aligned_pairs.push_back(ap);
}
int ref_start_pos = event_aligned_pairs.front().ref_pos;
int ref_end_pos = event_aligned_pairs.back().ref_pos;
// Extract the reference sequence for this region
int fetched_len = 0;
assert(ref_end_pos >= ref_start_pos);
std::string ref_seq = get_reference_region_ts(params.fai, contig.c_str(), ref_start_pos,
ref_end_pos, &fetched_len);
// Remove non-ACGT bases from this reference segment
ref_seq = gDNAAlphabet.disambiguate(ref_seq);
// Scan the sequence for CpGs
std::vector<int> cpg_sites;
assert(ref_seq.size() != 0);
for(size_t i = 0; i < ref_seq.size() - 1; ++i) {
if(ref_seq[i] == 'C' && ref_seq[i+1] == 'G') {
cpg_sites.push_back(i);
}
}
// Batch the CpGs together into groups that are separated by some minimum distance
int min_separation = 10;
size_t curr_idx = 0;
while(curr_idx < cpg_sites.size()) {
// Find the endpoint of this group of sites
size_t end_idx = curr_idx + 1;
while(end_idx < cpg_sites.size()) {
if(cpg_sites[end_idx] - cpg_sites[end_idx - 1] > min_separation)
break;
end_idx += 1;
}
// the coordinates on the reference substring for this group of sites
int sub_start_pos = cpg_sites[curr_idx] - min_separation;
int sub_end_pos = cpg_sites[end_idx - 1] + min_separation;
if(sub_start_pos > min_separation && cpg_sites[end_idx - 1] - cpg_sites[curr_idx] < 200) {
std::string subseq = ref_seq.substr(sub_start_pos, sub_end_pos - sub_start_pos + 1);
std::string rc_subseq = mtest_alphabet->reverse_complement(subseq);
// using the reference-to-event map, look up the event indices for this segment
AlignedPairRefLBComp lb_comp;
AlignedPairConstIter start_iter = std::lower_bound(event_aligned_pairs.begin(), event_aligned_pairs.end(),
sub_start_pos + ref_start_pos, lb_comp);
AlignedPairConstIter stop_iter = std::lower_bound(event_aligned_pairs.begin(), event_aligned_pairs.end(),
sub_end_pos + ref_start_pos, lb_comp);
// Only process this region if the the read is aligned within the boundaries
// and the span between the start/end is not unusually short
if(start_iter != event_aligned_pairs.end() && stop_iter != event_aligned_pairs.end() &&
abs(start_iter->read_pos - stop_iter->read_pos) > 10)
{
uint32_t hmm_flags = HAF_ALLOW_PRE_CLIP | HAF_ALLOW_POST_CLIP;
// Set up event data
HMMInputData data;
data.read = &sr;
data.anchor_index = -1; // unused
data.strand = strand_idx;
data.rc = alignment_output.front().rc;
data.event_start_idx = start_iter->read_pos;
data.event_stop_idx = stop_iter->read_pos;
data.event_stride = data.event_start_idx <= data.event_stop_idx ? 1 : -1;
// Calculate the likelihood of the unmethylated sequence
HMMInputSequence unmethylated(subseq, rc_subseq, mtest_alphabet);
double unmethylated_score = profile_hmm_score(unmethylated, data, hmm_flags);
// Methylate all CpGs in the sequence and score again
std::string mcpg_subseq = mtest_alphabet->methylate(subseq);
std::string rc_mcpg_subseq = mtest_alphabet->reverse_complement(mcpg_subseq);
// Calculate the likelihood of the methylated sequence
HMMInputSequence methylated(mcpg_subseq, rc_mcpg_subseq, mtest_alphabet);
double methylated_score = profile_hmm_score(methylated, data, hmm_flags);
// Aggregate score
int start_position = cpg_sites[curr_idx] + ref_start_pos;
auto iter = site_score_map.find(start_position);
if(iter == site_score_map.end()) {
// insert new score into the map
ScoredSite ss;
ss.chromosome = contig;
ss.start_position = start_position;
ss.end_position = cpg_sites[end_idx - 1] + ref_start_pos;
ss.n_cpg = end_idx - curr_idx;
// extract the CpG site(s) with a k-mers worth of surrounding context
size_t site_output_start = cpg_sites[curr_idx] - k + 1;
size_t site_output_end = cpg_sites[end_idx - 1] + k;
ss.sequence = ref_seq.substr(site_output_start, site_output_end - site_output_start);
// insert into the map
iter = site_score_map.insert(std::make_pair(start_position, ss)).first;
}
// set strand-specific score
// upon output below the strand scores will be summed
iter->second.ll_unmethylated[strand_idx] = unmethylated_score;
iter->second.ll_methylated[strand_idx] = methylated_score;
}
}
curr_idx = end_idx;
}
} // for strands
#pragma omp critical(methyltest_write)
{
// these variables are sums over all sites within a read
double ll_ratio_sum_strand[2] = { 0.0f, 0.0f };
double ll_ratio_sum_both = 0;
size_t num_positive = 0;
// write all sites for this read
for(auto iter = site_score_map.begin(); iter != site_score_map.end(); ++iter) {
const ScoredSite& ss = iter->second;
double sum_ll_m = ss.ll_methylated[0] + ss.ll_methylated[1];
double sum_ll_u = ss.ll_unmethylated[0] + ss.ll_unmethylated[1];
double diff = sum_ll_m - sum_ll_u;
num_positive += diff > 0;
fprintf(handles.site_writer, "%s\t%d\t%d\t", ss.chromosome.c_str(), ss.start_position, ss.end_position);
fprintf(handles.site_writer, "ReadIdx=%zu;", read_idx);
fprintf(handles.site_writer, "LogLikMeth=%.2lf;LogLikUnmeth=%.2lf;LogLikRatio=%.2lf;", sum_ll_m, sum_ll_u, diff);
fprintf(handles.site_writer, "LogLikMethByStrand=%.2lf,%.2lf;", ss.ll_methylated[0], ss.ll_methylated[1]);
fprintf(handles.site_writer, "LogLikUnmethByStrand=%.2lf,%.2lf;", ss.ll_unmethylated[0], ss.ll_unmethylated[1]);
fprintf(handles.site_writer, "NumCpGs=%d;Sequence=%s\n", ss.n_cpg, ss.sequence.c_str());
ll_ratio_sum_strand[0] += ss.ll_methylated[0] - ss.ll_unmethylated[0];
ll_ratio_sum_strand[1] += ss.ll_methylated[1] - ss.ll_unmethylated[1];
ll_ratio_sum_both += diff;
}
std::string complement_model = sr.pore_model[C_IDX].name;
fprintf(handles.read_writer, "%s\t%.2lf\t%zu\t%s\tNumPositive=%zu\n", fast5_path.c_str(), ll_ratio_sum_both, site_score_map.size(), complement_model.c_str(), num_positive);
for(size_t si = 0; si < NUM_STRANDS; ++si) {
std::string model = sr.pore_model[si].name;
fprintf(handles.strand_writer, "%s\t%.2lf\t%zu\t%s\n", fast5_path.c_str(), ll_ratio_sum_strand[si], site_score_map.size(), model.c_str());
}
}
}
// Update the training data with aligned events from a read
void add_aligned_events(const Fast5Map& name_map,
const faidx_t* fai,
const bam_hdr_t* hdr,
const bam1_t* record,
size_t read_idx,
int region_start,
int region_end,
size_t round,
ModelTrainingMap& training)
{
// Load a squiggle read for the mapped read
std::string read_name = bam_get_qname(record);
std::string fast5_path = name_map.get_path(read_name);
// load read
SquiggleRead sr(read_name, fast5_path);
// replace the models that are built into the read with the current trained model
sr.replace_models(opt::trained_model_type);
for(size_t strand_idx = 0; strand_idx < NUM_STRANDS; ++strand_idx) {
// skip if 1D reads and this is the wrong strand
if(!sr.has_events_for_strand(strand_idx)) {
continue;
}
// set k
uint32_t k = sr.pore_model[strand_idx].k;
// Align to the new model
EventAlignmentParameters params;
params.sr = &sr;
params.fai = fai;
params.hdr = hdr;
params.record = record;
params.strand_idx = strand_idx;
params.alphabet = mtrain_alphabet;
params.read_idx = read_idx;
params.region_start = region_start;
params.region_end = region_end;
std::vector<EventAlignment> alignment_output = align_read_to_ref(params);
if (alignment_output.size() == 0)
return;
// Update pore model based on alignment
std::string curr_model = sr.pore_model[strand_idx].metadata.get_short_name();
double orig_score = -INFINITY;
if (opt::output_scores) {
orig_score = model_score(sr, strand_idx, fai, alignment_output, 500, NULL);
#pragma omp critical(print)
std::cout << round << " " << curr_model << " " << read_idx << " " << strand_idx << " Original " << orig_score << std::endl;
}
if ( opt::calibrate ) {
double resid = 0.;
recalibrate_model(sr, strand_idx, alignment_output, mtrain_alphabet, resid, true);
if (opt::output_scores) {
double rescaled_score = model_score(sr, strand_idx, fai, alignment_output, 500, NULL);
#pragma omp critical(print)
{
std::cout << round << " " << curr_model << " " << read_idx << " " << strand_idx << " Rescaled " << rescaled_score << std::endl;
std::cout << round << " " << curr_model << " " << read_idx << " " << strand_idx << " Delta " << rescaled_score-orig_score << std::endl;
}
}
}
// Get the training data for this model
auto& emission_map = training[curr_model];
for(size_t i = 0; i < alignment_output.size(); ++i) {
const EventAlignment& ea = alignment_output[i];
std::string model_kmer = ea.model_kmer;
// Grab the previous/next model kmer from the alignment_output table.
// If the read is from the same strand as the reference
// the next kmer comes from the next alignment_output (and vice-versa)
// other the indices are swapped
int next_stride = ea.rc ? -1 : 1;
std::string prev_kmer = "";
std::string next_kmer = "";
if(i > 0 && i < alignment_output.size() - 1) {
// check that the event indices are correct for the next expected position
assert(alignment_output[i + next_stride].event_idx - ea.event_idx == 1);
assert(alignment_output[i - next_stride].event_idx - ea.event_idx == -1);
// only set the previous/next when there was exactly one base of movement along the referenc
if( std::abs(alignment_output[i + next_stride].ref_position - ea.ref_position) == 1) {
next_kmer = alignment_output[i + next_stride].model_kmer;
}
if( std::abs(alignment_output[i - next_stride].ref_position - ea.ref_position) == 1) {
prev_kmer = alignment_output[i - next_stride].model_kmer;
}
}
// Get the rank of the kmer that we aligned to (on the sequencing strand, = model_kmer)
uint32_t rank = mtrain_alphabet->kmer_rank(model_kmer.c_str(), k);
assert(rank < emission_map.size());
auto& kmer_summary = emission_map[rank];
// We only use this event for training if its not at the end of the alignment
// (to avoid bad alignments around the read edges) and if its not too short (to
// avoid bad measurements from effecting the levels too much)
bool use_for_training = i > opt::min_distance_from_alignment_end &&
i + opt::min_distance_from_alignment_end < alignment_output.size() &&
alignment_output[i].hmm_state == 'M' &&
sr.get_duration( alignment_output[i].event_idx, strand_idx) >= opt::min_event_duration &&
sr.get_fully_scaled_level(alignment_output[i].event_idx, strand_idx) >= 1.0;
if(use_for_training) {
StateTrainingData std(sr, ea, rank, prev_kmer, next_kmer);
#pragma omp critical(kmer)
kmer_summary.events.push_back(std);
}
if(ea.hmm_state == 'M') {
#pragma omp atomic
kmer_summary.num_matches += 1;
} else if(ea.hmm_state == 'E') {
#pragma omp atomic
kmer_summary.num_stays += 1;
}
}
} // for strands
}