// Write an ascii pictogram of an overlap
// O = overhang of length block_size (region of no overlap)
// P = perfect match of length block_size
// I = matched block with mismatches
std::string ascii_overlap(const std::string& s0, const std::string& s1,
SequenceOverlap& ovr, int block_size = 50)
{
std::string out;
int pre_overhang = ovr.match[0].start / block_size;
int post_overhang = (ovr.length[0] - ovr.match[0].end - 1) / block_size;
std::string p0;
std::string p1;
ovr.makePaddedMatches(s0, s1, &p0, &p1);
out.append(pre_overhang, '-');
for(size_t i = 0; i < p0.size(); i += block_size)
{
if(p0.substr(i, block_size) == p1.substr(i, block_size))
out.append(1, '=');
else
out.append(1, 'x');
}
out.append(post_overhang, '-');
return out;
}
SequenceOverlapPairVector KmerOverlaps::retrieveMatches(const std::string& query, size_t k,
int min_overlap, double min_identity,
int bandwidth, const BWTIndexSet& indices)
{
PROFILE_FUNC("OverlapHaplotypeBuilder::retrieveMatches")
assert(indices.pBWT != NULL);
assert(indices.pSSA != NULL);
int64_t max_interval_size = 200;
SequenceOverlapPairVector overlap_vector;
// Use the FM-index to look up intervals for each kmer of the read. Each index
// in the interval is stored individually in the KmerMatchMap. We then
// backtrack to map these kmer indices to read IDs. As reads can share
// multiple kmers, we use the map to avoid redundant lookups.
// There is likely a faster algorithm which performs direct decompression
// of the read sequences without having to expand the intervals to individual
// indices. The current algorithm suffices for now.
KmerMatchMap prematchMap;
size_t num_kmers = query.size() - k + 1;
for(size_t i = 0; i < num_kmers; ++i)
{
std::string kmer = query.substr(i, k);
BWTInterval interval = BWTAlgorithms::findInterval(indices, kmer);
if(interval.isValid() && interval.size() < max_interval_size)
{
for(int64_t j = interval.lower; j <= interval.upper; ++j)
{
KmerMatch match = { i, static_cast<size_t>(j), false };
prematchMap.insert(std::make_pair(match, false));
}
}
kmer = reverseComplement(kmer);
interval = BWTAlgorithms::findInterval(indices, kmer);
if(interval.isValid() && interval.size() < max_interval_size)
{
for(int64_t j = interval.lower; j <= interval.upper; ++j)
{
KmerMatch match = { i, static_cast<size_t>(j), true };
prematchMap.insert(std::make_pair(match, false));
}
}
}
// Backtrack through the kmer indices to turn them into read indices.
// This mirrors the calcSA function in SampledSuffixArray except we mark each entry
// as visited once it is processed.
KmerMatchSet matches;
for(KmerMatchMap::iterator iter = prematchMap.begin(); iter != prematchMap.end(); ++iter)
{
// This index has been visited
if(iter->second)
continue;
// Mark this as visited
iter->second = true;
// Backtrack the index until we hit the starting symbol
KmerMatch out_match = iter->first;
while(1)
{
char b = indices.pBWT->getChar(out_match.index);
out_match.index = indices.pBWT->getPC(b) + indices.pBWT->getOcc(b, out_match.index - 1);
// Check if the hash indicates we have visited this index. If so, stop the backtrack
KmerMatchMap::iterator find_iter = prematchMap.find(out_match);
if(find_iter != prematchMap.end())
{
// We have processed this index already
if(find_iter->second)
break;
else
find_iter->second = true;
}
if(b == '$')
{
// We've found the lexicographic index for this read. Turn it into a proper ID
out_match.index = indices.pSSA->lookupLexoRank(out_match.index);
matches.insert(out_match);
break;
}
}
}
// Refine the matches by computing proper overlaps between the sequences
// Use the overlaps that meet the thresholds to build a multiple alignment
for(KmerMatchSet::iterator iter = matches.begin(); iter != matches.end(); ++iter)
{
std::string match_sequence = BWTAlgorithms::extractString(indices.pBWT, iter->index);
if(iter->is_reverse)
match_sequence = reverseComplement(match_sequence);
// Ignore identical matches
if(match_sequence == query)
continue;
// Compute the overlap. If the kmer match occurs a single time in each sequence we use
// the banded extension overlap strategy. Otherwise we use the slow O(M*N) overlapper.
SequenceOverlap overlap;
std::string match_kmer = query.substr(iter->position, k);
size_t pos_0 = query.find(match_kmer);
size_t pos_1 = match_sequence.find(match_kmer);
assert(pos_0 != std::string::npos && pos_1 != std::string::npos);
// Check for secondary occurrences
if(query.find(match_kmer, pos_0 + 1) != std::string::npos ||
match_sequence.find(match_kmer, pos_1 + 1) != std::string::npos) {
// One of the reads has a second occurrence of the kmer. Use
// the slow overlapper.
overlap = Overlapper::computeOverlap(query, match_sequence);
} else {
overlap = Overlapper::extendMatch(query, match_sequence, pos_0, pos_1, bandwidth);
}
bool bPassedOverlap = overlap.getOverlapLength() >= min_overlap;
bool bPassedIdentity = overlap.getPercentIdentity() / 100 >= min_identity;
if(bPassedOverlap && bPassedIdentity)
{
SequenceOverlapPair op;
op.sequence[0] = query;
op.sequence[1] = match_sequence;
op.overlap = overlap;
op.is_reversed = iter->is_reverse;
overlap_vector.push_back(op);
}
}
return overlap_vector;
}
// Align the haplotype to the reference genome represented by the BWT/SSA pair
void HapgenUtil::alignHaplotypeToReferenceKmer(size_t k,
const std::string& haplotype,
const BWTIndexSet& referenceIndex,
const ReadTable* pReferenceTable,
HapgenAlignmentVector& outAlignments)
{
PROFILE_FUNC("HapgenUtil::alignHaplotypesToReferenceKmer")
int64_t max_interval_size = 4;
if(haplotype.size() < k)
return;
std::vector<int> event_count_vector;
std::vector<HapgenAlignment> tmp_alignments;
int min_events = std::numeric_limits<int>::max();
// Align forward and reverse haplotype to reference
for(size_t i = 0; i <= 1; ++i)
{
bool is_reverse = i == 1;
std::string query = is_reverse ? reverseComplement(haplotype) : haplotype;
// Find shared kmers between the haplotype and the reference
CandidateVector candidates;
size_t nqk = query.size() - k + 1;
for(size_t j = 0; j < nqk; ++j)
{
std::string kmer = query.substr(j, k);
// Find the interval of this kmer in the reference
BWTInterval interval = BWTAlgorithms::findInterval(referenceIndex, kmer);
if(!interval.isValid() || interval.size() >= max_interval_size)
continue; // not found or too repetitive
// Extract the reference location of these hits
for(int64_t k = interval.lower; k <= interval.upper; ++k)
{
SAElem elem = referenceIndex.pSSA->calcSA(k, referenceIndex.pBWT);
// Make a candidate alignment
CandidateKmerAlignment candidate;
candidate.query_index = j;
candidate.target_index = elem.getPos();
candidate.target_extrapolated_start = candidate.target_index - candidate.query_index;
candidate.target_extrapolated_end = candidate.target_extrapolated_start + query.size();
candidate.target_sequence_id = elem.getID();
candidates.push_back(candidate);
}
}
// Remove duplicate candidates
std::sort(candidates.begin(), candidates.end(), CandidateKmerAlignment::sortByStart);
CandidateVector::iterator new_end = std::unique(candidates.begin(), candidates.end(), CandidateKmerAlignment::equalByStart);
candidates.resize(new_end - candidates.begin());
for(size_t j = 0; j < candidates.size(); ++j)
{
// Extract window around reference
size_t window_size = 200;
int ref_start = candidates[j].target_extrapolated_start - window_size;
int ref_end = candidates[j].target_extrapolated_end + window_size;
const SeqItem& ref_record = pReferenceTable->getRead(candidates[j].target_sequence_id);
const DNAString& ref_sequence = ref_record.seq;
if(ref_start < 0)
ref_start = 0;
if(ref_end > (int)ref_sequence.length())
ref_end = ref_sequence.length();
std::string ref_substring = ref_sequence.substr(ref_start, ref_end - ref_start);
// Align haplotype to the reference
SequenceOverlap overlap = alignHaplotypeToReference(ref_substring, query);
if(overlap.score < 0 || !overlap.isValid())
continue;
int alignment_start = ref_start + overlap.match[0].start;
int alignment_end = ref_start + overlap.match[0].end; // inclusive
int alignment_length = alignment_end - alignment_start + 1;
// Crude count of the number of distinct variation events
bool has_indel = false;
int num_events = overlap.edit_distance;
std::stringstream c_parser(overlap.cigar);
int len;
char t;
while(c_parser >> len >> t)
{
assert(len > 0);
// Only count one event per insertion/deletion
if(t == 'D' || t == 'I')
{
num_events -= (len - 1);
has_indel = true;
}
}
// Skip poor alignments
double mismatch_rate = 1.0f - (overlap.getPercentIdentity() / 100.f);
if(mismatch_rate > 0.05f || overlap.total_columns < 50)
{
if(Verbosity::Instance().getPrintLevel() > 4)
{
printf("Haplotype Alignment - Ignoring low quality alignment (%.3lf, %dbp, %d events) to %s:%d\n",
1.0f - mismatch_rate, overlap.total_columns, num_events, ref_record.id.c_str(), ref_start);
}
continue;
}
bool is_snp = !has_indel && overlap.edit_distance == 1;
HapgenAlignment aln(candidates[j].target_sequence_id,
alignment_start,
alignment_length,
overlap.score,
num_events,
is_reverse,
is_snp);
tmp_alignments.push_back(aln);
event_count_vector.push_back(num_events);
if(Verbosity::Instance().getPrintLevel() > 4)
{
printf("Haplotype Alignment - Accepting alignment (%.3lf, %dbp, %d events) to %s:%d\n",
1.0f - mismatch_rate, overlap.total_columns, num_events, ref_record.id.c_str(), ref_start);
}
// Record the best edit distance
if(num_events < min_events)
min_events = num_events;
}
}
// Copy the best alignments into the output
int MAX_DIFF_TO_BEST = 10;
int MAX_EVENTS = 8;
assert(event_count_vector.size() == tmp_alignments.size());
for(size_t i = 0; i < event_count_vector.size(); ++i)
{
if(event_count_vector[i] <= MAX_EVENTS && event_count_vector[i] - min_events <= MAX_DIFF_TO_BEST)
outAlignments.push_back(tmp_alignments[i]);
else if(Verbosity::Instance().getPrintLevel() > 3)
printf("Haplotype Alignment - Ignoring alignment with too many events (%d)\n", event_count_vector[i]);
}
}
// Align the haplotype to the reference genome represented by the BWT/SSA pair
void HapgenUtil::alignHaplotypeToReferenceKmer(size_t k,
const std::string& haplotype,
const BWTIndexSet& referenceIndex,
const ReadTable* pReferenceTable,
HapgenAlignmentVector& outAlignments)
{
PROFILE_FUNC("HapgenUtil::alignHaplotypesToReferenceKmer")
int64_t max_interval_size = 4;
if(haplotype.size() < k)
return;
std::vector<int> event_count_vector;
std::vector<HapgenAlignment> tmp_alignments;
int min_events = std::numeric_limits<int>::max();
// Align forward and reverse haplotype to reference
for(size_t i = 0; i <= 1; ++i)
{
bool is_reverse = i == 1;
std::string query = is_reverse ? reverseComplement(haplotype) : haplotype;
// Find shared kmers between the haplotype and the reference
CandidateVector candidates;
size_t nqk = query.size() - k + 1;
for(size_t j = 0; j < nqk; ++j)
{
std::string kmer = query.substr(j, k);
// Find the interval of this kmer in the reference
BWTInterval interval = BWTAlgorithms::findInterval(referenceIndex, kmer);
if(!interval.isValid() || interval.size() >= max_interval_size)
continue; // not found or too repetitive
// Extract the reference location of these hits
for(int64_t k = interval.lower; k <= interval.upper; ++k)
{
SAElem elem = referenceIndex.pSSA->calcSA(k, referenceIndex.pBWT);
// Make a candidate alignment
CandidateKmerAlignment candidate;
candidate.query_index = j;
candidate.target_index = elem.getPos();
candidate.target_extrapolated_start = candidate.target_index - candidate.query_index;
candidate.target_extrapolated_end = candidate.target_extrapolated_start + query.size();
candidate.target_sequence_id = elem.getID();
candidates.push_back(candidate);
}
}
// Remove duplicate candidates
std::sort(candidates.begin(), candidates.end(), CandidateKmerAlignment::sortByStart);
CandidateVector::iterator new_end = std::unique(candidates.begin(), candidates.end(), CandidateKmerAlignment::equalByStart);
candidates.resize(new_end - candidates.begin());
for(size_t j = 0; j < candidates.size(); ++j)
{
// Extract window around reference
size_t window_size = 200;
int ref_start = candidates[j].target_extrapolated_start - window_size;
int ref_end = candidates[j].target_extrapolated_end + window_size;
const DNAString& ref_sequence = pReferenceTable->getRead(candidates[j].target_sequence_id).seq;
if(ref_start < 0)
ref_start = 0;
if(ref_end > (int)ref_sequence.length())
ref_end = ref_sequence.length();
std::string ref_substring = ref_sequence.substr(ref_start, ref_end - ref_start);
// Align haplotype to the reference
SequenceOverlap overlap = Overlapper::computeOverlap(query, ref_substring);
// Skip terrible alignments
double percent_aligned = (double)overlap.getOverlapLength() / query.size();
if(percent_aligned < 0.95f)
continue;
/*
// Skip alignments that are not full-length matches of the haplotype
if(overlap.match[0].start != 0 || overlap.match[0].end != (int)haplotype.size() - 1)
continue;
*/
int alignment_start = ref_start + overlap.match[1].start;
int alignment_end = ref_start + overlap.match[1].end; // inclusive
int alignment_length = alignment_end - alignment_start + 1;
// Crude count of the number of distinct variation events
int num_events = overlap.edit_distance;
std::stringstream c_parser(overlap.cigar);
int len;
char t;
while(c_parser >> len >> t)
{
assert(len > 0);
// Only count one event per insertion/deletion
if(t == 'D' || t == 'I')
num_events -= (len - 1);
}
HapgenAlignment aln(candidates[j].target_sequence_id, alignment_start, alignment_length, overlap.score, is_reverse);
tmp_alignments.push_back(aln);
event_count_vector.push_back(num_events);
// Record the best edit distance
if(num_events < min_events)
min_events = num_events;
}
}
// Copy the best alignments into the output
int MAX_DIFF_TO_BEST = 10;
int MAX_EVENTS = 8;
assert(event_count_vector.size() == tmp_alignments.size());
for(size_t i = 0; i < event_count_vector.size(); ++i)
{
if(event_count_vector[i] <= MAX_EVENTS && event_count_vector[i] - min_events <= MAX_DIFF_TO_BEST)
outAlignments.push_back(tmp_alignments[i]);
}
}
void OverlapExtractorWithCorrection::getRawOverlapsCached(const std::string& query, bool is_reverse, SequenceOverlapPairVector* out_vector)
{
PROFILE_FUNC("OverlapExtractorWithCorrection::getRawOverlaps")
size_t nk = query.size() - m_k + 1;
std::set<std::string> sequences_seen;
for(size_t i = 0; i < nk; ++i)
{
std::string kmer = query.substr(i, m_k);
std::string q_kmer = is_reverse ? reverseComplement(kmer) : kmer;
const std::vector<size_t>& indices = m_cache_map.find(q_kmer)->second;
for(size_t j = 0; j < indices.size(); ++j)
{
size_t index = indices[j];
std::string match_sequence = m_strings[index];
if(is_reverse)
match_sequence = reverseComplement(match_sequence);
if(sequences_seen.find(match_sequence) != sequences_seen.end())
continue;
sequences_seen.insert(match_sequence);
// Ignore identical matches
if(match_sequence.empty() || match_sequence == query)
continue;
// Compute the overlap. If the kmer match occurs a single time in each sequence we use
// the banded extension overlap strategy. Otherwise we use the slow O(M*N) overlapper.
SequenceOverlap overlap;
size_t pos_0 = query.find(kmer);
size_t pos_1 = match_sequence.find(kmer);
// If there is a single occurrence of the kmer in each read,
// use that position to seed the overlap calculation
if(pos_0 == std::string::npos ||
pos_1 == std::string::npos ||
query.find(kmer, pos_0 + 1) != std::string::npos ||
match_sequence.find(kmer, pos_1 + 1) != std::string::npos)
{
// One of the reads has a second occurrence of the kmer. Use
// the slow overlapper.
overlap = Overlapper::computeOverlap(query, match_sequence);
} else {
overlap = Overlapper::extendMatch(query, match_sequence, pos_0, pos_1, 2);
}
bool bPassedOverlap = overlap.getOverlapLength() >= m_minOverlap;
bool bPassedIdentity = overlap.getPercentIdentity() / 100 >= m_minIdentity;
if(bPassedOverlap && bPassedIdentity)
{
SequenceOverlapPair op;
op.sequence[0] = query;
op.sequence[1] = match_sequence;
op.overlap = overlap;
op.is_reversed = is_reverse;
out_vector->push_back(op);
}
}
}
}
void _approximateSeededMatch(const std::string& in_query,
int min_overlap,
double min_identity,
int bandwidth,
int max_interval,
bool do_reverse,
const BWTIndexSet& indices,
SequenceOverlapPairVector& out_vector)
{
Timer timer("test", true);
assert(indices.pBWT != NULL);
assert(indices.pSSA != NULL);
assert(indices.pCache != NULL);
static size_t n_calls = 0;
static size_t n_candidates = 0;
static size_t n_output = 0;
static double t_time = 0;
n_calls++;
int target_seed_length = 41;
int seed_stride = target_seed_length / 2;
size_t d = 1;
SequenceOverlapPairVector overlap_vector;
std::string strand_query = do_reverse ? reverseComplement(in_query) : in_query;
// Initialize seeds
int seed_end = strand_query.size();
int q = indices.pCache->getCachedLength();
std::queue<ApproxSeed> seeds;
while(seed_end > target_seed_length)
{
// For the last q bases of the seed, create all strings within edit distance d
std::string qmer = strand_query.substr(seed_end - q, q);
assert((int)qmer.size() == q);
// 0-distance seed
ApproxSeed seed;
seed.query_index = seed_end - q;
seed.interval = indices.pCache->lookup(qmer.c_str());
seed.length = q;
seeds.push(seed);
for(int i = 0; i < q; ++i)
{
// Switch base to other 3 symbols
char o = qmer[i];
for(size_t j = 0; j < 4; ++j)
{
char b = "ACGT"[j];
if(b != o)
{
qmer[i] = b;
ApproxSeed seed;
seed.query_index = seed_end - q;
seed.interval = indices.pCache->lookup(qmer.c_str());
seed.length = q;
seed.edits.push_back(SeedEdit(i + seed.query_index, b));
seeds.push(seed);
}
}
qmer[i] = o;
}
seed_end -= seed_stride;
}
// Extend seeds
std::vector<ApproxSeed> finished_seeds;
while(!seeds.empty())
{
ApproxSeed& seed = seeds.front();
// query_index is the index of the last base
// in the seed. get the next base
char qb = strand_query[seed.query_index - 1];
// Branch to inexact match
if(seed.edits.size() < d)
{
for(size_t j = 0; j < 4; ++j)
{
char b = "ACGT"[j];
if(b != qb)
{
ApproxSeed new_seed;
new_seed.query_index = seed.query_index - 1;
new_seed.interval = seed.interval;
BWTAlgorithms::updateInterval(new_seed.interval, b, indices.pBWT);
if(new_seed.interval.isValid())
{
new_seed.length = seed.length + 1;
new_seed.edits = seed.edits;
new_seed.edits.push_back(SeedEdit(new_seed.query_index, b));
if(new_seed.length < target_seed_length && new_seed.query_index > 0)
seeds.push(new_seed);
else
finished_seeds.push_back(new_seed);
}
}
}
}
// Extend with the actual query base without branching
seed.query_index = seed.query_index - 1;
seed.length += 1;
BWTAlgorithms::updateInterval(seed.interval, qb, indices.pBWT);
if(!seed.interval.isValid() || seed.length >= target_seed_length || seed.query_index == 0)
{
if(seed.interval.isValid())
finished_seeds.push_back(seed);
seeds.pop();
}
}
std::set<size_t> rank_set;
for(size_t i = 0; i < finished_seeds.size(); ++i)
{
if(finished_seeds[i].interval.size() > max_interval)
continue;
//std::cout << finished_seeds[i] << "\n";
std::string query_seed = strand_query.substr(finished_seeds[i].query_index, target_seed_length);
std::string match_seed = query_seed;
// Apply edits to the new sequence
for(size_t j = 0; j < finished_seeds[i].edits.size(); ++j)
match_seed[finished_seeds[i].edits[j].index - finished_seeds[i].query_index] = finished_seeds[i].edits[j].base;
// Flip the seeds to match the strand of the query
if(do_reverse)
{
query_seed = reverseComplement(query_seed);
match_seed = reverseComplement(match_seed);
}
// Extract the prefix of every occurrence of this seed
RankedPrefixVector extensions = BWTAlgorithms::extractRankedPrefixes(indices.pBWT, finished_seeds[i].interval);
// Extend the seeds to the full-length string
for(size_t j = 0; j < extensions.size(); ++j)
{
size_t rank = extensions[j].rank;
// The second element of the returned pair is
// false if the set already contains this rank
if(!rank_set.insert(rank).second)
continue;
// Extract the reminder of the read
std::string& prefix = extensions[j].prefix;
int64_t start_index_of_read = indices.pSSA->lookupLexoRank(rank);
std::string suffix = BWTAlgorithms::extractUntilInterval(indices.pBWT,
start_index_of_read,
finished_seeds[i].interval);
std::string match_sequence = prefix + suffix;
// Ignore identical matches
if(match_sequence == strand_query)
continue;
// Change strands
if(do_reverse)
match_sequence = reverseComplement(match_sequence);
// Compute the overlap
SequenceOverlap overlap;
size_t pos_0 = in_query.find(query_seed);
size_t pos_1 = match_sequence.find(match_seed);
assert(pos_0 != std::string::npos);
assert(pos_1 != std::string::npos);
if(in_query.find(query_seed, pos_0 + 1) != std::string::npos ||
match_sequence.find(match_seed, pos_1 + 1) != std::string::npos) {
// One of the reads has a second occurrence of the kmer. Use
// the slow overlapper.
overlap = Overlapper::computeOverlap(in_query, match_sequence);
} else {
overlap = Overlapper::extendMatch(in_query, match_sequence, pos_0, pos_1, bandwidth);
}
n_candidates += 1;
bool bPassedOverlap = overlap.getOverlapLength() >= min_overlap;
bool bPassedIdentity = overlap.getPercentIdentity() / 100 >= min_identity;
if(bPassedOverlap && bPassedIdentity)
{
//printf("Rank\t%zu\t%zu\t%s\t%.2lf\t%d\n", n_calls,
// rank, match_sequence.c_str(), overlap.getPercentIdentity(), overlap.getOverlapLength());
SequenceOverlapPair op;
op.sequence[0] = in_query;
op.sequence[1] = match_sequence;
op.overlap = overlap;
op.is_reversed = do_reverse;
out_vector.push_back(op);
n_output += 1;
}
}
}
t_time += timer.getElapsedCPUTime();
if(Verbosity::Instance().getPrintLevel() > 6 && n_calls % 100 == 0)
printf("[approx seeds] n: %zu candidates: %zu valid: %zu (%.2lf) time: %.2lfs\n",
n_calls, n_candidates, n_output, (double)n_output / n_candidates, t_time);
}
SequenceOverlapPairVector KmerOverlaps::PacBioRetrieveMatches(const std::string& query, size_t k,
int min_overlap, double min_identity,
int bandwidth, const BWTIndexSet& indices,
KmerDistribution& kd, int round)
{
PROFILE_FUNC("OverlapHaplotypeBuilder::PacBioRetrieveMatches")
assert(indices.pBWT != NULL);
assert(indices.pSSA != NULL);
//size_t numStringCount[query.size()+1] = 0;
int64_t intervalSum = 0;
static size_t n_calls = 0;
static size_t n_candidates = 0;
static size_t n_output = 0;
static double t_time = 0;
size_t count = 0;
size_t numKmer = 0;
size_t numRepeatKmer = 0;
size_t totalKmer = 0;
size_t numNoSeedRead = 0;
size_t repeatCutoff = kd.getRepeatKmerCutoff();
size_t errorCutoff = kd.getMedian() - kd.getSdv();
Timer timer("test", true);
n_calls++;
//std::cout<<"PacBioRetrieveMatches\n";
std::cout<<"\tk :\t"<<k<<"\n";
SequenceOverlapPairVector overlap_vector;
std::vector<long> identityVector(100);
for(int j = 0;j < identityVector.size(); j++)
identityVector[j] = 0;
// Use the FM-index to look up intervals for each kmer of the read. Each index
// in the interval is stored individually in the KmerMatchMap. We then
// backtrack to map these kmer indices to read IDs. As reads can share
// multiple kmers, we use the map to avoid redundant lookups.
// There is likely a faster algorithm which performs direct decompression
// of the read sequences without having to expand the intervals to individual
// indices. The current algorithm suffices for now.
KmerMatchMap prematchMap;
size_t num_kmers = query.size() - k + 1;
clock_t search_seeds_s = clock(), search_seeds_e;
for(size_t i = 0; i < num_kmers; i++)
{
std::string kmer = query.substr(i, k);
BWTInterval interval = BWTAlgorithms::findInterval(indices, kmer);
if(interval.upper - interval.lower < errorCutoff)
numNoSeedRead++;
if((interval.upper - interval.lower) > 20 && (interval.upper - interval.lower) < repeatCutoff)
{
numKmer++;
totalKmer++;
}
//To avoid the repeat region
/*if((interval.upper - interval.lower) > repeatCutoff)
{
numRepeatKmer++;
totalKmer++;
continue;
}
else
interval.upper = ((interval.upper - interval.lower)>20)?interval.lower + 20 : interval.upper;*/
if(interval.isValid() && interval.size())
{
//std::cout<<"\tinterval size : "<<interval.upper - interval.lower<<std::endl;
for(int64_t j = interval.lower; j <= interval.upper; ++j)
{
KmerMatch match = { i, static_cast<size_t>(j), false };
prematchMap.insert(std::make_pair(match, false));
}
intervalSum += interval.upper - interval.lower;
count++;
}
kmer = reverseComplement(kmer);
interval = BWTAlgorithms::findInterval(indices, kmer);
interval.upper = ((interval.upper - interval.lower)>20)?interval.lower + 20 : interval.upper;
if(interval.isValid() && interval.size())
{
for(int64_t j = interval.lower; j <= interval.upper; ++j)
{
KmerMatch match = { i, static_cast<size_t>(j), true };
prematchMap.insert(std::make_pair(match, false));
}
intervalSum += interval.upper - interval.lower;
count++;
}
}
if(numNoSeedRead == num_kmers)
std::cout<<"\tnoSeedRead : 1"<<std::endl;
std::cout<<"\tnumber of kmer : "<<numKmer<<std::endl;
std::cout<<"\tnumber of RepeatKmer : "<<numRepeatKmer<<std::endl;
std::cout<<"\tnumber of totalkmer : "<<totalKmer<<std::endl;
// Backtrack through the kmer indices to turn them into read indices.
// This mirrors the calcSA function in SampledSuffixArray except we mark each entry
// as visited once it is processed.
//std::cout<<"\tintervalSum : "<<intervalSum<<std::endl;
//std::cout<<"\tintervalCount : "<<count<<std::endl;
std::cout<<"\tprematchMap :\t"<<prematchMap.size()<<std::endl;
KmerMatchSet matches;
for(KmerMatchMap::iterator iter = prematchMap.begin(); iter != prematchMap.end(); ++iter)
{
//std::cout<<"iter->first.position : "<<iter->first.position<<std::endl;
// This index has been visited
if(iter->second)
continue;
// Mark this as visited
iter->second = true;
// Backtrack the index until we hit the starting symbol
KmerMatch out_match = iter->first;
while(1)
{
char b = indices.pBWT->getChar(out_match.index);
out_match.index = indices.pBWT->getPC(b) + indices.pBWT->getOcc(b, out_match.index - 1);
// Check if the hash indicates we have visited this index. If so, stop the backtrack
KmerMatchMap::iterator find_iter = prematchMap.find(out_match);
if(find_iter != prematchMap.end())
{
// We have processed this index already
if(find_iter->second)
break;
else
find_iter->second = true;
}
if(b == '$')
{
// We've found the lexicographic index for this read. Turn it into a proper ID
out_match.index = indices.pSSA->lookupLexoRank(out_match.index);
//std::cout<<"out_match.position"<<out_match.position<<std::endl;
matches.insert(out_match);
break;
}
}
}
search_seeds_e = clock();
std::cout<<"\tmatchset :\t"<<matches.size()<<"\n";
// Refine the matches by computing proper overlaps between the sequences
// Use the overlaps that meet the thresholds to build a multiple alignment
clock_t extrac_s, extrac_e;
clock_t overlapE_s, overlapE_e;
clock_t overlapC_s, overlapC_e;
double extrac_sum = 0.0;
double overlapE_sum = 0.0, overlapC_sum = 0.0;
int compute_count = 0,extend_count = 0;
size_t acNumber = 0;
for(KmerMatchSet::iterator iter = matches.begin(); iter != matches.end(); ++iter)
{
extrac_s = clock();
std::string match_sequence;// = BWTAlgorithms::extractString(indices.pBWT, iter->index);
if(indices.pReadTable != NULL)
match_sequence = indices.pReadTable->getRead(iter->index).seq.toString();
/*else
match_sequence = BWTAlgorithms::extractString(indices.pBWT, iter->index);*/
extrac_e = clock();
extrac_sum += (double)extrac_e - extrac_s;
if(iter->is_reverse)
match_sequence = reverseComplement(match_sequence);
// Ignore identical matches
if(match_sequence == query)
continue;
// Compute the overlap. If the kmer match occurs a single time in each sequence we use
// the banded extension overlap strategy. Otherwise we use the slow O(M*N) overlapper.
SequenceOverlap overlap;
std::string match_kmer = query.substr(iter->position, k);
size_t pos_0 = iter->position;//query.find(match_kmer);
size_t pos_1 = match_sequence.find(match_kmer);
assert(pos_0 != std::string::npos && pos_1 != std::string::npos);
//Timer* sTimer = new Timer("seeds overlap");
// Check for secondary occurrences
/*if(query.find(match_kmer, pos_0 + 1) != std::string::npos ||
match_sequence.find(match_kmer, pos_1 + 1) != std::string::npos) {
// One of the reads has a second occurrence of the kmer. Use
// the slow overlapper.
overlapC_s = clock();
compute_count++;
overlap = Overlapper::computeOverlap(query, match_sequence);
overlapC_e = clock();
overlapC_sum += (double)overlapC_e - overlapC_s;
}
else {*/
overlapE_s = clock();
extend_count++;
overlap = Overlapper::PacBioExtendMatch(query, match_sequence, pos_0, pos_1, bandwidth);
overlapE_e = clock();
overlapE_sum += (double)overlapE_e - overlapE_s;
//}
//delete sTimer;
n_candidates += 1;
bool bPassedOverlap = overlap.getOverlapLength() >= min_overlap;
bool bPassedIdentity = overlap.getPercentIdentity() >= min_identity;
identityVector[(int)overlap.getPercentIdentity()] += 1;
//overlap.printTotal_columns();
//overlap.printEdit_distance();
//std::cout<<"min_overlap == "<<overlap.getOverlapLength()<<"\n";
//std::cout<<"overlap.getOverlapLength() / 100 == "<<overlap.getOverlapLength() / 100<<"\n";
//std::cout<<"min_identity == "<<min_identity<<"\n";
//std::cout<<"bPassedOverlap == "<<bPassedOverlap<<"\n";
//std::cout<<"bPassedIdentity == "<<bPassedIdentity<<"\n";
//std::cout<<match_sequence<<"\n";
if(bPassedOverlap && bPassedIdentity)
{
SequenceOverlapPair op;
op.sequence[0] = query;
op.sequence[1] = match_sequence;
op.overlap = overlap;
op.is_reversed = iter->is_reverse;
overlap_vector.push_back(op);
n_output += 1;
acNumber += 1;
//numStringCount
}
}
std::cout<<"\tacceptable number of seeds == "<<acNumber<<"\n";
std::cout<<"\tsearch seeds time : "<<(double)(search_seeds_e - search_seeds_s)/CLOCKS_PER_SEC<<std::endl;
std::cout<<"\textract time : "<<extrac_sum/CLOCKS_PER_SEC<<std::endl;
//std::cout<<"\tcompute_count : "<<compute_count<<std::endl;
//std::cout<<"\tbanded_count : "<<extend_count<<std::endl;
//std::cout<<"\tcompute overlap time : "<<overlapC_sum/CLOCKS_PER_SEC<<std::endl;
//std::cout<<"\tbanded overlap time : "<<overlapE_sum/CLOCKS_PER_SEC<<std::endl;
/*------------------output-identity------------------------------------
double mean = 0.0, temp_mean = 0.0,temp = 0.0;
for(int i = 0; i < 100; i++)
{ //count*identity
mean+=identityVector[i]*i;
temp+=identityVector[i];
}
mean=mean/temp;
for(int i = 0; i < 100; i++)
//count*identity^2
temp_mean+=identityVector[i]*pow(i,2);
std::cout<<"-----------outputIdentity------------"<<std::endl;
std::cout<<"\tround "<<round;
std::cout<<"\tmean identity :\t"<<mean<<std::endl;
std::cout<<"\tSD identity :\t"<<sqrt(temp_mean/temp - pow(mean,2))<<std::endl;
std::cout<<"-------------------------------------\n"<<std::endl;
/*---------------------------------------------------------------------*/
t_time += timer.getElapsedCPUTime();
if(Verbosity::Instance().getPrintLevel() > 6 && n_calls % 100 == 0)
printf("[kmer overlaps] n: %zu candidates: %zu valid: %zu (%.2lf) time: %.2lfs\n",
n_calls, n_candidates, n_output, (double)n_output / n_candidates, t_time);
return overlap_vector;
}