size_t BWTAlgorithms::countSequenceOccurrencesSingleStrand(const std::string& w, const BWTIndexSet& indices)
{
assert(indices.pBWT != NULL);
assert(indices.pCache != NULL);
BWTInterval interval = findIntervalWithCache(indices.pBWT, indices.pCache, w);
return interval.isValid() ? interval.size() : 0;
}
// Extract all strings found from a backwards search starting at the given interval
RankedPrefixVector BWTAlgorithms::extractRankedPrefixes(const BWT* pBWT, BWTInterval interval)
{
std::string curr;
RankedPrefixVector output;
output.reserve(interval.size());
_extractRankedPrefixes(pBWT, interval, curr, &output);
return output;
}
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;
}
// Extract reads from an FM-index that have a k-mer match to any given haplotypes
// Returns true if the reads were successfully extracted, false if there are
// more reads than maxReads
bool HapgenUtil::extractHaplotypeReads(const StringVector& haplotypes,
const BWTIndexSet& indices,
int k,
bool doReverse,
size_t maxReads,
int64_t maxIntervalSize,
SeqRecordVector* pOutReads,
SeqRecordVector* pOutMates)
{
PROFILE_FUNC("HapgenUtil::extractHaplotypeReads")
// Extract the set of reads that have at least one kmer shared with these haplotypes
// This is a bit of a lengthy procedure with a few steps:
// 1) extract all the kmers in the haplotypes
// 2) find the intervals for the kmers in the fm-index
// 3) compute the set of read indices of the reads from the intervals (using the sampled suffix array)
// 4) finally, extract the read sequences from the index
// Make a set of kmers from the haplotypes
std::set<std::string> kmerSet;
for(size_t i = 0; i < haplotypes.size(); ++i)
{
const std::string& h = haplotypes[i];
if((int)h.size() < k)
continue;
for(size_t j = 0; j < h.size() - k + 1; ++j)
{
std::string ks = h.substr(j, k);
if(doReverse)
ks = reverseComplement(ks);
kmerSet.insert(ks);
}
}
// Compute suffix array intervals for the kmers
std::vector<BWTInterval> intervals;
for(std::set<std::string>::const_iterator iter = kmerSet.begin(); iter != kmerSet.end(); ++iter)
{
BWTInterval interval = BWTAlgorithms::findInterval(indices, *iter);
if(interval.size() < maxIntervalSize)
intervals.push_back(interval);
}
// Compute the set of reads ids
std::set<int64_t> readIndices;
for(size_t i = 0; i < intervals.size(); ++i)
{
BWTInterval interval = intervals[i];
for(int64_t j = interval.lower; j <= interval.upper; ++j)
{
// Get index from sampled suffix array
SAElem elem = indices.pSSA->calcSA(j, indices.pBWT);
readIndices.insert(elem.getID());
}
}
// Check if we have hit the limit of extracting too many reads
if(readIndices.size() > maxReads)
return false;
for(std::set<int64_t>::const_iterator iter = readIndices.begin(); iter != readIndices.end(); ++iter)
{
int64_t idx = *iter;
// Extract the read
std::stringstream namer;
namer << "idx-" << idx;
SeqRecord record;
record.id = namer.str();
record.seq = BWTAlgorithms::extractString(indices.pBWT, idx);
assert(indices.pQualityTable != NULL);
record.qual = indices.pQualityTable->getQualityString(idx, record.seq.length());
if(!record.seq.empty())
pOutReads->push_back(record);
// Optionally extract its mate
// If the index is constructed properly,
// paired reads are in adjacent indices with the
// first read at even indices
if(pOutMates != NULL)
{
int64_t mateIdx = idx;
if(idx % 2 == 0)
mateIdx += 1;
else
mateIdx -= 1;
std::stringstream mateName;
mateName << "idx-" << mateIdx;
SeqRecord mateRecord;
mateRecord.id = mateName.str();
mateRecord.seq = BWTAlgorithms::extractString(indices.pBWT, mateIdx);
mateRecord.qual = indices.pQualityTable->getQualityString(mateIdx, mateRecord.seq.length());
if(!record.seq.empty() && !mateRecord.seq.empty())
pOutMates->push_back(mateRecord);
}
}
return true;
}
// 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]);
}
}
AlphaCount64 BWTAlgorithms::calculateDeBruijnExtensionsSingleIndex(const std::string str,
const BWT* pBWT,
EdgeDir direction,
const BWTIntervalCache* pFwdCache)
{
size_t k = str.size();
size_t p = k - 1;
std::string pmer;
// In the sense direction, we extend from the 3' end
if(direction == ED_SENSE)
pmer = str.substr(1, p);
else
pmer = str.substr(0, p);
assert(pmer.length() == p);
std::string rc_pmer = reverseComplement(pmer);
// As we only have a single index, we can only directly look up
// the extensions for either the pmer or its reverse complement
// In the SENSE extension direction, we directly look up for
// the reverse complement. In ANTISENSE we directly look up for
// the pmer.
// Get the extension bases
AlphaCount64 extensions;
AlphaCount64 rc_extensions;
// Set up pointers to the data to fill in/query
// depending on the direction of the extension
AlphaCount64* pDirectEC;
AlphaCount64* pIndirectEC;
std::string* pDirectStr;
std::string* pIndirectStr;
if(direction == ED_SENSE)
{
pDirectEC = &rc_extensions;
pDirectStr = &rc_pmer;
pIndirectEC = &extensions;
pIndirectStr = &pmer;
}
else
{
pDirectEC = &extensions;
pDirectStr = &pmer;
pIndirectEC = &rc_extensions;
pIndirectStr = &rc_pmer;
}
// Get the interval for the direct query string
BWTInterval interval;
// Use interval cache if available
if(pFwdCache)
interval = BWTAlgorithms::findIntervalWithCache(pBWT, pFwdCache, *pDirectStr);
else
interval = BWTAlgorithms::findInterval(pBWT, *pDirectStr);
// Fill in the direct count
if(interval.isValid())
*pDirectEC = BWTAlgorithms::getExtCount(interval, pBWT);
// Now, for the non-direct index, query the 4 possible k-mers that are adjacent to the pmer
// Setup the query sequence
std::string query(k, 'A');
int varIdx = query.size() - 1;
query.replace(0, p, *pIndirectStr);
for(int i = 0; i < BWT_ALPHABET::size; ++i)
{
// Transform the query
char b = BWT_ALPHABET::getChar(i);
query[varIdx] = b;
// Perform lookup
if(pFwdCache)
interval = BWTAlgorithms::findIntervalWithCache(pBWT, pFwdCache, query);
else
interval = BWTAlgorithms::findInterval(pBWT, query);
// Update the extension count
if(interval.isValid())
pIndirectEC->add(b, interval.size());
}
// Switch the reverse-complement extensions to the same strand as the str
rc_extensions.complement();
extensions += rc_extensions;
return extensions;
}
size_t BWTAlgorithms::countSequenceOccurrencesSingleStrand(const std::string& w, const BWT* pBWT)
{
BWTInterval interval = findInterval(pBWT, w);
return interval.isValid() ? interval.size() : 0;
}
GraphCompareResult GraphCompare::process(const SequenceWorkItem& item) const
{
PROFILE_FUNC("GraphCompare::process")
GraphCompareResult result;
SeqRecord currRead = item.read;
std::string w = item.read.seq.toString();
int len = w.size();
int num_kmers = len - m_parameters.kmer + 1;
// Check if the read is long enough to be used
if(w.size() < m_parameters.kmer)
return result;
// Process the kmers that have not been previously visited
// We only try to find variants from one k-mer per read.
// This heurestic handles the situation where we process a kmer,
// spend a lot of time trying to assemble it into a string and fail,
// then try again with the next k-mer in the read. Since all the k-mers
// in a read are connected in the de Bruijn graph, if the first attempt
// to assemble a k-mer into a variant fails, it is very unlikely that
// subsequent attempts will succeed.
bool variantAttempted = false;
for(int j = 0; j < num_kmers; ++j)
{
std::string kmer = w.substr(j, m_parameters.kmer);
std::string rc_kmer = reverseComplement(kmer);
// Use the lexicographically lower of the kmer and its pair as the key in the bloom filter
std::string& key_kmer = kmer < rc_kmer ? kmer : rc_kmer;
// Check if this k-mer is marked as used by the bloom filter
if(m_parameters.pBloomFilter->test(key_kmer.c_str(), key_kmer.size()))
continue;
// Get the interval for this kmer
BWTInterval interval = BWTAlgorithms::findInterval(m_parameters.variantIndex, kmer);
BWTInterval rc_interval = BWTAlgorithms::findInterval(m_parameters.variantIndex, rc_kmer);
size_t count = interval.size();
if(rc_interval.isValid())
count += rc_interval.size();
bool both_strands = interval.size() > 0 && rc_interval.size() > 0;
size_t min_base_coverage = 1;
if(count >= m_parameters.minDiscoveryCount && count < m_parameters.maxDiscoveryCount && !variantAttempted && both_strands)
{
// Update the bloom filter to contain this kmer
m_parameters.pBloomFilter->add(key_kmer.c_str(), key_kmer.size());
// Check if this k-mer is present in the other base index
size_t base_count = BWTAlgorithms::countSequenceOccurrences(kmer, m_parameters.baseIndex);
if(Verbosity::Instance().getPrintLevel() > 6)
std::cout << "Read: " << currRead.id << " k: " << j << " CV: " << interval.size() << "/" << rc_interval.size() << " " << base_count << "\n";
// k-mer present in the base read set, skip it
if(base_count >= min_base_coverage)
continue;
if(Verbosity::Instance().getPrintLevel() > 0)
std::cout << "Variant read: " << w << "\n";
// variant k-mer, attempt to assemble it into haplotypes
GraphBuildResult build_result = processVariantKmer(kmer, count);
variantAttempted = true;
// Mark the kmers of the variant haplotypes as being visited
for(size_t vhi = 0; vhi < build_result.variant_haplotypes.size(); ++vhi)
markVariantSequenceKmers(build_result.variant_haplotypes[vhi]);
// If we assembled anything, run Dindel on the haplotypes
if(build_result.variant_haplotypes.size() > 0)
{
if(Verbosity::Instance().getPrintLevel() > 0)
std::cout << "Running dindel\n";
std::stringstream baseVCFSS;
std::stringstream variantVCFSS;
std::stringstream callsVCFSS;
DindelReadReferenceAlignmentVector alignments;
DindelReturnCode drc = DindelUtil::runDindelPairMatePair(kmer,
build_result.base_haplotypes,
build_result.variant_haplotypes,
m_parameters,
baseVCFSS,
variantVCFSS,
callsVCFSS,
&alignments);
//
if(Verbosity::Instance().getPrintLevel() > 0)
{
std::cout << "Dindel returned " << drc << "\n";
std::cout << "base vcf records:\n" << baseVCFSS.str() << "\n";
std::cout << "variant vcf records:\n" << variantVCFSS.str() << "\n";
}
// DINDEL ran without error, push its results to the output
if(drc == DRC_OK)
{
result.baseVCFStrings.push_back(baseVCFSS.str());
result.variantVCFStrings.push_back(variantVCFSS.str());
result.calledVCFStrings.push_back(callsVCFSS.str());
result.varStrings.insert(result.varStrings.end(),
build_result.variant_haplotypes.begin(), build_result.variant_haplotypes.end());
result.projectedReadAlignments = alignments;
}
}
}
}
return result;
}
MetAssembleResult MetAssemble::process(const SequenceWorkItem& item)
{
MetAssembleResult result;
SeqRecord currRead = item.read;
std::string w = item.read.seq.toString();
if(w.size() <= m_parameters.kmer)
{
return result;
}
// Perform a backwards search using the read sequence
// Check which k-mers have already been visited using the
// shared bitvector. If any bit in the range [l,u] is set
// for a suffix of the read, then we do not visit those kmers
// later
int len = w.size();
int num_kmers = len - m_parameters.kmer + 1;
std::vector<bool> visitedKmers(false, num_kmers);
int j = len - 1;
char curr = w[j];
BWTInterval interval;
BWTAlgorithms::initInterval(interval, curr, m_parameters.pBWT);
--j;
for(;j >= 0; --j)
{
curr = w[j];
BWTAlgorithms::updateInterval(interval, curr, m_parameters.pBWT);
assert(interval.isValid());
// At this point interval represents the suffix [j,len)
// Check if the starting point of this interval is set
if(j < num_kmers)
visitedKmers[j] = m_parameters.pBitVector->test(interval.lower);
}
// Process the kmers that have not been previously visited
for(j = 0; j < num_kmers; ++j)
{
if(visitedKmers[j])
continue; // skip
std::string kmer = w.substr(j, m_parameters.kmer);
// Get the interval for this kmer
BWTInterval interval = BWTAlgorithms::findIntervalWithCache(m_parameters.pBWT, m_parameters.pBWTCache, kmer);
// Check if this interval has been marked by a previous iteration of the loop
assert(interval.isValid());
if(m_parameters.pBitVector->test(interval.lower))
continue;
BWTInterval rc_interval = BWTAlgorithms::findIntervalWithCache(m_parameters.pBWT, m_parameters.pBWTCache, reverseComplement(kmer));
size_t count = interval.size();
if(rc_interval.isValid())
count += rc_interval.size();
if(count >= m_parameters.kmerThreshold)
{
// Process the kmer
std::string contig = processKmer(kmer, count);
// We must determine if this contig has been assembled by another thread.
// Break the contig into lexicographically ordered set of kmers. The lowest kmer is chosen
// to represent the contig. If this kmer has been marked as visited, we discard the contig
// otherwise we mark all kmers in the contig and output the contig.
StringVector kmers = getLexicographicKmers(contig);
// Get the lowest kmer in the set
assert(!kmers.empty());
std::string lowest = kmers.front();
BWTInterval lowInterval = BWTAlgorithms::findIntervalWithCache(m_parameters.pBWT, m_parameters.pBWTCache, lowest);
BWTInterval lowRCInterval = BWTAlgorithms::findIntervalWithCache(m_parameters.pBWT, m_parameters.pBWTCache, reverseComplement(lowest));
bool marked = false;
// If the kmer exists in the read set (the interval is valid), we attempt to mark it
// Otherwise, we attempt to mark its reverse complement. If either call succeeds
// we output the contig. This block of code gives the synchronization between threads.
// If multiple threads attempt to assemble the same contig, marked will be true for only
// one of the threads.
if(lowInterval.isValid())
marked = m_parameters.pBitVector->updateCAS(lowInterval.lower, false, true);
else
marked = m_parameters.pBitVector->updateCAS(lowRCInterval.lower, false, true);
// Mark all the kmers in the contig so they will not be visited again
markSequenceKmers(contig);
// If the collision check passed, output the contig
if(marked)
{
if(contig.size() >= m_parameters.minLength)
result.contigs.push_back(contig);
}
}
// Update the bit vector for the source kmer
for(int64_t i = interval.lower; i <= interval.upper; ++i)
m_parameters.pBitVector->updateCAS(i, false, true);
for(int64_t i = rc_interval.lower; i <= rc_interval.upper; ++i)
m_parameters.pBitVector->updateCAS(i, false, true);
}
return result;
}
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;
}