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#pragma once
#include "Compat.h"
#include "FixedSizeMap.h"
#include "BigAlloc.h"
#include "exit.h"
#include "Genome.h"
#include "Read.h"
#include "Tables.h"
#include "LandauVishkin.h"
const int MAX_ALPHABET_SIZE = 5;
//
// These are global so there are only one for both senses of the template
//
extern double *lv_indelProbabilities; // Maps indels by length to probability of occurance.
extern double *lv_phredToProbability; // Maps ASCII phred character to probability of error, including
extern double *lv_perfectMatchProbability; // Probability that a read of this length has no mutations
// #define PRINT_SCORES
//
// Computes the affine gap score between two strings
//
template<int TEXT_DIRECTION = 1> class AffineGap {
public:
AffineGap()
{
init(1, 4, 6, 1);
}
AffineGap(
int i_matchReward,
int i_subPenalty,
int i_gapOpenPenalty,
int i_gapExtendPenalty)
{
init(i_matchReward, i_subPenalty, i_gapOpenPenalty, i_gapExtendPenalty);
}
static size_t getBigAllocatorReservation() { return sizeof(AffineGap<TEXT_DIRECTION>); }
~AffineGap()
{
}
void init(
int i_matchReward,
int i_subPenalty,
int i_gapOpenPenalty,
int i_gapExtendPenalty)
{
matchReward = i_matchReward;
subPenalty = -i_subPenalty;
gapOpenPenalty = i_gapOpenPenalty + i_gapExtendPenalty; // First gap costs (gapOpen + gapExtend)
gapExtendPenalty = i_gapExtendPenalty;
// Initialize score arrays
memsetint(H, 0, MAX_READ_LENGTH + 1);
memsetint(E, 0, MAX_READ_LENGTH + 1);
//
// Initialize nucleotide <-> nucleotide transition matrix
//
int i, j, k = 0;
for (i = 0; i < (MAX_ALPHABET_SIZE - 1); i++) {
for (j = 0; j < (MAX_ALPHABET_SIZE - 1); j++) {
ntTransitionMatrix[k++] = (i == j) ? matchReward : subPenalty;
}
ntTransitionMatrix[k++] = -1; // FIXME: What penalty to use for N ?
}
for (i = 0; i < MAX_ALPHABET_SIZE; i++) {
ntTransitionMatrix[k++] = -1;
}
}
int computeScore(
const char* text,
int textLen,
const char* pattern,
const char *qualityString,
int patternLen,
int w,
int scoreInit,
int *o_textOffset = NULL,
int *o_patternOffset = NULL,
int *o_nEdits = NULL,
double *matchProbability = NULL)
{
#ifdef PRINT_SCORES
printf("\n");
#endif
_ASSERT(w < MAX_K);
_ASSERT(textLen <= MAX_READ_LENGTH + MAX_K);
int localTextOffset, localPatternOffset;
if (NULL == o_textOffset) {
//
// If the user doesn't want textOffset, just use a stack local to avoid
// having to check it all the time.
//
o_textOffset = &localTextOffset;
}
if (NULL == o_patternOffset) {
o_patternOffset = &localPatternOffset;
}
w = __min(MAX_K - 1, w); // enforce limit even in non-debug builds
if (NULL == text) {
// This happens when we're trying to read past the end of the genome.
if (NULL != matchProbability) {
*matchProbability = 0.0;
}
if (NULL != o_nEdits) {
*o_nEdits = -1;
}
return -1;
}
// FIXME: Find better way to do this based on usage
double localMatchProbability;
if (NULL == matchProbability) {
//
// If the user doesn't want matchProbability, just use a stack local to avoid
// having to check it all the time.
//
matchProbability = &localMatchProbability;
}
int localnEdits;
if (NULL == o_nEdits) {
//
// If the user doesn't want matchProbability, just use a stack local to avoid
// having to check it all the time.
//
o_nEdits = &localnEdits;
}
//
// Start with perfect match probability and work our way down.
//
*matchProbability = 1.0;
if (TEXT_DIRECTION == -1) {
text--; // so now it points at the "first" character of t, not after it.
}
//
// Generate query profile
//
for (int i = 0, k = 0; i < MAX_ALPHABET_SIZE; i++) {
for (int j = 0; j < patternLen; j++) {
_uint8 bp = BASE_VALUE[pattern[j]]; // 2b encoded base pair
qProfile[k++] = ntTransitionMatrix[i * MAX_ALPHABET_SIZE + bp];
}
}
// Initialize score arrays
memsetint(H, 0, patternLen + 1);
memsetint(E, 0, patternLen + 1);
//
// Initialize H array
//
H[0] = scoreInit;
H[1] = scoreInit > gapOpenPenalty ? scoreInit - gapOpenPenalty : 0;
for (int j = 2; j < patternLen && H[j - 1] > gapExtendPenalty; ++j) {
H[j] = H[j - 1] - gapExtendPenalty;
}
int score = -1; // Final alignment score to be returned.
// We keep track of the best score and text offset for global and local alignment separately.
// These are used to choose between global and local alignment for the {text, pattern} pair
int bestGlobalAlignmentScore = -1;
int bestGlobalAlignmentTextOffset = -1;
int bestLocalAlignmentScore = -1;
int bestLocalAlignmentTextOffset = -1;
int bestLocalAlignmentPatternOffset = -1;
*o_textOffset = -1; // # Characters of text used for aligning against the pattern
*o_patternOffset = -1; // # Characters of pattern used for obtaining the maximum score. Ideally we would like to use the full pattern
*o_nEdits = -1;
int beg = 0, end = patternLen;
// Iterate over all rows of text
for (int i = 0; i < textLen; i++) {
// Compute only a 2w+1 band
if (beg < i - w) beg = i - w;
if (end > i + w + 1) end = i + w + 1;
if (end > patternLen) end = patternLen;
const char* t = (text + i * TEXT_DIRECTION);
// Get the query profile for the row
_int8* qRowProfile = &qProfile[BASE_VALUE[*t] * patternLen];
_mm_prefetch((const char*)&qProfile[BASE_VALUE[*(t + TEXT_DIRECTION)] * patternLen], _MM_HINT_T0);
int hLeft = 0, fCurr = 0;
// Initialize first column
if (beg == 0) {
hLeft = scoreInit - (gapOpenPenalty + i * gapExtendPenalty);
}
// H -1 0 j-1 j .. (pattern)
// -1
// 0
// 1
// i-1 hDiag
// i hLeft hCurr
// ...
//
// (text)
int maxScoreRow = 0;
#ifdef PRINT_SCORES
for (int j = 0; j < beg; ++j) {
printf("0,");
}
#endif
// Iterate over all columns of pattern (within the band)
for (int j = beg; j < end; ++j) {
//
// Compute H(i,j).
// H(i,j) = max(H(i-1,j-1) + qRowProfile[j], E(i,j), F(i,j))
//
int hDiag = H[j];
hDiag = hDiag > 0 ? hDiag + qRowProfile[j] : 0;
char action = (hDiag >= E[j]) ? 'M' : 'D';
int hCurr = __max(hDiag, E[j]);
action = (hCurr >= fCurr) ? action : 'I';
hCurr = __max(hCurr, fCurr);
backtraceAction[i][j][0] = action;
bestLocalAlignmentPatternOffset = (hCurr >= maxScoreRow) ? j : bestLocalAlignmentPatternOffset;
maxScoreRow = __max(maxScoreRow, hCurr);
#ifdef PRINT_SCORES
printf("%d,", hCurr);
#endif
// hLeft will be used as hDiag for the next row i+1
H[j] = hLeft;
// hCurr will be used as hLeft for the next column j+1
hLeft = hCurr;
//
// Compute E(i+1,j) and F(i,j+1)
// E(i+1,j) = max(H(i,j) - gapOpen, E(i,j) - gapExtend)
// F(i,j+1) = max(H(i,j) - gapOpen, F(i,j) - gapExtend)
// Note hDiag is used instead of hCurr below for H(i,j) to disallow insertions followed by deletions (e.g., 1I1D) in CIGAR
//
int hTemp = __max(hDiag - gapOpenPenalty, 0);
E[j] -= gapExtendPenalty;
action = (E[j] > hTemp) ? 'D' : 'M';
E[j] = __max(hTemp, E[j]);
backtraceAction[i][j][1] = action;
fCurr -= gapExtendPenalty;
action = (fCurr > hTemp) ? 'I' : 'M';
fCurr = __max(hTemp, fCurr);
backtraceAction[i][j][2] = action;
} // end pattern
H[end] = hLeft;
E[end] = 0;
#ifdef PRINT_SCORES
printf("\n");
#endif
if (end == patternLen) { // we got a semi-global alignment
int globalAlignmentScore = H[end];
if (globalAlignmentScore >= bestGlobalAlignmentScore) {
bestGlobalAlignmentScore = globalAlignmentScore;
bestGlobalAlignmentTextOffset = i;
}
}
// All scores in row are zero. Break out and report local alignment
if (maxScoreRow == 0) {
break;
}
if (maxScoreRow > bestLocalAlignmentScore) { // If we obtained a better score this round
bestLocalAlignmentScore = maxScoreRow;
bestLocalAlignmentTextOffset = i;
}
// BWA-MEM's band narrowing heuristic
int j;
for (j = beg; j < end && H[j] == 0 && E[j] == 0; ++j);
beg = j;
for (j = end; j >= beg && H[j] == 0 && E[j] == 0; --j);
end = j + 2;
} // end text
// Choose between local and global alignment for patternOffset
if ((bestLocalAlignmentScore != bestGlobalAlignmentScore) && (bestLocalAlignmentScore >= bestGlobalAlignmentScore + 5)) { // FIXME: Change 5 to a clipping penalty
// Local alignment preferred
*o_patternOffset = bestLocalAlignmentPatternOffset;
*o_textOffset = bestLocalAlignmentTextOffset;
score = bestLocalAlignmentScore;
}
else {
// Global alignment preferred
*o_patternOffset = patternLen - 1;
*o_textOffset = bestGlobalAlignmentTextOffset;
score = bestGlobalAlignmentScore;
}
if (bestGlobalAlignmentScore > scoreInit) {
int rowIdx = bestGlobalAlignmentTextOffset, colIdx = patternLen - 1, matrixIdx = 0;
if (abs(rowIdx - colIdx) > w) return -1;
char action = 'M', prevAction = 'M';
int actionCount = 1;
int nMatches = 0, nMismatches = 0, nGaps = 0;
// Start traceback from the cell (i,j) with the maximum score
while (rowIdx >= 0 && colIdx >= 0) {
action = backtraceAction[rowIdx][colIdx][matrixIdx];
_ASSERT(action == 'M' || action == 'I' || action == 'D');
if (action == 'M') {
if (pattern[colIdx] != text[rowIdx * TEXT_DIRECTION]) {
// Compute probabilties of mismatches
*matchProbability *= lv_phredToProbability[qualityString[colIdx]];
nMismatches++;
}
else {
nMatches++;
}
rowIdx--; colIdx--; matrixIdx = 0;
} else if (action == 'D') {
rowIdx--; matrixIdx = 1;
} else if (action == 'I') {
colIdx--; matrixIdx = 2;
}
// Ignore transitions from H. Compute number of indels in sequence for matchProbability
if (prevAction != 'M') {
if (prevAction == action) {
actionCount++;
}
else {
// Update match probabilties with indel probabilties here
nGaps += actionCount;
*matchProbability *= lv_indelProbabilities[actionCount];
actionCount = 1;
}
}
prevAction = action;
/*
if (nMismatches + nGaps > w) {
return -1;
}
*/
}
if (rowIdx >= 0) { // deletion just after the seed
actionCount = rowIdx + 1;
nGaps += actionCount;
*matchProbability *= lv_indelProbabilities[actionCount];
}
if (colIdx >= 0) { // insertion just after the seed
actionCount = colIdx + 1;
nGaps += actionCount;
*matchProbability *= lv_indelProbabilities[actionCount];
}
*o_nEdits = nMismatches + nGaps;
*o_nEdits = (*o_nEdits <= w) ? *o_nEdits : -1; // return -1 if we have more edits than threshold w
*matchProbability *= lv_perfectMatchProbability[nMatches];
*o_textOffset += 1;
*o_patternOffset += 1;
*o_textOffset = patternLen - *o_textOffset;
*o_patternOffset = patternLen - *o_patternOffset;
return score;
}
else {
return -1;
}
}
void *operator new(size_t size) { return BigAlloc(size); }
void operator delete(void *ptr) { BigDealloc(ptr); }
void *operator new(size_t size, BigAllocator *allocator) { _ASSERT(size == sizeof(AffineGap<TEXT_DIRECTION>)); return allocator->allocate(size); }
void operator delete(void *ptr, BigAllocator *allocator) {/*Do nothing. The memory is freed when the allocator is deleted.*/ }
private:
//
// Scores for each nucleotide <-> nucleotide transition
//
int ntTransitionMatrix[MAX_ALPHABET_SIZE * MAX_ALPHABET_SIZE];
//
// Precompute query profile which is a table containing the result of
// matching each letter of the alphabet with each character of the pattern
// and filling in the appropriate transition score from the ntTransMatrix.
// This precomputation saves looking up the ntTransition matrix for each
// (ref_i, query_j) in the inner dynamic programming loop.
//
_int8 qProfile[MAX_ALPHABET_SIZE * MAX_READ_LENGTH];
//
// H and E arrays used for storing scores in every row
//
int H[MAX_READ_LENGTH + 1];
int E[MAX_READ_LENGTH + 1];
//
// Pointers to traceback alignment, one for each of the three affine-gap matrices, H, E and F
//
char backtraceAction[(MAX_READ_LENGTH + MAX_K)][MAX_READ_LENGTH][3];
//
// Affine gap scoring parameters
//
int matchReward;
int subPenalty;
int gapOpenPenalty;
int gapExtendPenalty;
};
class AffineGapWithCigar {
public:
AffineGapWithCigar(); // FIXME: Pass scoring parameters
AffineGapWithCigar(int i_matchReward, int i_subPenalty, int i_gapOpenPenalty, int i_gapExtendPenalty);
// Compute the affine gap score between two strings and write the CIGAR string in cigarBuf.
// Returns -1 if the edit distance exceeds k or -2 if we run out of space in cigarBuf.
int computeGlobalScore(const char* text, int textLen, const char* pattern, int patternLen, int w,
char* cigarBuf, int cigarBufLen, bool useM,
CigarFormat format = COMPACT_CIGAR_STRING,
int* o_cigarBufUsed = NULL, int *o_netDel = NULL, int *o_tailIns = NULL);
int computeGlobalScoreNormalized(const char* text, int textLen,
const char* pattern, int patternLen,
int k,
char *cigarBuf, int cigarBufLen, bool useM,
CigarFormat format, int* o_cigarBufUsed,
int* o_addFrontClipping, int *o_netDel = NULL, int* o_tailIns = NULL);
bool writeCigar(char** o_buf, int* o_buflen, int count, char code, CigarFormat format);
private:
//
// Scores for each nucleotide <-> nucleotide transition
//
int ntTransitionMatrix[MAX_ALPHABET_SIZE * MAX_ALPHABET_SIZE];
//
// Precompute query profile which is a table containing the result of
// matching each letter of the alphabet with each character of the pattern
// and filling in the appropriate transition score from the ntTransMatrix.
// This precomputation saves looking up the ntTransition matrix for each
// (ref_i, query_j) in the inner dynamic programming loop.
//
_int8 qProfile[MAX_ALPHABET_SIZE * MAX_READ_LENGTH];
//
// H and E arrays used for storing scores in every row
//
int H[MAX_READ_LENGTH + 1];
int E[MAX_READ_LENGTH + 1];
//
// Pointers to traceback alignment, one for each of the three affine-gap matrices, H, E and F
//S
char backtraceAction[(MAX_READ_LENGTH + MAX_K)][MAX_READ_LENGTH][3];
//
// Structure used for storing (action, count) pairs from backtracking
//
typedef struct {
char action[MAX_READ_LENGTH];
int count[MAX_READ_LENGTH];
} LocalCigarResult;
//
// Affine gap scoring parameters
//
int matchReward;
int subPenalty;
int gapOpenPenalty;
int gapExtendPenalty;
};
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