bam2bcf.c

#include <math.h>
#include <stdint.h>
#include <assert.h>
#include <float.h>
#include <htslib/sam.h>
#include <htslib/kstring.h>
#include <htslib/kfunc.h>
#include "bam2bcf.h"
#include "errmod.h"

extern	void ks_introsort_uint32_t(size_t n, uint32_t a[]);
extern const char bam_nt16_nt4_table[];

#define CALL_DEFTHETA 0.83
#define DEF_MAPQ 20

#define CAP_DIST 25

bcf_callaux_t *bcf_call_init(double theta, int min_baseQ)
{
	bcf_callaux_t *bca;
	if (theta <= 0.) theta = CALL_DEFTHETA;
	bca = calloc(1, sizeof(bcf_callaux_t));
	bca->capQ = 60;
	bca->openQ = 40; bca->extQ = 20; bca->tandemQ = 100;
	bca->min_baseQ = min_baseQ;
	bca->e = errmod_init(1. - theta);
	bca->min_frac = 0.002;
	bca->min_support = 1;
    bca->per_sample_flt = 0;
    bca->npos = 100;
    bca->ref_pos = malloc(bca->npos*sizeof(int));
    bca->alt_pos = malloc(bca->npos*sizeof(int));
    bca->nqual = 60;
    bca->ref_mq  = malloc(bca->nqual*sizeof(int));
    bca->alt_mq  = malloc(bca->nqual*sizeof(int));
    bca->ref_bq  = malloc(bca->nqual*sizeof(int));
    bca->alt_bq  = malloc(bca->nqual*sizeof(int));
    bca->fwd_mqs = malloc(bca->nqual*sizeof(int));
    bca->rev_mqs = malloc(bca->nqual*sizeof(int));
 	return bca;
}

void bcf_call_destroy(bcf_callaux_t *bca)
{
	if (bca == 0) return;
	errmod_destroy(bca->e);
    if (bca->npos) { free(bca->ref_pos); free(bca->alt_pos); bca->npos = 0; }
    free(bca->ref_mq); free(bca->alt_mq); free(bca->ref_bq); free(bca->alt_bq); 
    free(bca->fwd_mqs); free(bca->rev_mqs);
    bca->nqual = 0;
	free(bca->bases); free(bca->inscns); free(bca);
}

// position in the sequence with respect to the aligned part of the read
static int get_position(const bam_pileup1_t *p, int *len)
{
    int icig, n_tot_bases = 0, iread = 0, edist = p->qpos + 1;
    for (icig=0; icig<p->b->core.n_cigar; icig++) 
    {
        int cig  = bam_get_cigar(p->b)[icig] & BAM_CIGAR_MASK;
        int ncig = bam_get_cigar(p->b)[icig] >> BAM_CIGAR_SHIFT;
        if ( cig==BAM_CMATCH || cig==BAM_CEQUAL || cig==BAM_CDIFF )
        {
            n_tot_bases += ncig;
            iread += ncig;
            continue;
        }
        if ( cig==BAM_CINS )
        {
            n_tot_bases += ncig;
            iread += ncig;
            continue;
        }
        if ( cig==BAM_CSOFT_CLIP )
        {
            iread += ncig;
            if ( iread<=p->qpos ) edist -= ncig;
            continue;
        }
        if ( cig==BAM_CDEL ) continue;
        fprintf(stderr,"todo: cigar %d\n", cig);
        assert(0);
    }
    *len = n_tot_bases;
    return edist;
}

void bcf_callaux_clean(bcf_callaux_t *bca)
{
    memset(bca->ref_pos,0,sizeof(int)*bca->npos);
    memset(bca->alt_pos,0,sizeof(int)*bca->npos);
    memset(bca->ref_mq,0,sizeof(int)*bca->nqual);
    memset(bca->alt_mq,0,sizeof(int)*bca->nqual);
    memset(bca->ref_bq,0,sizeof(int)*bca->nqual);
    memset(bca->alt_bq,0,sizeof(int)*bca->nqual);
    memset(bca->fwd_mqs,0,sizeof(int)*bca->nqual);
    memset(bca->rev_mqs,0,sizeof(int)*bca->nqual);
}

/*
    Notes: 
    - Called from bam_plcmd.c by mpileup. Amongst other things, sets the bcf_callret1_t.qsum frequencies
        which are carried over via bcf_call_combine and bcf_call2bcf to the output BCF as the QS annotation.
        Later it's used for multiallelic calling by bcftools -m
    - ref_base is the 4-bit representation of the reference base. It is negative if we are looking at an indel. 
 */
/* 
 * This function is called once for each sample.
 * _n is number of pilesups pl contributing reads to this sample
 * pl is pointer to array of _n pileups (one pileup per read)
 * ref_base is the 4-bit representation of the reference base. It is negative if we are looking at an indel.
 * bca is the settings to perform calls across all samples
 * r is the returned value of the call
 */
int bcf_call_glfgen(int _n, const bam_pileup1_t *pl, int ref_base, bcf_callaux_t *bca, bcf_callret1_t *r)
{
	int i, n, ref4, is_indel, ori_depth = 0;
	memset(r, 0, sizeof(bcf_callret1_t));
	if (ref_base >= 0) {
		ref4 = bam_nt16_nt4_table[ref_base];
		is_indel = 0;
	} else ref4 = 4, is_indel = 1;
	if (_n == 0) return -1;
	// enlarge the bases array if necessary
	if (bca->max_bases < _n) {
		bca->max_bases = _n;
		kroundup32(bca->max_bases);
		bca->bases = (uint16_t*)realloc(bca->bases, 2 * bca->max_bases);
	}
	// fill the bases array
	for (i = n = r->n_supp = 0; i < _n; ++i) {
		const bam_pileup1_t *p = pl + i;
		int q, b, mapQ, baseQ, is_diff, min_dist, seqQ;
		// set base
		if (p->is_del || p->is_refskip || (p->b->core.flag&BAM_FUNMAP)) continue;
		++ori_depth;
		mapQ  = p->b->core.qual < 255? p->b->core.qual : DEF_MAPQ; // special case for mapQ==255
        if ( !mapQ ) r->mq0++;
		baseQ = q = is_indel? p->aux&0xff : (int)bam_get_qual(p->b)[p->qpos]; // base/indel quality
		seqQ = is_indel? (p->aux>>8&0xff) : 99;
		if (q < bca->min_baseQ) continue;
		if (q > seqQ) q = seqQ;
		mapQ = mapQ < bca->capQ? mapQ : bca->capQ;
		if (q > mapQ) q = mapQ;
		if (q > 63) q = 63;
		if (q < 4) q = 4;       // MQ=0 reads count as BQ=4
		if (!is_indel) {
			b = bam_seqi(bam_get_seq(p->b), p->qpos); // base
			b = bam_nt16_nt4_table[b? b : ref_base]; // b is the 2-bit base
			is_diff = (ref4 < 4 && b == ref4)? 0 : 1;
		} else {
			b = p->aux>>16&0x3f;
			is_diff = (b != 0);
		}
		if (is_diff) ++r->n_supp;
		bca->bases[n++] = q<<5 | (int)bam_is_rev(p->b)<<4 | b;
		// collect annotations
		if (b < 4) r->qsum[b] += q;
		++r->anno[0<<2|is_diff<<1|bam_is_rev(p->b)];
		min_dist = p->b->core.l_qseq - 1 - p->qpos;
		if (min_dist > p->qpos) min_dist = p->qpos;
		if (min_dist > CAP_DIST) min_dist = CAP_DIST;
		r->anno[1<<2|is_diff<<1|0] += baseQ;
		r->anno[1<<2|is_diff<<1|1] += baseQ * baseQ;
		r->anno[2<<2|is_diff<<1|0] += mapQ;
		r->anno[2<<2|is_diff<<1|1] += mapQ * mapQ;
		r->anno[3<<2|is_diff<<1|0] += min_dist;
		r->anno[3<<2|is_diff<<1|1] += min_dist * min_dist;

        // collect for bias tests
        if ( baseQ > 59 ) baseQ = 59;
        if ( mapQ > 59 ) mapQ = 59;
        int len, pos = get_position(p, &len);
        int epos = (double)pos/(len+1) * bca->npos;
        int ibq  = baseQ/60. * bca->nqual;
        int imq  = mapQ/60. * bca->nqual;
        if ( bam_is_rev(p->b) ) bca->rev_mqs[imq]++;
        else bca->fwd_mqs[imq]++;
        if ( bam_seqi(bam_get_seq(p->b),p->qpos) == ref_base )
        {
            bca->ref_pos[epos]++;
            bca->ref_bq[ibq]++;
            bca->ref_mq[imq]++;
        }
        else
        {
            bca->alt_pos[epos]++;
            bca->alt_bq[ibq]++;
            bca->alt_mq[imq]++;
        }
	}
	r->depth = n; r->ori_depth = ori_depth;
	// glfgen
	errmod_cal(bca->e, n, 5, bca->bases, r->p); // calculate PL of each genotype
	return r->depth;
}


/*
 *  calc_vdb() - returns value between zero (most biased) and one (no bias)
 *               on success, or HUGE_VAL when VDB cannot be calculated because
 *               of insufficient depth (<2x)
 *
 *  Variant Distance Bias tests if the variant bases are positioned within the
 *  reads with sufficient randomness. Unlike other tests, it looks only at
 *  variant reads and therefore gives different kind of information than Read
 *  Position Bias for instance. VDB was developed for detecting artefacts in
 *  RNA-seq calls where reads from spliced transcripts span splice site
 *  boundaries.  The current implementation differs somewhat from the original
 *  version described in supplementary material of PMID:22524474, but the idea
 *  remains the same. (Here the random variable tested is the average distance
 *  from the averaged position, not the average pairwise distance.)
 *
 *  For coverage of 2x, the calculation is exact but is approximated for the
 *  rest. The result is most accurate between 4-200x. For 3x or >200x, the
 *  reported values are slightly more favourable than those of a true random
 *  distribution.
 */
double calc_vdb(int *pos, int npos)
{
    // Note well: the parameters were obtained by fitting to simulated data of
    // 100bp reads. This assumes rescaling to 100bp in bcf_call_glfgen().
    const int readlen = 100;
    assert( npos==readlen );

    #define nparam 15
    const float param[nparam][3] = { {3,0.079,18}, {4,0.09,19.8}, {5,0.1,20.5}, {6,0.11,21.5},
        {7,0.125,21.6}, {8,0.135,22}, {9,0.14,22.2}, {10,0.153,22.3}, {15,0.19,22.8},
        {20,0.22,23.2}, {30,0.26,23.4}, {40,0.29,23.5}, {50,0.35,23.65}, {100,0.5,23.7},
        {200,0.7,23.7} };

    int i, dp = 0;
    float mean_pos = 0, mean_diff = 0;
    for (i=0; i<npos; i++)
    {
        if ( !pos[i] ) continue;
        dp += pos[i];
        mean_pos += pos[i]*i;
    }
    if ( dp<2 ) return HUGE_VAL;     // one or zero reads can be placed anywhere

    mean_pos /= dp;
    for (i=0; i<npos; i++)
    {
        if ( !pos[i] ) continue;
        mean_diff += pos[i] * fabs(i - mean_pos);
    }
    mean_diff /= dp;

    int ipos = mean_diff;   // tuned for float-to-int implicit conversion
    if ( dp==2 )
        return (2*readlen-2*(ipos+1)-1)*(ipos+1)/(readlen-1)/(readlen*0.5);

    if ( dp>=200 ) 
        i = nparam; // shortcut for big depths
    else 
    {
        for (i=0; i<nparam; i++)
            if ( param[i][0]>=dp ) break;
    }
    float pshift, pscale;
    if ( i==nparam )
    {
        // the depth is too high, go with 200x
        pscale = param[nparam-1][1];
        pshift = param[nparam-1][2];
    }
    else if ( i>0 && param[i][0]!=dp )
    {
        // linear interpolation of parameters
        pscale = (param[i-1][1] + param[i][1])*0.5;
        pshift = (param[i-1][2] + param[i][2])*0.5;
    }
    else
    {
        pscale = param[i][1];
        pshift = param[i][2];
    }
    return 0.5*kf_erfc(-(mean_diff-pshift)*pscale);
}

double calc_chisq_bias(int *a, int *b, int n)
{
    int na = 0, nb = 0, i, ndf = n;
    for (i=0; i<n; i++) na += a[i];
    for (i=0; i<n; i++) nb += b[i];
    if ( !na || !nb ) return HUGE_VAL;

    double chisq = 0;
    for (i=0; i<n; i++)
    {
        if ( !a[i] && !b[i] ) ndf--;
        else 
        {
            double tmp = a[i] - b[i];
            chisq += tmp*tmp/(a[i]+b[i]);
        }
    }
    /*
        kf_gammq: incomplete gamma function Q(a,x) = 1 - P(a,x) = Gamma(a,x)/Gamma(a)
        1 if the distributions are identical, 0 if very different
    */
    double prob = kf_gammaq(0.5*ndf, 0.5*chisq);
    return prob;
}

double mann_whitney_1947(int n, int m, int U)
{
    if (U<0) return 0;
    if (n==0||m==0) return U==0 ? 1 : 0;
    return (double)n/(n+m)*mann_whitney_1947(n-1,m,U-m) + (double)m/(n+m)*mann_whitney_1947(n,m-1,U);
}

double mann_whitney_1947_cdf(int n, int m, int U)
{
    int i;
    double sum = 0;
    for (i=0; i<=U; i++) 
        sum += mann_whitney_1947(n,m,i);
    return sum;
}

double calc_mwu_bias_cdf(int *a, int *b, int n)
{
    int na = 0, nb = 0, i;
    double U = 0, ties = 0;
    for (i=0; i<n; i++) 
    {
        na += a[i];
        U  += a[i] * (nb + b[i]*0.5);
        nb += b[i];
        if ( a[i] && b[i] )
        {
            double tie = a[i] + b[i];
            ties += (tie*tie-1)*tie;
        }
    }
    if ( !na || !nb ) return HUGE_VAL;

    // Always work with the smaller U
    double U_min = ((double)na * nb) - U;
    if ( U < U_min ) U_min = U;

    if ( na==1 ) return 2.0 * (floor(U_min)+1) / (nb+1);
    if ( nb==1 ) return 2.0 * (floor(U_min)+1) / (na+1);

    // Normal approximation, very good for na>=8 && nb>=8 and reasonable if na<8 or nb<8
    if ( na>=8 || nb>=8 )
    {
        double mean = ((double)na*nb)*0.5;
        // Correction for ties:
        //      double N = na+nb;
        //      double var2 = (N*N-1)*N-ties;
        //      if ( var2==0 ) return 1.0;
        //      var2 *= ((double)na*nb)/N/(N-1)/12.0; 
        // No correction for ties:
        double var2 = ((double)na*nb)*(na+nb+1)/12.0;
        double z = (U_min - mean)/sqrt(2*var2);   // z is N(0,1)
        return 2.0 - kf_erfc(z);  // which is 1 + erf(z)
    }

    // Exact calculation
    double pval = 2*mann_whitney_1947_cdf(na,nb,U_min);
    return pval>1 ? 1 : pval;
}

double calc_mwu_bias(int *a, int *b, int n)
{
    int na = 0, nb = 0, i;
    double U = 0, ties = 0;
    for (i=0; i<n; i++) 
    {
        na += a[i];
        U  += a[i] * (nb + b[i]*0.5);
        nb += b[i];
        if ( a[i] && b[i] )
        {
            double tie = a[i] + b[i];
            ties += (tie*tie-1)*tie;
        }
    }
    if ( !na || !nb ) return HUGE_VAL;
    if ( na==1 || nb==1 ) return 1.0;       // Flat probability, all U values are equally likely

    double mean = ((double)na*nb)*0.5;
    if ( na==2 || nb==2 )
    {
        // Linear approximation
        return U>mean ? (2.0*mean-U)/mean : U/mean;
    }
    // Correction for ties:
    //      double N = na+nb;
    //      double var2 = (N*N-1)*N-ties;
    //      if ( var2==0 ) return 1.0;
    //      var2 *= ((double)na*nb)/N/(N-1)/12.0;
    // No correction for ties:
    double var2 = ((double)na*nb)*(na+nb+1)/12.0;
    if ( na>=8 || nb>=8 )
    {
        // Normal approximation, very good for na>=8 && nb>=8 and reasonable if na<8 or nb<8
        return exp(-0.5*(U-mean)*(U-mean)/var2);
    }

    // Exact calculation
    return mann_whitney_1947(na,nb,U) * sqrt(2*M_PI*var2);
}

static inline double logsumexp2(double a, double b)
{
    if ( a>b )
        return log(1 + exp(b-a)) + a;
    else
        return log(1 + exp(a-b)) + b;
}

void calc_SegBias(const bcf_callret1_t *bcr, bcf_call_t *call)
{
    call->seg_bias = HUGE_VAL;
    if ( !bcr ) return;

    int nr = call->anno[2] + call->anno[3]; // number of observed non-reference reads
    if ( !nr ) return;

    int avg_dp = (call->anno[0] + call->anno[1] + nr) / call->n;    // average depth
    double M   = floor((double)nr / avg_dp + 0.5);   // an approximate number of variants samples in the population
    if ( M>call->n ) M = call->n;       // clamp M at the number of samples
    else if ( M==0 ) M = 1;
    double f = M / 2. / call->n;        // allele frequency
    double p = (double) nr / call->n;   // number of variant reads per sample expected if variant not real (poisson)
    double q = (double) nr / M;         // number of variant reads per sample expected if variant is real (poisson)
    double sum = 0;
    const double log2 = log(2.0);

    // fprintf(stderr,"M=%.1f  p=%e q=%e f=%f  dp=%d\n",M,p,q,f,avg_dp);
    int i;
    for (i=0; i<call->n; i++)
    {
        int oi = bcr[i].anno[2] + bcr[i].anno[3];       // observed number of non-ref reads
        double tmp;
        if ( oi )
        {
            // tmp = log(f) + oi*log(q/p) - q + log(2*(1-f) + f*pow(2,oi)*exp(-q)) + p; // this can under/overflow
            tmp = logsumexp2(log(2*(1-f)), log(f) + oi*log2 - q);
            tmp += log(f) + oi*log(q/p) - q + p;
        }
        else
            tmp = log(2*f*(1-f)*exp(-q) + f*f*exp(-2*q) + (1-f)*(1-f)) + p;
        sum += tmp;
        // fprintf(stderr,"oi=%d %e\n", oi,tmp);
    }
    call->seg_bias = sum;
}

/**
 *  bcf_call_combine() - sets the PL array and VDB, RPB annotations, finds the top two alleles
 *  @n:         number of samples
 *  @calls:     each sample's calls
 *  @bca:       auxiliary data structure for holding temporary values
 *  @ref_base:  the reference base
 *  @call:      filled with the annotations
 *
 *  Combines calls across the various samples being studied
 *  1. For each allele at each base across all samples the quality is summed so
 *     you end up with a set of quality sums for each allele present 2. The quality
 *     sums are sorted.
 *  3. Using the sorted quality sums we now create the allele ordering array
 *     A\subN. This is done by doing the following: 
 *     a) If the reference allele is known it always comes first, otherwise N
 *        comes first.  
 *     b) Then the rest of the alleles are output in descending order of quality
 *        sum (which we already know the qsum array was sorted).  Any allelles with
 *        qsum 0 will be excluded.
 *  4. Using the allele ordering array we create the genotype ordering array.
 *     In the worst case with an unknown reference this will be:  A0/A0 A1/A0 A1/A1
 *     A2/A0 A2/A1 A2/A2 A3/A0 A3/A1 A3/A2 A3/A3 A4/A0 A4/A1 A4/A2 A4/A3 A4/A4 
 *  5. The genotype ordering array is then used to extract data from the error
 *     model 5*5 matrix and is used to produce a Phread likelihood array for each
 *     sample.
 */
int bcf_call_combine(int n, const bcf_callret1_t *calls, bcf_callaux_t *bca, int ref_base /*4-bit*/, bcf_call_t *call)
{
	int ref4, i, j;
    unsigned int qsum[5] = {0,0,0,0,0};
	int64_t tmp;
	if (ref_base >= 0) {
		call->ori_ref = ref4 = bam_nt16_nt4_table[ref_base];
		if (ref4 > 4) ref4 = 4;
	} else call->ori_ref = -1, ref4 = 0;
	// calculate qsum, this is done by calculating combined qsum across all samples
	for (i = 0; i < n; ++i)
		for (j = 0; j < 4; ++j)
			qsum[j] += calls[i].qsum[j];
	// then encoding the base in the first two bits
    int qsum_tot=0;
    for (j=0; j<4; j++) { qsum_tot += qsum[j]; call->qsum[j] = 0; }
	for (j=0; j<4; j++) 
    { 
        assert( !(qsum[j]>>(sizeof(unsigned int)*8-2)) );   // if qsum is too big, insert sort will break
        qsum[j] = qsum[j] << 2 | j; 
    }
	// find the top 2 alleles
	for (i = 1; i < 4; ++i) // insertion sort, ascending
		for (j = i; j > 0 && qsum[j] < qsum[j-1]; --j)
			tmp = qsum[j], qsum[j] = qsum[j-1], qsum[j-1] = tmp;

	// Set the reference allele and alternative allele(s)

	// Set the alleles and QS values
	for (i = 0; i < 5; ++i) call->a[i] = -1;
	for (i = 0; i < 5; ++i) call->qsum[i] = 0;
	call->unseen = -1;
	call->a[0] = ref4;
	for (i = 3, j = 1; i >= 0; --i) // i: alleles sorted by QS; j, a[j]: output allele ordering
    {
		if ((qsum[i]&3) == ref4) 
            call->qsum[0] = qsum_tot ? (float)(qsum[i]>>2)/qsum_tot : 0;    // REF's qsum
        else
        {
			if ( !(qsum[i]>>2) ) break; // qsum is 0, this allele is not seen in the pileup
            call->qsum[j] = (float)(qsum[i]>>2)/qsum_tot;
            call->a[j++]  = qsum[i]&3;
		}
	}
	if (ref_base >= 0) { // for SNPs, find the "unseen" base
		if (((ref4 < 4 && j < 4) || (ref4 == 4 && j < 5)) && i >= 0)
			call->unseen = j, call->a[j++] = qsum[i]&3;
		call->n_alleles = j;
	} else {
		call->n_alleles = j;
		if (call->n_alleles == 1) return -1; // no reliable supporting read. stop doing anything
	}
	/*
     * Set the phread likelihood array (call->PL) This array is 15 entries long
     * for each sample because that is size of an upper or lower triangle of a
     * worst case 5x5 matrix of possible genotypes. This worst case matrix will
     * occur when all 4 possible alleles are present and the reference allele
     * is unknown.  The sides of the matrix will correspond to the reference
     * allele (if known) followed by the alleles present in descending order of
     * quality sum
	 */
	{
		int x, g[15], z;
		double sum_min = 0.;
		x = call->n_alleles * (call->n_alleles + 1) / 2;
		// get the possible genotypes
		// this is done by creating an ordered list of locations g for call (allele a, allele b) in the genotype likelihood matrix
		for (i = z = 0; i < call->n_alleles; ++i) {
			for (j = 0; j <= i; ++j) {
				g[z++] = call->a[j] * 5 + call->a[i];
			}
		}
		// for each sample calculate the PL
		for (i = 0; i < n; ++i) 
        {
			uint32_t *PL = call->PL + x * i;
			const bcf_callret1_t *r = calls + i;
            float min = FLT_MAX;
            for (j = 0; j < x; ++j) {
                if (min > r->p[g[j]]) min = r->p[g[j]];
            }
            sum_min += min;
            for (j = 0; j < x; ++j) {
                int y;
                y = (int)(r->p[g[j]] - min + .499);
                if (y > 255) y = 255;
                PL[j] = y;
            }
        }
        if ( call->DP )
            for (i=0; i<n; i++) call->DP[i] = calls[i].depth;
        if ( call->DV ) 
            for (i=0; i<n; i++) call->DV[i] = calls[i].n_supp;

//		if (ref_base < 0) fprintf(stderr, "%d,%d,%f,%d\n", call->n_alleles, x, sum_min, call->unseen);
		call->shift = (int)(sum_min + .499);
	}
	// combine annotations
	memset(call->anno, 0, 16 * sizeof(double));
    call->ori_depth = 0;
    call->depth     = 0;
    call->mq0       = 0;
	for (i = 0; i < n; ++i) {
		call->depth += calls[i].depth;
		call->ori_depth += calls[i].ori_depth;
        call->mq0 += calls[i].mq0;
		for (j = 0; j < 16; ++j) call->anno[j] += calls[i].anno[j];
	}

    calc_SegBias(calls, call);

    // calc_chisq_bias("XPOS", call->bcf_hdr->id[BCF_DT_CTG][call->tid].key, call->pos, bca->ref_pos, bca->alt_pos, bca->npos);
    // calc_chisq_bias("XMQ", call->bcf_hdr->id[BCF_DT_CTG][call->tid].key, call->pos, bca->ref_mq, bca->alt_mq, bca->nqual);
    // calc_chisq_bias("XBQ", call->bcf_hdr->id[BCF_DT_CTG][call->tid].key, call->pos, bca->ref_bq, bca->alt_bq, bca->nqual);

    call->mwu_pos = calc_mwu_bias(bca->ref_pos, bca->alt_pos, bca->npos);
    call->mwu_mq  = calc_mwu_bias(bca->ref_mq,  bca->alt_mq,  bca->nqual);
    call->mwu_bq  = calc_mwu_bias(bca->ref_bq,  bca->alt_bq,  bca->nqual);
    call->mwu_mqs = calc_mwu_bias(bca->fwd_mqs, bca->rev_mqs, bca->nqual);

#if CDF_MWU_TESTS
    call->mwu_pos_cdf = calc_mwu_bias_cdf(bca->ref_pos, bca->alt_pos, bca->npos);
    call->mwu_mq_cdf  = calc_mwu_bias_cdf(bca->ref_mq,  bca->alt_mq,  bca->nqual);
    call->mwu_bq_cdf  = calc_mwu_bias_cdf(bca->ref_bq,  bca->alt_bq,  bca->nqual);
    call->mwu_mqs_cdf = calc_mwu_bias_cdf(bca->fwd_mqs, bca->rev_mqs, bca->nqual);
#endif

    call->vdb = calc_vdb(bca->alt_pos, bca->npos);

	return 0;
}

int bcf_call2bcf(bcf_call_t *bc, bcf1_t *rec, bcf_callret1_t *bcr, int fmt_flag, const bcf_callaux_t *bca, const char *ref)
{
	extern double kt_fisher_exact(int n11, int n12, int n21, int n22, double *_left, double *_right, double *two);
	int i, j, nals = 1;

    bcf_hdr_t *hdr = bc->bcf_hdr;
    rec->rid  = bc->tid;
    rec->pos  = bc->pos;
    rec->qual = 0;

	bc->tmp.l = 0;
	if (bc->ori_ref < 0)    // indel
    {
		// REF
		kputc(ref[bc->pos], &bc->tmp); 
		for (j = 0; j < bca->indelreg; ++j) kputc(ref[bc->pos+1+j], &bc->tmp);

		// ALT
		for (i=1; i<4; i++) 
        {
			if (bc->a[i] < 0) break;
            kputc(',', &bc->tmp); kputc(ref[bc->pos], &bc->tmp);

			if (bca->indel_types[bc->a[i]] < 0) { // deletion
				for (j = -bca->indel_types[bc->a[i]]; j < bca->indelreg; ++j)
					kputc(ref[bc->pos+1+j], &bc->tmp);
			} else { // insertion; cannot be a reference unless a bug
				char *inscns = &bca->inscns[bc->a[i] * bca->maxins];
				for (j = 0; j < bca->indel_types[bc->a[i]]; ++j)
					kputc("ACGTN"[(int)inscns[j]], &bc->tmp);
				for (j = 0; j < bca->indelreg; ++j) kputc(ref[bc->pos+1+j], &bc->tmp);
			}
            nals++;
		}
	} 
    else    // SNP
    {
		kputc("ACGTN"[bc->ori_ref], &bc->tmp);
		for (i=1; i<5; i++) 
        {
			if (bc->a[i] < 0) break;
			kputc(',', &bc->tmp);
			kputc(bc->unseen == i? 'X' : "ACGT"[bc->a[i]], &bc->tmp);
            nals++;
		}
	}
    bcf_update_alleles_str(hdr, rec, bc->tmp.s);

    bc->tmp.l = 0;

	// INFO
	if (bc->ori_ref < 0) 
    {
        bcf_update_info_flag(hdr, rec, "INDEL", NULL, 1);
        bcf_update_info_int32(hdr, rec, "IDV", &bca->max_support, 1);
        bcf_update_info_float(hdr, rec, "IMF", &bca->max_frac, 1);
    }
    bcf_update_info_int32(hdr, rec, "DP", &bc->ori_depth, 1);

    float tmpf[16];
    for (i=0; i<16; i++) tmpf[i] = bc->anno[i];
    bcf_update_info_float(hdr, rec, "I16", tmpf, 16);

    for (i=4; i>0; i--)
        if ( bc->qsum[i]!=0 ) break;
    bcf_update_info_float(hdr, rec, "QS", bc->qsum, i+1);

    if ( bc->vdb != HUGE_VAL )      bcf_update_info_float(hdr, rec, "VDB", &bc->vdb, 1);
    if ( bc->seg_bias != HUGE_VAL ) bcf_update_info_float(hdr, rec, "SGB", &bc->seg_bias, 1);
    if ( bc->mwu_pos != HUGE_VAL )  bcf_update_info_float(hdr, rec, "RPB", &bc->mwu_pos, 1);
    if ( bc->mwu_mq != HUGE_VAL )   bcf_update_info_float(hdr, rec, "MQB", &bc->mwu_mq, 1);
    if ( bc->mwu_mqs != HUGE_VAL )  bcf_update_info_float(hdr, rec, "MQSB", &bc->mwu_mqs, 1);
    if ( bc->mwu_bq != HUGE_VAL )   bcf_update_info_float(hdr, rec, "BQB", &bc->mwu_bq, 1);
#if CDF_MWU_TESTS
    if ( bc->mwu_pos_cdf != HUGE_VAL )  bcf_update_info_float(hdr, rec, "RPB2", &bc->mwu_pos_cdf, 1);
    if ( bc->mwu_mq_cdf != HUGE_VAL )   bcf_update_info_float(hdr, rec, "MQB2", &bc->mwu_mq_cdf, 1);
    if ( bc->mwu_mqs_cdf != HUGE_VAL )  bcf_update_info_float(hdr, rec, "MQSB2", &bc->mwu_mqs_cdf, 1);
    if ( bc->mwu_bq_cdf != HUGE_VAL )   bcf_update_info_float(hdr, rec, "BQB2", &bc->mwu_bq_cdf, 1);
#endif
    tmpf[0] = bc->ori_depth ? (float)bc->mq0/bc->ori_depth : 0;
    bcf_update_info_float(hdr, rec, "MQ0F", tmpf, 1);

	// FORMAT
    rec->n_sample = bc->n;
    bcf_update_format_int32(hdr, rec, "PL", bc->PL, nals*(nals+1)/2 * rec->n_sample);
    if (bc->DP) bcf_update_format_int32(hdr, rec, "DP", bc->DP, rec->n_sample);
    if (bc->DV) bcf_update_format_int32(hdr, rec, "DV", bc->DV, rec->n_sample);
    // todo: SP, per-sample strand-bias?

	return 0;
}