/* Function: esl_dst_XPairId()
* Synopsis: Pairwise identity of two aligned digital seqs.
* Incept: SRE, Tue Apr 18 09:24:05 2006 [St. Louis]
*
* Purpose: Digital version of <esl_dst_PairId()>: <adsq1> and
* <adsq2> are digitized aligned sequences, in alphabet
* <abc>. Otherwise, same as <esl_dst_PairId()>.
*
* Args: abc - digital alphabet in use
* ax1 - aligned digital seq 1
* ax2 - aligned digital seq 2
* opt_pid - optRETURN: pairwise identity, 0<=x<=1
* opt_nid - optRETURN: # of identities
* opt_n - optRETURN: denominator MIN(len1,len2)
*
* Returns: <eslOK> on success. <opt_distance>, <opt_nid>, <opt_n>
* contain the answers, for any of these that were passed
* non-<NULL> pointers.
*
* Throws: <eslEINVAL> if the strings are different lengths (not aligned).
*/
int
esl_dst_XPairId(const ESL_ALPHABET *abc, const ESL_DSQ *ax1, const ESL_DSQ *ax2,
double *opt_distance, int *opt_nid, int *opt_n)
{
int status;
int idents; /* total identical positions */
int len1, len2; /* lengths of seqs */
int i; /* position in aligned seqs */
idents = len1 = len2 = 0;
for (i = 1; ax1[i] != eslDSQ_SENTINEL && ax2[i] != eslDSQ_SENTINEL; i++)
{
if (esl_abc_XIsCanonical(abc, ax1[i])) len1++;
if (esl_abc_XIsCanonical(abc, ax2[i])) len2++;
if (esl_abc_XIsCanonical(abc, ax1[i]) && esl_abc_XIsCanonical(abc, ax2[i])
&& ax1[i] == ax2[i])
idents++;
}
if (len2 < len1) len1 = len2;
if (ax1[i] != eslDSQ_SENTINEL || ax2[i] != eslDSQ_SENTINEL)
ESL_XEXCEPTION(eslEINVAL, "strings not same length, not aligned");
if (opt_distance != NULL) *opt_distance = ( len1==0 ? 0. : (double) idents / (double) len1 );
if (opt_nid != NULL) *opt_nid = idents;
if (opt_n != NULL) *opt_n = len1;
return eslOK;
ERROR:
if (opt_distance != NULL) *opt_distance = 0.;
if (opt_nid != NULL) *opt_nid = 0;
if (opt_n != NULL) *opt_n = 0;
return status;
}
/* Function: esl_dst_XJukesCantor()
* Synopsis: Jukes-Cantor distance for two aligned digitized seqs.
* Incept: SRE, Tue Apr 18 15:26:51 2006 [St. Louis]
*
* Purpose: Calculate the generalized Jukes-Cantor distance between two
* aligned digital strings <ax> and <ay>, in substitutions/site,
* using alphabet <abc> to evaluate identities and differences.
* The maximum likelihood estimate for the distance is optionally returned in
* <opt_distance>. The large-sample variance for the distance
* estimate is optionally returned in <opt_variance>.
*
* Identical to <esl_dst_CJukesCantor()>, except that it takes
* digital sequences instead of character strings.
*
* Args: abc - bioalphabet to use for comparisons
* ax - 1st digital aligned seq
* ay - 2nd digital aligned seq
* opt_distance - optRETURN: ML estimate of distance d
* opt_variance - optRETURN: large-sample variance of d
*
* Returns: <eslOK> on success. As in <esl_dst_CJukesCantor()>, the
* distance and variance may be infinite, in which case they
* are returned as <HUGE_VAL>.
*
* Throws: <eslEINVAL> if the two strings aren't the same length (and
* thus can't have been properly aligned).
* <eslEDIVZERO> if no aligned residues were counted.
* On either failure, the distance and variance are set
* to <HUGE_VAL>.
*/
int
esl_dst_XJukesCantor(const ESL_ALPHABET *abc, const ESL_DSQ *ax, const ESL_DSQ *ay,
double *opt_distance, double *opt_variance)
{
int status;
int n1, n2; /* number of observed identities, substitutions */
int i; /* position in aligned seqs */
n1 = n2 = 0;
for (i = 1; ax[i] != eslDSQ_SENTINEL && ay[i] != eslDSQ_SENTINEL; i++)
{
if (esl_abc_XIsCanonical(abc, ax[i]) && esl_abc_XIsCanonical(abc, ay[i]))
{
if (ax[i] == ay[i]) n1++;
else n2++;
}
}
if (ax[i] != eslDSQ_SENTINEL || ay[i] != eslDSQ_SENTINEL)
ESL_XEXCEPTION(eslEINVAL, "strings not same length, not aligned");
return jukescantor(n1, n2, abc->K, opt_distance, opt_variance);
ERROR:
if (opt_distance != NULL) *opt_distance = HUGE_VAL;
if (opt_variance != NULL) *opt_variance = HUGE_VAL;
return status;
}
/* Function: esl_msaweight_PB()
* Synopsis: PB (position-based) weights.
* Incept: SRE, Sun Nov 5 08:59:28 2006 [Janelia]
*
* Purpose: Given a multiple alignment <msa>, calculate sequence
* weights according to the position-based weighting
* algorithm (Henikoff and Henikoff, JMB 243:574-578,
* 1994). These weights are stored internally in the <msa>
* object, replacing any weights that may have already been
* there. Weights are $\geq 0$ and they sum to <msa->nseq>.
*
* The <msa> may be in either digitized or text mode.
* Digital mode is preferred, so that the algorithm
* deals with degenerate residue symbols properly.
*
* The Henikoffs' algorithm does not give rules for dealing
* with gaps or degenerate residue symbols. The rule here
* is to ignore them. This means that longer sequences
* initially get more weight; hence a "double
* normalization" in which the weights are first divided by
* sequence length in canonical residues (to compensate for
* that effect), then normalized to sum to nseq.
*
* An advantage of the PB method is efficiency.
* It is $O(1)$ in memory and $O(NL)$ time, for an alignment of
* N sequences and L columns. This makes it a good method
* for ad hoc weighting of very deep alignments.
*
* When the alignment is in simple text mode, IUPAC
* degenerate symbols are not dealt with correctly; instead,
* the algorithm simply uses the 26 letters as "residues"
* (case-insensitively), and treats all other residues as
* gaps.
*
* Returns: <eslOK> on success, and the weights inside <msa> have been
* modified.
*
* Throws: <eslEMEM> on allocation error, in which case <msa> is
* returned unmodified.
*
* Xref: [Henikoff94b]; squid::weight.c::PositionBasedWeights().
*/
int
esl_msaweight_PB(ESL_MSA *msa)
{
int *nres = NULL; /* counts of each residue observed in a column */
int ntotal; /* number of different symbols observed in a column */
int rlen; /* number of residues in a sequence */
int idx, pos, i;
int K; /* alphabet size */
int status;
/* Contract checks
*/
ESL_DASSERT1( (msa->nseq >= 1) );
ESL_DASSERT1( (msa->alen >= 1) );
if (msa->nseq == 1) { msa->wgt[0] = 1.0; return eslOK; }
/* Initialize
*/
if (! (msa->flags & eslMSA_DIGITAL))
{ ESL_ALLOC(nres, sizeof(int) * 26); K = 26; }
#ifdef eslAUGMENT_ALPHABET
else
{ ESL_ALLOC(nres, sizeof(int) * msa->abc->K); K = msa->abc->K; }
#endif
esl_vec_DSet(msa->wgt, msa->nseq, 0.);
/* This section handles text alignments */
if (! (msa->flags & eslMSA_DIGITAL))
{
for (pos = 0; pos < msa->alen; pos++)
{
/* Collect # of letters A..Z in this column, and total */
esl_vec_ISet(nres, K, 0.);
for (idx = 0; idx < msa->nseq; idx++)
if (isalpha((int) msa->aseq[idx][pos]))
nres[toupper((int) msa->aseq[idx][pos]) - 'A'] ++;
for (ntotal = 0, i = 0; i < K; i++) if (nres[i] > 0) ntotal++;
/* Bump weight on each seq by PB rule */
if (ntotal > 0) {
for (idx = 0; idx < msa->nseq; idx++) {
if (isalpha((int) msa->aseq[idx][pos]))
msa->wgt[idx] += 1. /
(double) (ntotal * nres[toupper((int) msa->aseq[idx][pos]) - 'A'] );
}
}
}
/* first normalization by # of residues counted in each seq */
for (idx = 0; idx < msa->nseq; idx++) {
for (rlen = 0, pos = 0; pos < msa->alen; pos++)
if (isalpha((int) msa->aseq[idx][pos])) rlen++;
if (ntotal > 0) msa->wgt[idx] /= (double) rlen;
/* if rlen == 0 for this seq, its weight is still 0.0, as initialized. */
}
}
/* This section handles digital alignments. */
#ifdef eslAUGMENT_ALPHABET
else
{
for (pos = 1; pos <= msa->alen; pos++)
{
/* Collect # of residues 0..K-1 in this column, and total # */
esl_vec_ISet(nres, K, 0.);
for (idx = 0; idx < msa->nseq; idx++)
if (esl_abc_XIsCanonical(msa->abc, msa->ax[idx][pos]))
nres[(int) msa->ax[idx][pos]] ++;
for (ntotal = 0, i = 0; i < K; i++) if (nres[i] > 0) ntotal++;
/* Bump weight on each sequence by PB rule */
if (ntotal > 0) {
for (idx = 0; idx < msa->nseq; idx++) {
if (esl_abc_XIsCanonical(msa->abc, msa->ax[idx][pos]))
msa->wgt[idx] += 1. / (double) (ntotal * nres[msa->ax[idx][pos]]);
}
}
}
/* first normalization by # of residues counted in each seq */
for (idx = 0; idx < msa->nseq; idx++)
{
for (rlen = 0, pos = 1; pos <= msa->alen; pos++)
if (esl_abc_XIsCanonical(msa->abc, msa->ax[idx][pos])) rlen++;
if (rlen > 0) msa->wgt[idx] /= (double) rlen;
/* if rlen == 0 for this seq, its weight is still 0.0, as initialized. */
}
}
#endif
/* Make weights normalize up to nseq, and return. In pathological
* case where all wgts were 0 (no seqs contain any unambiguous
* residues), weights become 1.0.
*/
esl_vec_DNorm(msa->wgt, msa->nseq);
esl_vec_DScale(msa->wgt, msa->nseq, (double) msa->nseq);
msa->flags |= eslMSA_HASWGTS;
free(nres);
return eslOK;
ERROR:
if (nres != NULL) free(nres);
return status;
}