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5bb077e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | """In-process progressive MSA fallback (pure Biopython).
Used when every EBI MSA endpoint is unreachable or times out, so the pipeline
never stalls on the MSA step. Produces a sensible progressive alignment plus an
UPGMA guide-tree Newick. Marked as ``method: "in-process fallback"`` in the
result so the UI can indicate it is not a Clustal Omega alignment.
"""
from __future__ import annotations
import logging
logger = logging.getLogger(__name__)
def _aligner(stype: str):
from Bio.Align import PairwiseAligner
from Bio.Align import substitution_matrices
aligner = PairwiseAligner()
aligner.mode = "global"
if stype == "protein":
try:
aligner.substitution_matrix = substitution_matrices.load("BLOSUM62")
except Exception:
aligner.substitution_matrix = None
aligner.match_score = 1.0
aligner.mismatch_score = -1.0
aligner.open_gap_score = -11.0
aligner.extend_gap_score = -1.0
else:
aligner.substitution_matrix = None
aligner.match_score = 2.0
aligner.mismatch_score = -1.0
aligner.open_gap_score = -2.0
aligner.extend_gap_score = -0.5
return aligner
def _pairwise(aligner, s1: str, s2: str) -> tuple[str, str]:
"""Return (gapped_s1, gapped_s2) from the best global alignment."""
aln = aligner.align(s1, s2)[0]
ncol = int(aln.shape[1])
return _gapped(s1, aln.aligned[0], ncol), _gapped(s2, aln.aligned[1], ncol)
def _gapped(seq: str, aligned, ncol: int) -> str:
"""Turn an alignment block list (start, end) pairs into a gapped string of
exactly ``ncol`` columns (pad for un-aligned overhangs)."""
out: list[str] = []
prev = 0
for start, end in aligned:
out.append("-" * (start - prev))
out.append(seq[start:end])
prev = end
out.append("-" * (ncol - len("".join(out))))
return "".join(out)
def _identity(g1: str, g2: str) -> float:
aligned = sum(1 for a, b in zip(g1, g2) if a != "-" and b != "-")
if aligned == 0:
return 0.0
matches = sum(1 for a, b in zip(g1, g2) if a == b and a != "-")
return matches / aligned
def _upgma_newick(labels: list[str], dist: list[list[float]]) -> str:
"""UPGMA clustering to a Newick tree. Falls back to a star tree on error."""
try:
n = len(labels)
d = {i: {j: dist[i][j] for j in range(n)} for i in range(n)}
size = {i: 1 for i in range(n)}
names = {i: _safe_label(labels[i]) for i in range(n)}
active = set(range(n))
next_id = n
while len(active) > 1:
best = None
for i in active:
for j in active:
if i < j and (best is None or d[i][j] < best[0]):
best = (d[i][j], i, j)
if best is None:
break
_, i, j = best
si, sj = size[i], size[j]
merged = next_id
next_id += 1
d[merged] = {}
for k in active:
if k in (i, j):
continue
d[merged][k] = d[k][merged] = (d[i][k] * si + d[j][k] * sj) / (si + sj)
names[merged] = f"({names[i]}:{d[i][j]/2:.4f},{names[j]}:{d[i][j]/2:.4f})"
size[merged] = si + sj
active.remove(i)
active.remove(j)
active.add(merged)
root = active.pop()
return names[root] + ";"
except Exception as e: # never let a tree build error block the fallback
logger.warning("UPGMA Newick build failed, using star tree: %s", e)
return _star_newick(labels)
def _safe_label(label: str) -> str:
clean = "".join(c for c in label if c.isalnum() or c in "_.")
return clean or "seq"
def _star_newick(labels: list[str]) -> str:
leaves = ",".join(_safe_label(l) for l in labels)
return f"({leaves});" if labels else "(root);"
def _expand(row: str, c_aln: str) -> str:
"""Map an existing profile row (old consensus columns) onto a new alignment
of the consensus against a new sequence. Every letter in ``c_aln`` consumes
one old column; every gap inserts a new gap column."""
out = []
ci = 0
for ch in c_aln:
if ch == "-":
out.append("-")
else:
out.append(row[ci] if ci < len(row) else "-")
ci += 1
return "".join(out)
def _consensus(rows: list[str]) -> str:
L = len(rows[0])
cons: list[str] = []
for col in range(L):
counts: dict[str, int] = {}
for r in rows:
c = r[col]
if c != "-":
counts[c] = counts.get(c, 0) + 1
cons.append(max(counts, key=counts.get) if counts else "X")
return "".join(cons)
def progressive_msa(sequences: list[tuple[str, str]], stype: str = "protein") -> tuple[str, str]:
"""Return ``(fasta, newick)`` from a pure-Biopython progressive MSA."""
ids = [s[0] for s in sequences]
seqs = [s[1] for s in sequences]
n = len(seqs)
if n == 0:
raise ValueError("No sequences to align")
aligner = _aligner(stype)
if n == 1:
fasta = _to_fasta([(ids[0], seqs[0])])
return fasta, _star_newick(ids)
# Distance matrix (1 - pairwise identity)
dist = [[0.0] * n for _ in range(n)]
for i in range(n):
for j in range(i + 1, n):
g1, g2 = _pairwise(aligner, seqs[i], seqs[j])
dist[i][j] = dist[j][i] = 1.0 - _identity(g1, g2)
newick = _upgma_newick(ids, dist)
# Progressive alignment: align each sequence against the current consensus
# and gap-transfer the result onto every already-added profile row.
rows = [seqs[0]]
cons = seqs[0]
for s in seqs[1:]:
c_aln, s_aln = _pairwise(aligner, cons, s)
rows = [_expand(row, c_aln) for row in rows]
rows.append(s_aln)
cons = _consensus(rows)
fasta = _to_fasta([(ids[k], rows[k]) for k in range(n)])
return fasta, newick
def _to_fasta(seqs: list[tuple[str, str]], width: int = 80) -> str:
lines: list[str] = []
for sid, sseq in seqs:
lines.append(f">{sid}")
for i in range(0, len(sseq), width):
lines.append(sseq[i : i + width])
return "\n".join(lines)
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