data/beta/pairmsa.py

import numpy as np
from string import ascii_uppercase, ascii_lowercase
import urllib.parse
import urllib.request
import time

def parse_a3m(a3m_lines=None, a3m_file=None, filter_qid=0.15, filter_cov=0.5, N=100000):
  
  def seqid(a, b):
    return sum(c1 == c2 for c1, c2 in zip(a, b))
  
  def nongaps(a):
    return sum(c != "-" for c in a)
  
  def chk(seq, ref_seq):
    rL = len(ref_seq)
    L = nongaps(seq)
    return not (L > filter_cov*rL and seqid(seq, ref_seq) > filter_qid*L)

  rm_lower = str.maketrans('','',ascii_lowercase)

  # prep inputs
  if a3m_lines is None: a3m_lines = open(a3m_file,"r")
  else: a3m_lines = a3m_lines.splitlines()

  # parse inputs
  n,nams,seqs,mtx = 0,[],[],[]
  def do_filter():
    seq = seqs[-1].translate(rm_lower)
    if "_UPI" in nams[-1] or chk(seq,ref_seq):
      nams.pop()
      seqs.pop()
    else:
      # deletion matrix
      deletion_vec = []
      deletion_count = 0
      for j in seqs[-1]:
        if j.islower():
          deletion_count += 1
        else:
          deletion_vec.append(deletion_count)
          deletion_count = 0
      mtx.append(deletion_vec)
      seqs[-1] = seq

  for line in a3m_lines:
    line = line.rstrip()
    if line.startswith(">"):
      if n == 1:
        ref_seq = seqs[0].translate(rm_lower)
      if n >= 1:
        # filter previous entry
        do_filter()
      # start new sequence entry
      nam = line.split()[0][1:]
      if "_" not in nam: nam = f"X_{nam}"
      nams.append(nam)
      seqs.append("")
      n += 1
    else:
      seqs[-1] += line

  # filter last entry
  do_filter()

  if len(seqs) > N+1:
    print(f"found too many sequences ({len(seqs)}), taking the top{N} (sorted by qid)")
    sid = np.argsort([seqid(seq,ref_seq) for seq in seqs])[::-1][:N+1]
    seqs = [seqs[i] for i in sid]
    mtx = [mtx[i] for i in sid]
    nams = [nams[i] for i in sid]
  return seqs[1:],mtx[1:],nams[1:]

def get_uni_jackhmmer(msa, mtx, lab, filter_qid=0.15, filter_cov=0.5):
  '''filter entries to uniprot'''
  lab_,msa_,mtx_ = [],[],[]
  ref_seq = np.array(list(msa[0]))
  rL = len(ref_seq)
  for l,s,x in zip(lab[1:],msa[1:],mtx[1:]):
    if l.startswith("UniRef"):
      l = l.split("/")[0]
      if "_UPI" not in l:
        tar_seq = np.array(list(s))
        L = (tar_seq != "-").sum()
        qid = (ref_seq == tar_seq).sum()
        if L > filter_cov * rL and qid > filter_qid * L:
          lab_.append(l)
          msa_.append(s)
          mtx_.append(x)
  return msa_, mtx_, lab_

def uni_num(ids):
  ########################################
  pa = {a:0 for a in ascii_uppercase}
  for a in ["O","P","Q"]: pa[a] = 1
  ma = [[{} for k in range(6)],[{} for k in range(6)]]
  for n,t in enumerate(range(10)):
    for i in [0,1]:
      for j in [0,4]: ma[i][j][str(t)] = n
  for n,t in enumerate(list(ascii_uppercase)+list(range(10))):
    for i in [0,1]:
      for j in [1,2]: ma[i][j][str(t)] = n
    ma[1][3][str(t)] = n
  for n,t in enumerate(ascii_uppercase):
    ma[0][3][str(t)] = n
    for i in [0,1]: ma[i][5][str(t)] = n
  ########################################
  nums = []
  for uni in ids:
    p = pa[uni[0]]
    tot, num = 1,0
    if len(uni) == 10:
      for n,u in enumerate(reversed(uni[-4:])):
        num += ma[p][n][u] * tot
        tot *= len(ma[p][n].keys())
    for n,u in enumerate(reversed(uni[:6])):
      num += ma[p][n][u] * tot
      tot *= len(ma[p][n].keys())
    nums.append(num)
  return nums

def map_retrieve(ids, call_uniprot=False):

  if call_uniprot:
    mode = "NF100" if "UniRef100" in ids[0] else "NF90"
    url = 'https://www.uniprot.org/uploadlists/'
    out = []
    for i in range(0,len(ids),5000):
      params = {
      'from': mode,
      'to': 'ACC',
      'format': 'tab',
      'query': " ".join(ids[i:i+5000])
      }
      data = urllib.parse.urlencode(params)
      data = data.encode('utf-8')
      req = urllib.request.Request(url, data)
      with urllib.request.urlopen(req) as f:
        response = f.read()
      out += [line.split() for line in response.decode('utf-8').splitlines()]
      time.sleep(5)

    # combine mapping
    mapping = {}
    for i,j in out:
      if i != "From":
        if i not in mapping:
          mapping[i] = [j]
        else:
          mapping[i].append(j)
  else:
    mapping = {}

  for i in ids:
    if i not in mapping:
      mapping[i] = [i.split("_")[1]]
  
  return mapping

def hash_it(_seq, _lab, _mtx, call_uniprot=False):
  if _seq is None or _lab is None:
    _seq, _lab = parse_a3m(a3m_lines)

  _lab_to_seq = {L:S for L,S in zip(_lab,_seq)}
  _lab_to_mtx = {L:M for L,M in zip(_lab,_mtx)}
  
  # call uniprot
  _lab_to_uni = map_retrieve(_lab, call_uniprot=call_uniprot)
  
  _uni_to_lab = {}
  for L,U in _lab_to_uni.items():
    for u in U: _uni_to_lab[u] = L

  _uni,__lab = [],[]
  for U,L in _uni_to_lab.items():
    _uni.append(U)
    __lab.append(L)
  
  _hash = uni_num(_uni)
  _uni_to_hash = {u:h for u,h in zip(_uni,_hash)}
  _hash_to_lab = {h:l for h,l in zip(_hash,__lab)}

  _lab_to_hash = {}
  for L,U in _lab_to_uni.items():
    _lab_to_hash[L] = []
    for u in U: _lab_to_hash[L].append(_uni_to_hash[u])

  return {"_lab_to_seq":_lab_to_seq,
          "_lab_to_mtx":_lab_to_mtx,
          "_lab_to_hash":_lab_to_hash,
          "_hash_to_lab":_hash_to_lab}

import tqdm.notebook
TQDM_BAR_FORMAT = '{l_bar}{bar}| {n_fmt}/{total_fmt} [elapsed: {elapsed} remaining: {remaining}]'

# keeping old function for compatability
def stitch(_hash_a,_hash_b, stitch_min=1, stitch_max=20, filter_id=None):
  o = _stitch(_hash_a, _hash_b, stitch_min, stitch_max)
  return (*o["seq"],*o["mtx"])

def _stitch(_hash_a,_hash_b, stitch_min=1, stitch_max=20):
  _seq, _mtx, _lab, _delta_gene = [[],[]],[[],[]],[[],[]],[]
  TOTAL = len(_hash_a["_lab_to_hash"])
  with tqdm.notebook.tqdm(total=TOTAL, bar_format=TQDM_BAR_FORMAT) as pbar:
    pbar.set_description("STITCHING")
    H_A = np.asarray(list(_hash_a["_hash_to_lab"].keys()))
    H_B = np.asarray(list(_hash_b["_hash_to_lab"].keys()))

    def hit(h,H):
      h = np.asarray(h)
      match = np.abs(h[:,None]-H[None,:]).min(0)
      match_min = match.min()
      if match_min >= stitch_min and match_min <= stitch_max:
        return True,H[match.argmin()],match_min
      else:
        return False,None,None

    for n,(l_a,h_a) in enumerate(_hash_a["_lab_to_hash"].items()):
      chk_b, h_b, dg = hit(h_a,H_B)
      if chk_b:
        l_b = _hash_b["_hash_to_lab"][h_b]
        h_b = _hash_b["_lab_to_hash"][l_b]
        chk_c, h_c, _ = hit(h_b,H_A)
        if chk_c and _hash_a["_hash_to_lab"][h_c] == l_a:
          _seq[0].append(_hash_a["_lab_to_seq"][l_a])
          _mtx[0].append(_hash_a["_lab_to_mtx"][l_a])
          _lab[0].append(l_a)
          _seq[1].append(_hash_b["_lab_to_seq"][l_b])
          _mtx[1].append(_hash_b["_lab_to_mtx"][l_b])
          _lab[1].append(l_b)
          _delta_gene.append(dg)
      pbar.update()

  return {"seq":_seq,
          "mtx":_mtx,
          "lab":_lab,
          "delta_gene":_delta_gene}