Exemplo n.º 1
0
def findVarDisGeneDrug(pmid, text):
    """
    >>> startup({})
    >>> list(findVarDisGeneDrug(0, "The R71G BRCA1 is a breast cancer founder mutation not treatable with Herceptin"))
    """
    textLow = text.lower()
    # very basic filter, remove documents without some basic keywords
    if " variant " not in textLow and " mutation" not in textLow and " substitution" not in textLow and \
            " mutant " not in textLow:
        return

    for section, sentStart, sentEnd, sentText in pubNlp.sectionSentences(text):
        genes = list(geneFinder.findGeneNames(sentText))
        if len(genes) == 0:
            continue
        #print "genes", genes, sentText

        conds = list(pubNlp.findDiseases(sentText))
        drugs = list(pubNlp.findDrugs(sentText))
        # remove diseases and drugs that are also genes
        drugs = pubNlp.rangeRemoveOverlaps(drugs, genes)
        conds = pubNlp.rangeRemoveOverlaps(conds, genes)
        # check if we still have a disease and drug left
        if len(conds) == 0 or len(drugs) == 0:
            continue
        print "drugs", drugs
        print "diseases", conds

        geneSnips = pubNlp.rangeTexts(sentText, genes)
        condSnips = pubNlp.rangeTexts(sentText, conds)
        drugSnips = pubNlp.rangeTexts(sentText, drugs)

        genePosSet = pubNlp.rangeToPosSet(genes)
        variants = varFinder.findVariantDescriptions(sentText,
                                                     exclPos=genePosSet)

        # the last field of the genes rows is the entrez ID
        entrezIds = [r[-1] for r in genes]

        # we need a protein variant, not DNA
        if "prot" not in variants:
            continue

        for variant, mentions in variants["prot"]:
            print "grounding variant", variant, mentions
            groundedMuts, ungroundVar, beds = \
                varFinder.groundVariant(pmid, sentText, variant, mentions, [], entrezIds)

            for mutInfo in groundedMuts:
                coords = [(m.start, m.end) for m in mentions]
                varSnips = pubNlp.rangeTexts(sentText, coords)
                row = [
                    section, "|".join(geneSnips), "|".join(condSnips),
                    "|".join(drugSnips), "|".join(varSnips), sentText
                ]
                yield row
Exemplo n.º 2
0
def findVarDisGeneDrug(pmid, text):
    """
    >>> startup({})
    >>> list(findVarDisGeneDrug(0, "The R71G BRCA1 is a breast cancer founder mutation not treatable with Herceptin"))
    """
    textLow = text.lower()
    # very basic filter, remove documents without some basic keywords
    if " variant " not in textLow and " mutation" not in textLow and " substitution" not in textLow and \
            " mutant " not in textLow:
        return

    for section, sentStart, sentEnd, sentText in pubNlp.sectionSentences(text):
        genes = list(geneFinder.findGeneNames(sentText))
        if len(genes)==0:
            continue
        #print "genes", genes, sentText

        conds = list(pubNlp.findDiseases(sentText))
        drugs = list(pubNlp.findDrugs(sentText))
        # remove diseases and drugs that are also genes
        drugs = pubNlp.rangeRemoveOverlaps(drugs, genes)
        conds = pubNlp.rangeRemoveOverlaps(conds, genes)
        # check if we still have a disease and drug left
        if len(conds)==0 or len(drugs)==0:
            continue
        print "drugs", drugs
        print "diseases", conds

        geneSnips = pubNlp.rangeTexts(sentText, genes)
        condSnips = pubNlp.rangeTexts(sentText, conds)
        drugSnips = pubNlp.rangeTexts(sentText, drugs)

        genePosSet = pubNlp.rangeToPosSet(genes)
        variants  = varFinder.findVariantDescriptions(sentText, exclPos=genePosSet)

        # the last field of the genes rows is the entrez ID
        entrezIds = [r[-1] for r in genes]

        # we need a protein variant, not DNA
        if "prot" not in variants:
            continue

        for variant, mentions in variants["prot"]:
            print "grounding variant", variant, mentions
            groundedMuts, ungroundVar, beds = \
                varFinder.groundVariant(pmid, sentText, variant, mentions, [], entrezIds)

            for mutInfo in groundedMuts:
                coords = [(m.start, m.end) for m in mentions]
                varSnips = pubNlp.rangeTexts(sentText, coords)
                row = [section, "|".join(geneSnips), "|".join(condSnips), "|".join(drugSnips), "|".join(varSnips), sentText]
                yield row
Exemplo n.º 3
0
def findDisGeneVariant(text):
    """
    >>> geneFinder.initData(exclMarkerTypes=["dnaSeq", "band"])
    >>> varFinder.loadDb(loadSequences=False)
    >>> list(findDisGeneVariant("Diabetes is caused by a PITX2 mutation, V234T and influenced by Herceptin."))
    [(0, 74, 'probablyAbstract', '64-73:Herceptin=Trastuzumab', '0-8:Diabetes=Diabetes Mellitus', '24-29:PITX2=symbol', 'V233T', 'Diabetes is caused by a PITX2 mutation, V234T and influenced by Herceptin.')]
    >>> #list(findDisGeneVariant("We undertook a quantitative review of the literature to estimate the effectiveness of desferrioxamine and deferiprone in decreasing hepatic iron concentrations (HIC) in thalassemia major."))
    >>> list(findDisGeneVariant("his mutation, we cotransfected C3H10T cells with expression vectors encoding SMO-WT or SMO-D473H "))
    """
    docGenes = list(geneFinder.findGeneNames(text))
    docEntrezIds = set([r[-1] for r in docGenes])

    for section, start, end, sentence in pubNlp.sectionSentences(text):
        conds = list(pubNlp.findDiseases(sentence))
        drugs = list(pubNlp.findDrugs(sentence))
        genes = list(geneFinder.findGeneNames(sentence))
        #print conds, drugs, genes, section, sentence
        # remove drugs and conds that are also genes
        drugs = rangeRemoveOverlaps(drugs, genes)
        conds = rangeRemoveOverlaps(conds, genes)

        #geneSnips = rangeTexts(sentence, genes, useSym=True)
        #condSnips = rangeTexts(sentence, conds)
        #drugSnips = rangeTexts(sentence, drugs)
#
        mutDescs = []
        mutDict = varFinder.findVariantDescriptions(sentence)
        if "prot" in mutDict:
            for varDesc, mentions in mutDict["prot"]:
                if varDesc.mutType!="sub":
                    continue
                logging.debug("grounding variant: %s %s"% (varDesc, mentions))
                groundedMuts, ungroundVar, beds = \
                    varFinder.groundVariant(None, sentence, varDesc, mentions, [], docEntrezIds)

                for mutInfo in groundedMuts:
                    shortDesc = varDesc.origSeq+str(varDesc.start+1)+varDesc.mutSeq # 0-based!!
                    mutDescs.append(shortDesc+"=%s:%s"%(mutInfo.geneSymbol,mutInfo.hgvsProt))
            
        #mutMatches =  list(mutRe.finditer(sentence))
        #mutDescs = [(m.group(1),m.group(2), m.group(3)) for m in mutMatches]
        #mutDescSet = set(mutDescs)
        #blackListMuts = mutDescSet.intersection(blackListStr)
        #if len(mutMatches)==0:
            #logging.debug("No mutation found, skipping")
            #continue
        #if len(blackListMuts)!=0:
            #logging.debug("At least one blacklisted mutation found, skipping")
            #continue
        #if len(drugs)==0:
            #logging.debug("No drugs found, skipping")
            #continue
        #if len(genes)==0:
            #logging.debug("No genes found, skipping")
            #continue
    
        mutDesc = "|".join(mutDescs)
        drugDesc = rangeDescs(sentence, drugs)
        condDesc = rangeDescs(sentence, conds)
        geneDesc = rangeDescs(sentence, genes, useSym=True)

        ret = (start, end, section, drugDesc, condDesc, geneDesc, mutDesc, sentence)
        yield ret
Exemplo n.º 4
0
def findDisGeneVariant(text):
    """
    >>> geneFinder.initData(exclMarkerTypes=["dnaSeq", "band"])
    >>> varFinder.loadDb(loadSequences=False)
    >>> list(findDisGeneVariant("Diabetes is caused by a PITX2 mutation, V234T and influenced by Herceptin."))
    [(0, 74, 'probablyAbstract', '64-73:Herceptin=Trastuzumab', '0-8:Diabetes=Diabetes Mellitus', '24-29:PITX2=symbol', 'V233T', 'Diabetes is caused by a PITX2 mutation, V234T and influenced by Herceptin.')]
    >>> #list(findDisGeneVariant("We undertook a quantitative review of the literature to estimate the effectiveness of desferrioxamine and deferiprone in decreasing hepatic iron concentrations (HIC) in thalassemia major."))
    >>> list(findDisGeneVariant("his mutation, we cotransfected C3H10T cells with expression vectors encoding SMO-WT or SMO-D473H "))
    """
    docGenes = list(geneFinder.findGeneNames(text))
    docEntrezIds = set([r[-1] for r in docGenes])

    for section, start, end, sentence in pubNlp.sectionSentences(text):
        conds = list(pubNlp.findDiseases(sentence))
        drugs = list(pubNlp.findDrugs(sentence))
        genes = list(geneFinder.findGeneNames(sentence))
        #print conds, drugs, genes, section, sentence
        # remove drugs and conds that are also genes
        drugs = rangeRemoveOverlaps(drugs, genes)
        conds = rangeRemoveOverlaps(conds, genes)

        #geneSnips = rangeTexts(sentence, genes, useSym=True)
        #condSnips = rangeTexts(sentence, conds)
        #drugSnips = rangeTexts(sentence, drugs)
        #
        mutDescs = []
        mutDict = varFinder.findVariantDescriptions(sentence)
        if "prot" in mutDict:
            for varDesc, mentions in mutDict["prot"]:
                if varDesc.mutType != "sub":
                    continue
                logging.debug("grounding variant: %s %s" % (varDesc, mentions))
                groundedMuts, ungroundVar, beds = \
                    varFinder.groundVariant(None, sentence, varDesc, mentions, [], docEntrezIds)

                for mutInfo in groundedMuts:
                    shortDesc = varDesc.origSeq + str(
                        varDesc.start + 1) + varDesc.mutSeq  # 0-based!!
                    mutDescs.append(shortDesc + "=%s:%s" %
                                    (mutInfo.geneSymbol, mutInfo.hgvsProt))

        #mutMatches =  list(mutRe.finditer(sentence))
        #mutDescs = [(m.group(1),m.group(2), m.group(3)) for m in mutMatches]
        #mutDescSet = set(mutDescs)
        #blackListMuts = mutDescSet.intersection(blackListStr)
        #if len(mutMatches)==0:
        #logging.debug("No mutation found, skipping")
        #continue
        #if len(blackListMuts)!=0:
        #logging.debug("At least one blacklisted mutation found, skipping")
        #continue
        #if len(drugs)==0:
        #logging.debug("No drugs found, skipping")
        #continue
        #if len(genes)==0:
        #logging.debug("No genes found, skipping")
        #continue

        mutDesc = "|".join(mutDescs)
        drugDesc = rangeDescs(sentence, drugs)
        condDesc = rangeDescs(sentence, conds)
        geneDesc = rangeDescs(sentence, genes, useSym=True)

        ret = (start, end, section, drugDesc, condDesc, geneDesc, mutDesc,
               sentence)
        yield ret