def __init__(self, filename):
self.orgmir2mimat = defaultdict(set)
self.mimat2mi = {}
self.mimat2orgmi = {}
self.mimat2orgmir = {}
self.mi2orgmi = defaultdict(set)
self.mi2orgmir = defaultdict(set)
self.orgmi2mi = defaultdict(set)
self.orgmir2mi = defaultdict(set)
allRelations = DataFrame.parseFromFile(filename,
bConvertTextToNumber=False)
for row in allRelations:
mimat = row['MIMAT']
orgmir = row['ORGMIR']
mi = row['MI']
orgmi = row['ORGMI']
self.mimat2mi[mimat] = mi
self.mimat2orgmir[mimat] = orgmir
self.mimat2orgmi[mimat] = orgmi
self.orgmir2mimat[orgmir].add(mimat)
self.mi2orgmi[mi].add(orgmi)
self.mi2orgmir[mi].add(orgmir)
self.orgmir2mi[orgmir].add(mi)
self.orgmi2mi[orgmi].add(mi)
def dfSummary(cls, thisObservation, mc=10):
df = DataFrame()
madeObs = cls.summarizeKmers(thisObservation, mc)
for idx, obs in enumerate(madeObs):
if idx == 0: # header
cols = [x for x in obs]
df.addColumns(cols)
dfrow = DataRow.fromDict(obs)
df.addRow(dfrow)
return df
pseudoCount = 1
allSamples = []
for x in args.groups:
for y in x:
if not y in allSamples:
allSamples.append(y)
print(allSamples)
for fidx, defile in enumerate(args.counts):
indf = DataFrame.parseFromFile(defile.name,
skipChar='#',
replacements={
"None": None,
"": None,
"NA": None
})
dfCols = indf.getHeader()
allDerivedSamples = []
replaceSamples = {}
for sample in allSamples:
if sample in dfCols:
replaceSamples[sample] = sample
allDerivedSamples.append(sample)
else:
else:
foundAcceptedInteractions[x].add(mirna)
for mirna in fInts:
mirnaFound = False
for defMirna in defInts:
if defMirna.accept(mirna):
mirnaFound = True
break
if mirnaFound == False:
additionalInteractions[x].add(miRNA(mirna))
missingDF = DataFrame()
missingDF.addColumns([
'chemokine', 'miRNA Group', 'miRNA', 'Original Network', 'PubMed',
'MIRECORD', 'MIRTARBASE', 'MIRWALK'
])
linkedDF = DataFrame()
linkedDF.addColumns([
'chemokine', 'miRNA Group', 'miRNA', 'Original Network', 'PubMed',
'MIRECORD', 'MIRTARBASE', 'MIRWALK'
])
totalMissing = 0
print("Missing miRNAs")
for x in missingInteractions:
print(x, len(missingInteractions[x]), len(interactions[x]),
import json
from collections import Counter
from collections import defaultdict
from porestat.utils.DataFrame import DataFrame, ExportTYPE
base = "/mnt/c/ownCloud/data/bcn/"
inputFile = base + "all/CV-IPN-Endothelial cell activation1.0.sif"
inputFile = base + "manual/CV-IPN-Endothelial cell activation1.0.jgf"
allNodesLabels = {}
allEdges = []
uniqueNodes = set()
if inputFile.endswith('.sif'):
interactions = DataFrame.parseFromFile(inputFile,
['source', 'interaction', 'target'])
for row in interactions:
src = row['source']
dst = row['target']
if src is None or dst is None:
continue
uniqueNodes.add(src)
uniqueNodes.add(dst)
for row in interactions:
src = row['source']
dst = row['target']
edge2support[cellpair].add(mirna)
print(stage, mirna, cellpair[0], cellpair[1], mirnaCellPairs[mirna][cellpair], stageMirnaCellPairs[cellpair], stageMir2CellEvidence[stage][mirna].get(cellpair[0]),stageMir2CellEvidence[stage][mirna].get(cellpair[1]) )
cellgraph = networkx.Graph()
allnodes = set()
for edge in edge2support:
allnodes.add(edge[0])
allnodes.add(edge[1])
for node in allnodes:
cellgraph.add_node(node[1] + " ("+node[0]+")", size=20 + stageCellCount[node])
cellCommunicatorDF = DataFrame()
cellCommunicatorDF.addColumns(["miRNA", "cells"])
mirna2cells = defaultdict(set)
for edge in edge2support:
cellgraph.add_edge(
edge[0][1] + " (" + edge[0][0] + ")",
edge[1][1] + " (" + edge[1][0] + ")",
label=", ".join(edge2support.get(edge, [])))
mirnas = edge2support.get(edge, [])
for mirna in mirnas:
mirna2cells[mirna].add(edge[0][1] + " (" + edge[0][0] + ")")
mirna2cells[mirna].add(edge[1][1] + " (" + edge[1][0] + ")")
from Bio import Entrez
from porestat.utils.DataFrame import DataFrame
from utils.idutils import miRExploreDir
dbData = DataFrame.parseFromFile(miRExploreDir + "/miR2Disease/AllEntries.txt", ['mirna', 'disease', 'effect', 'measurement', 'year', 'title'], bConvertTextToNumber=False)
pmidTitleIdx = dbData.getColumnIndex('title')
allTitles = []
for row in dbData:
title = row['title']
if title == None:
continue
title = title.strip()
if not title[-1] == '.':
title += "."
allTitles.append(title)
allTitles = list(set(allTitles))
print(len(allTitles))
titlesSearch = []
help='alignment files')
parser.add_argument('-o',
'--output',
type=str,
required=False,
help="output base")
args = parser.parse_args()
if args.output == None:
args.output = args.summary.name
indf = DataFrame.parseFromFile(args.summary.name,
skipChar='#',
replacements={
"None": None,
"": None,
"NA": None
})
allStatus = []
allCols = indf.getHeader()
allCols.remove("Status")
for row in indf:
allStatus.append(row["Status"])
sampleData = defaultdict(lambda: dict())
for row in indf:
from collections import Counter
from collections import defaultdict
from porestat.utils.DataFrame import DataFrame, ExportTYPE
interactions = DataFrame.parseFromFile(
"/home/users/joppich/ownCloud/data/chemokines_sfb/chemokine_interactions.tsv"
)
#interactions.export("/home/users/joppich/ownCloud/data/chemokines_sfb/chemokine_interactions.html", ExportTYPE.HTML)
uniqueEdges = set()
chemList = [
'CXCR2',
'CCL9',
'CXCL5',
'CXCL1',
'CXCL13',
'CXCL7',
'CCL2',
'CXCL9',
'CCL3',
'CXCL10',
'CCL22',
'CCR5',
'CCR7',
'CCL7',
'CCL4',
'CXCR4',
'CX3CL1',
import gzip
import os
from collections import defaultdict
from porestat.utils.DataFrame import DataFrame
from utils.idutils import miRExploreDir
hgncData = DataFrame.parseFromFile(miRExploreDir + "/hgnc.tsv")
allUniprotIDs = set()
for row in hgncData:
uniprotVals = row['UniProt ID(supplied by UniProt)']
if uniprotVals == None:
continue
uniprotVals = uniprotVals.strip()
uniprotIDs = uniprotVals.split(', ')
for x in uniprotIDs:
allUniprotIDs.add(x)
print(len(allUniprotIDs))
allUniprotIDs = sorted(allUniprotIDs)
uniprot2ipr = defaultdict(set)
neededUniprotIDs = miRExploreDir + "/interpro/relevant.uniprot.list"
# zgrep -f relevant.uniprot.list > relevant.uniprot.ipr.list
else:
foundAcceptedInteractions[x].add(mirna)
for mirna in fInts:
mirnaFound = False
for defMirna in defInts:
if defMirna.accept(mirna):
mirnaFound = True
break
if mirnaFound == False:
additionalInteractions[x].add(miRNA(mirna))
missingDF = DataFrame()
missingDF.addColumns(
['chemokine', 'miRNA Group', 'miRNA', 'Weber', 'PubMed', 'MIRTARBASE'])
linkedDF = DataFrame()
linkedDF.addColumns(
['chemokine', 'miRNA Group', 'miRNA', 'Weber', 'PubMed', 'MIRTARBASE'])
totalMissing = 0
print("Missing miRNAs")
for x in missingInteractions:
print(x, len(missingInteractions[x]), len(interactions[x]),
missingInteractions[x])
totalMissing += len(missingInteractions[x])
for article in record['PubmedArticle']:
pubmedID = article['PubmedData']['ArticleIdList'][0] if len(
article['PubmedData']['ArticleIdList']) > 0 else "-1"
pubID = int(pubmedID)
artInfo = article['MedlineCitation']['Article']
articleTitle = artInfo['ArticleTitle']
articleJournal = artInfo['Journal'][
'Title'] if 'Journal' in artInfo else ''
pmid2title[pubID] = articleTitle
return pmid2title
res = DataFrame()
res.addColumns(["SET", "PMID_ID", "PMID_TITLE", 'Common'])
print(ntd)
print("NTD", len(ntd))
pmidt = getPMIDTitles(ntd)
for x in sorted([x for x in pmidt]):
dataDict = {
'SET':
'NTinfect',
'PMID_ID':
"<a href='https://www.ncbi.nlm.nih.gov/pubmed/" + str(x) +
"' target='_blank'>" + str(x) + "</a>",
'PMID_TITLE':
dbs2pmids["CELLS"] = cellPMIDs
print(datetime.datetime.now(), "Loading ncit")
ncitPMIDs = easyPMIDFinder(args.pmidBase + "/ncit.pmid")
dbs2pmids["NCIT"] = ncitPMIDs
with open("/mnt/d/pmidsindims.pickle", 'wb') as fout:
pickle.dump(dbs2pmids, fout)
else:
with open("/mnt/d/pmidsindims.pickle", 'rb') as fout:
dbs2pmids = pickle.load(fout)
outdf = DataFrame()
outdf.addColumns(["Subset", "Number of PMIDs"])
allDims = [x for x in dbs2pmids]
allPowerSets = powerset(sorted(allDims))
allPMIDs = set()
for x in dbs2pmids:
allPMIDs = allPMIDs.union(dbs2pmids[x])
for pset in allPowerSets:
if len(pset) == 0:
continue
from collections import Counter
from neo4j.v1 import GraphDatabase, basic_auth
from porestat.utils.DataFrame import DataFrame
from utils.idutils import ltype2label, makeDBGeneID, dataDir
from database.Neo4JInterface import neo4jInterface
hgncGenes = DataFrame.parseFromFile(dataDir +
"/miRExplore/hgnc_ensembl_entrez.tsv",
bConvertTextToNumber=False)
allStatus = Counter()
db = neo4jInterface(simulate=False)
db.createUniquenessConstraint('GENE', 'id')
db.deleteRelationship('n', None, None, 'm', None, None, ['HAS_GENE'], None,
'r')
db.deleteNode(["GENE"], None)
for gene in hgncGenes:
hgncID = gene['HGNC ID']
hgncSym = gene['Approved Symbol']
hgncName = gene['Approved Name']
hgncEnsembl = gene['Ensembl ID(supplied by Ensembl)']
hgncEntrez = gene['Entrez Gene ID(supplied by NCBI)']
hgncStatus = gene['Status']
hgncLocusType = gene['Locus Type']
'miR-145', 'miR-155', 'miR-302a', 'miR-758', 'miR-223', 'miR-378'
]
networks['targetMirsCholEfflux'] = targetMirsCholEfflux
# SMC proliferation / migration
targetMirsSMCProlif = [
'miR-24', 'miR-26a', 'miR-31', 'miR-146a', 'miR-155', 'miR-208',
'miR-221', 'miR-222', 'miR-7d', 'let-7d', 'miR-1', 'miR-10a', 'miR-21',
'miR-29', 'miR-100', 'miR-132', 'miR-133', 'miR-143', 'miR-145',
'miR-195', 'miR-204', 'miR-424', 'miR-638', 'miR-663'
]
networks['targetMirsSMCProlif'] = targetMirsSMCProlif
summaryDF = DataFrame()
summaryDF.addColumns(
["Network", "Accepted miRNAs", 'Additional miRNAs', "Missing miRNAs"])
networkGraphs = {}
makeStory = []
allNetworks = [x for x in networks]
print(allNetworks)
#exit()
ignoreNetworks = []
networkRestrictions = {
'targetMirsECA': {
from collections import defaultdict
import editdistance
from nertoolkit.geneontology.GeneOntology import GeneOntology
from porestat.utils.DataFrame import DataFrame
from utils.idutils import miRExploreDir
dbData = DataFrame.parseFromFile(miRExploreDir + "/miR2Disease/mirna_disease.tsv", bConvertTextToNumber=False)
allDiseases = set()
for row in dbData:
disease = row['disease']
if disease == 'None':
continue
allDiseases.add(disease.upper())
print(len(allDiseases))
diseaseObo = GeneOntology(miRExploreDir + "/doid.obo")
disease2obo = defaultdict(set)
"""
find perfect matches
"""
"hgnc2sym2ens2uniprot") as fin:
for line in fin:
line = line.strip().split("\t")
sym = line[0]
approvSym = line[3]
sym2approvSym[sym] = approvSym
availSets = {}
setDF = DataFrame.parseFromFile(args.sets,
skipChar='#',
replacements={
"None": None,
"": None,
"NA": None
})
allSetGenes = set()
for row in setDF:
availSets[row['set_id']] = (row["set_descr"],
set([
sym2approvSym[x.strip()]
for x in row["genes"].split(";")
if x.strip() in sym2approvSym
]))
allSetGenes = allSetGenes.union(availSets[row['set_id']][1])
print("Got", len(availSets), "sets with a total of", len(allSetGenes),
def fetch(self,
fromEntity,
elements,
toEntities=[
GeneIdentity.UNIPROT, GeneIdentity.GENE_SYMBOL,
GeneIdentity.ORDERED_LOCUS
],
error_on_empty_result=True):
self._must_accept(fromEntity)
self._must_provide(toEntities)
elements = sorted(elements)
reqParams = self._make_params(fromEntity, elements, toEntities)
for x in reqParams:
lenReqParams = len(reqParams[x])
if lenReqParams < 100:
print(str(x) + " " + str(reqParams[x]))
else:
print(str(x) + " " + str(lenReqParams) + " elements")
resp = self._request(RequestMethod.POST, "", reqParams)
if (resp.text == None):
print(json.dumps(reqParams))
raise StoreException("Could not retrieve elements")
if len(resp.text) == 0 and error_on_empty_result:
raise StoreException("Empty result")
convData = DataFrame()
dfCols = toEntities + [fromEntity]
convData.addColumns(dfCols)
def addLineToReturn(lineData):
modLine = {}
for c, x in zip(dfCols, lineData):
if x == '':
modLine[c] = None
else:
modLine[c] = x
convData.addRow(DataRow.fromDict(modLine))
bFirstLine = True
for line in resp.text.split('\n'):
if bFirstLine:
bFirstLine = False
continue
if len(line) == 0:
continue
aline = line.split('\t')
if len(aline) == 0:
continue
aline = aline[:-1]
if ',' in aline[-1]:
elems = aline[-1].split(',')
elemCount = len(elems)
for i in range(0, elemCount):
modLine = []
for elem in aline[:-1]:
aelem = elem.split(' ')
if len(aelem) != elemCount:
modLine.append(elem)
else:
modLine.append(aelem[i])
modLine.append(elems[i])
addLineToReturn(modLine)
else:
addLineToReturn(aline)
return convData
homDB.homologies[combid] = elems
homDB.finalize()
homDB.save_to_file(fileLocation + "combed")
"""
for orgname in homDB.get_all_organisms():
genomDB.loadGenome(orgname)
allorgs = list(homDB.get_all_organisms())
mc = ['4_N1-031C1', '2_N1-025A2', '14_1-20A_UB64', '13_N5-004A1', '3_N1-029C1', '11_N4-029C2', '10_N2-085C2', '1_N1-024A1']
nmc = [x for x in allorgs if not x in mc] # and not x.startswith("15")
allData = DataFrame()
allData.addColumns(allorgs)
homClusterIDs = []
for homid in homDB.homologies:
val = homDB.get_homology_cluster(homid)
maxlength = 0
for org in val:
geneid = val[org]
seq = genomDB.get_sequence(org, geneid)
#parser.add_argument('-c', '--cutoff', type=float, help='alignment files', default=0.05)
parser.add_argument('-minfc',
'--min-foldchange',
type=float,
default=1.0,
required=False)
parser.add_argument('-minpval',
'--min-pvalue',
type=float,
default=0.05,
required=False)
args = parser.parse_args()
for fidx, defile in enumerate(args.de):
indf = DataFrame.parseFromFile(defile.name)
availMethods = set()
headername2idx = {}
indfHeader = indf.getHeader()
genesymname = None
if "gene_symbol" in indfHeader:
genesymname = "gene_symbol"
elif "Geneid" in indfHeader:
genesymname = "Geneid"
else:
genesymname = "id"
from urllib import request
from porestat.utils.DataFrame import DataFrame
allgenomes = DataFrame.parseFromFile("../../../ena_bacteria_list.csv")
print(allgenomes.getHeader())
for row in allgenomes:
protInfo = row['proteins']
print(protInfo)
for row in allgenomes:
protInfo = row['proteins']
if protInfo == None or len(protInfo) == 0 or len(
protInfo.strip()) == 0 or protInfo == 'n/a' or protInfo == 'None':
continue
downloadFile = row['seqID'] + ".gb"
downloadLocation = "../../../genomes/"
print(downloadFile)
request.urlretrieve(
"http://www.ebi.ac.uk/ena/data/view/" + row['seqID'] +
"&display=txt&expanded=true", downloadLocation + "/" + downloadFile)
from collections import defaultdict
from porestat.utils.DataFrame import DataFrame
from database.MIRFamily import MIRFamilyDB
from neo4j.v1 import GraphDatabase, basic_auth
from database.Neo4JInterface import neo4jInterface
from database.ORGMIRs import ORGMIRDB
from synonymes.mirnaID import miRNA, miRNAPART
from utils.idutils import dataDir
mirbase = DataFrame.parseFromFile(dataDir + "/miRExplore/mirnas_mirbase.csv",
bConvertTextToNumber=False)
filename = dataDir + "/miRExplore/miFam.dat"
familyDB = MIRFamilyDB(filename)
print(mirbase.getHeader())
db = neo4jInterface(simulate=False, printQueries=False)
db.deleteRelationship('n', None, None, 'm', None, None, ['IS_ORG_MI'], None,
'r')
db.deleteRelationship('n', None, None, 'm', None, None, ['IS_ORG_MIR'], None,
'r')
db.deleteRelationship('n', None, None, 'm', None, None, ['IS_ORG_MIRNA'], None,
'r')
db.deleteRelationship('n', ['MIRNA'], None, 'm', ['MIRNA_PRE'], None,
['MIRNA_MATURE_OF'], None, 'r')
db.deleteRelationship('n', ['MIRNA'], None, 'm', ['MIRNA_FAMILY'], None,
parser.add_argument("-p", "--prefixes", nargs='+', type=str, required=True)
parser.add_argument('-s2', '--samples', nargs='+', type=str, default=[])
parser.add_argument('-pc',
'--prefix-counts',
dest="prefix_counts",
action='store_true',
default=False,
help="run FC part")
args = parser.parse_args()
curDF = DataFrame.parseFromFile(args.de[0].name,
skipChar='#',
replacements={
"None": None,
"": None,
"NA": None
})
for didx, deTable in enumerate(args.de):
if didx == 0:
continue
indf2 = DataFrame.parseFromFile(deTable.name,
skipChar='#',
replacements={
"None": None,
"": None,
"NA": None
parser.add_argument('-p', '--pval', type=float, default=0.05)
parser.add_argument('-t', '--tools', nargs='+')
parser.add_argument('-o', '--output', type=argparse.FileType("w"), required=True)
#parser.add_argument('-g', '--gene', type=str, required=True, help="gene id column name")
args = parser.parse_args()
for fidx, defile in enumerate(args.counts):
indf = DataFrame.parseFromFile(defile.name, skipChar='#', replacements = {
"None": None,
"": None,
"NA": None
})
inHeaders = indf.getHeader()
#if not args.gene in inHeaders:
# print("Unknown gene id column", args.gene)
# print(inHeaders)
# exit(-1)
allconditions = []
for conditions in args.conditions:
for condition in conditions:
action='store_true',
default=False)
args = parser.parse_args()
enhancedData = loadEnhancement(args.enhanced)
geneLengths = loadGeneLengths(args.lengths)
if args.norrna and enhancedData == None:
raise argparse.ArgumentParser().error(
"removal of rRNA requires --enhanced!")
indf = DataFrame.parseFromFile(args.fc.name,
skipChar='#',
replacements={
"None": None,
"": None,
"NA": None
})
allheaders = indf.getHeader()
featureCountsColumns = [
y for y in ["Geneid", "Chr", "Start", "End", "Strand", "Length"]
if y in allheaders
]
sampleHeaders = [x for x in allheaders if not x in featureCountsColumns]
outdf = DataFrame()
sample2total = Counter()
from collections import Counter, defaultdict
from porestat.utils.DataFrame import DataFrame
from utils.idutils import ltype2label, makeDBGeneID, mirtarbase_exp_type, mirtarbase_function_label, speciesName2TaxID, \
dataDir
from database.Neo4JInterface import neo4jInterface
from utils.parallel import MapReduce
mirtarbaseEvidences = DataFrame.parseFromFile(dataDir + "/miRExplore/miRTarBase.csv", bConvertTextToNumber=False)
print(mirtarbaseEvidences.getHeader())
experimentTypes = Counter()
supportTypes = Counter()
referencesWithComma = Counter()
db = neo4jInterface(simulate=False, printQueries=False)
db.deleteRelationship('n', ['GENE'], None, 'm', ['MIRTARBASE'], None, ['GENE_MENTION'], None, 'r')
db.deleteRelationship('n', ['MIRTARBASE'], None, 'm', ['MIRNA'], None, ['MIRNA_MENTION'], None, 'r')
db.deleteRelationship('n', ['MIRTARBASE'], None, 'm', ['PUBMED'], None, ['MIRTARBASE_LITERATURE_SUPPORT'], None, 'r')
db.deleteRelationship('n', ['MIRTARBASE_SUPPORT'], None, 'm', ['MIRTARBASE'], None, ['MIRTARBASE_FUNCTIONAL_SUPPORT'], None, 'r')
db.deleteRelationship('n', ['MIRTARBASE_EXPERIMENT'], None, 'm', ['MIRTARBASE'], None, ['MIRTARBASE_EXPERIMENT_SUPPORT'], None, 'r')
db.deleteRelationship('n', ['MIRTARBASE'], None, 'm', ['TAX'], None, ['ORGANISM_SUPPORT'], None, 'r')
db.deleteNode(["MIRTARBASE"], None)
db.deleteNode(["MIRTARBASE_SUPPORT"], None)
db.deleteNode(["MIRTARBASE_EXPERIMENT"], None)
db.createUniquenessConstraint('MIRTARBASE', 'id')
if False:
db.close()
parser.add_argument('-s2', '--samples', nargs='+', type=str, default=[])
parser.add_argument('-pc',
'--prefix-counts',
dest="prefix_counts",
action='store_true',
default=False,
help="run FC part")
args = parser.parse_args()
indf1 = DataFrame.parseFromFile(args.de1.name,
skipChar='#',
replacements={
"None": None,
"": None,
"NA": None
})
indf2 = DataFrame.parseFromFile(args.de2.name,
skipChar='#',
replacements={
"None": None,
"": None,
"NA": None
})
allSamples = args.samples
print("all samples", allSamples)
nargs='+',
type=str,
required=False,
help="output base")
parser.add_argument('-p', '--pathname', action="store_true", default=False)
args = parser.parse_args()
if args.output == None:
args.output = [counts.name for counts in args.counts]
for fidx, defile in enumerate(args.counts):
indf = DataFrame.parseFromFile(defile.name,
skipChar='#',
replacements={
"None": None,
"": None,
"NA": None
})
inHeaders = indf.getHeader()
#if not args.gene in inHeaders:
# print("Unknown gene id column", args.gene)
# print(inHeaders)
# exit(-1)
for conditions in args.conditions:
for condition in conditions:
for orgname in homDB.get_all_organisms():
genomDB.loadGenome(orgname)
allorgs = list(homDB.get_all_organisms())
extra = ['AE001439', 'CP009259']
mc = [
'4_N1-031C1', '2_N1-025A2', '14_1-20A_UB64', '13_N5-004A1',
'3_N1-029C1', '11_N4-029C2', '10_N2-085C2', '1_N1-024A1'
]
nmc = [
x for x in allorgs
if not x in mc and not x in extra and not x.startswith("6_")
] # and not x.startswith("15")
allData = DataFrame()
allData.addColumns(allorgs)
homClusterIDs = []
for homid in homDB.homologies:
val = homDB.get_homology_cluster(homid)
maxlength = 0
for org in val:
geneid = val[org]
seq = genomDB.get_sequence(org, geneid)
if len(seq) > maxlength:
