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crispor.py
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#!/usr/bin/env python2.7
# the tefor crispr tool
# can be run as a CGI or from the command line
# OOF scores are WRONG for Cpf1! -> where is the cut site?
# OOF scores should not be shown for Cpf1... staggered cut!
# python std library
import subprocess, tempfile, optparse, logging, atexit, glob, shutil
import Cookie, time, sys, cgi, re, random, platform, os, pipes, gdbm
import hashlib, base64, string, logging, operator, urllib, sqlite3, time
import traceback, json, pwd, pickle, gzip, zlib
from StringIO import StringIO
from collections import defaultdict, namedtuple
from datetime import datetime
from itertools import product
from os.path import abspath, basename, dirname, isdir, isfile, join, relpath
try:
from collections import OrderedDict
except ImportError:
from ordereddict import OrderedDict # python2.6 users: run 'sudo pip install ordereddict'
# try to load external dependencies
# we're going into great lengths to create a readable error message
needModules = set(["pytabix", "twobitreader", "pandas", "matplotlib", "scipy"])
try:
import tabix # if not found, install with 'pip install pytabix'
needModules.remove("pytabix")
except:
pass
try:
import twobitreader # if not found, install with 'pip install twobitreader'
needModules.remove("twobitreader")
except:
pass
try:
import pandas # required by doench2016 score. install with 'pip install pandas'
needModules.remove("pandas")
import scipy # required by doench2016 score. install with 'pip install pandas'
needModules.remove("scipy")
import matplotlib # required by doench2016 score. install with 'pip install matplotlib'
needModules.remove("matplotlib")
import numpy # required by doench2016 score. install with 'pip install numpy'
needModules.remove("numpy")
except:
pass
if len(needModules)!=0:
print("Content-type: text/html\n")
print("Python interpreter path: %s<p>" % sys.executable)
print("These python modules were not found: %s<p>" % ",".join(needModules))
print("To install all requirements in one line, run: sudo pip install biopython numpy scikit-learn==0.16.1 pandas twobitreader<p>")
sys.exit(0)
# our own eff scoring library
import crisporEffScores
# don't report print as an error
# pylint: disable=E1601
# optional module for Excel export as native .xls files
# install with 'apt-get install python-xlwt' or 'pip install xlwt'
xlwtLoaded = True
try:
import xlwt
except:
sys.stderr.write("crispor.py - warning - the python xlwt module is not available\n")
xlwtLoaded = False
# optional module for mysql support
try:
import MySQLdb
mysqldbLoaded = True
except:
mysqldbLoaded = False
# version of crispor
versionStr = "4.92"
# contact email
contactEmail='crispor@tefor.net'
# url to this server
ctBaseUrl = "http://crispor-max.tefor.net/temp/customTracks"
# write debug output to stdout
DEBUG = False
#DEBUG = True
# use bowtie for off-target search?
useBowtie = False
# calculate the efficienc scores?
doEffScoring = True
# system-wide temporary directory
TEMPDIR = os.environ.get("TMPDIR", "/tmp")
# a hack for cluster jobs at UCSC:
# - default to ramdisk
if isdir("/scratch/tmp"):
TEMPDIR = "/dev/shm/"
# skipAlign is useful if your input sequence is not in the genome at all
# - don't do bwasw
# - this will trigger auto-ontarget: any perfect match is the on-target
# - do not calculate efficiency scores
skipAlign = False
# prefix in html statements before the directories "image/", "style/" and "js/"
HTMLPREFIX = ""
# alternative directory on local disk where image/, style/ and js/ are located
HTMLDIR = "/usr/local/apache/htdocs/crispor/"
# directory of crispor.py
baseDir = dirname(__file__)
# filename of this script, usually crispor.py
myName = basename(__file__)
# the segments.bed files use abbreviated genomic region names
segTypeConv = {"ex":"exon", "in":"intron", "ig":"intergenic"}
# directory for processed batches of offtargets ("cache" of bwa results)
batchDir = join(baseDir,"temp")
# use mysql or sqlite for the jobs?
jobQueueUseMysql = False
# the file where the sqlite job queue is stored
JOBQUEUEDB = join("/tmp/crisporJobs.db")
# alternatively: connection info for mysql
jobQueueMysqlConn = {"socket":None, "host":None, "user": None, "password" : None}
# directory for platform-independent scripts (e.g. Heng Li's perl SAM parser)
scriptDir = join(baseDir, "bin")
# directory for helper binaries (e.g. BWA)
binDir = abspath(join(baseDir, "bin", platform.system()))
# directory for genomes
genomesDir = join(baseDir, "genomes")
DEFAULTORG = 'hg19'
DEFAULTSEQ = 'cttcctttgtccccaatctgggcgcgcgccggcgccccctggcggcctaaggactcggcgcgccggaagtggccagggcgggggcgacctcggctcacagcgcgcccggctattctcgcagctcaccatgGATGATGATATCGCCGCGCTCGTCGTCGACAACGGCTCCGGCATGTGCAAGGCCGGCTTCGCGGGCGACGATGCCCCCCGGGCCGTCTTCCCCTCCATCGTGGGGCGCC'
# used if hg19 is not available
ALTORG = 'sacCer3'
ALTSEQ = 'ATTCTACTTTTCAACAATAATACATAAACatattggcttgtggtagCAACACTATCATGGTATCACTAACGTAAAAGTTCCTCAATATTGCAATTTGCTTGAACGGATGCTATTTCAGAATATTTCGTACTTACACAGGCCATACATTAGAATAATATGTCACATCACTGTCGTAACACTCT'
pamDesc = [ ('NGG','20bp-NGG - Sp Cas9, SpCas9-HF1, eSpCas9 1.1'),
('NNG','20bp-NNG - Cas9 S. canis'),
('NNGT','20bp-NNGT - Cas9 S. canis - high efficiency PAM, recommended'),
('NAA','20bp-NAA - iSpyMacCas9'),
#('TTN','TTN-23bp - Cpf1 F. Novicida'), # Jean-Paul: various people have shown that it's not usable yet
('NNGRRT','21bp-NNG(A/G)(A/G)T - Cas9 S. Aureus'),
('NNGRRT-20','20bp-NNG(A/G)(A/G)T - Cas9 S. Aureus with 20bp-guides'),
('NGK','20bp-NG(G/T) - xCas9, high efficiency PAM, recommended'),
('NGN','20bp-NGN or GA(A/T) - xCas9, low efficiency PAM'),
#('NGG-BE1','20bp-NGG - BaseEditor1, modifies C->T'),
('NNNRRT','21bp-NNN(A/G)(A/G)T - KKH SaCas9'),
('NNNRRT-20','20bp-NNN(A/G)(A/G)T - KKH SaCas9 with 20bp-guides'),
('NGA','20bp-NGA - Cas9 S. Pyogenes mutant VQR'),
('NNNNCC','24bp-NNNNCC - Nme2Cas9, NEW! 2019, A. Edraki'),
('NGCG','20bp-NGCG - Cas9 S. Pyogenes mutant VRER'),
('NNAGAA','20bp-NNAGAA - Cas9 S. Thermophilus'),
('NGGNG','20bp-NGGNG - Cas9 S. Thermophilus'),
('NNNNGMTT','20bp-NNNNG(A/C)TT - Cas9 N. Meningitidis'),
('NNNNACA','20bp-NNNNACA - Cas9 Campylobacter jejuni'),
('TTTV','TTT(A/C/G)-23bp - Cas12a (Cpf1) Acidaminoc. / Lachnosp. - recommended'),
('TTTN','TTTN-23bp - Cas12a (Cpf1) Acidaminoc. / Lachnosp - low efficiency')
#('TYCV','T(C/T)C(A/C/G)-23bp - TYCV As-Cpf1 K607R'),
#('TATV','TAT(A/C/G)-23bp - TATV As-Cpf1 K548V')
]
DEFAULTPAM = 'NGG'
# the default base editor modification window
DEFAULTBEWIN = "1-7"
# for some PAMs, there are alternative main PAMs. These are also shown on the main sequence panel
multiPams = {
"NGN" : ["GAW"]
}
# for some PAMs, we allow other alternative motifs when searching for offtargets
# MIT and eCrisp do that, they use the motif NGG + NAG, we add one more, based on the
# on the guideSeq results in Tsai et al, Nat Biot 2014
# The NGA -> NGG rule was described by Kleinstiver...Young 2015 "Improved Cas9 Specificity..."
# NNGTRRT rule for S. aureus is in the new protocol "SaCas9 User manual"
# ! the length of the alternate PAM has to be the same as the original PAM!
offtargetPams = {
"NGG" : ["NAG","NGA"],
"NGN" : ["GAW"],
"NGK" : ["GAW"],
"NGA" : ["NGG"],
"NNGRRT" : ["NNGRRN"]
}
# maximum size of an input sequence
MAXSEQLEN = 2000
# maximum input size when specifying "no genome"
MAXSEQLEN_NOGENOME = 25000
# maximum input size when using xCas9 or sCanis
MAXSEQLEN2 = 600
# BWA: allow up to X mismatches
maxMMs=4
# maximum number of occurences in the genome to get flagged as repeats.
# This is used in bwa samse, when converting the same file
# and for warnings in the table output.
MAXOCC = 60000
# the BWA queue size is 2M by default. We derive the queue size from MAXOCC
MFAC = 2000000/MAXOCC
# the length of the guide sequence, set by setupPamInfo
GUIDELEN=None
# length of the PAM sequence
PAMLEN=None
# the name of the base editor, if any. This is the flag to activate
# baseEditor mode in the UI
baseEditor = None
# input sequences are extended by X basepairs so we can calculate the efficiency scores
# and can better design primers
FLANKLEN=100
# the name of the currently processed batch, assigned only once
# in readBatchParams and only for json-type batches
batchName = ""
# are we doing a Cpf1 run?
# this variable changes almost all processing and
# has to be set on program start, as soon as we know
# the PAM we're running on
cpf1Mode=None
saCas9Mode=None
# Highly-sensitive mode (not for CLI mode):
# MAXOCC is increased in processSubmission() and in the html UI if only one
# guide seq is run
# Also, the number of allowed mismatches is increased to 5 instead of 4
#HIGH_MAXOCC=600000
#HIGH_maxMMs=5
# minimum off-target score of standard off-targets (those that end with NGG)
# This should probably be based on the CFD score these days
# But for now, I'll let the user do the filtering
MINSCORE = 0.0
# minimum off-target score for alternative PAM off-targets
# There is not a lot of data to support this cutoff, but it seems
# reasonable to have at least some cutoff, as otherwise we would show
# NAG and NGA like NGG and the data shows clearly that the alternative
# PAMs are not recognized as well as the main NGG PAM.
# so for now, I just filter out very degenerative ones. the best solution
# would be to have a special penalty on the CFD score, but CFS does not
# support non-NGG PAMs (is this actually true?)
ALTPAMMINSCORE = 1.0
# how much shall we extend the guide after the PAM to match restriction enzymes?
pamPlusLen = 5
# global flag to indicate if we're run from command line or as a CGI
commandLineMode = False
# names/order of efficiency scores to show in UI
cas9ScoreNames = ["fusi", "crisprScan"]
allScoreNames = ["fusi", "fusiOld", "chariRank", "ssc", "wuCrispr", "doench", "wang", "crisprScan", "aziInVitro", "ccTop"]
mutScoreNames = []
spCas9MutScoreNames = ["oof", 'lindel'] # lindel is only added for spCas9
otherMutScoreNames = ["oof"] # lindel is only added for spCas9
cpf1ScoreNames = ["seqDeepCpf1"]
saCas9ScoreNames = ["najm"]
# how many digits shall we show for each score? default is 0
scoreDigits = {
"ssc" : 1,
}
# List of AddGene plasmids, their long and short names:
addGenePlasmids = [
("43860", ("MLM3636 (Joung lab)", "MLM3636")),
("49330", ("pAc-sgRNA-Cas9 (Liu lab)", "pAcsgRnaCas9")),
("42230", ("pX330-U6-Chimeric_BB-CBh-hSpCas9 (Zhang lab) + derivatives", "pX330")),
("52961", ("lentiCRISPR v2 (Zhang lab)", "lentiCrispr")),
("52963", ("lentiGuide-Puro (Zhang lab)", "lentiGuide-Puro")),
]
addGenePlasmidsAureus = [
("61591", ("pX601-AAV-CMV::NLS-SaCas9-NLS-3xHA-bGHpA;U6::BsaI-sgRNA (Zhang lab)", "pX601")),
("61592", ("pX600-AAV-CMV::NLS-SaCas9-NLS-3xHA-bGHpA (Zhang lab)", "pX600")),
("61593", ("pX602-AAV-TBG::NLS-SaCas9-NLS-HA-OLLAS-bGHpA;U6::BsaI-sgRNA (Zhang lab)", "pX602")),
("65779", ("VVT1 (Joung lab)", "VVT1"))
]
# list of AddGene primer 5' and 3' extensions, one for each AddGene plasmid
# format: prefixFw, prefixRw, u6-G-suffix, restriction enzyme, link to protocol
addGenePlasmidInfo = {
"43860" : ("ACACC", "AAAAC", "G", "BsmBI", "https://www.addgene.org/static/data/plasmids/43/43860/43860-attachment_T35tt6ebKxov.pdf"),
"49330" : ("TTC", "AAC", "", "Bsp QI", "http://bio.biologists.org/content/3/1/42#sec-9"),
"42230" : ("CACC", "AAAC", "", "Bbs1", "https://www.addgene.org/static/data/plasmids/52/52961/52961-attachment_B3xTwla0bkYD.pdf"),
"52961" : ("CACC", "AAAC", "", "BsmBI", "https://www.addgene.org/static/data/plasmids/52/52961/52961-attachment_B3xTwla0bkYD.pdf"),
"61591" : ("CACC", "AAAC", "", "BsaI", "https://www.addgene.org/static/data/plasmids/61/61591/61591-attachment_it03kn5x5O6E.pdf"),
"61592" : ("CACC", "AAAC", "", "BsaI", "https://www.addgene.org/static/data/plasmids/61/61592/61592-attachment_iAbvIKnbqNRO.pdf"),
"61593" : ("CACC", "AAAC", "", "BsaI", "https://www.addgene.org/static/data/plasmids/61/61592/61592-attachment_iAbvIKnbqNRO.pdf"),
"65779": ("CACC", "AAAC", "", "BsmBI (aka Esp3l)", "https://www.addgene.org/static/data/plasmids/65/65779/65779-attachment_G8oNyvV6pA78.pdf"),
"52963": ("CACC", "AAAC", "", "BsmBI (aka Esp3l)", "https://www.addgene.org/static/data/plasmids/52/52963/52963-attachment_IPB7ZL_hJcbm.pdf")
}
# the barcodes for subpool tagging for oligo pool tables
satMutBarcodes = [
(0, "No Subpool barcode"),
(1, "Subpool 1: CGGGTTCCGT/GCTTAGAATAGAA"),
(2, "Subpool 2: GTTTATCGGGC/ACTTACTGTACC"),
(3, "Subpool 3: ACCGATGTTGAC/CTCGTAATAGC"),
(4, "Subpool 4: GAGGTCTTTCATGC/CACAACATA"),
(5, "Subpool 5: TATCCCGTGAAGCT/TTCGGTTAA"),
(6, "Subpool 6: TAGTAGTTCAGACGC/ATGTACCC"),
(7, "Subpool 7: GGATGCATGATCTAG/CATCAAGC"),
(8, "Subpool 8: ATGAGGACGAATCT/CACCTAAAG"),
(9, "Subpool 9: GGTAGGCACG/TAAACTTAGAACC"),
(10, "Subpool 10: AGTCATGATTCAG/GTTGCAAGTCTAG"),
]
# Restriction enzyme supplier codes
rebaseSuppliers = {
"B":"Life Technologies",
"C":"Minotech",
"E":"Agilent",
"I":"SibEnzyme",
"J":"Nippon Gene",
"K":"Takara",
"M":"Roche",
"N":"NEB",
"O":"Toyobo",
"Q":"Molecular Biology Resources",
"R":"Promega",
"S":"Sigma",
"V":"Vivantis",
"X":"EURx",
"Y":"SinaClon BioScience"
}
# labels and descriptions of eff. scores
scoreDescs = {
"doench" : ("Doench '14", "Range: 0-100. Linear regression model trained on 880 guides transfected into human MOLM13/NB4/TF1 cells (three genes) and mouse cells (six genes). Delivery: lentivirus. The Fusi score can be considered an updated version this score, as their training data overlaps a lot. See <a target='_blank' href='http://www.nature.com/nbt/journal/v32/n12/full/nbt.3026.html'>Doench et al.</a>"),
"wuCrispr" : ("Wu-Crispr", "Range 0-100. Aka 'Wong score'. SVM model trained on previously published data. The aim is to identify only a subset of efficient guides, many guides will have a score of 0. Takes into account RNA structure. See <a target='_blank' href='https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0784-0'>Wong et al., Gen Biol 2015</a>"),
"ssc" : ("Xu", "Range ~ -2 - +2. Aka 'SSC score'. Linear regression model trained on data from >1000 genes in human KBM7/HL60 cells (Wang et al) and mouse (Koike-Yusa et al.). Delivery: lentivirus. Ranges mostly -2 to +2. See <a target='_blank' href='http://genome.cshlp.org/content/early/2015/06/10/gr.191452.115'>Xu et al.</a>"),
"crisprScan" : ["Moreno-Mateos", "Also called 'CrisprScan'. Range: mostly 0-100. Linear regression model, trained on data from 1000 guides on >100 genes, from zebrafish 1-cell stage embryos injected with mRNA. See <a target=_blank href='http://www.nature.com/nmeth/journal/v12/n10/full/nmeth.3543.html'>Moreno-Mateos et al.</a>. Recommended for guides transcribed <i>in-vitro</i> (T7 promoter). Click to sort by this score."],
"wang" : ("Wang", "Range: 0-100. SVM model trained on human cell culture data on guides from >1000 genes. The Xu score can be considered an updated version of this score, as the training data overlaps a lot. Delivery: lentivirus. See <a target='_blank' href='http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3972032/'>Wang et al.</a>"),
"chariRank" : ("Chari", "Range: 0-100. Support Vector Machine, converted to rank-percent, trained on data from 1235 guides targeting sequences that were also transfected with a lentivirus into human 293T cells. See <a target='_blank' href='http://www.nature.com/nmeth/journal/v12/n9/abs/nmeth.3473.html'>Chari et al.</a>"),
"fusi" : ("Doench '16", "Aka the 'Fusi-Score', since V4.4 using the version 'Azimuth', scores are slightly different than before April 2018 but very similar (click 'show all' to see the old scores). Range: 0-100. Boosted Regression Tree model, trained on data produced by Doench et al (881 guides, MOLM13/NB4/TF1 cells + unpublished additional data). Delivery: lentivirus. See <a target='_blank' href='http://biorxiv.org/content/early/2015/06/26/021568'>Fusi et al. 2015</a> and <a target='_blank' href='http://www.nature.com/nbt/journal/v34/n2/full/nbt.3437.html'>Doench et al. 2016</a> and <a target=_blank href='https://crispr.ml/'>crispr.ml</a>. Recommended for guides expressed in cells (U6 promoter). Click to sort the table by this score."),
"fusiOld" : ("OldDoench '16", "The original implementation of the Doench 2016 score, as received from John Doench. The scores are similar, but not exactly identical to the 'Azimuth' version of the Doench 2016 model that is currently the default on this site, since Apr 2018."),
"najm" : ("Najm 2018", "A modified version of the Doench 2016 score ('Azimuth'), by Mudra Hegde for S. aureus Cas9. Range 0-100. See <a target=_blank href='https://www.nature.com/articles/nbt.4048'>Najm et al 2018</a>."),
"ccTop" : ("CCTop", "The efficiency score used by CCTop, called 'crisprRank'."),
"aziInVitro" : ("Azimuth in-vitro", "The Doench 2016 model trained on the Moreno-Mateos zebrafish data. Unpublished model, gratefully provided by J. Listgarden"),
"housden" : ("Housden", "Range: ~ 1-10. Weight matrix model trained on data from Drosophila mRNA injections. See <a target='_blank' href='http://stke.sciencemag.org/content/8/393/rs9.long'>Housden et al.</a>"),
"proxGc" : ("ProxGCCount", "Number of GCs in the last 4pb before the PAM"),
"seqDeepCpf1" : ("DeepCpf1", "Range: ~ 0-100. Convolutional Neural Network trained on ~20k Cpf1 lentiviral guide results. This is the score without DNAse information, 'Seq-DeepCpf1' in the paper. See <a target='_blank' href='https://www.nature.com/articles/nbt.4061'>Kim et al. 2018</a>"),
"oof" : ("Out-of-Frame", "Range: 0-100. Out-of-Frame score, only for deletions. Predicts the percentage of clones that will carry out-of-frame deletions, based on the micro-homology in the sequence flanking the target site. See <a target='_blank' href='http://www.nature.com/nmeth/journal/v11/n7/full/nmeth.3015.html'>Bae et al. 2014</a>. Click the score to show the predicted deletions."),
"lindel": ("Lindel", "Wei Chen Frameshift ratio (0-100). Predicts probability of a frameshift caused by any type of insertion or deletion. See <a href='https://www.biorxiv.org/content/10.1101/481069v1'>Wei Chen et al, 2018</a> (article under review). Click the score to see the most likely deletions and insertions.")
}
# the headers for the guide and offtarget output files
guideHeaders = ["guideId", "targetSeq", "mitSpecScore", "offtargetCount", "targetGenomeGeneLocus"]
offtargetHeaders = ["guideId", "guideSeq", "offtargetSeq", "mismatchPos", "mismatchCount", "mitOfftargetScore", "cfdOfftargetScore", "chrom", "start", "end", "strand", "locusDesc"]
# library descriptions
libLabels = [
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4486245/
("human_brunello" , "Human, Brunello, Doench Nat Bio 2016 (recommended)"),
("human_avana" , "Human, Avana, Doench Nat Bio 2016"),
("human_geckov2" , "Human, GeCKO V2, Sanjana Nat Meth 2014"),
("mouse_brie" , "Mouse, Brie, Doench Nat Bio 2016 (recommended)"),
("mouse_geckov2" , "Mouse, GeCKO V2, Sanjana Nat Meth 2014"),
("mouse_asiago" , "Mouse, Asiago, Doench Nat Bio 2016"),
]
# a file crispor.conf in the directory of the script allows to override any global variable
myDir = dirname(__file__)
confPath =join(myDir, "crispor.conf")
if isfile(confPath):
exec(open(confPath))
cgiParams = None
# ====== END GLOBALS ============
def setupPamInfo(pam):
" modify a few globals based on the current pam "
global GUIDELEN
global cpf1Mode
global addGenePlasmids
global PAMLEN
global scoreNames
global baseEditor
global saCas9Mode
global mutScoreNames
pamOpt = None
if "-" in pam:
pam, pamOpt = pam.split("-")
if pamOpt=="BE1":
baseEditor = "BE1"
PAMLEN = len(pam)
if pamIsCpf1(pam):
logging.debug("switching on Cpf1 mode, guide length is 23bp")
GUIDELEN = 23
cpf1Mode = True
scoreNames = cpf1ScoreNames
elif pam=="NNGRRT" or pam=="NNNRRT":
logging.debug("switching on S. aureus mode, guide length is 21bp")
addGenePlasmids = addGenePlasmidsAureus
GUIDELEN = 21
if pamOpt=="20":
GUIDELEN=20
saCas9Mode = True
cpf1Mode = False
scoreNames = saCas9ScoreNames
elif pam=="NNNNCC":
GUIDELEN = 24
cpf1Mode = False
scoreNames = cas9ScoreNames
else:
GUIDELEN = 20
cpf1Mode = False
scoreNames = cas9ScoreNames
if GUIDELEN==20 and pam=="NGG":
mutScoreNames = spCas9MutScoreNames
else:
mutScoreNames = otherMutScoreNames
return pam
# ==== CLASSES =====
class JobQueue:
"""
simple job queue, using a db table as a backend
jobs have different types and status. status can be updated while they run
job running times are kept and old job info is kept in a separate table
>>> q = JobQueue()
>>> q.clearJobs()
>>> q.waitCount()
0
>>> q.addJob("search", "abc123", "myParams")
True
only one job per jobId
>>> q.addJob("search", "abc123", "myParams")
False
>>> q.waitCount()
1
>>> q.getStatus("abc123")
u'Waiting'
>>> q.startStep("abc123", "bwa", "Alignment with BWA")
>>> q.getStatus("abc123")
u'Alignment with BWA'
>>> jobType, jobId, paramStr = q.popJob()
>>> q.waitCount()
0
>>> q.jobDone("abc123")
>>> q.waitCount()
0
can't pop from an empty queue
#>>> q.popJob()
#(None, None, None)
#>>> os.system("rm /tmp/tempCrisporTest.db")
#0
"""
_queueDef = (
'CREATE TABLE IF NOT EXISTS %s '
'('
' jobType text,' # either "index" or "search"
' jobId text %s,' # unique identifier
' paramStr text,' # parameters for jobs, like db, options, etc.
' isRunning int DEFAULT 0,' # indicates steps have started, done jobs are moved to doneJobs table
' stepName text,' # currently step, internal step name for timings
' stepLabel text,' # current step, human-readable status of job, for UI
' lastUpdate float,' # time of last update
' stepTimes text,' # comma-sep list of whole msecs, one per step
' startTime text ' # date+time when job was put into queue
')')
def __init__(self):
" no inheritance needed here "
if jobQueueUseMysql:
self.openMysql()
else:
self.openSqlite(JOBQUEUEDB)
def openSqlite(self, dbName):
self.dbName = dbName
self.conn = sqlite3.Connection(dbName)
try:
self.conn.execute(self._queueDef % ("queue", "PRIMARY KEY"))
except sqlite3.OperationalError:
errAbort("cannot open the file %s" % JOBQUEUEDB)
self.conn.execute(self._queueDef % ("doneJobs", ""))
self.conn.commit()
self._chmodJobDb()
def _chmodJobDb(self):
# umask is not respected by sqlite, bug http://www.mail-archive.com/sqlite-users@sqlite.org/msg59080.html
if not jobQueueUseMysql:
try:
os.chmod(JOBQUEUEDB, 0o666)
except OSError:
pass
def openMysql(self):
db = MySQLdb.connect(**jobQueueMysql)
self.conn = db.cursor()
def addJob(self, jobType, jobId, paramStr):
" create a new job, returns False if not successful "
self._chmodJobDb()
sql = 'INSERT INTO queue (jobType, jobId, isRunning, lastUpdate, ' \
'stepTimes, paramStr, stepName, stepLabel, startTime) VALUES (?, ?, ?, ?, ?, ?, ?, ?, datetime("now"))'
now = "%.3f" % time.time()
try:
self.conn.execute(sql, (jobType, jobId, 0, now, "", paramStr, "wait", "Waiting"))
self.conn.commit()
return True
except sqlite3.IntegrityError:
return False
except sqlite3.OperationalError:
errAbort("Cannot open DB file %s. Please contact %s" % (self.dbName, contactEmail))
def getStatus(self, jobId):
" return current job status label or None if job is not in queue"
sql = 'SELECT stepLabel FROM queue WHERE jobId=?'
try:
status = self.conn.execute(sql, (jobId,)).next()[0]
except StopIteration:
status = None
return status
def dump(self):
" for debugging, write the whole queue table to stdout "
sql = 'SELECT * FROM queue'
for row in self.conn.execute(sql):
print "\t".join([str(x) for x in row])
def jobInfo(self, jobId, isDone=False):
" for debugging, return all job info as a tuple "
if isDone:
sql = 'SELECT * FROM doneJobs WHERE jobId=?'
else:
sql = 'SELECT * FROM queue WHERE jobId=?'
try:
row = self.conn.execute(sql, (jobId,)).next()
except StopIteration:
return []
return row
def startStep(self, jobId, newName, newLabel):
" start a new step. Update lastUpdate, status and stepTime "
self.startTransaction()
sql = 'SELECT lastUpdate, stepTimes, stepName FROM queue WHERE jobId=?'
lastTime, timeStr, lastStep = self.conn.execute(sql, (jobId,)).next()
lastTime = float(lastTime)
# append a string in format "stepName:milliSecs" to the timeStr
now = time.time()
timeDiff = "%d" % int((1000.0*(now - lastTime)))
newTimeStr = timeStr+"%s=%s" % (lastStep, timeDiff)+","
sql = 'UPDATE queue SET lastUpdate=?, stepName=?, stepLabel=?, stepTimes=?, isRunning=? WHERE jobId=?'
self.conn.execute(sql, (now, newName, newLabel, newTimeStr, 1, jobId))
tryCount = 0
while tryCount < 10:
try:
self.conn.commit()
break
except sqlite3.OperationalError:
time.sleep(3)
tryCount += 1
if tryCount >= 10:
raise Exception("Database locked for a long time")
def startTransaction(self):
tryCount = 0
while tryCount < 10:
try:
self.conn.execute('BEGIN IMMEDIATE') # lock db
break
except sqlite3.OperationalError:
time.sleep(3)
tryCount += 1
if tryCount >= 10:
raise Exception("Database locked for a long time")
def jobDone(self, jobId):
" remove the job from the queue and add it to the queue log"
#self.conn.execute('BEGIN IMMEDIATE') # lock db
self.startTransaction()
sql = 'SELECT * FROM queue WHERE jobId=?'
try:
row = self.conn.execute(sql, (jobId,)).next()
except StopIteration:
# return if the job has already been removed
logging.warn("jobDone - jobs %s has been removed already" % jobId)
return
sql = 'DELETE FROM queue WHERE jobId=?'
self.conn.execute(sql, (jobId,))
sql = 'INSERT INTO doneJobs VALUES (?,?,?,?,?,?,?,?,?)'
self.conn.execute(sql, row)
self.conn.commit() # release lock
def waitCount(self):
" return number of waiting jobs "
sql = 'SELECT count(*) FROM queue WHERE isRunning=0'
count = None
while count is None:
try:
count = self.conn.execute(sql).next()[0]
except sqlite3.OperationalError:
time.sleep(1+random.random()/10)
pass
return count
def popJob(self):
" return (jobType, jobId, params) of first waiting job and set it to running state "
self.startTransaction()
sql = 'SELECT jobType, jobId, paramStr FROM queue WHERE isRunning=0 ORDER BY lastUpdate LIMIT 1'
try:
jobType, jobId, paramStr = self.conn.execute(sql).next()
except StopIteration:
self.conn.commit() # unlock db
return None, None, None
sql = 'UPDATE queue SET isRunning=1 where jobId=?'
self.conn.execute(sql, (jobId,))
self.conn.commit() # unlock db
return jobType, jobId, paramStr
def clearJobs(self):
" clear the job table, removing running jobs, too "
self.conn.execute("DELETE from queue")
#self.conn.execute("DROP TABLE queue")
self.conn.commit()
def close(self):
" "
self.conn.close()
# ====== FUNCTIONS =====
contentLineDone = False
# the queue workers should be able to never abort
doAbort = True
def getTwoBitFname(db):
" return the name of the twoBit file for a genome "
# at UCSC, try to use local disk, if possible
locPath = join("/scratch", "data", db, db+".2bit")
if isfile(locPath):
return locPath
path = join(genomesDir, db, db+".2bit")
return path
def errAbort(msg, isWarn=False):
" print err msg and exit "
if commandLineMode:
raise Exception(msg)
if not contentLineDone:
print "Content-type: text/html\n"
print('<div style="position: absolute; padding: 10px; left: 100; top: 100; border: 10px solid black; background-color: white; text-align:left; width: 800px; font-size: 18px">')
if isWarn:
print("<strong>Warning:</strong><p> ")
else:
print("<strong>Error:</strong><p> ")
print(msg+"<p>")
print("If you think this is a bug or you have any other suggestions, please do not hesitate to contact us %s<p>" % contactEmail)
if isWarn:
print("In the email, please also send us the full URL of the page.")
else:
print("Please also send us the full URL of the page where you see the error. Thanks!")
print('</div>')
if doAbort:
sys.exit(0) # cgi must not exit with 1
# allow only dashes, digits, characters, underscores and colons in the CGI parameters
# and +
notOkChars = re.compile(r'[^+a-zA-Z0-9/:\n\r_. -]')
def checkVal(key, inStr):
""" remove special characters from input string, to protect against injection attacks """
if key!="geneIds":
if len(inStr) > 10000:
errAbort("input parameter %s is too long" % key)
else:
if len(inStr) > 100000:
errAbort("Pasting more than tens of thousands of gene IDs makes little sense. Copy/paste error?")
matchObj =notOkChars.search(inStr)
if matchObj!=None:
errAbort("input parameter %s contains an invalid character %s (ASCII %d)" % (key, repr(matchObj.group()), ord(matchObj.group())))
return inStr
def cgiGetParams():
" get CGI parameters and return as dict "
form = cgi.FieldStorage()
global cgiParams
cgiParams = {}
# parameters are:
#"pamId", "batchId", "pam", "seq", "org", "download", "sortBy", "format", "ajax
for key in form.keys():
val = form.getfirst(key)
if val!=None:
# "seq" is cleaned by cleanSeq later
val = urllib.unquote(val)
if key not in ["seq", "name"]:
checkVal(key, val)
cgiParams[key] = val
if "pam" in cgiParams:
legalChars = set("ACTGNMKRYVBE120345/-")
illegalChars = set(cgiParams["pam"])-legalChars
if len(illegalChars)!=0:
errAbort("Illegal character in PAM-sequence. Only %s are allowed."+"".join(legalChars))
if "batchId" in cgiParams:
batchId = cgiParams["batchId"]
if not batchId.isalnum() or len(batchId) > 30:
errAbort("Invalid batchId")
return cgiParams
def cgiGetStr(params, argName, default=None):
val = params.get(argName, None)
if val==None and default==None:
errAbort("'%s' parameter must be specified" % argName)
if val==None:
return default
return val
def cgiGetNum(params, argName, default):
" get CGI parameter which must be a number "
val = params.get(argName, None)
if val==None:
return default
if not val.isdigit():
errAbort("'%s' parameter must be a number" % argName)
val = int(val)
return val
transTab = string.maketrans("-=/+_", "abcde")
def makeTempBase(seq, org, pam, batchName):
"create the base name of temp files using a hash function and some prettyfication "
hasher = hashlib.sha1(seq+org+pam+batchName)
batchId = base64.urlsafe_b64encode(hasher.digest()[0:20]).translate(transTab)[:20]
return batchId
def makeTempFile(prefix, suffix):
" return a temporary file that is deleted upon exit, unless DEBUG is set "
if DEBUG:
fname = join("/tmp", prefix+suffix)
fh = open(fname, "w")
else:
fh = tempfile.NamedTemporaryFile(prefix="primer3In", suffix=".txt")
return fh
def pamIsCpf1(pam):
" if you change this, also change bin/filterFaToBed! "
return (pam in ["TTN", "TTTN", "TYCV", "TATV", "TTTV"])
def pamIsSaCas9(pam):
" only used for notes and efficiency scores, unlike its Cpf1 cousin function "
return (pam.split("-")[0] in ["NNGRRT", "NNNRRT"])
def pamIsXCas9(pam):
" "
return (pam in ["NGK", "NGN"])
def pamIsSpCas9(pam):
" only used for notes and efficiency scores, unlike its Cpf1 cousin function "
return (pam in ["NGG", "NGA", "NGCG"])
def saveSeqOrgPamToCookies(seq, org, pam):
" create a cookie with seq, org and pam and print it"
cookies=Cookie.SimpleCookie()
expires = 365 * 24 * 60 * 60
if len(seq)<3000:
cookies['lastseq'] = seq
else:
cookies['lastseq'] = "(last sequence was too long, could not be saved in Internet Browser cookie)"
cookies['lastseq']['expires'] = expires
cookies['lastorg'] = org
cookies['lastorg']['expires'] = expires
cookies['lastpam'] = pam
cookies['lastpam']['expires'] = expires
print cookies
def debug(msg):
if commandLineMode:
logging.debug(msg)
elif DEBUG:
print msg
print "<br>"
def gcContent(seq):
" return GC content as a float "
c = 0
for x in seq:
if x in ["G","C"]:
c+= 1
return (float(c)/len(seq))
def findPat(seq, pat):
""" yield positions where pat matches seq, stupid brute force search
"""
seq = seq.upper()
pat = pat.upper()
patLen = len(pat)
for i in range(0, len(seq)-patLen+1):
subseq = seq[i:i+patLen]
if patMatch(subseq, pat):
yield i
def rndSeq(seqLen):
" return random seq "
seq = []
alf = "ACTG"
for i in range(0, seqLen):
seq.append(alf[random.randint(0,3)])
return "".join(seq)
def cleanSeq(seq, db):
""" remove fasta header, check seq for illegal chars and return (filtered
seq, user message) special value "random" returns a random sequence.
"""
#print repr(seq)
if seq.startswith("random"):
seq = rndSeq(800)
lines = seq.strip().splitlines()
#print "<br>"
#print "before fasta cleaning", "|".join(lines)
if len(lines)>0 and lines[0].startswith(">"):
line1 = lines.pop(0)
#print "<br>"
#print "after fasta cleaning", "|".join(lines)
#print "<br>"
newSeq = []
nCount = 0
for l in lines:
if len(l)==0:
continue
for c in l:
if c not in "actgACTGNn":
nCount +=1
else:
newSeq.append(c)
seq = "".join(newSeq)
msgs = []
tooLongHint = """
Please split your input sequence into shorter sequences or use
the <a href='downloads/'>stand-alone version</a> on your own Linux or Mac server to process longer sequences in batch.<br>
"""
if len(seq)>MAXSEQLEN and db!="noGenome":
errMsg = "<strong>Sorry, this tool cannot handle sequences longer than %d bp</strong><br>" % (MAXSEQLEN)
errAbort(errMsg+tooLongHint)
if len(seq)>MAXSEQLEN_NOGENOME and db=="noGenome":
errMsg = "<strong>Sorry, this tool cannot handle sequences longer than %d bp when using the 'No Genome' option.</strong><br>" % (MAXSEQLEN_NOGENOME)
errAbort(errMsg+tooLongHint)
if nCount!=0:
msgs.append("Sequence contained %d non-ACTGN letters. They were removed." % nCount)
return seq, "<br>".join(msgs)
revTbl = {'A' : 'T', 'C' : 'G', 'G' : 'C', 'T' : 'A', 'N' : 'N' , 'M' : 'K', 'K' : 'M',
"R" : "Y" , "Y":"R" , "g":"c", "a":"t", "c":"g","t":"a", "n":"n", "V" : "B", "v":"b",
"B" : "V", "b": "v", "W" : "W", "w" : "w"}
def revComp(seq):
" rev-comp a dna sequence with UIPAC characters "
newSeq = []
for c in reversed(seq):
newSeq.append(revTbl[c])
return "".join(newSeq)
def docTestInit():
global cpf1Mode
global GUIDELEN
cpf1Mode=False
GUIDELEN=20
def findPams (seq, pam, strand, startDict, endSet):
""" return two values: dict with pos -> strand of PAM and set of end positions of PAMs
Makes sure to return only values with at least GUIDELEN bp left (if strand "+") or to the
right of the match (if strand "-")
If the PAM is cpf1, then this is inversed: pos-strand matches must have at least GUIDELEN
basepairs to the right, neg-strand matches must have at least GUIDELEN bp on their left
>>> docTestInit()
>>> findPams("GGGGGGGGGGGGGGGGGGGGGGG", "NGG", "+", {}, set())
({20: '+'}, set([23]))
>>> findPams("CCAGCCCCCCCCCCCCCCCCCCC", "CCA", "-", {}, set())
({0: '-'}, set([3]))
>>> findPams("TTTNCCCCCCCCCCCCCCCCCTTTN", "TTTN", "+", {}, set())
({0: '+'}, set([3]))
>>> findPams("CCCCCCCCCCCCCCCCCCCCCAAAA", "NAA", "-", {}, set())
({}, set([]))
>>> findPams("AAACCCCCCCCCCCCCCCCCCCCC", "NAA", "-", {}, set())
({}, set([]))
>>> findPams("CCCCCCCCCCCCCCCCCCCCCCCCCAA", "NAA", "-", {}, set())
({}, set([]))
>>> findPams("GTTGTGTTTTACAATGCAGAGAGTGGAGGATGCTTTTTATACATTGGTGAGAGAGATCCGACAGTACAGATTGAAAAAAATCAGCAAAGAAGAAAAGACTCCTGGCTGTGTGAAAATTAAAAAATGCGTTATAATGTAATCTGGTAAGTTGAGCATATTCATTCTGGTACAAAGCAGATGTCTTCAGAGGTAACA", "TATV", "-", {}, set())
({129: '-', 37: '-'}, set([41, 133]))
>>> findPams("GTTGTGTTTTACAATGCAGAGAGTGGAGGATGCTTTTTATACATTGGTGAGAGAGATCCGACAGTACAGATTGAAAAAAATCAGCAAAGAAGAAAAGACTCCTGGCTGTGTGAAAATTAAAAAATGCGTTATAATGTAATCTGGTAAGTTGAGCATATTCATTCTGGTACAAAGCAGATGTCTTCAGAGGTAACA", "TATV", "+", {}, set())
({129: '+', 37: '+'}, set([41, 133]))
"""
assert(cpf1Mode is not None)
if cpf1Mode:
maxPosPlus = len(seq)-(GUIDELEN+len(pam))
minPosMinus = GUIDELEN
else:
# -------------------
# OKOKOKOKOK
minPosPlus = GUIDELEN
# -------------------
# OKOKOKOKOK
maxPosMinus = len(seq)-(GUIDELEN+len(pam))
#print "new search", seq, pam, "<br>"
for start in findPat(seq, pam):
if cpf1Mode:
# need enough flanking seq on one side
#print "found", start,"<br>"
if strand == "+" and start > maxPosPlus:
continue
if strand == "-" and start < minPosMinus:
continue
else:
if strand=="+" and start < minPosPlus:
continue
if strand=="-" and start > maxPosMinus:
continue
#print "match", strand, start, end, "<br>"
startDict[start] = strand
end = start+len(pam)
endSet.add(end)
return startDict, endSet
def rulerString(maxLen):
" return line with positions every 10 chars "
texts = []
for i in range(0, maxLen, 10):
numStr = str(i)
texts.append(numStr)
spacer = "".join([" "]*(10-len(numStr)))
texts.append(spacer)
return "".join(texts)
def varDictToHtml(varDict, seq, varShortLabel):
" make a list of one html string per position in the sequence "
if varDict is None:
return None