PoPoolaon - storage.googleapis.com · Calculate natural variaon for genes (allows subsequent GO...
Transcript of PoPoolaon - storage.googleapis.com · Calculate natural variaon for genes (allows subsequent GO...
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PoPoola%on
Es%ma%ngnaturalvaria%oninpooledpopula%onsusingnextgenera%on
sequencing
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WhatcanyoudowithPoPoola%on?
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Genomewideoverviewofnaturalvaria%on;example:PiinD.mel.
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Detailedinspec%onofcandidategenes;exampleCyp6g1inD.mel.
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Calculatedivergencebetweenspecies
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Calculatenaturalvaria%onforgenes(allowssubsequentGOanalysis)
Gene ID SNPs cov-frac Tajima's Pi CG8493-RB 2 0.940 0.001171327 CG8877-RA 17 0.985 0.001175603 CG13159-RA 1 1.000 0.001213136 CG8290-RB 12 0.987 0.001254184 CG8841-RC 9 0.859 0.001480358 CG8857-RA 4 0.981 0.001549875
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Introduc%on
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Quickupdateonposi%veselec%on
Whenanalleleincreasesinitspopula%onfrequency,nearbyvariantsalsoincreaseinitsfrequency‐>“Hitchhiking”
Thisleadstoaselec%vesweepwhicherasesvaria%onaroundaposi%velyselectedallele
Source: Sabeti P.C. et al. (2006) – Positive Natural Selection in the Human Lineage Robert Kofler
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ASerthesweepNewmuta%onsappearandrestorediversity,buttheyappear
veryslowly(muta%onsarerare)andtheyareini%allyoflowfrequency.
Positive selection thus creates a signature consisting of a region of low overall diversity with an excess of rare alleles => rare alleles are very important (SNP identification) -> Low Tajima’s D
Of course there are many more signatures of positive selection, like the proportion of functional change (McDonald Kreitman Test), length of the haplotypes (iHS and linkage disequilibrium) or differences in allele frequencies between populations (Fst)
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PoPoola%oncurrentlycalculates:
• TajimasPi• WaWersonsTheta
• Tajima’sD
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MajorrequirementforPoPoola%on:
Apooledpopula%on!Disclaimer:Iexpectittoworkwithsequencedindividualsaswell,butIhavenottestedthis
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Twomajorstrategiesforpopula%ongenomics
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Whypooling?ProsandCons
Pros: • More cost effective (less sequencing is required) • Bioinformatics analysis is simpler (in my opinion..) • Standard population genetic estimator’s (Tajima’s D) may easily be
assessed Cons: • Haplotype information is not available • All individuals should contribute equal amount of DNA!!• Illumina reads have a high error rate it is necessary to introduce
minimum allele counts -> loosing singletons, doubletons etc • thus: Pooling requires a correction of standard Population
Genetics estimators like Tajima’s π ( missing singletons and multiple samplings of identical sequences)
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Wedevelopedcorrec%onfactorsforsomepopula%ongene%ces%mators:egTajima’sD
see also: Schloetterer and Futschik (2010):Massively Parallel Sequencing of Pooled DNA Samples--The Next Generation of Molecular Markers.
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PoPoola%onoverview
PoPoolation implements these correction factors
Genome wide pattern of variability may be assessed
Recent positive selection may be identified
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Valida%ngPoPoola%on
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Valida%onofPoPoola%onusingsimulateddata
• SimulatedTajima’sPialongachromosomeofD.melanogasterusingms
• Wecreatedar%ficialreadsandintroducedsequencingerrors
• Wefedthesear%ficialreadsintothePoPoola%onpipeline
• WecomparedtheobservedwiththeexpectedTajima’sPi
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DifferencebetweentheobservedandtheexpectedTajima’sPi
⇒ low error rate of 0.1% is necessary and a mac >=2 ⇒ Illumina has an error rate of ~1%
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Howtodecreasetheerrorrate
• Trimmingofreads;removelowqualitystretches
• Requireaminimumquality
• Requireaminimumallelecount(mac)
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TrimmingalgorithmofPoPoola%on
=>The algorithm finds the highest scoring substring of the read =>Individual bases may even be below the quality threshold, as long as a new high score can be achieved =>The algorithm is very similar to dynamic programming (Smith-Waterman) => handles single end as well as paired end reads!!
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Trimmingsta%s%cwithdifferentqualitythresholds
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Qualityandminorallelecount
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Iden%fica%onoffalseposi%veSNPsusingPhiXandPoPoola%on
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novelerrorrate(aSertrimmingandqualitycontrol)• Remember:therequirederrorrateis0.1‐0.2%• Remember:Illuminahasanerrorrateof~1%
• Trimmingandtherequirementforaminimumqualitydrama%callyreducetheerrorrate:
trim min.qual. Error rate 20 0 0.15% 20 20 0.07%
=> Error rate is sufficiently reduced by trimming and minimum quality
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DifferencebetweentheobservedandtheexpectedTajima’sPi
⇒ low error rate of 0.1% is necessary and a mac >=2 ⇒ Illumina has an error rate of ~1%
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Influenceofcoverageandwindowsize
• Wesequencechromosome3RofD.melto100xcoverage
• Werandomlydrewasubsetsofthereadstoachievevaryingcoverages(5x‐>90x)
• WecomparedPiofthesubset(eg.cov.:10x)withthePiofthefulldataset(cov.:100x)
• Furthermoreweuseddifferentwindowsizes
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Influenceofcoverageandwindowsize(averageof2000windows)
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Walktrough
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Precondi%ons:• MacorLinux(Unix)
• PerlandRinstalled• bwa(Burrows‐WheelerAlignmentTool)• samtools
• IGV(Integra%veGenomicsViewer)• PoPoola%on1.016hWp://code.google.com/p/popoola%on
=>bwaandsamtoolsneedtobeinthe$PATH
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Getthedata:
Webpage:hWp://code.google.com/p/popoola%on
goto‘Downloads’anddownload:
popool‐teaching.sh
teaching‐data.zip
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Prepara%ons:
• Unzipteaching‐data.zip• copythefilepopool‐teaching.shintotheunzipedfolder(data)
• enterthecommandline
• changedirectoryintotheunzipedfolder
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Trimming
Enterthecommand:perl <local-popoolation-installation>/basic-
pipeline/trim-fastq.pl --input1 read_1.fastq --input2 read_2.fastq --output trim --quality-threshold 20 --min-length 50
<local‐popoola%on‐installa%on>...thisisthepathtoyourcopyofpopoola%on
e.g.:/Users/robertkofler/dev/popoola%on‐1.016
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Trimsta%s%cs
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Preparethereferencesequenceformapping
Commandline:mkdir wg mv dmel-2R-chromosome-r5.22.fasta wg awk '{print $1}' wg/dmel-2R-chromosome-r5.22.fasta >
wg/dmel-2R-short.fa bwa index wg/dmel-2R-short.fa
akw ‘{print $1}’ .. this only prints the first column. For a fasta file this removes anything after the first space. Therefore this command shortens the header for example: >2R name=blabla id=12345 transposons=many will be shortend to >2R => more reliable for mapping and downstream processing
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mappingusing‘BWA’
commandline:bwa aln wg/dmel-2R-short.fa trim_1 > trim_1.sai bwa aln wg/dmel-2R-short.fa trim_2 > trim_2.sai bwa sampe wg/dmel-2R-short.fa trim_1.sai trim_2.sai
trim_1 trim_2 > maped.sam
thesearesubotpimalparametersaswedonotwanttowaittherestofthedayforthemappingtofinish!
forop%malresultswerecommendthefollowing:bwa aln -l 100 -o 2 -d 12 -e 12 -n 0.01 wg/dmel-2R-
short.fa trim_1 > trim_1.sai
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Checkthesam‐file
Commandline:less maped.sam
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createapileupfile
Firstremoveambiguouslymappedreads(minimummappingquality20)andcreateasortedbam‐file:
samtools view -q 20 -bS maped.sam| samtools sort - maped.sort
Thancreateapileupfile:samtools pileup maped.sort.bam > cyp6g1.pileup
Controlthepileupfile:less cyp6g1.pileup
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pileup?
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CalculateTajima’sPiusingaslidingwindowapproach
firstcheckouttheop%ons:perl <local-popoolation-installation>/Variance-
sliding.pl --help
andrunthetests(sanitycontrol):perl <local-popoolation-installation>/Variance-
sliding.pl --test
thancalculateTajima’sPiperl <local-popoolation-installation>/Variance-
sliding.pl --measure pi --input cyp6g1.pileup --min-count 2 --min-qual 20 --min-coverage 4 --max-coverage 70 --pool-size 500 --window-size 1000 --step-size 1000 --output cyp6g1.varslid.pi --region 2R:7800000-8300000
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Output:
less cyp6g1.varslid.pi
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PrepareoutputforIGV
Commandline:perl <local-popoolation-installation>/
VarSliding2Wiggle.pl --input cyp6g1.varslid.pi --output cyp6g1.pi.wig --trackname "nat-pop-pi”
Indexthebamfile:samtools index maped.sort.bam
Whynotdirectlyawiggleasoutput??
youlooseinforma%oninwiggle,e.g.:snpcountorcoveredfrac%on
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VisualizeinIGV
• openIGV(./igv_mac-intel.command)• File‐>ImportGenome...Name:dmel‐2rSequenceFile:dmel‐short.fa
• File‐>LoadfromFile...cyp6g1.gsmaped.sort.bamcyp6g1.pi.wig
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Biology!HereIcome..
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Youmaytrythisathome!
TheWebPagecontainsthefullguide:
hWp://code.google.com/p/popoola%on/wiki/TeachingPoPoola%on
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$PATH
vitheconfigfileforyourshell
BASH:~/.bash_profile
ZSH:~/.zshrc
addthefollowinglineadaptedtoyoursystem:
exportPATH=/Users/robertkofler/programs/bwa‐0.5.7:/Users/robertkofler/programs/samtools‐0.1.7_i386‐darwin:$PATH
loadthenewconfigura%on:
source~/.zshrc
test:
bwa