Basics of high-throughput sequencing

Olivier Elemento, PhDTA: Jenny Giannopoulou, PhD

Institute for Computational Biomedicine

CSHL High Throughput Data Analysis Workshop, June 2012

Plan

1. What high-throughput sequencing is used for2. Illumina technology3. Primary data analysis (alignment, QC)4. Read formats5. Secondary Analysis (mutation calling, transcript level

quantification, etc) 6. Read data visualization7. Useful R/BioC packages8. Challenges and evolution of sequencing and its analysis

1. What high-throughput sequencing is used for

Full genome sequencing

Targeted sequencing

Exome sequencing

C C U T

After PCR

mC CC U

Bisulfite treatment

DNA methylation profiling

RNA-seq

ChIP-seq

Transcription factor of interest

Antibody

DNA

High-throughput mapping of chromatin interactions (HiC)

Elemento lab (more on this next week)

And many others

• Gene fusion detection• Translational profiling (which mRNAs localize

to ribosomes)• Small/miRNA sequencing• Bacterial communities• Protein-RNA interactions (PAR-CLIP, HITS-CLIP)• …

2. Illumina technology

DNA(0.1-1.0 ug)

Single molecule arraySample

preparation Cluster growth5’

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Image acquisition Base calling

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Sequencing

Illumina SBS TechnologyReversible Terminator Chemistry Foundation

© Illumina, Inc.http://www.illumina.com/technology/sequencing_technology.ilmnhttp://seqanswers.com/forums/showthread.php?t=21

Single end vs pair end sequencing

What comes out of the machine: short reads in fastq format

@D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/1CTCCTGGAAAACGCTTTGGTAGATTTGGCCAGGAGCTTTCTTTTATGTAAATTG+D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/1[^^cedeefee`cghhhfcRX`_gfghf^bZbecg^eeb[caef`ef^a_`eXa@D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/1TCCANCCATGGCAAATTCCATGGCACCGTCAAGGCTGAGAACGGGAAGCTTGTC+D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/1ab_eBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB@D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/1TACAAGTGCAGCATCAAGGAGCGAATGCTCTACTCCAGCTGCAAGAGCCGCCTC+D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/1_[_ceeec[^eeghdffffhh^efh_egfhfgeec_fbafhhhhd`caegfheh@D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/1GAAGGAGAGAAGGGGAGGAGGGCGGGGGGCACCTACTACATCGCCCTCCACATC+D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/1\^_accceg`gga`f[fgcb`Ucgfaa_LVV^[bbbbbRWW`W^Y[_[^bbbbb@D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/1GTGGCCGATTCCTGAGCTGTGTTTGAGGAGAGGGCGGAGTGCCATCTGGGTAGC+D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/1aa_eeeeegggggihhiiifgeghfeghbgcghifiidg^dbgggeeeee`dcd@D3B4KKQ1_0166:8:1101:2358:2174#CGATGT/1CTGACCTGGGTCCTGTGGTGCTCAGCCTTTTGAAGATGCCAGAAAAATACGTCG+D3B4KKQ1_0166:8:1101:2358:2174#CGATGT/1\^_cccccg^Y`ega`fg`ebegfhd^egghhghfffhghdhbfffhhhfgfcf

QS to int In R:as.integer(charToRaw(‘e'))-33

Pair end sequencings_8_1_sequence.txt.gz s_8_2_sequence.txt.gz

@D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/1CTCCTGGAAAACGCTTTGGTAGATTTGGCCAGGAGCTTTCTTTTATGTAAATTG+D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/1[^^cedeefee`cghhhfcRX`_gfghf^bZbecg^eeb[caef`ef^a_`eXa@D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/1TCCANCCATGGCAAATTCCATGGCACCGTCAAGGCTGAGAACGGGAAGCTTGTC+D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/1ab_eBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB@D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/1TACAAGTGCAGCATCAAGGAGCGAATGCTCTACTCCAGCTGCAAGAGCCGCCTC+D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/1_[_ceeec[^eeghdffffhh^efh_egfhfgeec_fbafhhhhd`caegfheh@D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/1GAAGGAGAGAAGGGGAGGAGGGCGGGGGGCACCTACTACATCGCCCTCCACATC+D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/1\^_accceg`gga`f[fgcb`Ucgfaa_LVV^[bbbbbRWW`W^Y[_[^bbbbb@D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/1GTGGCCGATTCCTGAGCTGTGTTTGAGGAGAGGGCGGAGTGCCATCTGGGTAGC+D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/1aa_eeeeegggggihhiiifgeghfeghbgcghifiidg^dbgggeeeee`dcd

@D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/2GGCATATTTAACAGCATTGAACAGAATTCTGTGTCCTGTAAAAAAATTAGCTTA+D3B4KKQ1_0166:8:1101:1960:2190#CGATGT/2a__aaa`ce`cgcffdf_acda^ea]befffbeged`g[a`e_caaac]cb`gb@D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/2TTGAGGCTGTTGTCATACTTCTCATGGTTCACACCCATGACGAACATGGGGGCG+D3B4KKQ1_0166:8:1101:2154:2137#CGATGT/2a__eeeeeggegefhhhiiihhhhhiieghhhghhiiffhiififhhiihegic@D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/2CGGGGTGCACCTCGTCGTAGAGGAACTCTGCCGTCAGCTCTGCCCCATCGCCAA+D3B4KKQ1_0166:8:1101:2249:2171#CGATGT/2^__ee__cge`cghghhfgddgfgi]ehhfffff^ec[beegidffhhfhadba@D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/2CTTAGTCTCAGTTTTCCTCCAGCAGCCTGAGGAAACTCAAAGGCACAGTTCCCA+D3B4KKQ1_0166:8:1101:2043:2187#CGATGT/2_abeaaacg^g^eghhhhgafghhdfghfedeghfiiicfbgdHYagfeecggf@D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/2TAGGCTCAAAGTCTAACGCCAATCCCGAACCTGGGCATCTGTACACACACACAC+D3B4KKQ1_0166:8:1101:2188:2232#CGATGT/2abbeceeegggcghiihiihhhhiifhiiiiihiiiiiiihegh`eggfebfhg

… …

Illumina sequencing using HiSeq2000

• Previously: GAIIx: ~30M reads per lane, 8 lanes (1QC)

• • Now: HiSeq2000 + TruSeq

v3: 200M reads per lane, 8-16 lanes (1-2QC) in parallel with HiSeq2000

• Multiplexing: attach barcode, mix samples, sequence, identify and remove barcode

Full Genome Sequencing using Illumina technology

• ~$4-6K reagent with Illumina (storage+analysis costs not included)

• Exercise: you want to sequence 1 human genome at 100X coverage; how many lanes ?

QC for Illumina (part 1)

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Sequencing

3. Primary data analysis (alignment, QC)

Read alignment programs

• BWA (Burrows-Wheeler Aligner)– http://bio-bwa.sourceforge.net/– Fast, accurate, can find (short) indels– Allow 1-3 mismatches by default– Can also align longer 454 reads

• Bowtie– http://bowtie-bio.sourceforge.net/index.shtml– Ultrafast, accurate, newest version finds indels too – Allow 1-3 mismatches by default– Integrated into TopHat (splice aligner)

• Others: Eland, Maq, SOAP, etc

BWA tutorial (for aligning single end reads to genome)

• Get genome, e.g., from UCSC– http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/chromFa.tar.gz

• Combine into 1 file– tar zvfx chromFa.tar.gz– cat *.fa > wg.fa

• Indexing the genome– bwa index -p hg19bwaidx -a bwtsw wg.fa

• Align– bwa aln -t 4 hg19bwaidx s_3_sequence.txt.gz > s_3_sequence.txt.bwa

• Convert to SAM format– bwa samse hg19bwaidx s_3_sequence.txt.bwa s_3_sequence.txt.gz >

s_3_sequence.txt.sam

Aligning pair end reads

• Align two files separately– bwa aln -t 4 hg19bwaidx s_3_1_sequence.txt.gz >

s_3_1_sequence.txt.bwa– bwa aln -t 4 hg19bwaidx s_3_2_sequence.txt.gz >

s_3_1_sequence.txt.bwa

• Convert to SAM format– bwa sampe hg19bwaidx s_3_1_sequence.txt.bwa

s_3_1_sequence.txt.bwa s_3_1_sequence.txt.gz s_3_1_sequence.txt.gz > s_3_sequence.txt.sam

TopHat (spliced alignment)

Trapnell et al, 2009tophat –r 100 –p 4 –o outdir/ hg18 s_1_1_sequence.txt s_1_2_sequence.txt

D~100bp

Download genome index ftp://ftp.cbcb.umd.edu/pub/data/bowtie_indexes/hg18.ebwt.zip

Basic QC

• Fraction of mapped reads • How many unique mappers ?• Fraction of clonal reads (PCR duplicates)

4. Read formats

Read formats

• SAM/BAM• Eland/Eland Export

SAM formatDH1608P1_0130:6:1103:10579:166379#TTAGGC 16 chr1 1249828 37 51M * 0 0 GGGCGTGACTCTGATCTCAGGCATCGTCTCCGCCGCGCTCCCGGACCCGCG eb`XXYbZdadee^ceV]X][ccTcc^ebeeceeeeWbeeeeeeeceeaee XX:Z:NM_017871,32 NM:i:0 MD:Z:51

DH1608P1_0130:6:1102:3415:150915#TTAGGC 16 chr1 1249828 37 51M * 0 0 GGGCGGGACTCTGATCTCAGGCATCGTCTCCGCCGCGCTCCCGGACCCGCG BBBBBBBBBBBac]bbbceedaeddeZceeea_ba_\_eeeeeeedaeeee XX:Z:NM_017871,32 NM:i:1 MD:Z:5T45

DH1608P1_0130:6:1102:13118:62644#TTAGGC 16 chr1 1249828 37 51M * 0 0 GGGCGTGCCTCGGATCTCAGGCATCGTCTCCGCCGCGCTCCCGGACCCGCG BBBBBBBBBBBBBBBBBBBBB`XTbSa`cffegdggeccbeeffdeggggg XX:Z:NM_017871,32 NM:i:2 MD:Z:7A3T39

DH1608P1_0130:6:1203:3012:157120#TTAGGC 16 chr1 1249826 25 51M * 0 0 AAGGCCGTGACTCTGATCTCAGCCCTCGTCTCCGCCGCGCTCCCGGACCCG BBBBBBBB^`QWZZ]UXYSZSTFRU]Z__SO[adcc[acdV\`Y]YWY][_ XX:Z:NM_017871,34 NM:i:3 MD:Z:4G17G1A26

DH1608P1_0130:6:2206:4445:12756#TTAGGC 16 chr1 1246336 25 1M3487N50M * 0 0 CCAAAGGGTGTGACTCTGATCTCGGGCATCGTCTCCGCCGCGCTCCCGGAC BBBBBBBBBBBBBBBBBBBBBBBB`YdddYdc\cacaNddddcdddaeeee XX:Z:NM_017871,37 NM:i:3 MD:Z:2C5C14A27

DH1608P1_0130:6:2203:7903:43788#TTAGGC 16 chr1 1246336 37 1M3487N50M * 0 0 CCCAAGGGCGTGACTCTGATCTCAGGCATCGTCTCCGCCGCGCTCCCGGAC adbe[fbcbccb_cb^cb^^c^edgegggggdfggefffgfbfggggegeg XX:Z:NM_017871,37 NM:i:0 MD:Z:51

MD tag, e.g, MD:Z:4T46 = 5 matches, 1 mismatch (T in read), 46 matches

CIGAR string, eg 5M3487N46M = 5bp-long block, 3487 insert, 46bp-long block

XT tag, e.g. XT:A:U = unique mapper; XT:A:R = more than 1 high-scoring matches

Pair end SAMD3B4KKQ1_0161:8:2206:11080:31374#CTTGTA 83 chr1 4481348 255 51M = 4481165 0

TTAGATGCATTTTCTTACCATTGTAAGAAAAATGAAAATTTTACAATTAAG hiiiiiiihihhdhghggdiiihihffihhheihihhhgggggeeeeebbb NM:i:0 NH:i:1

D3B4KKQ1_0161:8:2206:8294:192062#CTTGTA 147 chr1 4481355 255 51M = 4481284 0 CATTTTCTTACCATTGTAAGAAAAATGAAAATTTTACAATTAAGTATACAC efehffhgfdiihhhhhihghiiihfhihdhiihgghigefggeeeeebbb NM:i:0 NH:i:1

D3B4KKQ1_0161:8:2204:6985:145082#CTTGTA 147 chr1 4481360 255 51M = 4481202 0 TCTTACCATTGTAAGAAAAATGAAAATTTTACAATTAAGTATACACTTCTA ghfhgihihghgihgiiiifiiiiihhhhfifhihhiigggeeceeeea__ NM:i:0 NH:i:1

D3B4KKQ1_0161:8:2205:15014:60805#CTTGTA 83 chr1 4481360 255 51M = 4481238 0 TCTTACCATTGTAAGAAAAATGAAAATTTTACAATTAAGTATACACTTCTA hihheiihiiiiiiiiiiiiiiiiiifhiefhiiiiiigggggeceeebba NM:i:0 NH:i:1

D3B4KKQ1_0161:8:1105:17802:25847#CTTGTA 83 chr1 4481362 255 51M = 4481198 0 TTACCATTGTAAGAAAAATGAAAATTTTACAATTAAGTATACACTTCTAAT gheiiiihhhiiiiiiiiiihiiiiiihgfiiiiiiiigeggceeeeebb_ NM:i:0 NH:i:1

D3B4KKQ1_0161:8:1208:2232:73719#CTTGTA 147 chr1 4481366 255 51M = 4481277 0 CATTGTAAGAAAAATGAAAATTTTACAATTAAGTATACACTTCTAATTGTA fhghiiiiiiiiiiiiiiiiiiihghiihiiiiihgggegfggeeeeebbb NM:i:0 NH:i:1

D3B4KKQ1_0161:8:2104:18142:93861#CTTGTA 83 chr1 4481367 255 51M = 4481198 0 ATTGTAAGAAAAATGAAAATTTTACAATTAAGTATACACTTCTAATTGTAT ihghiiiheiiiiihhihfhifgghhhhfgfhiggge_ggggeeeeee_bb NM:i:0 NH:i:1NM=edit distance NH=number of alignments for that read

BAM format

• Compressed, indexable version of SAM• Can be uploaded to UCSC Genome Browser

SAMtools• http://samtools.sourceforge.net/• Convert SAM to BAM

– samtools view –bS file.sam > file.bam • Sort BAM file

– samtools sort file.bam file.sorted # (will create file.sorted.bam) • Index BAM file

– samtools index file.sorted.bam • Convert BAM to SAM

– samtools view file.bam > file.sam

• Rsamtools• http://www.bioconductor.org/packages/2.6/bioc/html/Rsamtools.html

• Get alignment statistics– samtools flagstat pairendfile.bam

149923886 in total0 QC failure0 duplicates124520915 mapped (83.06%)149923886 paired in sequencing74961943 read174961943 read2120504218 properly paired (80.38%)121586068 with itself and mate mapped2934847 singletons (1.96%)482748 with mate mapped to a different chr143256 with mate mapped to a different chr (mapQ>=5)

SAMtools

• Get pileup– samtools pileup file.sorted.bam

chr1 1156 T 26 tTttTTTtTttTttTtTtTTGTTTTT ggggeggggg^Vgf_fggggJceb_gchr1 1157 T 26 tTttTTTtTttTttTtTtTTTTTTTT ggggfggggg[RgfNfgfgg`ed^]fchr1 1158 G 26 g$GggGGGgGggGggGgGgGGGGGGGG gggg_ggggg[Ugfddgggga_eW\cchr1 1159 A 25 AaaAAAaAaaAaaAaAaAAAAAAAA gggaefggg_Xgf_fggggadd]Zgchr1 1160 A 25 AaaAAAaAaaAaaAaAaAAAAAAAA ggefggggdNVgbZbgggg`ee[\gchr1 1161 C 25 C$c$c$CCCcCccCccCcCcCCCCCCCC gfgfggfggYYgeadgggg`ea^\gchr1 1162 C 23 C$CCcCccCccCcCcCCCCCCCC^FC fgggge_`gf_dgggge_e]_ggchr1 1163 T 22 T$T$tTttTttTtTtTTTTTTTTT ggffg\Rgf_dggeggde]_cgchr1 1164 C 20 cCccCccCcCcCCCCCCCCC ggg`[gf_dggggg\d[]fgchr1 1165 A 22 a$AaaAaaAaAaAAAAAAAAA^FA^FA ged_]ggadffgggecX^ggfgchr1 1166 G 21 G$g$g$GggGgGgGGGGGGGGGGG ggc`gfWfggfggcaSdggfechr1 1167 C 19 CccCcCcCCCCCCCCCCC^FC agg\dgggggbZUdfgfggchr1 1168 T 19 TttTtTtTTTTTTTTTTTT eggcbfgfgg_cXdegfggchr1 1169 T 19 TttTtTtTTTTTTTTTTTT aggccggdggccZdggfgfchr1 1170 T 19 TttTtTtTTTTTTTTTTTT `gfcfgggggccUcggcggchr1 1171 A 19 AaaAaAaAAAAAAAAAAAA ege_fgggggcc[aggcggchr1 1172 A 19 A$aaAaAaAAAAAAAAAAAA XggLfggfggdeM_ggaggchr1 1173 G 18 g$gGgGgGGGGGGGGGGGG gf\fgggggcfPcggeggchr1 1174 A 17 a$AaAaAAAAAAAAAAAA fce[gggg_eL]ggfdfchr1 1175 A 16 A$aAaAAAAAAAAAAAA dfggfggdfS[ggegg

^ = start of read at that position $ = end of read at that position

SAMtools

• Removing clonal reads– Multiple reads that map to

same position, with same orientation as usually considered PCR duplicates

– For mutation detection (less important for RNA-seq), need to collapse them into 1 read (e.g. read with highest quality score)

– samtools rmdup –s file.bam file_noclonal.bam

SAMtools

5. Secondary Analysis (transcript level quantification, mutation calling)

RPKM

Reads per kilobase of transcript per million reads

• R: Count how many reads map to a transcript• K: Divide by ( length of transcript / 1,000 ) • M: Divide by (total number of mapped reads in

sample / 1,000,000 )

CuffLinks uses FPKM (same as RPKM, F=fragment, for paired end reads)

CuffLinks

Trapnell et al, 2010

cufflinks -p 4 –o outdir/ s_1_sequence.txt.sorted.bam

http://www.broadinstitute.org/software/scripture/

http://genes.mit.edu/burgelab/miso/

Detecting Single Nucleotide Variations (SNVs)

AAAAATTCTCCCAAAACAAAAAAATACGCGTATTCTCCCAAAACAATATCTTACAAGATGTAAATATACCCAAGATG

Reference Human Genome (hg18)

AAAATACGCGTATTCTCCCAAAACAATATC

Short read

AAAAATTCTCCCAAAACAAAAAAATACGCGTATTCTCCCAAAACAATATCTTACAAGATGTAAATATACCCAAGATG

Reference Human Genome (hg18)

AAAATACGCCTATTCTCCCAAAACAATATC

Short read

AAAAATTCTCCCAAAACAAAAAAATACGCGTATTCTCCCAAAACAATATCTTACAAGATGTAAATATACCCAAGATG

Reference Human Genome (hg18)

AAAATACGCCTATTCTCCCATAACAATATC

Short read

Sequencing has high error rate

Mismatch = real variation OR sequencing error

AAAAATTCTCCCAAAACAAAAAAATACGCGTATTCTCCCAAAACAATATCTTACAAGATGTAAATATACCCAAGATG

Reference Human Genome (hg18)

AAAATACGCCTATTCTCCCAAAACAATATC

Short read

Typical mismatch rate of entire datasets = 0.5-2% (errors >> real variations)

chr2, pos=85623221 bp

Single Nucleotide Variation

chr14, pos=35859525 bp

Single Nucleotide Variation

chr1, pos=220952447

Single Nucleotide Variation

All cells in tumor have heterozygous mutation

A fraction of cells have heterozygous mutation

Loss of heterozygocity due to loss of genetic material

Cancer mutations

The error/mismatch rate is not uniform across read length

Mismatch

Popular SNV calling programs

• GATK http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit

• VarScan• http://varscan.sourceforge.net/

genome

N reads at considered position

k reads with mutation

Is k greater than expected by chance, given error rates pi ?

p1p3p5p6p8 p9p10p11p14p17

The Poisson-Binomial distribution

Wacker et al, 2012; Jiang et al, 2012 Chen & Liu, 1997

SNVseeqer: Single Nucleotide Variation detection from deep sequencing data

Indel calling• Complicated because indels often occur within

microsatellite regions, eg CACACACA– CA--CACACA as good as CACA--CACA, CACACA--CA

• Since reads are aligned independently, local realignment is needed

• DINDEL (used in 1000 Genomes Project)

http://www.sanger.ac.uk/resources/software/dindel/

Variant annotation• Variants can be either mutation or (more often) polymorphism. dbSNP catalogs all known polymorphisms

• Missense, nonsense, intron, 3’UTR, 5’UTR, etc– SeattleSNP http://pga.gs.washington.edu/

• Severity of missense mutations– PolyPhen http://genetics.bwh.harvard.edu/pph2/ – Mutation Assessor http://mutationassessor.org/

• GATK for variant annotation http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit

• Cross-species conservation

6. Read data visualization

SAMtoolssamtools tview file.sorted.bam wg.fa

UCSC Genome Browser

• Upload BAM file to genome browser or make it accessible to UCSC from your own web page

Integrated Genome Viewer (IGV)

Read count

genome

genome T A T T A A T T A T C C C C A T A T A T G A T A T

Read densities

Wiggle files for Genome BrowservariableStep chrom=chr1 span=101471 0.31481 0.61491 0.61501 0.61511 0.61521 0.61531 1.11541 1.71551 1.91561 2.11571 2.51581 2.81591 3.21601 3.91611 3.91621 4.51631 4.81641 4.21651 3.91661 3.81671 3.21681 2.41691 1.91701 1.41711 1.31721 0.81871 1.41881 4.91891 9.11901 9.71911 10.71921 11.21931 12.31941 16.51951 23.41961 29.91971 32.61981 31.81991 28.02001 29.62011 30.62021 32.72031 32.72041 29.2

http://genome.ucsc.edu/goldenPath/help/bigWig.htmlhttp://genome.ucsc.edu/goldenPath/help/wiggle.html

7. BioConductor packages for high-througput sequencing

BioC packages• IRanges

http://bioconductor.org/packages/release/bioc/html/IRanges.html• Rsamtools

http://bioconductor.org/packages/2.7/bioc/html/Rsamtools.html• ShortRead

http://bioconductor.org/packages/release/bioc/html/ShortRead.html• rtracklayer

http://bioconductor.org/packages/2.8/bioc/html/rtracklayer.html• BSgenome

http://bioconductor.org/packages/release/bioc/html/BSgenome.html

And many more

SAMTools, Unix programs and R/BioC

• RSAMtools• Unix commands can be ran in R

system(“samtools rmdup –s file.bam file_noclonal.bam”)

http://manuals.bioinformatics.ucr.edu/home/ht-seq

8. Challenges and evolution of sequencing and its analysis

Storage is becoming a real problem

Kahn, 2011, Science

Sequencing is becoming faster

Reads are becoming longerPacBio

How do you interpret sequencing data in a clinical context ?

Data integrationChIP-seq for BCL6, BCOR, SMRT, H3K79me2, H3K4me1, H3K4me3, H3K27Ac, H3K9Ac, H3K27me3, and DNA methylation (HELP) in LY1 cells

Integrative statistical model

Predictions /Mechanisms

ExperimentsChIP-seq / siRNA etc

HiC

The end

• ole2001@med.cornell.edu• eug2002@med.cornell.edu