High-Throughput Sequencing

Richard Mottwith contributions from Gil Mcvean, Gerton

Lunter, Zam Iqbal, Xiangchao Gan, Eric Belfield

Sequencing Technologies

• Capillary (eg ABI 3700)• Roche/454 FLX• Illumina GAII• ABI Solid• Others….

Capillary Sequencing

– based on electrophoresis– used to sequence human and mouse genomes– read lengths currently around 600bp (but used to

be 200-400 bp)– relatively slow – 384 sequences per run in x hours– expensive ???

Capillary Sequencing Tracehttp://wheat.pw.usda.gov/genome/sequence/

ACGT represented by continuous traces.

Base-calling requires the identification of well-defined peaks

PHRED Quality Scores

• PHRED is an accurate base-caller used for capillary traces (Ewing et al Genome Research 1998)

• Each called base is given a quality score Q• Quality based on simple metrics (such as peak spacing)

callibrated against a database of hand-edited data• Q = 10 * log10(estimated probability call is wrong)

10 prob = 0.1 20 prob = 0.01 30 prob = 0.001 [Q30 often used as a threshold for useful sequence data]

Capillary sequence assembly and editing

http://www.jgi.doe.gov/education/how/how_12.html

CONSED screen shots

Illumina Sequencing machines

GA-II HiSeq

The Illumina Flow-cell

• Each flow-cell has 8 lanes (16 on HiSeq)• A different sample can be run in each lane• It it possible to multiplex up to 12 samples in

a lane• Each lane comprises 2*60=120• square tiles• Each tile is imaged and analysed separately• Sometimes a control phiX lane is run (in a

control, the genome sequenced is identical to the reference and its GC content is not too far from 50%)

Illumina GA-II “traces”

Discontinuous – a set of 4 intensities at each base position

Cross talk: base-calling errors

Whiteford et al Bioinformatics 2009

Base-calling errors

Typical base-calling error rate ~ 1%, Error rate increases towards end of readUsually read2 has more errors than read1

Assessing Sequence QualityExample summary for a lane of 51bp paired-end data

# reads

% good quality reads(passed chastity filter) % mapped to

reference using ELAND(the read-mappersupplied by Illumina)

% opticalduplicates

% of differences fromreference (upper bound on error rate) in mapped reads

Illumina Throughput (April 2013)

Note: 1 human genome = 3Gb.20-30x coverage of one human genome = 2=3 lanes of HiSeq

Machine Read Length Run Time Output/lane Output/flow cell cost/laneHiSeq2000 2 x 50bp 1 week 15-18Gb 120-144Gb Gb HiSeq2000 2 x 100 bp 12 days 150-200 Gb 150-200 Gb £1.5-2k HiSeq2500 2 x 150bp >30hrs >4.5Gb £0.85kMiSeq 2 x 150 bp 24-27hrs 40-60Gb £2.5-3k

Illumina HiSeq Throughput (Feb 2011)

Read Length Run Time Output

1 x 35bp 1.5 days 26-35 Gb

2 x 50bp 4 days 75-100 Gb

1 x 100 bp 8 days 150-200 Gb

Note: 1 human genome = 3Gb.

Illumina GA-II throughput per flow-cell

Note: these are correct as of February 2010. Output is constantly improving due to changes in chemistry and software.Consumables costs are indicative only

- they don’t include labour, depreciation, overheads or bioinformatics- true costs are roughly double

Pooling and Multiplexing

PrimerPrimer

Barcode 6bpRead

Up to 96 distinct barcodes can be added to one end of a readuseful for low-coverage sequencing of many samples in a simple lane

Up to a further 96 barcodes can be added to other end of a read = 96*96 = 9216 samples

Useful for bacterial sequencing

Pooling Costs

• Library Preps – £80-150 per sample, depending on type of

sequencing– £<50 per sample for 96-plex genomic DNA

• Pooling costs are dominated by library prep, not HiSeq lane costs

• eg 96-plex of gDNA on on HiSeq lane = £4k

Data Formats

• Sequencing produces vast amounts of data• Rate of data growth exceeds Moore’s law

The FastQ format(standard text representation of short reads)

• A FASTQ text file normally uses four lines per sequence. – Line 1 begins with a '@' character and is followed by a sequence

identifier and an optional description (like a FASTA title line). – Line 2 is the raw sequence letters. – Line 3 begins with a '+' character and is optionally followed by the same

sequence identifier (and any description) again. – Line 4 encodes the quality values for the sequence in Line 2, and must

contain the same number of symbols as letters in the sequence. The letters encode Phred Quality Scores from 0 to 93 using ASCII 33 to 126

– Example• @SEQ_ID• GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT• +• !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65

Binary FastQ

• Computer-readable compressed form of FASTQ

• About 1/3 size of FASTQ• Enables much faster reading and writing• Standard utility programs will interconvert (eg.

maq) • Becoming obsolete……

SAM and SAMTOOLShttp://samtools.sourceforge.net/

• SAM (Sequence Alignment/Map) format is a generic format for storing large nucleotide sequence alignments.

• SAM aims to be a format that:– Is flexible enough to store all the alignment information generated by various

alignment programs;– Is simple enough to be easily generated by alignment programs or converted

from existing alignment formats;– Is compact in file size;– Allows most of operations on the alignment to work on a stream without

loading the whole alignment into memory;– Allows the file to be indexed by genomic position to efficiently retrieve all

reads aligning to a locus.– SAM Tools provide various utilities for manipulating alignments in the SAM

format, including sorting, merging, indexing and generating alignments in a per-position format.

BAM files• SAM, BAM are equivalent formats for describing alignments of

reads to a reference genome• SAM: text• BAM: compressed binary, indexed, so it is possible to access reads

mapping to a segment without decompressing the entire file• BAM is used by lookseq, IGV and other software• Current Standard Binary Format• Contains:

– Meta Information (read groups, algorithm details)– Sequence and Quality Scores– Alignment information

• one alignment per read

@HD VN:1.0 GO:none SO:coordinate@SQ SN:chr10 LN:135534747@SQ SN:chr11 LN:135006516...@SQ SN:chrX LN:155270560@SQ SN:chrY LN:59373566@RG ID:WTCHG_7618 PL:ILLUMINA PU:101001_GAII06_00018_FC_5 LB:070/10_MPX SM:1772/10 CN:WTCHG@PG ID:stampy VN:1.0.5_(r710) CL:--processpart=1/4 --readgroup=ID:WTCHG_7618,SM:1772/10,PL:ILLUMINA,PU:101001_GAII06_00018_FC_5,LB:070/10_MPX,CN:WTCHG --comment=@Lane_5_comments.txt --keepreforder --solexa -v0 -g /tmp/Human37 -h /tmp/Human37 -M s_5_1_sequence.txt,s_5_2_sequence.txt --bwaoptions=-t 2 -q10 /tmp/Human37 -o s_5.1_stampy.sam@PG ID:stampy.1 VN:1.0.5_(r710) CL:--processpart=2/4 --readgroup=ID:WTCHG_7618,SM:1772/10,PL:ILLUMINA,PU:101001_GAII06_00018_FC_5,LB:070/10_MPX,CN:WTCHG --comment=@Lane_5_comments.txt --keepreforder --solexa -v0 -g /tmp/Human37 -h /tmp/Human37 -M s_5_1_sequence.txt,s_5_2_sequence.txt --bwaoptions=-t 2 -q10 /tmp/Human37 -o s_5.2_stampy.sam@PG ID:stampy.2 VN:1.0.5_(r710) CL:--processpart=3/4 --readgroup=ID:WTCHG_7618,SM:1772/10,PL:ILLUMINA,PU:101001_GAII06_00018_FC_5,LB:070/10_MPX,CN:WTCHG --comment=@Lane_5_comments.txt --keepreforder --solexa -v0 -g /tmp/Human37 -h /tmp/Human37 -M s_5_1_sequence.txt,s_5_2_sequence.txt --bwaoptions=-t 2 -q10 /tmp/Human37 -o s_5.3_stampy.sam@PG ID:stampy.3 VN:1.0.5_(r710) CL:--processpart=4/4 --readgroup=ID:WTCHG_7618,SM:1772/10,PL:ILLUMINA,PU:101001_GAII06_00018_FC_5,LB:070/10_MPX,CN:WTCHG --comment=@Lane_5_comments.txt --keepreforder --solexa -v0 -g /tmp/Human37 -h /tmp/Human37 -M s_5_1_sequence.txt,s_5_2_sequence.txt --bwaoptions=-t 2 -q10 /tmp/Human37 -o s_5.4_stampy.sam@CO TM:Tue, 26 Oct 2010 09:21:06 BST WD:/data1/GA-DATA/101001_GAII06_00018_FC/Data/Intensities/BaseCalls/Demultiplexed-101009/004/GERALD_09-10-2010_johnb.2 HN:comp04.well.ox.ac.ukUN:johnb@CO IX:GCCAAT SN:085 B-cell ID:070/10_MPX GE:Human37 SR:gDNA Indexed CT:false PR:P100116 SM:1771/10@CO TM:Tue, 26 Oct 2010 09:21:06 BST WD:/data1/GA-DATA/101001_GAII06_00018_FC/Data/Intensities/BaseCalls/Demultiplexed-101009/004/GERALD_09-10-2010_johnb.2 HN:comp04.well.ox.ac.ukUN:johnb@CO IX:GCCAAT SN:085 B-cell ID:070/10_MPX GE:Human37 SR:gDNA Indexed CT:false PR:P100116 SM:1771/10@CO TM:Tue, 26 Oct 2010 09:21:06 BST WD:/data1/GA-DATA/101001_GAII06_00018_FC/Data/Intensities/BaseCalls/Demultiplexed-101009/004/GERALD_09-10-2010_johnb.2 HN:comp04.well.ox.ac.ukUN:johnb@CO IX:GCCAAT SN:085 B-cell ID:070/10_MPX GE:Human37 SR:gDNA Indexed CT:false PR:P100116 SM:1771/10@CO TM:Tue, 26 Oct 2010 09:21:06 BST WD:/data1/GA-DATA/101001_GAII06_00018_FC/Data/Intensities/BaseCalls/Demultiplexed-101009/004/GERALD_09-10-2010_johnb.2 HN:comp04.well.ox.ac.ukUN:johnb@CO IX:GCCAAT SN:085 B-cell ID:070/10_MPX GE:Human37 SR:gDNA Indexed CT:false PR:P100116 SM:1771/10WTCHG_7618:5:40:5848:3669#GCCAAT 145 chr10 69795 3 11I40M chr16 24580964 0 TCAGAAAAAAGAAAATGTGGTATATATACACAATGGAGTACTATTCAGCCC GFIIIIIIIIHIIIIHIIIIIIIIFIIHIIHIIHIIIIIHIIIIIIIHHII PQ:i:394 SM:i:0 UQ:i:250 MQ:i:96 XQ:i:40 RG:Z:WTCHG_7618WTCHG_7618:5:77:5375:15942#GCCAAT 99 chr10 82805 0 51M = 83055 301 GCAGGGAGAATGGAACCAAGTTGGAAAACACTCTGCAGGATATTATCCAGG GBBHHEBG<GGGGGGEGGGEGDGDGGDBGDHHHFHGGEGEBGGDGHEHHFH PQ:i:91 SM:i:0 UQ:i:38 MQ:i:0 XQ:i:33 RG:Z:WTCHG_7618WTCHG_7618:5:77:5375:15942#GCCAAT 147 chr10 83055 0 51M = 82805 -301 AGCTGATCTCTCAGCAGAAACCGTACAAGCCAGAAGAGAGTGGGGGCCAAC #################DB-B?B8B>G>GGBID?FHBBI@IGGGGEEDGGG PQ:i:91 SM:i:0 UQ:i:33 MQ:i:0 XQ:i:38 RG:Z:WTCHG_7618WTCHG_7618:5:49:18524:13016#GCCAAT 163 chr10 83516 0 51M = 83734 269 CCCATCTCACGTGCAGAGACACACATAGACTCAAAATAAAAGGATGGAGGA EHHIIIHIIIIHIIIIIFIDIIIEGEIIIHIHIIIIIIHHIHBDHEGFDEI PQ:i:57 SM:i:0 UQ:i:0 MQ:i:0 XQ:i:39 RG:Z:WTCHG_7618WTCHG_7618:5:2:1789:11020#GCCAAT 161 chr10 83598 0 2M2D49M chrM 2220 0 GTGGGTTGCAATCCTAGTCTCTGATAAAACAGACTTTAAACCAATAAAGAT GGGGG>DDBGGGGGGIIGIBDFGE?IIDIHIIIIBIGIBIHIIHII<DAI< PQ:i:192 SM:i:0 UQ:i:48 MQ:i:96 XQ:i:0 RG:Z:WTCHG_7618WTCHG_7618:5:5:8834:6028#GCCAAT 163 chr10 83702 0 51M = 83876 225 AGAAGAGCTAACTATCCTAAATATATATGCACCCAATACAGGAGCACCCAG EIIIIHHHGGIDIIIHEGIGIHGIGIDFIGBGGGEGGGGGIHDHIDIIHGH PQ:i:23 SM:i:0 UQ:i:0 MQ:i:0 XQ:i:0 RG:Z:WTCHG_7618WTCHG_7618:5:49:18524:13016#GCCAAT 83 chr10 83734 0 51M = 83516 -269 CCAATACAGGAGCACCCAGATTCATAAAGCAAGTCCTGAGTGACCTACAAT BHHHHGHHHHHHHHFHHHHHHHGHHHGGGGBHHEHHHHHHHHHHHHHHHHH PQ:i:57 SM:i:0 UQ:i:39 MQ:i:0 XQ:i:0 RG:Z:WTCHG_7618WTCHG_7618:5:5:8834:6028#GCCAAT 83 chr10 83876 0 51M = 83702 -225 TACCCAGGAATTGAACTCAGCTCTGCACCAAGCAGACCTAATAGACATCTA DEHIIIHIIIIDIGIHFHHGIHIGIIIIIIIIHIGIIIHIIHIIIIIIGII PQ:i:23 SM:i:0 UQ:i:0 MQ:i:0 XQ:i:0 RG:Z:WTCHG_7618

Inside a BAM file

samtools view -h WTCHG_7618.bam

SAMtools• A package for manipulating sequence data

– import: SAM-to-BAM conversion – view: BAM-to-SAM conversion and subalignment retrieval – sort: sorting alignment – merge: merging multiple sorted alignments – index: indexing sorted alignment – faidx: FASTA indexing and subsequence retrieval – tview: text alignment viewer – pileup: generating position-based output and consensus/indel calling

• Li H.*, Handsaker B.*, Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R. and 1000 Genome Project Data Processing Subgroup (2009) The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics, 25, 2078-9

Pileup Alignments

seq1 272 T 24 ,.$.....,,.,.,...,,,.,..^+. <<<+;<<<<<<<<<<<=<;<;7<& seq1 273 T 23 ,.....,,.,.,...,,,.,..A <<<;<<<<<<<<<3<=<<<;<<+ seq1 274 T 23 ,.$....,,.,.,...,,,.,... 7<7;<;<<<<<<<<<=<;<;<<6seq1 275 A 23 ,$....,,.,.,...,,,.,...^l. <+;9*<<<<<<<<<=<<:;<<<< seq1 276 G 22 ...T,,.,.,...,,,.,.... 33;+<<7=7<<7<&<<1;<<6< seq1 277 T 22 ....,,.,.,.C.,,,.,..G. +7<;<<<<<<<&<=<<:;<<&< seq1 278 G 23 ....,,.,.,...,,,.,....^k. %38*<<;<7<<7<=<<<;<<<<< seq1 279 C 23 A..T,,.,.,...,,,.,..... ;75&<<<<<<<<<=<<<9<<:<<

Applications

Genome Resequencing

• Align reads to reference genome– assumed to be very similar, most reads will align

• Identify sequence differences– SNPs, indels, rearrangements– Focus may be on • producing a catalogue of variants (1000 genomes) • producing a small number of very accurate genomes

(mouse, Arabidopsis)

• Generate new genome sequences

Mapping Accuracy in simulated human data Effects of Indels

Read Mapping:read length matters

2008 18: 810-820 Genome Res.

E.Coli 5.4 MbS. cerevisiae 12.5 MbA thaliana 120 MbH sapiens 2.8 Gb

Read Mapping(1): Hashing• Each nucleotide can be represented as a 2-bit binary number A=00, C=01, G=10,

T=11• A string of K nucleotides can be represented as a string of 2K bits eg AAGTC =

0000101101• Each binary string can be interpreted as a unique integer• All DNA strings of length K can be mapped to the integers 0,1,…..4K-1

– k=10 65,535– k=11 262,143– k=12 1,048,575– k=13 4,194,303– k=14 16,777,215– k=15 67,108,864 (effective limit for 32-bit 4-byte words)

• Can use this relationship to index DNA for fast mapping• Need not use contiguous nucleotides – spaced seeds, templates• Trade-off between unique indexing/high memory use

Package for read mapping, SNP calling and management of read data and alignments

Genome Research 2008

Easy to use - unix command-line based

Although no longer state of the art and comparatively slow , generally produces good results

MAQ

MAQ Read Mapping

– Indexes all reads in memory and then scans through genome– Uses the first 28bp of each read for seed mapping– Guarantees to find seed hits with no more than two mismatches, and it also finds

57% of hits with three mismatches– Uses a combination of 6 hash tables that index different parts of each read to do

this– Defines a PHRED-like read mapping quality

• Qs = −10log10 Pr{read is wrongly mapped}.• Based on summing the base-call PHRED scores at mismatched positions

– Reads that map equally well to multiple loci are randomly assigned one location (and have Q=0)

– Uses mate pair information to look for pairs of reads correctly oriented within a set distance• Defines mapping quality for a pair of consistent reads as the sum of their individual

mapping qualities

Read-Mapping (2)Bowtie, BWA, Stampy

all use the Burrows-Wheeler transform

Burrows Wheeler transform

• Represents a sequence in a form such that– The original sequence can be recovered – Is more compressible (human genome fits into

RAM)– similar substrings tend to occur together (fast to

find words)

Bowtiehttp://bowtie-bio.sourceforge.net/index.shtml

http://genomebiology.com/2009/10/3/R25

• Uses the BWA algorithm

• Indexes the genome, not the reads

• Not quite guaranteed to find all matching positions with <= 2 mismatches in first 28 bases (Maq’s criterion)

• Very fast (15-40 times faster than Maq)

• Low memory usage (1.3 Gb for human genome)

• Paper focuses on speed and # of mapped reads, not accuracy.“[…] Bowtie’s sensitivity […] is equal to SOAP’s and somewhat less than Maq’s”

Stampy (Gerton Lunter, WTCHG)

• “Statistical Mapper in Python” (+ core in C)

• Uses BWA and hashing

• <= 3 mismatches in first 34 bp match guaranteedMore mismatches: gradual loss of sensitivity

• Algorithm scans full read, rather than just beginning(and no length limit)

• Handles indels well: Reads are aligned to reference at all candidate positions

• Faster than Maq, slower than Bowtie

• 2.7 Gb memory (shared between instances)

Performance – sensitivityM

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stampy 72 SEbwa 72 SEeland 72 SEmaq 72 SE

Top panel:Sensitivity for reads with indels

Right-hand panel:Sensitivity as function of divergence

(Genome: human)

Viewing Read Alignments: lookseqhttp://www.sanger.ac.uk/resources/software/lookseq/

Viewing Read Alignments: IGVhttp://www.broadinstitute.org/igv/

Variant calling• A hard problem, several SNP callers exist eg MAQ/SAMTOOLS, Platypus

(WTCHG) GATK (Broad)• Issue is to distinguish between sequencing errors and sequence variants• If variant has been seen before in other samples then problem is easier

– genotyping vs variant discovery• VCF Variant call format is now standard file format • MAQ

Assumes genome is diploid by default– Error model initially assumes that sequence positions are independent, attempts

to compute probability of sequence variant– Has to use number of heuristics to deal with misalignments– SNP caller now part of SAMTOOLS varFilter.pl– acts as a filter on a large number of statistics tabulated about each sequence

position

Problems with Variant Calling• Variant Calling is difficult because

– a diploid genome will have two haplotypes present, which can differ significantly, eg due to polymorphic indels• should be easier with haploid or inbred genomes• but even harder when looking at low-coverage pools of individuals (eg 1000 genomes)

– Coverage can vary depending on GC content• problem is sporadic

– Optical duplicates may give the impression there is more support for a variant• often all reads with the same start and end points are thinned to a single representative, but

this can cause problems if the coverage is very high

– read misalignments can produce false positives• repetitive reads can be mapped to the wrong place• indels near the ends of reads can cause local read misalignments, where mismatches (SNPs)

are favoured over indels

– very divergent sequence is hard to align• may fail to give any mapping signal and will look like a deletion• problem addressed by local indel realignment (GATK)

coverage

global

GC content

• Possible causes:• Sanger identified that melting the gel slice by heating to 50 °C in chaotropic buffer decreased the

representation of A+T-rich sequences. Nature methods | VOL.5 NO.12 | DECEMBER 2008• PCR bias during library amplification. Nature methods | VOL.6 NO.4 | APRIL 2009 | 291

GC content can affect read coverage(Arabidopsis data from Plant Sciences, thanks to Eric Belfield and Nick Harberd)

Deletions can cause SNP artefacts, by inducing misalignments at ends of reads

Arabidopsis Data from Eric Belfield and Nick Harberd, Plant Sciences, Oxford

de-Novo genome assembly

• No close reference genome available• Harder than resequencing– Only about 80-90% of genome is assembleable due to

repeats– contiguation– scaffolding

• Different Algorithms• More data required:– greater depth of coverage– range of paired-end insert sizes

Assembling Genomes from Scratchde-novo assembly

• Software include:– VELVET– ABySS– ALL_PAIRS– SOAPdenovo – CORTEX

Computational limitations

• Traditional approach to take reads as fundamental objects, and build algorithms/data structures to encode their overlaps– essentially quadratic in the number of reads

• Next-generation sequencing machines generate too many reads!– simply holding the base-calls requires tens of terabytes

for large projects– analysis produces lots of large intermediate files

• Whatever we do, it has to scale slower than coverage

The de Bruijn Grapha representation of all possible paths joining reads together

Pevsner, PNAS 2001

AACTACTTACGCG

AACTA

Choose a word length k (5 in this example, but larger in applications)

The de Bruijn Graph

AACTAACTACGCG

AACTA ACTAA

The de Bruijn Graph

AACTAACTACGCG

AACTA ACTAA CTAAC

The de Bruijn Graph

AACTAACTACGCG

AACTA ACTAA CTAAC TAACT

The de Bruijn Graph

AACTAACTACGCG

AACTA ACTAA CTACT TAACT

Same sequence, different k=3

ACTACTACTGCAGACTACT

ACT

CTATAC CTGTGC

GCA

CAG

AGAGAC

Same sequence, different k=17

ACTACTACTGCAGACTACT

ACTACTACTGCAGACTA

CTACTACTGCAGACTACTACTACTGCAGACTACT

Recovering unambiguous contigs

bulge– two different paths;in a diploid genome both might be correct

Outline of de-Novo Assembly with the deBruijn Graph

2010 20: 265-272 Genome Res.

2010 20: 265-272 Genome Res.

Cortex ABYSS Velvet0

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Comparison of RAM requirements for whole human genome

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Examples

Resequencing Inbred Lines

• Mouse– 15 inbred strains, at Sanger (PI David Adams)– 2.8Mbp– sequenced to 20x

• Arabidopsis thaliana (plant model)– 19 inbred accessions, here– 120Mb– sequenced to 20-30x

Iterative Reassembly (IMQ/DENOM)Xiangchao Gan

• Basic idea– Should only be one haplotype present (but not always true)– Align reads to reference (Stampy)– Identify high-confidence SNPs and indels (SAMTOOLS)– Modify reference accordingly– Realign reads to modified reference– Iterate until convergence– 5 iterations usually sufficient– Combine with denovo assembled contigs to improve

assembly

Iterative reassembly of inbred strains

Iterative Alignment + deNovo Assembly

Low Coverage sequencing

• “Cheap” alternative to SNP genotyping chips• Sequence populations at <1x coverage• Impute compute genotypes from population

data + haplotype data (1000 genomes…)

CONVERGE study of Major Depression

• 12000 Chinese Women– 6000 cases with Major Depression– 6000 matched controls– Sequenced at ~1x coverage

Imputation from 1x coverageComparison with 16 samples genotyped on Illumina Omnichip

orange is after imputation with 570 asian Thousand Genomes Project haplotypesgreen is before imputation (just using genotype likelihoods)

• 2000 commercial outbred mice• descended from standard laboratory inbred

strains• phenotyped for ~300 traits• sequenced at ~ 0.1x coverage

Outbred Mice

Haplotype reconstruction as probabalistic mosaics

QTLs

Other Applications• RNA-Seq

– gene expression• Chip-Seq

– DNA-protein binding sites– Histone marks– Nucleosome positioning– DNAse hypersensitive sites

• Methylation– bisulphite sequencing

• Mutation detection– from mutagenesis experiments– from human trios

• Multiplex Pooling– random genotyping from low coverage read data