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High-Throughput Sequencing Richard Mott with contributions from Gil Mcvean, Gerton Lunter, Zam Iqbal, Xiangchao Gan, Eric Belfield.

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Presentation on theme: "High-Throughput Sequencing Richard Mott with contributions from Gil Mcvean, Gerton Lunter, Zam Iqbal, Xiangchao Gan, Eric Belfield."— Presentation transcript:

1 High-Throughput Sequencing Richard Mott with contributions from Gil Mcvean, Gerton Lunter, Zam Iqbal, Xiangchao Gan, Eric Belfield

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

3 Capillary Sequencing – based on electrophoresis – used to sequence human and mouse genomes – read lengths currently around 600bp (but used to be bp) – relatively slow – 384 sequences per run in x hours – expensive ???

4 Capillary Sequencing Trace ACGT represented by continuous traces. Base-calling requires the identification of well-defined peaks

5 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 = prob = prob = [Q30 often used as a threshold for useful sequence data]

6 Capillary sequence assembly and editing CONSED screen shots

7 Illumina Sequencing machines GA-IIHiSeq

8 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%)



11 Illumina GA-II traces Discontinuous – a set of 4 intensities at each base position

12 Cross talk: base-calling errors Whiteford et al Bioinformatics 2009

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

14 Assessing Sequence Quality Example summary for a lane of 51bp paired-end data # reads % good quality reads (passed chastity filter) % mapped to reference using ELAND (the read-mapper supplied by Illumina) % optical duplicates % of differences from reference (upper bound on error rate) in mapped reads

15 Illumina Throughput (April 2013) Note: 1 human genome = 3Gb x coverage of one human genome = 2=3 lanes of HiSeq MachineRead LengthRun TimeOutput/laneOutput/flow cellcost/lane HiSeq20002 x 50bp1 week15-18Gb Gb Gb HiSeq20002 x 100 bp12 days Gb £1.5-2k HiSeq25002 x 150bp>30hrs >4.5Gb£0.85k MiSeq2 x 150 bp24-27hrs 40-60Gb£2.5-3k

16 Illumina HiSeq Throughput (Feb 2011) Read LengthRun TimeOutput 1 x 35bp1.5 days26-35 Gb 2 x 50bp4 days Gb 1 x 100 bp8 days Gb Note: 1 human genome = 3Gb.

17 Illumina GA-II throughput per flow-cell Note: these are correct as of February Output is constantly improving due to changes in chemistry and software. Consumables costs are indicative only - they dont include labour, depreciation, overheads or bioinformatics - true costs are roughly double

18 Pooling and Multiplexing Primer Barcode 6bp Read Up to 96 distinct barcodes can be added to one end of a read useful 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

19 Pooling Costs Library Preps – £ 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

20 Data Formats Sequencing produces vast amounts of data Rate of data growth exceeds Moores law

21 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 – GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT + !''*((((***+))%%++)(%%).1***-+*''))**55CCF>>>>>>CCCCCCC65

22 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……

23 SAM and SAMTOOLS 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.

24 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

25 @PGID:stampyVN: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 --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 --readgroup=ID:WTCHG_7618,SM:1772/10,PL:ILLUMINA,PU:101001_GAII06_00018_FC_5,LB:070/10_MPX,CN:WTCHG --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 --readgroup=ID:WTCHG_7618,SM:1772/10,PL:ILLUMINA,PU:101001_GAII06_00018_FC_5,LB:070/10_MPX,CN:WTCHG --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 --readgroup=ID:WTCHG_7618,SM:1772/10,PL:ILLUMINA,PU:101001_GAII06_00018_FC_5,LB:070/10_MPX,CN:WTCHG --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 26 Oct :21:06 B-cellID:070/10_MPXGE:Human37SR:gDNA 26 Oct :21:06 B-cellID:070/10_MPXGE:Human37SR:gDNA 26 Oct :21:06 B-cellID:070/10_MPXGE:Human37SR:gDNA 26 Oct :21:06 B-cellID:070/10_MPXGE:Human37SR:gDNA IndexedCT:falsePR:P100116SM:1771/10 WTCHG_7618:5:40:5848:3669#GCCAAT145chr I40Mchr TCAGAAAAAAGAAAATGTGGTATATATACACAATGGAGTACTATTCAGCCCGFIIIIIIIIHIIIIHIIIIIIIIFIIHIIHIIHIIIIIHIIIIIIIHHIIPQ:i:394SM:i:0UQ:i:250MQ:i:96XQ:i:40RG:Z:WTCHG_7618 WTCHG_7618:5:77:5375:15942#GCCAAT99chr M= GCAGGGAGAATGGAACCAAGTTGGAAAACACTCTGCAGGATATTATCCAGGGBBHHEBGDDBGGGGGGIIGIBDFGE?IIDIHIIIIBIGIBIHIIHII { "@context": "", "@type": "ImageObject", "contentUrl": "", "name": "@HDVN:1.0GO:noneSO:coordinate @SQSN:chr10LN:135534747 @SQSN:chr11LN:135006516...", "description": "@SQSN:chrXLN:155270560 @SQSN:chrYLN:59373566 @RGID:WTCHG_7618PL:ILLUMINAPU:101001_GAII06_00018_FC_5LB:070/10_MPXSM:1772/10CN:WTCHG @PGID:stampyVN: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 @PGID:stampy.1VN: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 @PGID:stampy.2VN: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 @PGID:stampy.3VN: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 @COTM:Tue, 26 Oct 2010 09:21:06 BSTWD:/data1/GA-DATA/101001_GAII06_00018_FC/Data/Intensities/BaseCalls/Demultiplexed-101009/004/ @COIX:GCCAATSN:085 B-cellID:070/10_MPXGE:Human37SR:gDNA IndexedCT:falsePR:P100116SM:1771/10 @COTM:Tue, 26 Oct 2010 09:21:06 BSTWD:/data1/GA-DATA/101001_GAII06_00018_FC/Data/Intensities/BaseCalls/Demultiplexed-101009/004/ @COIX:GCCAATSN:085 B-cellID:070/10_MPXGE:Human37SR:gDNA IndexedCT:falsePR:P100116SM:1771/10 @COTM:Tue, 26 Oct 2010 09:21:06 BSTWD:/data1/GA-DATA/101001_GAII06_00018_FC/Data/Intensities/BaseCalls/Demultiplexed-101009/004/ @COIX:GCCAATSN:085 B-cellID:070/10_MPXGE:Human37SR:gDNA IndexedCT:falsePR:P100116SM:1771/10 @COTM:Tue, 26 Oct 2010 09:21:06 BSTWD:/data1/GA-DATA/101001_GAII06_00018_FC/Data/Intensities/BaseCalls/Demultiplexed-101009/004/ @COIX:GCCAATSN:085 B-cellID:070/10_MPXGE:Human37SR:gDNA IndexedCT:falsePR:P100116SM:1771/10 WTCHG_7618:5:40:5848:3669#GCCAAT145chr1069795311I40Mchr16245809640TCAGAAAAAAGAAAATGTGGTATATATACACAATGGAGTACTATTCAGCCCGFIIIIIIIIHIIIIHIIIIIIIIFIIHIIHIIHIIIIIHIIIIIIIHHIIPQ:i:394SM:i:0UQ:i:250MQ:i:96XQ:i:40RG:Z:WTCHG_7618 WTCHG_7618:5:77:5375:15942#GCCAAT99chr1082805051M=83055301GCAGGGAGAATGGAACCAAGTTGGAAAACACTCTGCAGGATATTATCCAGGGBBHHEBGG>GGBID?FHBBI@IGGGGEEDGGGPQ:i:91SM:i:0UQ:i:33MQ:i:0XQ:i:38RG:Z:WTCHG_7618 WTCHG_7618:5:49:18524:13016#GCCAAT163chr1083516051M=83734269CCCATCTCACGTGCAGAGACACACATAGACTCAAAATAAAAGGATGGAGGAEHHIIIHIIIIHIIIIIFIDIIIEGEIIIHIHIIIIIIHHIHBDHEGFDEIPQ:i:57SM:i:0UQ:i:0MQ:i:0XQ:i:39RG:Z:WTCHG_7618 WTCHG_7618:5:2:1789:11020#GCCAAT161chr108359802M2D49MchrM22200GTGGGTTGCAATCCTAGTCTCTGATAAAACAGACTTTAAACCAATAAAGATGGGGG>DDBGGGGGGIIGIBDFGE?IIDIHIIIIBIGIBIHIIHII

26 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,

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

28 Applications

29 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


31 Mapping Accuracy in simulated human dataEffects of Indels

32 Read Mapping: read length matters : Genome Res. E.Coli 5.4 Mb S. cerevisiae 12.5 Mb A thaliana 120 Mb H sapiens 2.8 Gb

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

34 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

35 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 Q s = 10log 10 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

36 Read-Mapping (2) Bowtie, BWA, Stampy all use the Burrows-Wheeler transform

37 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)

38 Bowtie 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 (Maqs 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. […] Bowties sensitivity […] is equal to SOAPs and somewhat less than Maqs


40 Stampy (Gerton Lunter, WTCHG) Statistical Mapper in Python (+ core in C) Uses BWA and hashing <= 3 mismatches in first 34 bp match guaranteed More 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)

41 Performance – sensitivity Mapping sensitivity Indel size Top panel: Sensitivity for reads with indels Right-hand panel: Sensitivity as function of divergence (Genome: human)

42 Viewing Read Alignments: lookseq

43 Viewing Read Alignments: IGV

44 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 – acts as a filter on a large number of statistics tabulated about each sequence position

45 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)

46 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)

47 Deletions can cause SNP artefacts, by inducing misalignments at ends of reads Arabidopsis Data from Eric Belfield and Nick Harberd, Plant Sciences, Oxford

48 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

49 Assembling Genomes from Scratch de-novo assembly Software include: – VELVET – ABySS – ALL_PAIRS – SOAPdenovo – CORTEX

50 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

51 The de Bruijn Graph a 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)







58 Recovering unambiguous contigs bulge– two different paths; in a diploid genome both might be correct

59 Outline of de-Novo Assembly with the deBruijn Graph : Genome Res.



62 Examples




66 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

67 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

68 Iterative reassembly of inbred strains

69 Iterative Alignment + deNovo Assembly

70 Low Coverage sequencing Cheap alternative to SNP genotyping chips Sequence populations at <1x coverage Impute compute genotypes from population data + haplotype data (1000 genomes…)

71 CONVERGE study of Major Depression Chinese Women – 6000 cases with Major Depression – 6000 matched controls – Sequenced at ~1x coverage


73 Imputation from 1x coverage Comparison with 16 samples genotyped on Illumina Omnichip orange is after imputation with 570 asian Thousand Genomes Project haplotypes green is before imputation (just using genotype likelihoods)

74 2000 commercial outbred mice descended from standard laboratory inbred strains phenotyped for ~300 traits sequenced at ~ 0.1x coverage Outbred Mice

75 Haplotype reconstruction as probabalistic mosaics

76 QTLs

77 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

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