1 Next Generation Sequencing: Technology, Mapping, and Analysis Gary Benson Computer Science, Biology, Bioinformatics Boston University
3 The Human Genome Project took 10 years and cost roughly $3,000,000,000 by 2001.
4 The Human Genome Project took 10 years and cost roughly $3,000,000,000 by But, we had only one, maybe two, versions of the human genome, therefore, little data for a comprehensive, systematic study of human genetic diversity.
5 The Human Genome Project took 10 years and cost roughly $3,000,000,000 by But, we had only one, maybe two, versions of the human genome, therefore, little data for a comprehensive, systematic study of human genetic diversity. My lab has data from 2 whole human genomes, stored on a hard drive. Each cost roughly $40,000 (in 2010). Today (2013) sequencing a genome costs $5000 $10,000.
6 Outline Next Generation Sequencing Technologies Algorithms for Mapping Reads Detecting Structural Variants Visualization Software
7 Why Sequence DNA? DNA is the molecule of genetic inheritance. Sequencing data provide a fundamental basis for understanding the biology of an organism. The data allow comprehensive comparisons of organisms on a genomic level to find regions of similarity, difference, and functional significance.
8 Why Sequence DNA? The data allow us to understand Human variation on a molecular level, for example, the genetic differences between tumor and normal tissue. This will hopefully lead to more specific medical treatments (personalized medicine).
9 Current Experimental Methods That Use Sequencing RNA Seq Measurement of gene expression in a tissue by counting the number of RNA fragments sequenced from each gene. Also used for alternative splicing detection. ChIP Seq (Chromatin Immunoprecipitation) Identification of protein binding sites on DNA by determining where DNA fragments bound to a specific protein map onto the genome.
10 Current Experimental Methods That Use Sequencing Genome sequencing allows us to detect SNPs (single nucleotide polymorphisms) and structural variations among individuals: within a population, from different populations
11 Next Generation Sequencing Technologies Current Illumina Genome Analyzer Roche 454 Applied Biosystems Solid Future Ion Torrent Pacific Biosciences RS
12 Sanger Sequencing
13 Sanger vs NGS technology Next-generation DNA sequencing by Jay Shendure and Hanlee Ji, Nature Biotechnology 26, (2008), doi: /nbt1486
14 Sanger vs NGS technology $/.50 per kilobase Next-generation DNA sequencing by Jay Shendure and Hanlee Ji, Nature Biotechnology 26, (2008), doi: /nbt1486 $/ 1.50 per megabase
15 Emulsion and bridge amplification Next-generation DNA sequencing by Jay Shendure and Hanlee Ji, Nature Biotechnology 26, (2008), doi: /nbt1486
16 Illumina sequencing technology
17 Illumina sequencing technology
18 Illumina sequencing technology
19 Video of Illumina Sequencing
20 SOLiD and 454 technologies Sequencing technologies the next generation by Michael L. Metzker Nature Reviews Genetics 11, 31-46, doi: /nrg2626
21 Most Common Error Types: 454 vs Illumina Roche/454 Illumina/Solexa Lower overall error
22 Next Generation Sequencing Data These technologies generate millions to billions of short DNA reads sampled from a whole DNA genome, targeted genetic regions, or transcribed RNA. Length: nt (Illumina) nt (454)
24 Mapping Reads the Problem Given 100+ million reads from an experiment, for each: 1. find the genomic coordinates, chromosome and first base, where it has the best match in a reference genome, either with the forward or reverse strand. 2. best match means zero or a small number of differences with the reference. 3. differences include mismatches and indels. 4. determine if it has multiple matches or none at all.
25 Algorithms for Read Mapping
26 Structural Variants Structural variants are any rearrangements of the genome relative to a reference. They include: insertions/deletions inversions translocations tandem repeat variations Many can be detected with paired end or mate pair reads.
27 Paired-Ends and Mate-Pairs Accurate whole human genome sequencing using reversible terminator chemistry, David Bentley et al., Nature 456, 53-59, doi: /nature07517
28 Mate-pairs vs paired-ends Detection of large indels requires large insert sizes. Current Illumina technology allows for paired-end insert sizes very close to 250 bp, which, depending on coverage, allows for detection of small and medium-size indels only. Mate-pair libraries allow for generation of large inserts at the expense of more insert-size variability.
29 Normally Mapped Reads 1 2 paired reads sequenced fragment (insert) Subject A B C Reference 1 2 A B C mapped reads Apparent insert size in the normally expected range.
30 Deletion 1 2 paired reads sequenced fragment (insert) Subject A C Reference 1 2 A B C mapped reads
31 Deletion 1 2 Subject A C 1 2 Reference A B C Apparent insert size longer than expected indicating deletion of B.
32 Insertion 1 2 Subject A B C 1 2 Reference A C
33 Insertion 1 2 Subject A B C 1 2 Reference A C Apparent insert size shorter than expected indicating insertion of B.
34 Distribution to determine unusually long or short apparent insert length Insertions Deletions
35 Singletons A singleton is a read which maps, but whose pair does not map. Possible causes: 1. Split read 2. Novel insertion
36 Singleton Split Read 1 2 Subject A C Reference A B C Parts of read 2 map to two locations. It is split. Some mapping programs cannot detect the split mapping.
37 Singleton Split Read 1 2 Subject A C 1 Reference A B C Only one read mapped Parts of read 2 map to two locations. It is split. Some mapping programs cannot detect the split mapping.
38 Homozygous deletion Bentley, et al, Nature 456, (6 November 2008) doi: /nature07517 ;
39 Bentley, et al, Nature 456, (6 November 2008) doi: /nature07517 ; Gap Heterozygous deletion No gap
40 Inversion 1 3 prime 5 prime Reference A B C D E F G H 5 prime 3 prime
41 Inversion 2 B C D E F G 3 prime 5 prime Reference A H 5 prime 3 prime
42 Inversion 3 B C D E F G 3 prime 5 prime Reference A H 5 prime 3 prime
43 Inversion 4 3 prime B C D E F G 5 prime Reference A H 5 prime 3 prime
44 Inversion 5 B C D E F G 3 prime 5 prime Reference A H 5 prime 3 prime
45 Inversion 6 G F E D C B 3 prime 5 prime Reference A H 5 prime 3 prime
46 Inversion 7 Subject 3 prime A G F E D C B 5 prime H 5 prime 3 prime
47 Inversion Subject A G F E D C B H Reference A B C D E F G H
48 Inversion Subject A G F E D C B H Reference A B C D E F G H Paired reads map in the same direction and are farther apart than expected
49 Inversion Subject A G F E D C B H Reference A B C D E F G H Paired reads map in the same direction and are farther apart than expected
50 Inversion Subject A G F E D C B H Reference A B C D E F G H A split read will generally go undetected.
51 Inversion Subject A G F E D C B H 2 1 Reference A B C D E F G H An insert entirely contained in the inversion will look normal, although the positions are swapped.
52 Bentley, et al, Nature 456, (6 November 2008) doi: /nature07517 ; Homozygous inversion Red is pair mapped in the same direction
53 Breakpoints of an Inversion No normally mapped reads span the breakpoints.
54 Tandem Repeat Variants or VNTRs (Variable Number of Tandem Repeats)
55 Tandem Repeat tcgctggtcata cgt cgt cgt cgt cgt tacaaacgtcttccgt
56 Tandem Repeat tcgctggtcata cgt cgt cgt cgt cgt tacaaacgtcttccgt left flank sequence tandem array of copies right flank sequence
57 Tandem Repeat left flank sequence tandem array of copies right flank sequence consensus sequence multiple alignment
59 Tandem Repeat Variants Tandem Repeat polymorphisms occur as differences in: copy number individual copy motifs (SNPs/indels) order of motifs in the tandem array
60 Why are Tandem Repeat Variants Important? They are associated with human disease: Triple repeat diseases Fragile X mental retardation Myotonic dystrophy Huntington s disease Friedreich s ataxia Epilepsy Diabetes Ovarian cancer They co occur with transcription factor binding sites and so may be involved in gene regulation.
61 Why is detecting variants difficult? Read mapping in the presence of large indels (copy number difference) is computationally costly.
62 Why is detecting variants difficult? Read mapping in the presence of large indels (copy number difference) is computationally costly. Motif differences (indels and SNPs) and motif order differences are additional complications for both seed indexing and BWT/Suffix Array matching approaches.
63 Why is detecting variants difficult? Read mapping in the presence of large indels (copy number difference) is computationally costly. Motif differences (indels and SNPs) and motif order differences are additional complications for both seed indexing and BWT/Suffix Array matching approaches. Mapping and indel detection is typically oblivious to sequence annotation.
64 Outline of Strategy 1. Detect repeats in the subject and in the human reference using TRF software. 2. Map read TRs to reference TRs using: indexing of reference patterns fast bit wise edit distance with threshold profile alignment of TR arrays flanking sequence alignment (bit wise) 3. Compare copy number of reference TRs with those of mapped read TRs and identify variants
65 Subject data comes from the Watson genome. 454 technology: 74 million reads, avg. length 261 nt. avg. coverage ~6
66 Reference tandem repeats come from the Tandem Repeats Database (TRDB).
67 Reference tandem repeats come from the Tandem Repeats Database (TRDB) ,671
68 VNTR 1 copy shorter (heterozygous?)
69 VNTR 2 copies shorter
70 VNTR 1 copy longer
71 VNTR 2 alleles observed
72 VNTR 2 copies shorter
73 VNTR and SNP alleles
74 VNTR internal motif duplicated and flanking SNPs
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