Mapping of Next Generation Sequencing Data

Transcription

1 Mapping of Next Generation Sequencing Data Agnes Hotz-Wagenblatt Bioinformatik (HUSAR)

2 Next Generation Sequencers Next (or 3 rd ) generation sequencers came onto the scene in the early 2000 s General characteristics include: Amplification of genetic material by PCR Ligation of amplified material to a solid surface Sequence of the target genetic material is determined using Sequenceby-Synthesis (using labelled nucleotides or pyrosequencing for detection) or Sequence by ligation Sequencing done in a massively parallel fashion and sequence information is captured by a computer

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4 Sanger sequencing DNA is fragmented Cloned to a plasmid vector Cyclic sequencing reaction Separation by electrophoresis Readout with fluorescent tags

5 Cyclic-array methods DNA is fragmented Adaptors ligated to fragments Several possible protocols yield array of PCR colonies. Enyzmatic extension with fluorescently tagged nucleotides. Cyclic readout by imaging the array.

6 Emulsion PCR Fragments, with adaptors, are PCR amplified within a water drop in oil. One primer is attached to the surface of a bead. Used by 454, Polonator and SOLiD.

7 Bridge PCR DNA fragments are flanked with adaptors. A flat surface coated with two types of primers, corresponding to the adaptors. Amplification proceeds in cycles, with one end of each bridge tethered to the surface. Used by Solexa.

8 Comparison of existing methods

9 Read length and pairing ACTTAAGGCTGACTAGC TCGTACCGATATGCTG Short reads are problematic, because short sequences do not map uniquely to the genome. Solution #1: Get longer reads. Solution #2: Get paired reads.

10 Third generation Nanopore sequencing Nucleic acids driven through a nanopore. Differences in conductance of pore provide readout. Real-time monitoring of PCR activity Read-out by fluorescence resonance energy transfer between polymerase and nucleotides or Waveguides allow direct observation of polymerase and fluorescently labeled nucleotides

11 Analysis tasks Base calling / polymorphism detection Mapping to a reference genome De novo or assisted genome assembly

12 Next Gen. Sequencers Cont. Sequencing platform ABI3730xl Genome Analyzer Roche (454) FLX Illumina Genome Analyzer ABI SOLiD HeliScope Sequencing chemistry Automated Sanger sequencing Pyrosequencing on solid support Sequencing-bysynthesis with reversible terminators Sequencing by ligation Sequencing-bysynthesis with virtual terminators Template amplification method In vivo amplification via cloning Emulsion PCR Bridge PCR Emulsion PCR None (single molecule) Read length bp bp bp 35 bp bp Sequencing throughput Mb/h 13 Mb/h 25 Mb/h Mb/h 83 Mb/h

13 Usage of Sequencing Resequencing Map reads back to genome Call bases RNA-seq Map reads back to genome Count tags to determine gene expression levels Chip Seq Map reads back to genome Peaks determine binding sites. Nearly all experiments have the same first step!

14 Bioinformatics Because of the massively parallel nature of next gen sequencers, huge amounts of data are produced quickly requiring terabytes of storage New bioinformatics tools were developed to utilize the huge number of much shorter reads (~35bp vs ~800bp) Bowtie - Ultrafast, memory-efficient short read aligner SOAPdenovo - Part of the SOAP suite, used to build reference genome TopHat - TopHat is a fast splice junction mapper for RNA-Seq reads

15 Mapping Methods Hash table (Lookup table) - fast, but requires perfect matches Array Scanning - can handle mismatches, but not gaps Dynamic Programming eq Smith-Waterman - Indels, mathematically optimal, slow - most programs use hash mapping as prefilter Burrows-Wheeler Transform - fast and memory efficient, but less suited for - gaps and mismatches

16 Programs Hash Tables: Eland, SOAP, SeqMap, MAQ, RMAP, ZOOM, Novoalign BW transform, FM index: Bowtie, BWA, SOAP2, BWA-SW Briefings in Bioinformatics Advance Access published online on May 11, 2010 Heng Li and Nils Homer A survey of sequence alignment algorithms for next-generation sequencing

17 Hash table A hash table is a data structure that stores things and allows insertions, lookups, and deletions to be performed in O(1) time. An algorithm converts an object, typically a string, to a number. Then the number is compressed according to the size of the table and used as an index. There is the possibility of distinct items being mapped to the same key. This is called a collision and must be resolved.

18 Key Hash Code Generator Number Compression Index Smith Bob Smith 123 Main St. Orlando, FL bob@myisp.com

19 First Hash Table Lookup Blast was the first program using this algorithm Kmer (11mer by default) seed from the query searched in All database sequences, keeps result in hash tables.

20 Hash table algorithm Each tool builds a hash table of short oligomers present in - either the reads (SHRiMP, Maq, RMAP, and ZOOM) -or the reference (SOAP). ZOOM uses 'spaced seeds' to significantly outperform RMAP, Algorithm Yaetes and Perleberg. Spaced seeds have been shown to yield higher sensitivity than contiguous seeds of the same length. SHRiMP employs a combination of spaced seeds and the Smith-Waterman algorithm to align reads at expense of speed. Eland is a commercial alignment program available from Illumina that uses a hash-based algorithm with spaced seeds to align reads.

21 Spaced seeds A template requiring 11 matches at the 1 positions is 55% more sensitive than BLAST s default template for two sequences of 70% similarity. A seed allowing internal mismatches is called spaced seed; the number of matches in the seed is its weight. Eland was the first program that utilized spaced seed in short-read alignment. It uses six seed templates spanning the entire short read such that a two-mismatch hit is guaranteed to be identified by at least one of the templates, SOAP adopts almost the same strategy except that it indexes the genome rather than reads. SeqMap and MAQ extends the method to allow k-mismatches,

22 Borrows Wheeler Transform identifying exact matches and building inexact alignments supported by exact matches. First step by: suffix tree, enhanced suffix array or FM-index. The design of the FM-index is based upon the relationship between the Burrows-Wheeler compression algorithm and the suffix array data structure. The advantage of using a trie is that alignment to multiple identical copies of a substring in the reference is only needed to be done once. Used by Bowtie: Langmead et al. Genome Biology :R25 doi: /gb r25

23 What is a suffix tree? S = M A L A Y A L A M $ A LA M YALAM$ $ $M YALAM$ AL $M YALAM$ $M YALAM$ ALAYALAM$ $

24 Finding a (short) Pattern in a (long) String Build a suffix tree of the string. Starting from the root, traverse a path matching characters of the pattern. If stuck, pattern not present in string. Otherwise, each leaf below gives a position of the pattern in the string.

25 Finding a Pattern in a String Find ALA $ A YALAM$ LA M 5 10 $ AL M$ ALAYALAM$ YALAM$ YALAM$ M$ YALAM$ M$ Two matches - at 6 and 2

26 Suffix Array Suffixe of abracadabra (11): abracadabra bracadabra racadabra etc. Order lexicographically: a abra abracadabra acadabra adabra bra bracadabra cadabra dabra ra racadabra The suffix array is a array of indices starting with 1 or 0 in lexicographical order. For the string "abracadabra" the suffix array is {11,8,1,4,6,9,2,5,7,10,3}, because suffix "a" starts at the 11th letter, "abra" starts at the 8th letter, etc.

27 mississippi# ississippi#m ssissippi#mi sissippi#mis issippi#miss ssippi#missi sippi#missis ippi#mississ ppi#mississi pi#mississip i#mississipp #mississippi Every column is a permutation of T. Given row i, char L[i] precedes F[i] in original T. Sort the rows Consecutive char s in L are adjacent to similar strings in T. Therefore L usually contains long runs of identical char s. F L # mississipp i I #mississip p I ppi#missis s I ssippi#mis s I ssissippi# m M ississippi # P i#mississi p P pi#mississ i S ippi#missi s S issippi#mi s S sippi#miss i S sissippi#m i

28 Reminder: Recovering T from L 1. Find F by sorting L 2. First char of T? m 3. Find m in L 4. L[i] precedes F[i] in T. Therefore we get mi 5. How do we choose the correct i in L? The i s are in the same order in L and F As are the rest of the char s 6. i is followed by s: mis 7. And so on. F L

29 Replace each char in L with the number of distinct char s seen since its last occurrence. Keep MTF[1,, Σ ] array, sorted lexicographically. Runs of identical char s are transformed into runs of zeroes in L(MTF) MTF L L i p s s m # p i s s i i # i m p s i # m p s p i Bad # example m s And so on 0 1For 2larger 3 texts 4 we will receive more runs of zeroes, and dominancy of smaller numbers. s p i # m 0 1The 2reason 3 being 4 that BWT creates clusters of similar char s.

30 Burrows-Wheeler transform. (a) The Burrows-Wheeler matrix and transformation for 'acaacg'. (b) Steps taken by EXACTMATCH to identify the range of rows, and thus the set of reference suffixes, prefixed by 'aac'. (c) UNPERMUTE repeatedly applies the last first (LF) mapping to recover the original text (in red on the top line) from the Burrows-Wheeler transform (in black in the rightmost column).

31 Data structures based on a prefix tree String: AGGAGC Li, H. et al. Brief Bioinform :bbq015v1-15; doi: /bib/bbq015 (A) Prefix trie of string AGGAGC where symbol ^ marks the start of the string. The two numbers in each node give the suffix array interval of the substring represented by the node, which is the string concatenation of edge symbols from the node to the root. (B) Compressed prefix trie by contracting nodes with in- and out-degree both being one. (C) Prefix tree by representing the substring on each edge as the interval on the original string. (D) Prefix directed word graph (prefix DAWG) created by collapsing nodes of the prefix trie with identical suffix array interval. (E) Constructing the suffix array and Burrows Wheeler transform of AGGAGC. Copyright restrictions may apply.

32 Exact matching versus inexact alignment. Illustration of how EXACTMATCH (top) and Bowtie's aligner (bottom) proceed when there is no exact match for query 'ggta' but there is a one-mismatch alignment when 'a' is replaced by 'g'.

33 Role of paired-end and mate-pair mapping Some sequencing technologies produce read pairs such that the two reads are known to be close to each other in physical chromosomal distance. These reads are called paired-end or mate-pair reads. - With this mate-pair information, a repetitive read will be reliably placed if its mate can be placed unambiguously. - Alignment errors may be detected and fixed when wrong alignments break the mate-pair requirement

34 Effect of paired end alignment

35 Effect of quality values

36 Aligning bisulfite-treated reads Bisulfite sequencing is a technology to identify methylation patterns - Cytosines with underlines are not methylated. - Denaturation and bisulfite treatment will convert these cytosines to uracils. - After amplification, four different sequences from the original double-strand DNA result.

37 Aligning bisulfite reads 1) Increased search space due to the cytosine-thymine conversion in the bisulfite treatment. 2) Mapping asymmetry: thymines in bisulfite reads can be aligned with cytosines in the reference (illustrated in blue) but not the reverse. Xi and Li BMC Bioinformatics :232 doi: /

38 Aligning bisufite treated reads -two reference sequences: one with all C bases converted to T bases (the C-to-T reference) the other with all G bases converted to A bases (the G-to-A reference). -alignment: C bases are converted to T base for reads and are mapped to the C-to-T reference (then a C T mismatch is effectively regarded as a match); a similar procedure is performed for the G-to-A conversion in the next round of alignment. -The results from two rounds of alignment are combined to generate the final report. If there are no mutations or sequencing errors, a bisulfite treated read can always be mapped exactly in one of the two rounds.

39 Aligning spliced reads RNA-seq produces reads from transcribed sequences with introns and intergenetic regions excluded. When RNA-seq reads are aligned against the genomic sequence, a read may be mapped to a splicing junction. This will fail with a standard alignment algorithm. -> Special alignment e.g. TopHat

40 TOPHAT Pipeline Trapnell, C. et al. Bioinformatics : ; doi: /bioinformatics/btp120 Copyright restrictions may apply.

41 Eukaryotic genes (exons & introns) Splicing Translation

42 Alternative splicing: One gene, several proteins! Alternative Splicing Mature splice variant I Mature splice variant II

43 Types of alternative splicing

44 TopHat and Cufflinks - Use next generation sequence Data for alternative splicing

45 Comparison of some mapping programs Table 1: Popular short-read alignment software Program Algorithm SOLiD Long a Gapped PE b Q c Bfast hashing ref. Yes No Yes Yes No Bowtie FM-index Yes No No Yes Yes BWA FM-index Yes d Yes e Yes Yes No MAQ hashing reads Yes No Yes f Yes Yes Mosaik hashing ref. Yes Yes Yes Yes No Novoalign g hashing ref. No No Yes Yes Yes a Work well for Sanger and 454 reads, allowing gaps and clipping. b Paired end mapping. c Make use of base quality in alignment. d BWA trims the primer base and the first color for a color read. e Long-read alignment implemented in the BWA-SW module. f MAQ only does gapped alignment for Illumina paired-end reads. g Free executable for non-profit projects only.