NextGenMap and the impact of hhighly polymorphic regions. Arndt von Haeseler

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1 NextGenMap and the impact of hhighly polymorphic regions Arndt von Haeseler

2 Joint work with:

3 The Technological Revolution Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) Available at: Accessed [ ].

4 Cost categories The graph accounts for Labor, administration, management, utilities, reagents, and consumables Sequencing instruments and other large equipment (amortized over three years) Informatics activities directly related to sequence production (e.g., laboratory information management systems and initial data processing) Shotgun library construction (required for preparing DNA to be sequenced) Submission of data to a public database Indirect Costs The graph does not include Quality assessment/control for sequencing projects Technology development to improve sequencing pipelines Development of bioinformatics/computational tools to improve sequencing pipelines or to improve downstream sequence analysis Management of individual sequencing projects Informatics equipment Data analysis downstream of initial data processing (e.g., sequence assembly, sequence alignments, identifying variants, and interpretation of results)

5 High Throughput Sequencing

6 High Throughput Sequencing Fragment, reverse transcribe Sequence, map onto genome Pepke, Wold, Mortazavi. Nat. Methods 2009

7 High Throughput Sequencing: the Basics Fragment, reverse transcribe Sequence, map onto genome Pepke, Wold, Mortazavi. Nat. Methods 2009

8 Approaches for Read Mapping

9 Comparison of Methods

10 Comparison of Methods: BWT

11 Comparison of Methods: BWT

12 Approaches for Read Mapping: Hash

13 Approaches for Read Mapping: Hash

14 Goal to develop a Mapper matches the speed of BWT methods AND the flexibility of alignment methods deals with different technologies (Illumina, 454, Ion Torrent) is user-friendly (installation, minimum user interaction) runs on desktop computers and on clusters

15 Optimizations Technicalities: Memory access

16 Optimizations 1. Indexing the reference genome 2. Identification of Candidate Mapping Regions (CMR) 3. Reducing alignment score computation 4. Alignment computation

17 1. Indexing the Reference Genome Count the k-mer frequency

18 1. Indexing the Reference Genome Count the k-mer frequency

19 1. Indexing the Reference Genome Constructing an indexing structure Hash table Genomic position

20 1. Indexing the Reference Genome Constructing an indexing structure Hash table Genomic position

21 1. Indexing the reference genome 2. Identification of Candidate Mapping Regions (CMR) 3. Reducing Alignment score computation 4. Alignment computation

22 Identification of CMRs Detection of seed-words (k-match between read and reference K-mer size 3, step size 2

23 Identification of CMRs Shift to the potential starting point of the read new old K-mer size 3, step size 2

24 Identification of CMRs Accounting for insertions or deletions new old K-mer size 3, step size 2

25 Identification of CMRs Seed word distribution F R : K-mer size 3, step size 2

26 Identification of CMRs

27 1. Indexing the reference genome 2. Identification of Candidate Mapping Regions (CMR) 3. Reducing alignment score computation 4. Alignment computation

28 Reducing Alignment Score Computation Typical seed word distributions

29 Reducing Alignment score computation Typical seed word distributions t=

30 Reducing Alignment score computation Typical seed word distributions t= Choose t with respect to the max of F R

31 Reducing Alignment Score Computation Computation of a read and genome dependent threshold t R 1.) maximal number S max of seed words per read:

32 Reducing Alignment Score Computation Computation of a read and genome dependent threshold t R 2.) Average maximal number of seed words per read estimated from B random reads

33 Reducing Alignment Score Computation Computation of a read and genome dependent threshold t R 3.) Compute similarity ratio σ 0 reads are on average very different from reference genome. σ 1 reads and reference genome are almost identical

34 Reducing Alignment Score Computation Computation of a read and genome dependent threshold t R 4.) adaptation to specific reads The similarity ratio σ describes the average! Very different signals can occur:

35 Reducing Alignment Score Computation Computation of a read and genome dependent threshold t R 4.) adaptation to specific reads The similarity ratio σ describes the average! Very different signals can occur:

36 Reducing Alignment Score Computation Computation of a read and genome dependent threshold t R 4.) adaptation to specific reads The similarity ratio σ describes the average! Very different signals can occur:!! : =!!max!{!! }

37 Reducing Alignment Score Computation!! : =!!max!{!! }

38 1. Indexing the reference genome 2. Identification of Candidate Mapping Regions (CMR) 3. Reducing alignment score computation 4. Alignment computation

39 Alignment Computation

40 Alignment Computation

41 Results: Human Technology

42 Results: Human Technology

43 Results: Human Technology

44 Results: Human Technology

45 Results: Human Technology

46 Results: Human Technology

47 Results: Human Technology

48 Before NextGenMap

49 After NextGenMap

50 After NextGenMap

51 Summary: NextGenMap (CPU/GPU) is a fast and SNP tolerant mapper. NextGenMap works for Illumina, 454 and IonTorrent data NextGenMap matches the mapping accuracy of Stampy independent of degree of polymorphism. NextGenMap is also well suited for non-model organisms or organisms that show a higly polymorphic genome or maps well in regions that show a high number of differences, i.e. many SNPs. Fritz J Sedlazeck; Philipp Rescheneder; AvH. Bioinformatics (2013) 29: HOMEPAGE:

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