2. WGS Assembly and Reads Clustering Zemin Ning Production Software Group Informatics Division

3. Outline of the Talk:  Whole Genome Shotgun Sequencing  Insert Sizes  Repeats in the Genomes  Kmer Words Hashing and Distribution  Relational Matrix  Profile of unique kmer words  Phusion Steps  How to run Phusion – parameter selections

4. Clone-by-Clone Sequencing –ADV. Easy assembly –DIS. Build library & physical map; redundant sequencing Whole Genome Shotgun (WGS) –ADV. No mapping, no redundant sequencing –DIS. Difficult to assemble and resolve repeats WGS Sequencing: The WGS method begins by fragmenting the genome into many pieces of various sizes. This fragmentation can be done in several ways, including physically shaking the DNA and cutting it with restriction enzymes. Depending on the size of the resulting fragment, various hosts are used to clone these regions.

5. Whole Genome Shotgun Sequencing cut many times at random genome forward-reverse paired reads plasmids (2 – 10 Kbp) cosmids (40 Kbp) known dist ~500 bp

6. Automatic Sequencing

7. Base Calling - Phred Idealized traces would consist of evenly spaced, nonoverlapping peaks. Real traces deviate from this ideal due to imper- fections of the sequencing reactions, of gel electro- phoresis, and of trace processing. The first 50 or so peaks and peaks over 500 or so are particularly noisy. Quality: high – no ambiguities medium – some ambiguities Poor – low confidence

8. Historical Context 1995: H.influenzae sequenced using TIGR by Craig Venter. H. influenzae is the first free living organism to be sequnced. It has roughly 2 million base pairs. The sequencing used a shotgun method that assembled 25,000 fragments of 500 bp each. 1997: Whole Genome Shotgun paper written by Weber & Meyers. This is the first time that a shotgun method has been suggested for sequencing the human genome. By this time, the public Human Genome Project has already started using a clone-by-clone method. 1997: Phil Green writes review against WGS. 1998: Celera founded. Celera entered into a competition with the public Human Genome Project to sequence the human genome first. Celera’s main advantage was using the Whole Genome Shotgun method, which had a chance of failing, but if successful would produce faster results.

9. 1999: Fly genome (180Mbs) sequenced by Celera using the Celera assembler. The genome is available by subscription to Celera’s database 2001: Human Genome published. The genome was sequenced using data from the public Human Genome Project and Celera. The public effort used the clone-by-clone method, while Celera used the Whole Genome Shotgun method. Celera gives access to the genome through subscription to the database. The results from the public project are free to access. 2001: Mouse Genome sequenced by Celera using the Whole Genome Shotgun method. It is made available by Celera on a subscription basis. 2002: The Mouse Genome published. Whitehead’s ARACHNE and Sanger’s Phusion were involved.

10. Whole Genome Assemblers TIGR Assembler G.G. Sutton et al., Genome Sci Technol 1, 9-19 (1995) PHRAP P. Green (1996) Celera Assembler CAP3 X. Huang, A. Madan, Genome Res 9, 868-877 (1999) RePS J. Wang et al. Genome Res 12, 824-831 (2002) Phusion (Sanger)J.C. Mullikin, Z. Ning, Genome Res 13, 81-90 (2003) Arachne (Whitehead/MIT) Euler (UCSD, USC) P.A. Pevzner, H. Tang, M.S. Waterman, RECOMB (2001) most assemblers follow the same approach: overlap – layout - consensus

11. Unique and Repetitive DNA Sections Depth Unique Section Depth Repetitive Section A X’ B X’’ C

12. Repetitive Contig and Read Pairs Depth Depth Depth Grouped Reads by Phusion

13. Gap-Hash4x3 ATGGGCAGATGT ATGGGCAGATGT TGGCCAGTTGTT TGGCCAGTTGTT GGCGAGTCGTTC GGCGAGTCGTTC GCGTGTCCTTCG GCGTGTCCTTCG ATGGCGTGCAGTCCATGTTCGGATCA ATGGCGTGCAGTCCATGTTCGGATCA ATGGCGTGCAGT TGGCGTGCAGTC TGGCGTGCAGTC GGCGTGCAGTCC GGCGTGCAGTCC GCGTGCAGTCCA GCGTGCAGTCCA CGTGCAGTCCAT CGTGCAGTCCAT ATGGCGTGCAGTCCATGTTCGGATCA ATGGCGTGCAGTCCATGTTCGGATCA Contiguous Base Hash Base Hash K = 12 Kmer Word Hashing

14. Word use distribution for the mouse sequence data at ~7.5 fold Useful Region Poisson Curve Real Data Curve

15. Sorted List of Each k-Mer and Its Read Indices ACAGAAAAGC10h06.p1c ACAGAAAAGC12a04.q1c ACAGAAAAGC13d01.p1c ACAGAAAAGC16d01.p1c ACAGAAAAGC26g04.p1c ACAGAAAAGC33h02.q1c ACAGAAAAGC37g12.p1c ACAGAAAAGC40d06.p1c ACAGAAAAGG16a02.p1c ACAGAAAAGG20a10.p1c ACAGAAAAGG22a03.p1c ACAGAAAAGG26e12.q1c ACAGAAAAGG30e12.q1c ACAGAAAAGG47a01.p1c High bits Low bits 64 -2k 2k

16. 1 2 3 4 5 6 … j … N 3 1 4 2 6 5 i N 227 0 0 0 0 R(i,j) Relation Matrix: R(i,j) – number of kmer words shared between read i and read j 227 187 0 0 0 0 187 0 170 0 0 0 170 0 0 0 0 0 0 213 0 0 0 213 0 Group 1: (1,2,3,5) Group 2: (4,6)

17. 1 2 3 4 5 6 … j … 500 3 1 4 2 5 R(i,j) Relation Matrix: R(i,j) – Implementation Read index Number of shared kmer words (< 63) N......

18. Phusion Iterations – Cutting The Weakest Link

19. This graph shows the effect of k-mer on relative contig N50 size for C. briggsae assemblies. At k = 15, 4 ^ 15 is about 10 times the genome size.

20. Profile of Unique kmer Words ATGGCGTGCAGTCCATT ATGGCGTGCAGTCCATT TCGGATCATCCGTTAACGT TCGGATCATCCGTTAACGT P=Kmer P=P2 P=P1 Unique sequence Non-unique sequence Quality values are reset over the read

21. Phusion Steps  Hashing the kmer words;  Calculate kmer words distribution;  Get the list of kmer words – only use those occur 2-D times;  Combine the kmer words with read index;  Sort the combined list;  Build up relational matrix;  Group the reads;  Output.

22. Phusion command line for Zfish./phusion –kmer 18 –depth 13 –depth 13 [-fill 6] [-gap5] -match6 -match26 -matrix500 -break1 -set 12000 mates mates fasta/fastq files

23. Phusion2 ?

24. Acknowledgements:  Jim Mullkin  Richard Durbin  David Jaffe – Broad Institute