1. DNA Sequencing with Longer Reads Byung G. Kim Computer Science Dept. Univ. of Mass. Lowell kim@cs.uml.edu

2. Outline DNA distribution  Counting problem DNA sequencing  Reassemble DNA fragments  Part of $1,000 genome sequencing

3. Bioinformatics What is bioinformatics ?  Intersection biology statistics computer science Bioinformatics problems  DNA sequences  Protein sequences/structures  Modeling/inference

4. DNA and RNA  DNA (deoxyribonucleic acid) and RNA (ribonucleic acid) are composed of linear chains of monomeric units of nucleotides  A nucleotide has three parts: a sugar, a phophate and a base  Four bases (monomers)

5.  Double helix – 1953 Watson and Crick using X-ray diffraction  Sugar-phosphate backbone is the outer part of the helix  Two strands run in antiparallel directions  Two strands are complementary  Base pairing: A-T; G-C DNA Secondary Structure

6.  In double strands  # of A = # of T; # of G = # of C  Erwin Chargaff’s 1 st Parity Rule, 1951  In a single strand ?  # of A = # of T; # of G = # of C  Erwin Chargaff’s 2nd Parity Rule  How about oligomer (a few successive bases) frequencies ? Monomer Counts in DNA

7. Oligomer Frequencies  Oligomer length = k  Window of k sliding by one base (overlapping k-1 bases)  A simple counting program  May have to contend with long sequences  An oligomer and its reverse complement  ACT vs. AGT A C T A A G C G ……

8. Trimer Frequencies in Yeast

9. Trimer Frequencies in a Few Species

10. Observations  Symmetric in oligomer and reverse complements  Very similar among chromosomes  Issues  Two successive windows share (k-1) bases  Are they independent ? -- probably not (consider a large k)  Need jumping window  Given an oligomer distribution with k, generate a random sequence according to the same distribution  Distributions with (k+1) from the random and the real sequences

11. DNA Sequencing Problem A sample (genome) is amplified, and broken into multiple fragments Reconstruct the original sequence from its fragments (reads)

12. Shot-gun Sequencing

13. Base coverage  A sample (genome) is amplified  A base in the sample is copied into many reads  But, reads are randomly generated  Poisson distribution  How many copies of reads to cover the whole genome ?  Called sequencing depth (c)  From Poisson, prob. of non-coverage is P(X=0) = exp(-c)  # of uncovered bases G*exp(-c)<1  Human genome (3 Gb): c>22

14. Sequencing By Hybridization (SBH)  More recent sequencing machines produce short k-mers, called reads  Given an unknown DNA sequence, DNA array provides  All strings of length l that the sequence contains  No information about their positions Sequencing by Hybridization (SBH) Problem Reconstruct a string from its k - mer composition

15. SBH  Spectrum ( s, k )  For string s of length n, the k -mer composition with a multiset of n-k +1 k -mers in s  k =3, s =TATGGTGC  Spectrum( s.k ) = {TAT, ATG, TGG, GGT, GTG, TGC} Sequencing by Hybridization (SBH) Problem Reconstruct a string from its l- mer composition Input: A set S, representing all k -mers from unknown string s Output: string s such that Spectrum( s, k ) = S

16. Shortest Superstring Problem  Find superstring of the reads, but shortest one Shortest Superstring Problem Given a set of strings, find a shortest string that contain all of them Input: Strings s 1, s 2, …., s n Output: A shortest string s that contains all strings s 1, s 2, …., s n {001}111110101100011010000 0 0 0 1 1 1 0 1 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 0 0 1 1 0 1 0 0 0 0

17. SBH as a Hamiltonian Path Problem  Two k -mers overlap if overlap(p,q) = k-1  Hamiltonian Path Problem  Given Spectrum ( s, k ), and a vertex for every k -mer in Spectrum ( s, k )  Connect every two vertices if two vertices overlap, and visit every vertex  Overlap-Layout-Consensus (OLC)  NP-complete

18. OLC  Conventional shotgun sequencing  Overlap-layout-consensus  Use a computer to search for overlap: trying for all possible pairs of fragments  Layout: putting fragments together  Consensus: error correction  Good for short prokaryote genomes  For n fragments, # of possible overlaps is 2n(n-1)  Difficult  No solution for “repeat problem” to find correct path in the layout step  Produce sequencing errors  Programs  PHRAP, CAP, TIGR, CELERA

19. SBH as an Eulerian Path Problem  Break up reads into short k -mers ( k = 27, for example)  A graph with all ( k -1)-mers : De Bruijn Graph  Edges corresponding to k -mers from Spectrum ( s, k )  Find a path visiting every edge exactly once

20. Eulerian Path Problem  Repeatedly find Eulerian cycles in the graph  Linear time

21. OLC vs. De Bruijn Graph

23. Scaffold linkage Connecting contigs Paired-end read Mate-pair

24.  ABySS (Assembly By Short Sequencing)  Simpson, 2009  www.cs.uml.edu/~kim/580/08_abyss.pdf  Velvet  Zerbino and Birny, 2008  www.cs.uml.edu/~kim/580/08_velvet.pdf  Euler  Pevzner, 2001  www.cs.uml.edu/~kim/580/01_pevzner.pdf www.cs.uml.edu/~kim/580/01_pevzner.pdf  www.cs.uml.edu/~kim/580/09_chaisson.pdf  SOAPdenovo (Short Oligonucleotide Alignment Program)  Beijing Genomics Institute  www.cs.uml.edu/~kim/580/09_soap.pdf SBH Programs

25. SBH Schematic Overview

26. ABySS  Proceeds in two stages  Stage 1  All possible k-mers are generated from reads  Remove read errors and construct initial contigs  Stage 2  Use mate-pairs to extend contigs  Distributed implementation of de Bruijn graph in a cluster using Message Passing Interface (MPI) over multiple computers

27. Velvet  Construct a graph  Transform reads into roadmaps  From a read, generate k-mers with read ID and position in the read (called roadmaps)  Each read is transformed to a set of k-mers with overlaps and hash links to previous reads with the same k-mers  2 nd database  For each read, which k-mers are overlapped by subsequent reads  Trace reads through the graph using roadmaps

28. EULER, 2001  EULER  Implement Eulerian Path problem  Issues with real data  Reads may have errors  Error correction is typically done in ‘consensus’ stage  EULER corrects errors in the first step  Repeat problem  De Bruijn graph

29. EULER +, 2004  EULER+, 2004  A-Bruijn graph  To handle errors in reads, introduce vertices with ungapped alignments that allow mismtaches rather than exact l -mer in de Bruijn assembly  Graph simplication algorithms to remove errors in edges  De Bruijn graph is proportional to the coverage and requires a large memory with a higher coverage with short reads than long–read sequencing

30. EULER – SR, 2008  EULER-SR, 2008  Focus on memory-efficient algorithm dealing with Short Reads  Results  Error correction  E. coli – 68% error-free reads  99.6% errors are corrected  12h on a single processor, 1/2h for assembly

31. EULER – USR, 2009  EULER-USR  Show results of EULER-SR on error-prone Illumina read data  Show 35-nt reads are sufficient when mate-pairs are used

32. Comparisons

33. OLC vs. de Bruijn Graph

35. Difficulties in Graph-based Approaches  De Bruijn Graph (DBG) approach  A read (150-500 bp) is broken into a number of k-mers  A read of 150 bp produces 123 k-mers when k=27 (n-k+1)  New sequencing machines produce longer reads  Problem Sizes  OLC  For a large genome, more than giga reads  Eulerian Path  k = 27 => ( k -1) = 26  4**(26) = 2**(52) (2**30 = 1 G)

36. New OLC Approach  Preserve read info  Data Partition  Partition data according to front and rear k-mers  Utilize known oligomer distribution of species

37. New OLC Approach  Native OLC  Pair-wise fragment overlap  Partial OLC  Partition data  Create consensus string

38. New OLC Approach  Data Partition  Front buckets of identical first k-mers of reads  Rear buckets of identical last k-mers of reads  k=15, 4 15 = 2 30 = 1 billion buckets * 2  Join F and R buckets with the same k-mers  Produce consensus contigs with base matches over a threshold  Repeat generating new F & R buckets,  Each time dealing with longer contigs  Per-bucket sequencing

39. Benefits  Hybrid of OLC and fixed-size de Bruijn graph  First k or last k bp’s can be rapidly compared  Parallelize the program with all possible combinations of s

40. Implementation  Dual Index Bucket Map data structure according to prefix and suffix  A set of reads  A hash table (Java HashMap) mapping prefixes to a Java Vector of references (pointers) to fragments in the set  Another hash table mapping suffixes to a vector of references to fragments in the set.  Bucket size is a parameter

41. Algorithms  Focused merge  Start with a random (or the largest fragment) and keep merging from there until there is no way to merge further or the resulting contig is at least as large as the original input sequence.  Sweep merge  Sweep through each bucket repeatedly, making the best merge each time until no merges can be done  Cutoff for maximum difference between merge of 2 fragments’ base ratios and the original sequence’s base ratios.  Heuristic “oversize penalty” for merges that are significantly larger than the original sequence  Heuristics for base ratio difference  Average: calculate the base ratios for each sequence, average the differences for each base  Maximum: take the maximum of the differences of ratio for any base.

42. Comparison of OLC algorithms Yeast genome Artificial Data with no read errors * with optional simulated annealing. StrategyContigsHeuristicTime Mixed Chromosome Support Worst Case Accuracy Ease of Use FocusedBeam Search* SingleBase ratios 2-15 seconds Unreliable/ Poor LowNot too bad SweepWide Search* MultipleBase ratios 1-30** seconds Excellent Good Needs Micro managi ng NaiveBrute Force SingleActual overlap ½ hour to a few hours NoneOptimalSimple

43. Summary  Base and oligomer distributions in species are unique  New OLC sequencing  DBG may not be suitable for longer reads  Utilize  Data partition  Base distribution