1. ALLPATHS: De Novo Assembly of Whole-Genome Shotgun Microreads
Jonathan Butler, Iain MacCallum, Michael Kleber, Ilya A. Shlyakhter, Matthew K. Belmonte, Eric S. Lander, Chad Nusbaum, and David B. Jaffe
Presented by: Mohit Jain

2. Motivation (P) DNA Sequencing:
Introduction
Overview
Algorithm
Results
Motivation
(P) DNA Sequencing:
Chr length: ~ ~250,000,000 bps
Longest sequence-able fragment: ~600 bps
(S) Shotgun Method: determine sequence by breaking genome into many small segments (reads)
(P) Sequence/Genome Assembly: combining these reads to reconstruct the genome
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3. Motivation (S) Original Genome: (P) Repeats (S) Overlap Graph
Shortest Common Superstring (SCS) Problem = The shortest sequence that contains every read as a substring
(P) Repeats
Genomes have repeats, and SCS represents repeats only once
(S) Overlap Graph

4. (S) Overlap Graph Algorithm:
Each read forms a Node
Edge exists between two nodes if the reads overlap
Algorithm:
Step 1: Removing redundant edges, classify edges as required/optional
Step 2: Find the shortest walk which includes all required edges
Red/Thin: False Overlaps

5. (P) Overlap Graph
Microreads = only bases long (for HTS)
shorter reads = shorter overlap => more reads => more overlaps
- Very large number of (mostly false) overlaps
- Large number of reads + short overlap + higher error rate

6. (S) De Bruijn Graph To construct de Bruijn graph:
all reads are broken in to overlapping subsequences of length k (k-mer)
Each k-1 subsequence represents a Node
A directed Edge e exists between two nodes a and b iff there exists a k-mer such that its prefix = a and its suffix = b

7. (S) De Bruijn Graph Condensed by collapsing non-ambiguous paths
Genome: An Eulerian path (Superwalk: walk including all edges) in this graph

8. Paired Reads (Mate pairs)
Sequence two ends of a fragment of known size
Results better assemblies, but more complicated

9. Current Approaches Velvet EULER-USR ALLPATHS
(Velvet and Euler USR are based on De Bruijn Graph method)

10. ALLPATHS Step I. Builds Unipath Graph
Step II. Localizes reads sequences before assembly
Unipath: maximal unbranched sequence

11. Read Localization Select a unipath with ‘normal’ coverage
Avoid large repeat regions
All other unipaths and paired tags connected to this unipath is considered to be in its neighborhood
Assemble each neighborhood separately

12. Short Fragment Pair Merger
Fills the gap in between two paired reads
Builds a local unipath graph
Extend both ends (of all reads) based on the local unipath graph
For each pair, search for other pairs which overlap on both ends, and merge to obtain longer reads

13. Short Fragment Pair Merger
Repeat the process for all pairs.
Once sequence is complete, update the local unipath graph
Iteratively merge local unipath graphs to obtain a global unipath graph, representing the genome

14. ALLPATHS Paired-Read Assembly Algorithm
Step 1: Creating Approximate Unipaths
1a: Error correction
1b: k-mer numbering and searchable data structure (Ignoring any overlaps between reads)
1c: Computing unipaths from the data structure by walking along the reads until a branch is encountered
Read pairs  Unipaths  Localize

15. Step 2: Selecting Seeds
Seeds = Unipaths around which assemblies of genomic regions are build
Ideal seed: Long Unipaths with Low Copy Number (=1)
Copy Number = Inferred from read coverage of the unipaths
2a: For each unipath, compute the closest unipaths in the set that are to the left and to the right of the given unipath
2b: If the distance between left and right neighbours is less than 4 kb, then the middle unipath is removed
2c: After all such unipaths are removed, remaining forms the seeds unipaths

16. Step 3: Assembling neighbourhoods around the seeds
Neighbourhood = Seed + 10 kb on each side
3a: Define a collection of low-copy number unipaths, using iterative linking
3b: Construct two sets of read clouds:
primary (B): only reads, whose true genomic locations are near the seed
secondary (C): contains all the short-fragment read pairs (~0.5 kb) near the seed C
paired-read links
partners
unipaths
“primary read cloud,” generally containing only reads whose true genomic locations are near the seed (but not all such reads), and the “secondary read cloud,” generally containing all the short-fragment read pairs near the seed, and some outsiders as well.
Problem of too-many closures persists, hence use Short-Fragment Pair Merger (progressively merge the secondary read cloud pairs)

17. Step 4: Finding All Paths
compute the closures (include false closures) of all the merged short-fragment pairs
Step 5: Gluing Together the Local Assembly
sequence graph is formed by iteratively joining closures
Step 6: Building the Global Assembly
outputs of local assemblies are glued together to yield a single sequence graph

18. Step 7: Editing the Assembly
To remove detritus, eliminate ambiguity, and pull apart regions where repeats are assembled on top of each other

19. Experiments Simulated Data Real Data
10 reference genomes from bacteria and fungi, and 1 10-Mb segment of human genome; with introduced errors
Real Data
Solexa

20. Results Simulated Data Real Data
Highly complete and contiguous assemblies (Proportion of genome covered > 96%)
Assembly ambiguities regions <20 per megabase
Assemblies of C.jejuni and E.coli have no errors. Very high accuracy, less than one error per 106 bases
Real Data
High coverage (99.1%)
High continuity
High accuracy (Final assembly matches the reference sequence exactly, with only 12 exceptions)

21. + / - + Read Localization + Multi-CPU compatible
+ Extremely good (accurate) results
- Slow
- Very memory intensive
- Impractical assumptions on input data
(500bp +/- 5bp insert size)

22. Thank you