Gena Tang Pushkar Pande Tianjun Ye Xing Liu Racchit Thapliyal Robert Arthur Kevin Lee

Gena TangPushkar Pande

Tianjun YeXing Liu

Racchit ThapliyalRobert Arthur

Kevin Lee

Biology background Algorithms

De novo - Overlap-Layout-Consensus - De Bruijn graphs

Reference Tools and Techniques Work flow & strategy Group management




Flow OrderTACG

1-mer

2-mer

3-mer

4-mer

KEY (TCAG)

Measures the presence or absence of each nucleotide at any given position

Margulies et al., 2005

http://www.youtube.com/watch?v=kYAGFrbGl6E

~300nt per read 40X coverage (but widely varying, 12-80)

~ 2 Mb genome (1.8-2.0Mb) Mostly coding sequences

◦ Good for assembly Reference genomes of 4 closely related

species◦ H. influenzae◦ H. parasuis◦ H. ducreyi◦ H. somnus

High degree of genomic plasticity◦ 10% of genes in clinically isolated strains are

novel In situ horizontal gene transfer Supragenome – distributed genome

hypothesis Reference mapping relatively ineffective Making life difficult!!!

De novo Reference mapping

Number of large contigs

50 500+

Unmapped reads 1300 68000+

N50 contig size 28500 3000

Bases in large contigs (Mb)

2 1.3

Assemble all of the H. hemolyticus genomes together

Should give a more complete mapping because of higher coverage ◦ 40X * 5 genomes 200X coverage

But … we get 3700 contigs ◦ (average of 50 for single strain assembly)

These data hint at rampant recombination Reference mapping relatively worthless

Whole-genome alignment (intra-species)◦ On average, 27 insertions, 147 deletions (>90bp)◦ Average length of non-matching seq = 321kb (18%)




Read: a 500-900 long word that comes out of sequencer

Mate pair: a pair of reads from two ends of the same insert fragment

Contig: a contiguous sequence formed by several overlapping reads with no gaps.

Supercontig (scaffold) an ordered and oriented set of contigs, usually by mate pairs.

Consensus sequence: sequence derived from the multiple alignment of reads in a contig

Goal: Find the shortest common sequence of a set of reads.

Input: reads {s1, s2, s3, …} Output: find the shortest string T such that

every s_i is a substring of T. Comment: This is NP-hard problem, we

need to use some approximation algorithm.

Process:(1) Calculate pairwise alignments of all

fragments.(2) Choose two fragments with the largest

overlap.(3) Merge chosen fragments.(4) Repeat step 2 and 3 until only one

fragment is left.

Best one

Take pairwise alignment

Merge the best one

Input reads

Comment: Greedy algorithm was the first successful

assembly algorithm implemented. Used for organisms such as bacteria, single-

celled eukaryotes. It has some efficiency limitation

This approach is based on graph theory. Assemblers based on this approach:

Arachne, Celera, Newbler etc.

Sort all k-mers in reads (k~24)(1) Find pairs of reads sharing a k-mer(2) Extend to full alignment-throw away if not

> 95% similar

TACATAGATTACACAGATTACT GA

| | | | | | | | | | | | | | | | | | | | | |

TAGTTAGATTACACAGATTACTAGA

One caveat: repeats A k-mer that appears N times, initiates N^2

comparisons. Solution: Discard all k-mers that appear more than

c*Coverage, (c~10)

A graph is constructed:(1) Nodes are reads(2) Edges represent overlapping reads

CGTAGTGGCAT

ATTCACGTAG

Overlap graph

A graph is constructed:(1) Nodes are reads(2) Edges represent overlapping reads

CGTAGTGGCAT

ATTCACGTAG

Overlap graph

Terminology in graph theory:(1) Simple path--- a path in the graph contains each node at most

once.(2) Longest simple path---a simple path that cannot be extended.(3) Hamiltonian path– a path in the graph contains each node exactly

once.

CGTAGTGGCAT

ATTCACGTAG

Recall: Now we got several contigs(i.e. several longest simple paths)

Find the multiple alignments of these contigs, and get one consensus sequence as our final contig.

Summary:(1) Based on graph theory(2) Eularian path: a path in a graph which

visits every edge exactly once. (3) Example: Euler, Velvet, Allpath, Abyss,

SOAPdenovo…(4) Eularian path is more efficient, however, in

partice both are equally fast.

Break reads into overlapping k-mers.

Example: 10bp read: ATTCGACTCC for k=5-mers: ATTCG TTCGA TCGAC CGACT GACTC ACTCC

Nodes: k-mers Edges: if (k-1) suffix of a node equals (k-1)

prefix of a node, add a directional edge between them.

ATTCG TTCGA TCGAC

Whenever a node A has only one outgoing arc that points to another node B that has only one ingoing arc, the two nodes are merged.

ATTGC

TGCAT

TGCAG

TTGCA

ATTGCA

In Velvet:(1) Error removal(2) Removing tipsTip: a chain of nodes that is disconnected on

one end.

Consider two paths redundant if they start and end at the same nodes (forming a “bubble”) and contain similar sequences.

Such bubbles can be created by errors or biological variants, such as SNPs or cloning artifacts prior to sequencing. Erroneous bubbles are removed by an algorithm called “Tour Bus”.

Algorithm for directed graphs: (1) Start with an empty stack and an empty circuit (Eulerian path).

- If all vertices have same out-degrees as in-degrees - choose any of them.- If all but 2 vertices have same out-degree as in-degree, and one of those 2 vertices has out-degree with one greater than its in-degree, and the other has in-degree with one greater than its out-degree - then choose the vertex that has its out-degree with one greater than its in-degree.- Otherwise no Euler circuit or path exists.

(2) If current vertex has no out-going edges (i.e. neighbors) - add it to circuit, remove the last vertex from the stack and set it as the current one. Otherwise (in case it has out-going edges, i.e. neighbors) - add the vertex to the stack, take any of its neighbors, remove the edge between that vertex and selected neighbor, and set that neighbor as the current vertex.

(3) Repeat step 2 until the current vertex has no more out-going edges (neighbors) and the stack is empty.

C_k=C*(L-k+1)/L N50 size: 50% of genome is in contigs larger than

N50Example:1Mbp genomeContigs: 300, 100, 50, 45, 30, 20, 15, 15, 10,…N50=30kbp(300+100+50+45+30=525>=500kbp)Note: N50 is meaningful for comparison only when

genome size is the same

Map k-mer on the reference sequence, get a “location map”.

Map each read onto the “location map” according to the k-mer.

AATTG GGTTA

location map of 5-ker

CCCAATTGAAAAATGGTTACCA




Standard flowgram format (SFF) A binary file format used to encode results

of pyrosequencing from the 454 Life Sciences platform for high-throughput sequencing.

a header section + read data sections

A summary of general information regarding the file content

Reads' universal accession numbers (h),sequence information (s), quality scores of basecalls (q), clipping positions (c), flowgram values (f)flowgram indices (i)

the nucleotide bases + the quality scores

6 genomes, 6 .sff files Number of reads ranges from 72548 to

391117

Reads Contigs/Scaffold

Assembler

GS De Novo Assembler: a software package designed specifically for assembling sequence data generated by pyrosequencing platforms

De novo assembly Overlap-Layout-Consensus methodology Better deal with reads greater than 250bp in length GS Reference Mapper

Algorithms for de novo assembly Short read assembly (25~50bp) Using de Bruijn graphs. Applying Velvet to very short reads and

paired-ends information only can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs.

Open-source whole genome assembly software- Assemblers: Minimus2- Validation and Visualization: Hawkeye- Scaffolding: Bambus- Trimming, Overlapping, & Error Correction

Celera MIRA Edena

Finishing is a big challenge !

Sequencing errors: base pair misread, poly A…

It is possible that some portions of genomes are unsequenced

Identical and nearly identical sequences (repeats) can increase the time and space complexity of algorithms exponentially

Gaps & errors

• Finishing is taking contigs and yielding a complete sequence.

• Scaffolder orders contigs into scaffolds based on clone-mate pair information.

• Some assemblers have a simple quality-control method

• Check and manually assemble unresolved repeat regions

• Resequencing




Plan will evolve◦ Different beast than expected

Write scripts to automate the pipeline Velvet did not work MIRA3 requires much time Edena is not optimized for 454 data

454 sequencing reads

.sff files

Newbler

Newbler?

Reference genome

What can we use to reconcile assemblies?

Merged assembly contigs?

Scaffolding?

How do we visualize and evaluate assemblies?

Convert to .fasta

Make all reads equal

sizedVelvet


.sff files

Newbler

Newbler?

Reference genome

What can we use to reconcile assemblies?

Merged assembly contigs?

Scaffolding?

How do we visualize and evaluate assemblies?

Convert to .fasta

Make all reads equal

sizedVelvet

Human and chimp = 99% sequence similarity

H. influenzae and H. influenzae = 80% s.s. H. influenzae and H. hemolyticus = ???

(<80%)


.sff files

NewblerMIRA3

Add a third?AMOScmp

Reference genome

Minimus2

Merged assembly contigs

Scaffolding: Bambus

Visualize and evaluate: Mauve,

Hawkeye, amosvalidate

Specialized algorithm

MIRA3 Hash tagsContigs for each

genome

Hash tag indicates repeat?

BLAST ends against contigs of conspecifics

BLAST ends against

completed genomes

Ends from different contigs match same contig in 1 or more

genomes

Link together

No mapping or multimapping

Further processing?

Ends from different contigs map to similar RefSeq

regions

Link together

No mapping or multimapping

Further processing?

Yes No

Plans change, and knowledge changes An automated pipeline is invaluable

◦ What if 30 15 contigs? Gene finding group◦ Just re-run the scripts

Core genes vs unique genes Gene clustering Codon usage




Some task can be divided, some are not. Complexity of communication

The optimal group size is 3-6

Mini groups

Group meetings Wiki

main page discussion page

Everyone is special, thus valuable Start earlier, get closer to perfection

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Gena Tang Pushkar Pande Tianjun Ye Xing Liu Racchit Thapliyal Robert Arthur Kevin Lee