Foundations Algorithmic of Computational Biology Professor Istrail
Genome Assembly
Assembly Progression (Macro View)
Algorithmic Foundations of Computational Biology Professor Istrail
Algorithmic Foundations of Computational Biology
Review-Assembly
Professor Istrail
Step 1: Compare sequences all against all and find all fragment intersections of at least 40 bases with up to 6% error. (For the human genome this took 10,000 CPU hours) Step 2: Cluster into groups of overlapping fragments that agree on a common sequence, and do not overlap fragments that dispute this sequence. Such clusters are called contigs.
Algorithmic Foundations of Computational Biology
Review-Assembly
Professor Istrail
Step 3: Identify contigs that originated from repeats by using the "depth" of the fragments.
Step 4: Determine the consensus sequence of contig.
Algorithmic Foundations of Computational Biology
Conditions
Professor Istrail
Question: When can assembly of shot gun sequencing be performed? Requirements: Contains only a small (~1.5%) amount of repeats. Can be uniformly sampled at random Problem: The human genome is filled (>50%) with repeated sequences. Large duplicated segments 50-500kb. High sequence identity 98-99.9%
Algorithmic Foundations of Computational Biology
Repeats
Professor Istrail
Classes of Repeats Transposon derived repeats (45% of genome) Pseudugenes (inactive copies of genes) Short Kmer repeats ( (A)n (CA)n ) Segmental duplication Blocks of tandemly repeated segments Uses of repeats Passively repeats help study evolution Actively repeats case genome rearrangements
Algorithmic Foundations of Computational Biology
Repeats in the Human Genome
Professor Istrail
Hitch-hikers: molecules that use our genetic machinery for their replication - viruses and repeats:
DNA transposons
3% of our genome Use our DNA replication machinery, encode transposase. Many small unrelated families (common ancestor). 41% of our genome, Alu 400bpX106 copies Use our transcription machinery, encode reverse transcriptase.
RNA transposons (retroposons)
Algorithmic Foundations of Computational Biology
History of Sequencing
Professor Istrail
BAC to BAC sequencing: Used by HGP in the early stages when sequencing was slow and time consuming. BAC end shotgun sequencing: Used by HGP in later stages. Whole genome shotgun sequencing: Used by Celera.
The success of whole genome shotgun sequencing is a victory for computer science.
Algorithmic Foundations of Computational Biology
Whole genome shotgun sequencing
Professor Istrail
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Several copies of the whole are randomly cut into pieces of about 2000bp and 10000bp Sequence 500 bp of both ends from each fragment. Each such pair of sequences ends are called mates. Perform assembly over all sequences to create contigs. Use the mates to put contigs together.
Algorithmic Foundations of Computational Biology
Whole genome shotgun sequencing
Professor Istrail
We know each mate pair is either 2000 or 10000 bps apart and we know their orientation. The process of ordering and placing the contigs is called scaffolding.
More than one mate pair supports each pair of contigs The long 10000bp sequences allow us to jump over problematic repetitive regions.
Algorithmic Foundations of Computational Biology
Handling repeats
Professor Istrail
Assembler classifies repeat sequences by size and reliability. Rocks are the most reliable and must be supported by at least 2 mates one for each neighboring contig Stones are linked by only one mate Finally pebbles fill in the holes
Algorithmic Foundations of Computational Biology Professor Istrail
Whole-Genome Shotgun Sequencing
Whole Genome Shotgun Sequencing
genome
Algorithmic Foundations of Computational Biology Professor Istrail
cut many times at random
plasmids (2 10 Kbp) cosmids (40 Kbp) ~500 bp known dist
forward-reverse paired reads
~500 bp
Algorithmic Foundations of Computational Biology
Steps to Assemble a Genome
1. Find overlapping reads 2. Merge good pairs of reads into longer contigs
Professor Istrail
3. Link contigs to form supercontigs
4. Derive consensus sequence
..ACGATTACAATAGGTT..
Algorithmic Foundations of Computational Biology
1. Find Overlapping Reads
Professor Istrail
Sort all k-mers in reads
(k ~ 24)
Find pairs of reads sharing a k-mer to Extend full alignment throw away if not >95% similar
TACA TAGATTACACAGATTAC T GA || ||||||||||||||||| | || TAGT TAGATTACACAGATTAC TAGA
Algorithmic Foundations of Computational Biology
1. Find Overlapping Reads
One caveat: repeats
Professor Istrail
A k-mer that appears N times, initiates N2 comparisons ALU: 1,000,000 times Solution: Discard all k-mers that appear more than c Coverage, (c ~ 10)
Algorithmic Foundations of Computational Biology
1. Find Overlapping Reads
Professor Istrail
Create local multiple alignments from the overlapping reads
TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA
Algorithmic Foundations of Computational Biology
1. Find Overlapping Reads (cont'd)
Professor Istrail
Correct errors using multiple alignment
TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA
C: C: T: C: C: A: A: A: A:
20 35 30 35 40 15 25 40 25
C: C: C: C: C: A: A: A: A: A:
20 35 0 35 40 15 25 0 40 25
Score alignments
Accept alignments with good scores
Algorithmic Foundations of Computational Biology
2. Merge Reads into Contigs
repeat region
Professor Istrail
Merge reads up to potential repeat boundaries
Algorithmic Foundations of Computational Biology
Repeats, errors, and contig lengths
Professor Istrail
Repeats shorter than read length are OK Repeats with more base pair diffs than sequencing error rate are OK To make a smaller portion of the genome appear repetitive, try to:
Increase read length Decrease sequencing error rate
Role of error correction: Discards ~90% of single-letter sequencing errors decreases error rate decreases effective repeat content increases contig length
Algorithmic Foundations of Computational Biology
2. Merge Reads into Contigs
repeat region
Professor Istrail
Ignore non-maximal reads Merge only maximal reads into contigs
Algorithmic Foundations of Computational Biology
2. Merge Reads into Contigs
repeat boundary??? b a
Professor Istrail
sequencing error
Ignore "hanging" reads, when detecting repeat boundaries
Algorithmic Foundations of Computational Biology
2. Merge Reads into Contigs
?????
Professor Istrail
Unambiguous
Insert non-maximal reads whenever unambiguous
Algorithmic Foundations of Computational Biology
3. Link Contigs into Supercontigs
Normal density
Professor Istrail
Too dense: Overcollapsed? (Myers et al. 2000)
Inconsistent links: Overcollapsed?
Algorithmic Foundations of Computational Biology
3. Link Conti...