Genome sequencing and assembling

1. Genome sequencing and assembling Chitta Baral

2. Basic ideas and limitations • Current lab techniques can sequence small (say 700 base pairs) DNA pieces. • Use restriction enzymes to cut DNA pieces • Sort pieces of different sizes using gel electrophoresis and use the sorting to read them • Mapping and Walking • Sequence one piece, get 700 letters, make a primer that allowed you to read the next 700, and work sequentially down the clone • Estimate for human genome sequencing using this method: 100 years • Shotgun sequencing (introduced by Sanger et al. 1977) for sequencing genomes • Obtain random sequence reads from a genome • Assemble them into contigs on the basis of sequence overlaps • Straightforward for simple genomes (with no or few repeat sequences) • Merge reads containing overlapping sequence • Shotgun sequencing is more challenging for complex (repeat-rich) genomes: two approaches

3. Shotgun sequencing – 2 approaches • Hierarchical shotgun approach • Generating an overlapping set of intermediate-sized (e.g. bacterial artificial chromosomes with 200 KB inserts) clones, and keeping a map of that (it took 2 yrs for mapping e-coli) • Subjecting each of these clones to shotgun sequencing, and using the map to get the whole sequence. • Used in S. cerevisiae (yeast), C. elegans (nematode), A. thaliana (mustard weed) and by the International Human Genome Sequencing Consortium (started in 1990, draft made available in 2000) • Whole-genome shotgun (WGS) approach • Generating sequence reads directly from a whole-genome library • Using computational techniques to reassemble in one step. • Used for Drosophila melanogaster (fruit fly) and by Celera Genomics (formed 1998) for human genome.

4. Sequencing small DNA pieces • Use DNA cloning or PCR to make multiple copies. • Put in 4 testtubes marked G, A, T and C • In testtube G use restriction enzymes that cuts at G. • Do the above step for the other testubes. • Use gel electrophoresis separately for the content in each testtube. • The data results in the table on the left. • Reading the table we get G has lengths 1, 7, 12, 13, 19; A has lengths 2, 6, 8, 11, 14,15,16; T has length 4, 5, 9, 18 and C has length 3, 10, 17. • This gives us the sequence.

5. The ARACHNE WGS assembler: outline of assembly algorithm • Input data: • Paired end reads obtained by sequencing both ends of a plasmid of known insert size. • Assumes each base in each read has an associated quality score (say one obtained by PHRED program) • Quality score q corresponds to the probability 10-q/10 that the base is incorrect (40 corresponds to 99.99% accuracy) • Initial step: eliminates terminal regions whose quality is low. • Eliminates reads containing very little high-quality sequence • Eliminates known vector sequences and known contaminants (eg. Sequence from the bacterial host or cloning vector)

6. Cont. • Overlap detection and alignment • Create a sorted table of each k-letter subword (k-mer) together with its source (which read) and its position within the read. • Exclude k-mers that occur with extremely high frequency • corresponds to highly repeated sequences; • used to increase the efficiency of the overlap detection process • Identify all instances of read pairs that share one or more overlapping k-mer, and a 3 step process (similar to FASTA) to align the reads effciently • (i) Merge overlapping shared k-mers, (ii) Extend the shared k-mers to alignments, (iii) Refine the alignment by dynamic programming. • Some valid alignments may be missed and some invalid ones may result.

7. ARACHNE: Error correction • Error detection and correction • Generate multiple alignments among overlapping reads • Identify instances where a base is overwhelmingly outvoted by bases aligned to it (taking into account the score quality) • Similarly correct occasional inserts and deletes (mostly due to sequencing errors)

8. ARACHNE: Evaluation of alignments • Evaluation of alignments • Assign a penalty score to each aligned pair of overlapping reads • Penalty scores are assigned to each discrepant base, based on the sequence quality score at the base and flanking bases on either side. • Discrepancies in high quality sequences are assigned high penalty, and discrepancies in low quality sequences are penalized less heavily. • The penalty scores for individual discrepancies are combined to yield an overall penalty score for the alignment. • Overlaps incurring too high a penalty are discarded • Likely chimeric reads are also detected and discarded • Reads that contain genomic sequence from two disparate locations are termed chimeric.

9. ARACHNE: paired pairs • Identification of paired pairs • Paired reads: reads which are known to be related with respect to orientation and distance. • Searches for instances of two plasmids of similar insert size with sequence overlap occurring at both ends. (together called paired pairs) • These instances are extended by building complexes of such pairs • *********** ********* • ********** ********** • ********* ******** • Collection of paired pairs are merged together into contigs.

10. ARACHNE: Contig assembly • When repeats are absent: correct assembly can be easily obtained by merging all the overlapping reads. • In presence of repeats, false overlaps may arise between reads derived from different copies of a repeat • ARACHNE identifies potential repeat boundaries and avoids assembling contigs across such boundaries • Potential repeat boundary: a read r can be extended by x and y, but x and y don’t overlap • Merge overlapping read pairs that do not cross a marked repeat boundary.

11. ARACHNE: repeat contigs and supercontigs • Detection of repeat contigs – identified 2 ways • Unusually high depth of coverage • Conflicting links to multiple, distinct, non-overlapping contigs, reflecting the multiple regions that flank the repeat in the genome. • aRb, cRd, eRf … will result in –aR-, -cR-, … • Creation of supercontigs • After marking repeat contigs the unmarked contigs (called unitigs) are assembled. • Use forward-reverse links from reads to order and orient unique contigs into supercontigs

12. ARACHNE: Filling gaps in supercontigs • Layout is a set of contigs each of which is an ordered list of contigs with interleaved gaps: corresponding to 2 kind of regions • Regions marked as repeat contigs (which were omitted in supercontig construction) • Regions for which there are insufficient number of shotgun reads to allow assembly • Fill gap using repeat contigs • For every pair of consecutive contigs with an interleaving gap in a supercontig S, the program tries to find a path of pairwise overlapping contigs that fill the gap. • Forward-reverse links from S guide the construction of the path by identifying contigs likely to fall in the gap.

13. Consensus derivation and postconsensus merger • The layout of overlapping reads is converted into consensus sequence with quality scores. • Done by converting pair-wise alignments of reads into multiple alignments, and deriving the consensus base by weighed voting.