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Lecture_12_genome_sequencing

Weighted voting problems the main problem with this

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Unformatted text preview: ere are two classes of conCgs, unique conCgs and repeat conCgs. •  Unique conCgs are composed of reads that can be unambiguously assembled. •  Repeat conCgs are conCgs with an abnormally high read coverage or connected to an abnormally large number of other conCgs 29 SeparaCng ConCgs Normal density Too dense: Overcollapsed? Inconsistent links: Overcollapsed? CreaCng Scaffolds •  Unique conCgs are joined into larger sequences, called scaffolds. T •  The most common way to piece conCgs into scaffolds is through mate- pair informaCon. •  With mate- pair informaCon, assemblers can idenCfy how far reads and unique conCgs should be apart from each other. –  e.g. if a 2kb fragment of a genome were sequenced 100bp on each end, then we know these reads and the unique conCgs they are in should be roughly 2kb apart. 31 Link ConCgs into SuperconCgs Find all links between unique conCgs Connect conCgs incrementally, if ≥ 2 links Link ConCgs into SuperconCgs Fill gaps in supercontigs with paths of overcollapsed contigs Consensus •  A consensus sequence is derived from a profile of the assembled fragments •  A sufficient number of reads is required to ensure a staCsCcally significant consensus •  Reading errors are corrected Derive Consensus Sequence TAGATTACACAGATTACTGA TTGATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAAACTA TAG TTACACAGATTATTGACTTCATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGGGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAA CTA Derive mulCple alignment from pairwise read alignments Derive each consensus base by weighted voting Problems! •  The main problem with this approach is that it is very, very, very slow and will only work on small genomes or low coverage. •  Not commonly used for complete assembly, however, some sosware tools sCll use this approach: –  Celera: genome assembler for 454, PacBio, and...
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