Assembly algorithms for next-generation sequencing data - PubMed (original) (raw)

Review

Assembly algorithms for next-generation sequencing data

Jason R Miller et al. Genomics. 2010 Jun.

Abstract

The emergence of next-generation sequencing platforms led to resurgence of research in whole-genome shotgun assembly algorithms and software. DNA sequencing data from the Roche 454, Illumina/Solexa, and ABI SOLiD platforms typically present shorter read lengths, higher coverage, and different error profiles compared with Sanger sequencing data. Since 2005, several assembly software packages have been created or revised specifically for de novo assembly of next-generation sequencing data. This review summarizes and compares the published descriptions of packages named SSAKE, SHARCGS, VCAKE, Newbler, Celera Assembler, Euler, Velvet, ABySS, AllPaths, and SOAPdenovo. More generally, it compares the two standard methods known as the de Bruijn graph approach and the overlap/layout/consensus approach to assembly.

Copyright 2010 Elsevier Inc. All rights reserved.

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Conflict of interest statement

CONFLICT OF INTEREST: None.

Figures

Figure 1

Figure 1

A read represented by K-mer graphs. (a) The read is represented by two types of K-mer graph with K=4. Larger values of K are used for real data. (b) The graph has a node for every K-mer in the read plus a directed edge for every pair of K-mers that overlap by K-1 bases in the read. (c) An equivalent graph has an edge for every K-mer in the read and the nodes implicitly represent overlaps of K-1 bases. In these examples, the paths are simple because the value K=4 is larger than the 2bp repeats in the read. The read sequence is easily reconstructed from the path in either graph.

Figure 2

Figure 2

A pair-wise overlap represented by a K-mer graph. (a) Two reads have an error-free overlap of 4 bases. (b) One K-mer graph, with K=4, represents both reads. The pair-wise alignment is a by-product of the graph construction. (c) The simple path through the graph implies a contig whose consensus sequence is easily reconstructed from the path.

Figure 3

Figure 3

Complexity in K-mer graphs can be diagnosed with read multiplicity information. In these graphs, edges represented in more reads are drawn with thicker arrows. (a) An errant base call toward the end of a read causes a “spur” or short dead-end branch. The same pattern could be induced by coincidence of zero coverage after polymorphism near a repeat. (b) An errant base call near a read middle causes a “bubble” or alternate path. Polymorphisms between donor chromosomes would be expected to induce a bubble with parity of read multiplicity on the divergent paths. (c) Repeat sequences lead to the “frayed rope” pattern of convergent and divergent paths.

Figure 4

Figure 4

Three methods to resolve graph complexity. (a) Read threading joins paths across collapsed repeats that are shorter than the read lengths. (b) Mate threading joins paths across collapsed repeats that are shorter than the paired-end distances. (c) Path following chooses one path if its length fits the paired-end constraint. Reads and mates are shown as patterned lines. Not all tangles can be resolved by reads and mates. The non-branching paths are illustrative; they could be simplified to single edges or nodes.

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