ALLPATHS: De novo assembly of whole-genome shotgun microreads

Abstract

New DNA sequencing technologies deliver data at dramatically lower costs but demand new analytical methods to take full advantage of the very short reads that they produce. We provide an initial, theoretical solution to the challenge of de novo assembly from whole-genome shotgun “microreads.” For 11 genomes of sizes up to 39 Mb, we generated high-quality assemblies from 80× coverage by paired 30-base simulated reads modeled after real Illumina-Solexa reads. The bacterial genomes of Campylobacter jejuni and Escherichia coli assemble optimally, yielding single perfect contigs, and larger genomes yield assemblies that are highly connected and accurate. Assemblies are presented in a graph form that retains intrinsic ambiguities such as those arising from polymorphism, thereby providing information that has been absent from previous genome assemblies. For both C. jejuni and E. coli, this assembly graph is a single edge encompassing the entire genome. Larger genomes produce more complicated graphs, but the vast majority of the bases in their assemblies are present in long edges that are nearly always perfect. We describe a general method for genome assembly that can be applied to all types of DNA sequence data, not only short read data, but also conventional sequence reads.

Next-generation transcriptome assembly

“…Paired reads from long inserts (500-1000bp) also offer long range connectivity, similar to 454 reads. Some assemblers, such as ALLPATHS, require at least two libraries with different insert sizes, for this reason 8 .…”

“…The Rnnotator 16 , Multiple-k 21 , and Trans-ABySS 19 assemblers follow the same strategy; they assemble the dataset multiple times using a De Bruijn graph-based approach [6][7][8]58 to reconstruct transcripts from a broad range of expression levels, and then post-process the assembly to merge contigs and remove redundancy (Figure 2b). By contrast, other assemblers (Trinity 59 , and Oases 20 ) traverse the De Bruijn graph directly to assemble each isoform.…”

Evaluation of next-generation sequencing software in mapping and assembly

“…The third category of software based on de Bruijn graph approaches 40 are widely used in assembling data from the Solexa and SOLiD platforms. The tools in this category (such as ABySS, 51 ALLPATHS, 52 EULER-SR, 53 SOAPdenovo 54 and Velvet 55 ) have applied certain heuristic strategies to reduce the complexity of the de Bruijn graphs, which trivialize assembly problem by finding the path that would traverse each edge of the graph exactly once. EULER-SR 52 mitigates error sequencing impact by constructing different K-mer sizes De Bruijn graphs and reduces graph complexity by applying low-quality read ends and PE constraints.…”

ABySS-Explorer: Visualizing Genome Sequence Assemblies

“…This approach is not unique to ABySS [4,25,6]. Although some groups present static graphs of real genome assembly data for publication purposes [5], no interactive analysis tools exist for this data type.…”

“…Standard overlap search algorithms are not optimized for this very large number of short sequences, and thus a new generation of assembly algorithms has emerged. Our in-house assembly algorithm, ABySS [24], addresses the overlap search problem by representing DNA sequences as a de Bruijn graph, a notion pioneered by Pevzner et al [21] and employed in other recently published genome assembly tools [4,25,6]. A de Bruijn graph is a directed graph that compactly represents a uniform overlap between sequences.…”