Scaffolding pre-assembled contigs using SSPACE.

De novo assembly tools play a main role in reconstructing genomes from next-generation sequencing (NGS) data and usually yield a number of contigs. Using paired-read sequencing data it is possible to assess the order, distance and orientation of contigs and combine them into so-called scaffolds. Although the latter process is a crucial step in finishing genomes, scaffolding algorithms are often built-in functions in de novo assembly tools and cannot be independently controlled. We here present a new tool, called SSPACE, which is a stand-alone scaffolder of pre-assembled contigs using paired-read data. Main features are: a short runtime, multiple library input of paired-end and/or mate pair datasets and possible contig extension with unmapped sequence reads. SSPACE shows promising results on both prokaryote and eukaryote genomic testsets where the amount of initial contigs was reduced by at least 75%.

Boetzer M ，Henkel CV ，Jansen HJ ，Butler D ，Pirovano W ... -  《-》

gapFinisher: A reliable gap filling pipeline for SSPACE-LongRead scaffolder output.

Kammonen JI ，Smolander OP ，Paulin L ，Pereira PAB ，Laine P ，Koskinen P ，Jernvall J ，Auvinen P ... -  《PLoS One》

ELOPER: elongation of paired-end reads as a pre-processing tool for improved de novo genome assembly.

Silver DH ，Ben-Elazar S ，Bogoslavsky A ，Yanai I ... -  《-》

Multi-CAR: a tool of contig scaffolding using multiple references.

Chen KT ，Chen CJ ，Shen HT ，Liu CL ，Huang SH ，Lu CL ... -  《BMC BIOINFORMATICS》

Maptcha: an efficient parallel workflow for hybrid genome scaffolding.

Genome assembly, which involves reconstructing a target genome, relies on scaffolding methods to organize and link partially assembled fragments. The rapid evolution of long read sequencing technologies toward more accurate long reads, coupled with the continued use of short read technologies, has created a unique need for hybrid assembly workflows. The construction of accurate genomic scaffolds in hybrid workflows is complicated due to scale, sequencing technology diversity (e.g., short vs. long reads, contigs or partial assemblies), and repetitive regions within a target genome. In this paper, we present a new parallel workflow for hybrid genome scaffolding that would allow combining pre-constructed partial assemblies with newly sequenced long reads toward an improved assembly. More specifically, the workflow, called Maptcha, is aimed at generating long scaffolds of a target genome, from two sets of input sequences-an already constructed partial assembly of contigs, and a set of newly sequenced long reads. Our scaffolding approach internally uses an alignment-free mapping step to build a contig,contig graph using long reads as linking information. Subsequently, this graph is used to generate scaffolds. We present and evaluate a graph-theoretic "wiring" heuristic to perform this scaffolding step. To enable efficient workload management in a parallel setting, we use a batching technique that partitions the scaffolding tasks so that the more expensive alignment-based assembly step at the end can be efficiently parallelized. This step also allows the use of any standalone assembler for generating the final scaffolds. Our experiments with Maptcha on a variety of input genomes, and comparison against two state-of-the-art hybrid scaffolders demonstrate that Maptcha is able to generate longer and more accurate scaffolds substantially faster. In almost all cases, the scaffolds produced by Maptcha are at least an order of magnitude longer (in some cases two orders) than the scaffolds produced by state-of-the-art tools. Maptcha runs significantly faster too, reducing time-to-solution from hours to minutes for most input cases. We also performed a coverage experiment by varying the sequencing coverage depth for long reads, which demonstrated the potential of Maptcha to generate significantly longer scaffolds in low coverage settings ( - ).

Bhowmik O ，Rahman T ，Kalyanaraman A 《BMC BIOINFORMATICS》