SLIQ: Simple Linear Inequalities for Efficient Contig Scaffolding

Scaffolding is an important subproblem in "de novo" genome assembly in which mate pair data are used to construct a linear sequence of contigs separated by gaps. Here we present SLIQ, a set of simple linear inequalities derived from the geometry of contigs on the line that can be used to predict the relative positions and orientations of contigs from individual mate pair reads and thus produce a contig digraph. The SLIQ inequalities can also filter out unreliable mate pairs and can be used as a preprocessing step for any scaffolding algorithm. We tested the SLIQ inequalities on five real data sets ranging in complexity from simple bacterial genomes to complex mammalian genomes and compared the results to the majority voting procedure used by many other scaffolding algorithms. SLIQ predicted the relative positions and orientations of the contigs with high accuracy in all cases and gave more accurate position predictions than majority voting for complex genomes, in particular the human genome. Finally, we present a simple scaffolding algorithm that produces linear scaffolds given a contig digraph. We show that our algorithm is very efficient compared to other scaffolding algorithms while maintaining high accuracy in predicting both contig positions and orientations for real data sets.Comment: 16 pages, 6 figures, 7 table

Rascaf: Improving Genome Assembly with RNA Sequencing Data

Abundant but short second-generation sequencing reads make assembly difficult, leading to fragmented genomes and gene annotations. Gene structure information from RNA sequences can be used to improve the completeness and contiguity of an assembly, but bioinformatics methods have been lacking. Rascaf is a highly efficient tool leveraging long-range continuity information from intron spanning RNA sequencing (RNA-seq) read pairs to detect new contig connections. It determines a heaviest path in an exon block graph that simultaneously represents a gene and the underlying contig relationships. Rascaf is more accurate than its competitors, highly precise, and finds thousands of new verifiable connections in several draft Rosaceae genomes. Lightweight and practical, it can be readily incorporated into sequencing pipelines to improve an assembly and its gene annotations

An integer linear programming approach for genome scaffolding

This paper presents a simple and fast approach for genome scaffolding, combining constraint modeling and simple graph manipulation. We model the scaffolding problem as an optimization problem on a graph built from a paired-end reads alignment on contigs, then describe an heuristic to solve this problem with the iterative combination of local constraints solving and cycle breaking phases. We tested our approach on a benchmark of various genomes, and compared it with several usual scaffolders. The proposed method is quick, flexible, and provides results comparable to other scaffolders in terms of quality. In addition, contrarily to state of the art approaches that require dedicated servers, it can be run on a basic notebook computer even for large genomes

A comprehensive evaluation of assembly scaffolding tools

Background: Genome assembly is typically a two-stage process: contig assembly followed by the use of paired sequencing reads to join contigs into scaffolds. Scaffolds are usually the focus of reported assembly statistics; longer scaffolds greatly facilitate the use of genome sequences in downstream analyses, and it is appealing to present larger numbers as metrics of assembly performance. However, scaffolds are highly prone to errors, especially when generated using short reads, which can directly result in inflated assembly statistics. Results: Here we provide the first independent evaluation of scaffolding tools for second-generation sequencing data. We find large variations in the quality of results depending on the tool and dataset used. Even extremely simple test cases of perfect input, constructed to elucidate the behaviour of each algorithm, produced some surprising results. We further dissect the performance of the scaffolders using real and simulated sequencing data derived from the genomes of Staphylococcus aureus, Rhodobacter sphaeroides, Plasmodium falciparum and Homo sapiens. The results from simulated data are of high quality, with several of the tools producing perfect output. However, at least 10% of joins remains unidentified when using real data. Conclusions: The scaffolders vary in their usability, speed and number of correct and missed joins made between contigs. Results from real data highlight opportunities for further improvements of the tools. Overall, SGA, SOPRA and SSPACE generally outperform the other tools on our datasets. However, the quality of the results is highly dependent on the read mapper and genome complexity

OPERA-LG: efficient and exact scaffolding of large, repeat-rich eukaryotic genomes with performance guarantees

10.1186/s13059-016-0951-yGenome Biology17110

SOPRA: Scaffolding algorithm for paired reads via statistical optimization

<p>Abstract</p> <p>Background</p> <p>High throughput sequencing (HTS) platforms produce gigabases of short read (<100 bp) data per run. While these short reads are adequate for resequencing applications, <it>de novo </it>assembly of moderate size genomes from such reads remains a significant challenge. These limitations could be partially overcome by utilizing mate pair technology, which provides pairs of short reads separated by a known distance along the genome.</p> <p>Results</p> <p>We have developed SOPRA, a tool designed to exploit the mate pair/paired-end information for assembly of short reads. The main focus of the algorithm is selecting a sufficiently large subset of simultaneously satisfiable mate pair constraints to achieve a balance between the size and the quality of the output scaffolds. Scaffold assembly is presented as an optimization problem for variables associated with vertices and with edges of the contig connectivity graph. Vertices of this graph are individual contigs with edges drawn between contigs connected by mate pairs. Similar graph problems have been invoked in the context of shotgun sequencing and scaffold building for previous generation of sequencing projects. However, given the error-prone nature of HTS data and the fundamental limitations from the shortness of the reads, the ad hoc greedy algorithms used in the earlier studies are likely to lead to poor quality results in the current context. SOPRA circumvents this problem by treating all the constraints on equal footing for solving the optimization problem, the solution itself indicating the problematic constraints (chimeric/repetitive contigs, etc.) to be removed. The process of solving and removing of constraints is iterated till one reaches a core set of consistent constraints. For SOLiD sequencer data, SOPRA uses a dynamic programming approach to robustly translate the color-space assembly to base-space. For assessing the quality of an assembly, we report the no-match/mismatch error rate as well as the rates of various rearrangement errors.</p> <p>Conclusions</p> <p>Applying SOPRA to real data from bacterial genomes, we were able to assemble contigs into scaffolds of significant length (N50 up to 200 Kb) with very few errors introduced in the process. In general, the methodology presented here will allow better scaffold assemblies of any type of mate pair sequencing data.</p

Efficient hybrid de novo assembly of human genomes with WENGAN

International audienceGenerating accurate genome assemblies of large, repeat-rich human genomes has proved difficult using only long, error-prone reads, and most human genomes assembled from long reads add accurate short reads to polish the consensus sequence. Here we report an algorithm for hybrid assembly, WENGAN, that provides highest quality at low computational cost. We demonstrate de novo assembly of four human genomes using a combination of sequencing data generated on ONT PromethION, PacBio Sequel, Illumina and MGI technology. WENGAN implements efficient algorithms to improve assembly contiguity as well as consensus quality. The resulting genome assemblies have high contiguity (contig NG50:17.24-80.64 Mb), few assembly errors (contig NGA50:11.8-59.59 Mb), good consensus quality (QV:27.84-42.88), and high gene completeness (BUSCO complete: 94.6-95.2%), while consuming low computational resources (CPU hours:187-1,200). In particular, the WENGAN assembly of the haploid CHM13 sample achieved a contig NG50 of 80.64 Mb (NGA50:59.59 Mb), which surpasses the contiguity of the current human reference genome (GRCh38 contig NG50:57.88 Mb). This is a post-peer-review, pre-copyedit version of an article published in Nature Biotechnology