Depending on the design of sequencing libraries, paired reads can bridge over gaps in sequence ranging from hundreds to tens of thousands of base pairs, to order and orientate contigs as well as to estimate the length of gaps. This study compares the accuracy of methods that use paired sequences obtained from each end of DNA templates to bridge over regions of the genome that are difficult to sequence or assemble. However, assembly continuity can be vastly improved by linking contigs together into scaffolds. Analysing gene order and synteny, carrying out comparative or functional genomics or investigating patterns of recombination all rely heavily on obtaining an assembly with good continuity.Ĭontig sizes are determined by the under-representation of sequences, due to coverage variation or sequencing bias, read lengths, sequencing technology and the existence of repeats in the genome. The more fragmented the assembly is, the harder the downstream analysis becomes.
Depending on several factors, including the depth of sequence coverage, sequencing methodology and complexity of the genome, the sequence can be assembled into a variable, but large number of contigs. Through the process of de novo assembly, a genome is pieced together computationally, from overlapping randomly sequenced reads. Obtaining a genome sequence is a vital component for detailed molecular analysis of an organism and for several thousands of species, genome projects are now underway or complete. However, the quality of the results is highly dependent on the read mapper and genome complexity. Overall, SGA, SOPRA and SSPACE generally outperform the other tools on our datasets. Results from real data highlight opportunities for further improvements of the tools. The scaffolders vary in their usability, speed and number of correct and missed joins made between contigs. However, at least 10% of joins remains unidentified when using real data. The results from simulated data are of high quality, with several of the tools producing perfect output. 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. Even extremely simple test cases of perfect input, constructed to elucidate the behaviour of each algorithm, produced some surprising results. We find large variations in the quality of results depending on the tool and dataset used. Here we provide the first independent evaluation of scaffolding tools for second-generation sequencing data.
However, scaffolds are highly prone to errors, especially when generated using short reads, which can directly result in inflated assembly statistics.
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. Genome assembly is typically a two-stage process: contig assembly followed by the use of paired sequencing reads to join contigs into scaffolds.
0 kommentar(er)
