Abstract
Since the read lengths of high throughput sequencing (HTS) technologies are short, de novo assembly which plays significant roles in many applications remains a great challenge. Most of the state-of-the-art approaches base on de Bruijn graph strategy and overlap-layout strategy. However, these approaches which depend on k-mers or read overlaps do not fully utilize information of paired-end and single-end reads when resolving branches. Since they treat all single-end reads with overlapped length larger than a fix threshold equally, they fail to use the more confident long overlapped reads for assembling and mix up with the relative short overlapped reads. Moreover, these approaches have not been special designed for handling tandem repeats (repeats occur adjacently in the genome) and they usually break down the contigs near the tandem repeats. We present PERGA (Paired-End Reads Guided Assembler), a novel sequence-reads-guided de novo assembly approach, which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds using paired-end reads and different read overlap size ranging from O max to O min to resolve the gaps and branches. By constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. When the correct extension cannot be determined, PERGA will try to extend the contig by all feasible extensions and determine the correct extension by using look-ahead approach. Many difficult-resolved branches are due to tandem repeats which are close in the genome. PERGA detects such different copies of the repeats to resolve the branches to make the extension much longer and more accurate. We evaluated PERGA on both Illumina real and simulated datasets ranging from small bacterial genomes to large human chromosome, and it constructed longer and more accurate contigs and scaffolds than other state-of-the-art assemblers. PERGA can be freely downloaded at https://github.com/hitbio/PERGA.
Highlights
The high throughput sequencing (HTS) technologies have emerged for several years [1, 2] and are widely used in many biomedical applications, such as large scale DNA sequencing [3], re-sequencing [4] and SNP discovery [5, 6], etc
We present PERGA (Paired-End Reads Guided Assembler), a novel sequence-reads-guided de novo assembly approach, which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds using paired-end reads and different read overlap size ranging from Omax to Omin to resolve the gaps and branches
By constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases
Summary
The high throughput sequencing (HTS) technologies have emerged for several years [1, 2] and are widely used in many biomedical applications, such as large scale DNA sequencing [3], re-sequencing [4] and SNP discovery [5, 6], etc. The overlap-layout-based approaches firstly compute the overlaps among reads, and assemble according to the read overlaps, and it consists of the greedy extension strategy and the overlap graph strategy as two subcategories. The greedy extension approach was applied by first several de novo assemblers for the HTS data, such as SSAKE [12], VCAKE [13], SHARCGS [14]. These assemblers store the reads and their reverse complements inefficiently, so their memory consumptions are usually very large (especially when there are huge number of erroneous reads with high sequencing depth), which limits their application for large amount of HTS datasets
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