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.
There are many problems in security of Internet of Things (IOT) crying out for solutions, such as RFID tag security, wireless security, network transmission security, privacy protection, information processing security. This article is based on the existing researches of network security technology. And it provides a new approach for researchers in certain IOT application and design, through analyzing and summarizing the security of ITO from various angles.
BackgroundBecause of the short read length of high throughput sequencing data, assembly errors are introduced in genome assembly, which may have adverse impact to the downstream data analysis. Several tools have been developed to eliminate these errors by either 1) comparing the assembled sequences with some similar reference genome, or 2) analyzing paired-end reads aligned to the assembled sequences and determining inconsistent features alone mis-assembled sequences. However, the former approach cannot distinguish real structural variations between the target genome and the reference genome while the latter approach could have many false positive detections (correctly assembled sequence being considered as mis-assembled sequence).ResultsWe present misFinder, a tool that aims to identify the assembly errors with high accuracy in an unbiased way and correct these errors at their mis-assembled positions to improve the assembly accuracy for downstream analysis. It combines the information of reference (or close related reference) genome and aligned paired-end reads to the assembled sequence. Assembly errors and correct assemblies corresponding to structural variations can be detected by comparing the genome reference and assembled sequence. Different types of assembly errors can then be distinguished from the mis-assembled sequence by analyzing the aligned paired-end reads using multiple features derived from coverage and consistence of insert distance to obtain high confident error calls.ConclusionsWe tested the performance of misFinder on both simulated and real paired-end reads data, and misFinder gave accurate error calls with only very few miscalls. And, we further compared misFinder with QUAST and REAPR. misFinder outperformed QUAST and REAPR by 1) identified more true positive mis-assemblies with very few false positives and false negatives, and 2) distinguished the correct assemblies corresponding to structural variations from mis-assembled sequence. misFinder can be freely downloaded from https://github.com/hitbio/misFinder.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0818-3) contains supplementary material, which is available to authorized users.
BackgroundDespite the large volume of genome sequencing data produced by next-generation sequencing technologies and the highly sophisticated software dedicated to handling these types of data, gaps are commonly found in draft genome assemblies. The existence of gaps compromises our ability to take full advantage of the genome data. This study aims to identify a practical approach for biologists to complete their own genome assemblies using commonly available tools and resources.ResultsA pipeline was developed to assemble complete genomes primarily from the next generation sequencing (NGS) data. The input of the pipeline is paired-end Illumina sequence reads, and the output is a high quality complete genome sequence. The pipeline alternates the employment of computational and biological methods in seven steps. It combines the strengths of de novo assembly, reference-based assembly, customized programming, public databases utilization, and wet lab experimentation. The application of the pipeline is demonstrated by the completion of a bacterial genome, Thermotoga sp. strain RQ7, a hydrogen-producing strain.ConclusionsThe developed pipeline provides an example of effective integration of computational and biological principles. It highlights the complementary roles that in silico and wet lab methodologies play in bioinformatical studies. The constituting principles and methods are applicable to similar studies on both prokaryotic and eukaryotic genomes.
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