zoukankan      html  css  js  c++  java
  • Long-read error correction: a survey and qualitative comparison

    Long-read error correction: a survey and qualitative comparison

    长读纠错:调查和定性比较

    Pierre Morisse, Thierry Lecroq, Arnaud Lefebvre

    Abstract

    Third generation sequencing technologies Pacific Biosciences and Oxford Nanopore Technologies were respectively made available in 2011 and 2014. In contrast with second generation sequencing technologies such as Illumina, these new technologies allow the sequencing of long reads of tens to hundreds of kbps. These so called long reads are particularly promising, and are especially expected to solve various problems such as contig and haplotype assembly or scaffolding, for instance. However, these readers are also much more error prone than second generation reads, and display error rates reaching 10 to 30%, according to the sequencing technology and to the version the chemistry. Moreover, these errors are mainly composed of insertions and deletions, whereas most errors were substitutions in Illumina reads. As a result, long reads require efficient error correction, and a plethora of error correction tools, directly targeted at these reads, were developed in the past nine years. These methods can adopt an hybrid approach, using complementary short reads to perform correction, or a self-correction approach, only making use of the information contained in the long reads sequences. Both theses approaches make use of various strategies such as multiple sequence alignment, de Bruijn graphs, hidden Markov models, or even combine different strategies.

    In this paper, we describe a complete state-of-the-art of long-read error correction, reviewing all the different methodologies and tools existing up to date, for both hybrid and self-correction. Moreover, the long reads characteristics, such as sequencing depth, length, error rate, or even sequencing technology, can have an impact on how well a given tool or strategy performs, and can thus drastically reduce the correction quality. We thus also present an in depth benchmark of available long-read error correction tools, on a wide variety of datasets, composed of both simulated and real data, with various error rates, coverages, and read lengths, ranging from small bacterial to large mammal genomes.

     
  • 相关阅读:
    [Algorithm] Delete a node from Binary Search Tree
    [Javascript] Check both prop exists and value is valid
    对象的多态性
    spring 定时任务(3)--配置多个定时任务
    能上QQ无法打开网页
    [置顶] Ajax核心--XMLHttpRequest对象
    linux内核--进程地址空间(一)
    [SQL]一个删选数据的例子,使用GROUP、DISTINCT
    [置顶] 腾讯2014软件开发笔试题目
    DBS小结
  • 原文地址:https://www.cnblogs.com/wangprince2017/p/13755911.html
Copyright © 2011-2022 走看看