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本文引用的文献

1
A simple method to control over-alignment in the MAFFT multiple sequence alignment program.一种在MAFFT多序列比对程序中控制过度比对的简单方法。
Bioinformatics. 2016 Jul 1;32(13):1933-42. doi: 10.1093/bioinformatics/btw108. Epub 2016 Feb 26.
2
Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments.使用从头蛋白质结构预测来衡量非常大的多序列比对的质量。
Bioinformatics. 2016 Mar 15;32(6):814-20. doi: 10.1093/bioinformatics/btv592. Epub 2015 Nov 14.
3
Ultra-large alignments using phylogeny-aware profiles.使用系统发育感知概况的超大比对。
Genome Biol. 2015 Jun 16;16(1):124. doi: 10.1186/s13059-015-0688-z.
4
Simple chained guide trees give poorer multiple sequence alignments than inferred trees in simulation and phylogenetic benchmarks.在模拟和系统发育基准测试中,简单的链式引导树给出的多序列比对结果比推断树的结果更差。
Proc Natl Acad Sci U S A. 2015 Jan 13;112(2):E99-100. doi: 10.1073/pnas.1417526112. Epub 2015 Jan 6.
5
PASTA: Ultra-Large Multiple Sequence Alignment for Nucleotide and Amino-Acid Sequences.PASTA:用于核苷酸和氨基酸序列的超大多重序列比对
J Comput Biol. 2015 May;22(5):377-86. doi: 10.1089/cmb.2014.0156. Epub 2014 Dec 30.
6
Systematic exploration of guide-tree topology effects for small protein alignments.系统探索引导树拓扑结构效应对小蛋白比对的影响。
BMC Bioinformatics. 2014 Oct 4;15(1):338. doi: 10.1186/1471-2105-15-338.
7
Simple chained guide trees give high-quality protein multiple sequence alignments.简单的链式引导树可生成高质量的蛋白质多重序列比对。
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8
TCS: a new multiple sequence alignment reliability measure to estimate alignment accuracy and improve phylogenetic tree reconstruction.TCS:一种新的多重序列比对可靠性度量方法,用于估计比对准确性并改进系统发育树重建。
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9
Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era.在序列和结构丰富的时代评估基于共进化的残基-残基接触预测的效用。
Proc Natl Acad Sci U S A. 2013 Sep 24;110(39):15674-9. doi: 10.1073/pnas.1314045110. Epub 2013 Sep 5.
10
Adding unaligned sequences into an existing alignment using MAFFT and LAST.使用 MAFFT 和 LAST 将未对齐的序列添加到现有比对中。
Bioinformatics. 2012 Dec 1;28(23):3144-6. doi: 10.1093/bioinformatics/bts578. Epub 2012 Sep 27.

将MAFFT序列比对程序应用于对链式引导树实用性的大数据重新检验。

Application of the MAFFT sequence alignment program to large data-reexamination of the usefulness of chained guide trees.

作者信息

Yamada Kazunori D, Tomii Kentaro, Katoh Kazutaka

机构信息

Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan.

Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan.

出版信息

Bioinformatics. 2016 Nov 1;32(21):3246-3251. doi: 10.1093/bioinformatics/btw412. Epub 2016 Jul 4.

DOI:10.1093/bioinformatics/btw412
PMID:27378296
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5079479/
Abstract

MOTIVATION

Large multiple sequence alignments (MSAs), consisting of thousands of sequences, are becoming more and more common, due to advances in sequencing technologies. The MAFFT MSA program has several options for building large MSAs, but their performances have not been sufficiently assessed yet, because realistic benchmarking of large MSAs has been difficult. Recently, such assessments have been made possible through the HomFam and ContTest benchmark protein datasets. Along with the development of these datasets, an interesting theory was proposed: chained guide trees increase the accuracy of MSAs of structurally conserved regions. This theory challenges the basis of progressive alignment methods and needs to be examined by being compared with other known methods including computationally intensive ones.

RESULTS

We used HomFam, ContTest and OXFam (an extended version of OXBench) to evaluate several methods enabled in MAFFT: (1) a progressive method with approximate guide trees, (2) a progressive method with chained guide trees, (3) a combination of an iterative refinement method and a progressive method and (4) a less approximate progressive method that uses a rigorous guide tree and consistency score. Other programs, Clustal Omega and UPP, available for large MSAs, were also included into the comparison. The effect of method 2 (chained guide trees) was positive in ContTest but negative in HomFam and OXFam. Methods 3 and 4 increased the benchmark scores more consistently than method 2 for the three datasets, suggesting that they are safer to use.

AVAILABILITY AND IMPLEMENTATION

http://mafft.cbrc.jp/alignment/software/ CONTACT: katoh@ifrec.osaka-u.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

摘要

动机

由于测序技术的进步,由数千个序列组成的大型多序列比对(MSA)越来越普遍。MAFFT MSA程序有多种构建大型MSA的选项,但由于对大型MSA进行实际基准测试很困难,其性能尚未得到充分评估。最近,通过HomFam和ContTest基准蛋白质数据集使得此类评估成为可能。随着这些数据集的发展,提出了一个有趣的理论:链式引导树可提高结构保守区域MSA的准确性。该理论挑战了渐进比对方法的基础,需要通过与其他已知方法(包括计算量较大的方法)进行比较来检验。

结果

我们使用HomFam、ContTest和OXFam(OXBench的扩展版本)来评估MAFFT中启用的几种方法:(1)使用近似引导树的渐进方法,(2)使用链式引导树的渐进方法,(3)迭代优化方法和渐进方法的组合,以及(4)使用严格引导树和一致性得分的近似程度较低的渐进方法。用于大型MSA的其他程序Clustal Omega和UPP也被纳入比较。方法2(链式引导树)在ContTest中效果为正,但在HomFam和OXFam中为负。对于这三个数据集,方法3和4比方法2更一致地提高了基准分数,表明它们使用起来更安全。

可用性和实现方式

http://mafft.cbrc.jp/alignment/software/ 联系方式:katoh@ifrec.osaka-u.ac.jp 补充信息:补充数据可在《生物信息学》在线获取。