NANUQ+:网络估计的分而治之方法。

IF 1.7 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Elizabeth S Allman, Hector Baños, John A Rhodes, Kristina Wicke
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引用次数: 0

摘要

从基因组数据推断物种网络仍然是一个难题,最近的进展大多局限于1级情况。然而,对网络的Blobs树的推理,只显示网络的切割边缘,可以通过TINNiK对任何网络执行,这表明了一种分而治之的网络推理方法,其中树的多功能被单独解析以给出更详细的结构。在这里,我们开发了一种方法,NANUQ +,以快速执行这样的一级分辨率。作为快速一级推理的NANUQ管道的一部分,这为理解何时可能满足一级假设和探索所有高度支持的循环分辨率提供了工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

NANUQ<sup>+</sup>: A divide-and-conquer approach to network estimation.

NANUQ<sup>+</sup>: A divide-and-conquer approach to network estimation.

NANUQ<sup>+</sup>: A divide-and-conquer approach to network estimation.

NANUQ+: A divide-and-conquer approach to network estimation.

Inference of a species network from genomic data remains a difficult problem, with recent progress mostly limited to the level-1 case. However, inference of the Tree of Blobs of a network, showing only the network's cut edges, can be performed for any network by TINNiK, suggesting a divide-and-conquer approach to network inference where the tree's multifurcations are individually resolved to give more detailed structure. Here we develop a method, NANUQ + , to quickly perform such a level-1 resolution. Viewed as part of the NANUQ pipeline for fast level-1 inference, this gives tools for both understanding when the level-1 assumption is likely to be met and for exploring all highly-supported resolutions to cycles.

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来源期刊
Algorithms for Molecular Biology
Algorithms for Molecular Biology 生物-生化研究方法
CiteScore
2.40
自引率
10.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Algorithms for Molecular Biology publishes articles on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning. Areas of interest include but are not limited to: algorithms for RNA and protein structure analysis, gene prediction and genome analysis, comparative sequence analysis and alignment, phylogeny, gene expression, machine learning, and combinatorial algorithms. Where appropriate, manuscripts should describe applications to real-world data. However, pure algorithm papers are also welcome if future applications to biological data are to be expected, or if they address complexity or approximation issues of novel computational problems in molecular biology. Articles about novel software tools will be considered for publication if they contain some algorithmically interesting aspects.
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