Massively scalable inference of level-1 phylogenetic networks

Recent advancements in sequencing technologies have enabled large-scale phylogenomic analyses. While these analyses often rely on phylogenetic trees, increasing evidence suggests that non-treelike evolutionary events, such as hybridization and horizontal gene transfer, are prevalent in the evolutionary histories of many species, and in such cases, tree-based models are insufficient. Phylogenetic networks can capture such complex evolutionary histories, but current methods for accurately inferring them lack scalability. For instance, state-of-the-art model-based approaches are limited to around 30 taxa. Implicit network inference methods like NeighborNet and Consensus Networks are fast but lack biological interpretability. Here, we introduce a novel method called InPhyNet that merges a set of non-overlapping, independently inferred networks into a unified topology, achieving linear scalability while maintaining high accuracy under the multispecies network coalescent model. Our simulation..., , # Massively scalable inference of level-1 phylogenetic networks

All contents of this repository are contained in a single compressed file `data.tar.gz`. This repository contains the simulation study and empirical analysis for InPhyNet, a novel statistical method for inferring large phylogenetic networks. InPhyNet addresses the computational challenges of inferring species networks with hundreds of taxa by using a divide-and-conquer approach that decomposes the problem into smaller, manageable subproblems.

## Overview

Phylogenetic networks are mathematical models that represent evolutionary relationships among species, including both divergence (speciation) and reticulation (hybridization, horizontal gene transfer, introgression) events. Traditional network inference methods become computationally intractable for large datasets (>30 taxa). InPhyNet overcomes this limitation through:

1. **Subset decomposition**: Breaking the full taxon set into smaller, non-overlapping subsets
2. **Co...,

近年来测序技术的进步推动了大规模系统基因组学分析的开展。尽管此类分析通常以系统发育树为基础，但越来越多的证据表明，众多物种的演化历程中广泛存在杂交、水平基因转移等非树状演化事件，此时基于树的模型便不再适用。系统发育网络（phylogenetic network）能够刻画这类复杂的演化历程，但当前能够准确推断此类网络的方法却缺乏可扩展性。例如，当前最先进的基于模型的方法仅能处理约30个类群。诸如NeighborNet与Consensus Networks的隐式网络推断方法速度较快，但缺乏生物学可解释性。本研究提出一种名为InPhyNet的全新方法，该方法可将一组非重叠且独立推断得到的网络整合为统一的拓扑结构，在多物种网络溯祖模型（multispecies network coalescent model）下既能实现线性可扩展性，又能保持较高的推断精度。本研究的模拟实验……，# 大规模可扩展的一级系统发育网络（level-1 phylogenetic network）推断

本仓库的所有内容均包含在单个压缩文件`data.tar.gz`中。该仓库包含了针对InPhyNet的模拟研究与实证分析内容，InPhyNet是一种用于推断大规模物种网络的新型统计方法。InPhyNet通过分治策略将问题拆解为若干小型且易于处理的子问题，从而解决了数百个类群的物种网络推断所面临的计算难题。

## 概述

系统发育网络（phylogenetic network）是用于刻画物种间演化关系的数学模型，涵盖了分化（物种形成）与网状演化（杂交、水平基因转移、基因渐渗）两类事件。传统的网络推断方法在处理大规模数据集（类群数>30）时会出现计算不可行的问题。InPhyNet通过以下方式克服了这一局限：
1. **子集分解**：将完整的类群集合拆分为若干小型非重叠子集
2. **Co..., **