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1. Assessing the fit of the multi-species network coalescent to multi-locus data

   https://doi.org/10.1093/bioinformatics/btaa863  

   Cai, Ruoyi; Ané, Cécile (December 2020, Bioinformatics)

   Russell, Schwartz (Ed.)

   Abstract Motivation With growing genome-wide molecular datasets from next-generation sequencing, phylogenetic networks can be estimated using a variety of approaches. These phylogenetic networks include events like hybridization, gene flow or horizontal gene transfer explicitly. However, the most accurate network inference methods are computationally heavy. Methods that scale to larger datasets do not calculate a full likelihood, such that traditional likelihood-based tools for model selection are not applicable to decide how many past hybridization events best fit the data. We propose here a goodness-of-fit test to quantify the fit between data observed from genome-wide multi-locus data, and patterns expected under the multi-species coalescent model on a candidate phylogenetic network. Results We identified weaknesses in the previously proposed TICR test, and proposed corrections. The performance of our new test was validated by simulations on real-world phylogenetic networks. Our test provides one of the first rigorous tools for model selection, to select the adequate network complexity for the data at hand. The test can also work for identifying poorly inferred areas on a network. Availability and implementation Software for the goodness-of-fit test is available as a Julia package at https://github.com/cecileane/QuartetNetworkGoodnessFit.jl. Supplementary information Supplementary data are available at Bioinformatics online. 

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2. MCPNet: a parallel maximum capacity-based genome-scale gene network construction framework

   https://doi.org/10.1093/bioinformatics/btad373  

   Pan, Tony C; Chockalingam, Sriram P; Aluru, Maneesha; Aluru, Srinivas (June 2023, Bioinformatics)

   Cowen, Lenore (Ed.)

   Abstract MotivationGene network reconstruction from gene expression profiles is a compute- and data-intensive problem. Numerous methods based on diverse approaches including mutual information, random forests, Bayesian networks, correlation measures, as well as their transforms and filters such as data processing inequality, have been proposed. However, an effective gene network reconstruction method that performs well in all three aspects of computational efficiency, data size scalability, and output quality remains elusive. Simple techniques such as Pearson correlation are fast to compute but ignore indirect interactions, while more robust methods such as Bayesian networks are prohibitively time consuming to apply to tens of thousands of genes. ResultsWe developed maximum capacity path (MCP) score, a novel maximum-capacity-path-based metric to quantify the relative strengths of direct and indirect gene–gene interactions. We further present MCPNet, an efficient, parallelized gene network reconstruction software based on MCP score, to reverse engineer networks in unsupervised and ensemble manners. Using synthetic and real Saccharomyces cervisiae datasets as well as real Arabidopsis thaliana datasets, we demonstrate that MCPNet produces better quality networks as measured by AUPRC, is significantly faster than all other gene network reconstruction software, and also scales well to tens of thousands of genes and hundreds of CPU cores. Thus, MCPNet represents a new gene network reconstruction tool that simultaneously achieves quality, performance, and scalability requirements. Availability and implementationSource code freely available for download at https://doi.org/10.5281/zenodo.6499747 and https://github.com/AluruLab/MCPNet, implemented in C++ and supported on Linux. 

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3. Accurately modeling biased random walks on weighted networks using node2vec+

   https://doi.org/10.1093/bioinformatics/btad047  

   Liu, Renming; Hirn, Matthew; Krishnan, Arjun (January 2023, Bioinformatics)

   Martelli, Pier Luigi (Ed.)

   Abstract MotivationAccurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction networks, are dense, weighted graphs, node2vec is fundamentally limited in its ability to use edge weights during the biased random walk generation process, thus under-using all the information in the network. ResultsHere, we present node2vec+, a natural extension of node2vec that accounts for edge weights when calculating walk biases and reduces to node2vec in the cases of unweighted graphs or unbiased walks. Using two synthetic datasets, we empirically show that node2vec+ is more robust to additive noise than node2vec in weighted graphs. Then, using genome-scale functional gene networks to solve a wide range of gene function and disease prediction tasks, we demonstrate the superior performance of node2vec+ over node2vec in the case of weighted graphs. Notably, due to the limited amount of training data in the gene classification tasks, graph neural networks such as GCN and GraphSAGE are outperformed by both node2vec and node2vec+. Availability and implementationThe data and code are available on GitHub at https://github.com/krishnanlab/node2vecplus_benchmarks. All additional data underlying this article are available on Zenodo at https://doi.org/10.5281/zenodo.7007164. Supplementary informationSupplementary data are available at Bioinformatics online. 

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4. Identifiability of Level-1 Species Networks from Gene Tree Quartets

   https://doi.org/10.1007/s11538-024-01339-4  

   Allman, Elizabeth S; Baños, Hector; Garrote-Lopez, Marina; Rhodes, John A (September 2024, Bulletin of Mathematical Biology)

   Abstract When hybridization or other forms of lateral gene transfer have occurred, evolutionary relationships of species are better represented by phylogenetic networks than by trees. While inference of such networks remains challenging, several recently proposed methods are based on quartet concordance factors—the probabilities that a tree relating a gene sampled from the species displays the possible 4-taxon relationships. Building on earlier results, we investigate what level-1 network features are identifiable from concordance factors under the network multispecies coalescent model. We obtain results on both topological features of the network, and numerical parameters, uncovering a number of failures of identifiability related to 3-cycles in the network. Addressing these identifiability issues is essential for designing statistically consistent inference methods. 

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5. Incomplete lineage sorting and reticulate evolution mask species relationships in Brunelliaceae, an Andean family with rapid, recent diversification

   https://doi.org/10.1002/ajb2.16025  

   Murillo‐A, José; Valencia‐D, Janice; Orozco, Clara I; Parra‐O, Carlos; Neubig, Kurt M (July 2022, American Journal of Botany)

   Abstract PremiseTo date, phylogenetic relationships within the monogeneric Brunelliaceae have been based on morphological evidence, which does not provide sufficient phylogenetic resolution. Here we use target‐enriched nuclear data to improve our understanding of phylogenetic relationships in the family. MethodsWe used the Angiosperms353 toolkit for targeted recovery of exonic regions and supercontigs (exons + introns) from low copy nuclear genes from 53 of 70 species inBrunellia, and several outgroup taxa. We removed loci that indicated biased inference of relationships and applied concatenated and coalescent methods to inferBrunelliaphylogeny. We identified conflicts among gene trees that may reflect hybridization or incomplete lineage sorting events and assessed their impact on phylogenetic inference. Finally, we performed ancestral‐state reconstructions of morphological traits and assessed the homology of character states used to define sections and subsections inBrunellia. ResultsBrunelliacomprises two major clades and several subclades. Most of these clades/subclades do not correspond to previous infrageneric taxa. There is high topological incongruence among the subclades across analyses. ConclusionsPhylogenetic reconstructions point to rapid species diversification in Brunelliaceae, reflected in very short branches between successive species splits. The removal of putatively biased loci slightly improves phylogenetic support for individual clades. Reticulate evolution due to hybridization and/or incomplete lineage sorting likely both contribute to gene‐tree discordance. Morphological characters used to define taxa in current classification schemes are homoplastic in the ancestral character‐state reconstructions. While target enrichment data allows us to broaden our understanding of diversification inBrunellia, the relationships among subclades remain incompletely understood. 

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