Biology subjects

Ogilvie, H.

Publications and source records attributed to Ogilvie, H..

10 recordsLinked to original sources

bioRxiv2022

MaCroDNA: Accurate integration of single-cell DNA and RNA data for a deeper understanding of tumor heterogeneity

Cancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations, their transcriptomic effects, and predict proteomic changes with remarkable temporal and spatial precision. However, to connect genomic mutations with their transcriptomic and proteomic consequences, cells with either only DNA data or only RNA data must be mapped to a common domain. For this purpose, we present MaCroDNA, a novel method which uses maximum weighted bipartite matching of per-gene read counts from single-cell DNA and RNA-seq data. Using ground truth information from colorectal cancer data, we demonstrate the overwhelming advantage of MaCroDNA over existing methods in accuracy and speed. Exemplifying the utility of single-cell data integration in cancer research, we propose, based on results derived using MaCroDNA, that genomic mutations of large effect size increasingly contribute to differential expression between cells as Barretts esophagus progresses to esophageal cancer.

cancer biology↗

bioRxiv2022

MoTERNN: Classifying the Mode of Cancer Evolution Using Recursive Neural Networks

With the advent of single-cell DNA sequencing, it is now possible to infer the evolutionary history of thousands of tumor cells obtained from a single patient. This evolutionary history, which takes the shape of a tree, reveals the mode of evolution of the specific cancer under study and, in turn, helps with clinical diagnosis, prognosis, and therapeutic treatment. In this study we focus on the question of determining the mode of evolution of tumor cells from their inferred evolutionary history. In particular, we employ recursive neural networks that capture tree structures to classify the evolutionary history of tumor cells into one of four modes--linear, branching, neutral, and punctuated. We trained our model, MoTERNN, using simulated data in a supervised fashion and applied it to a real phylogenetic tree obtained from single-cell DNA sequencing data. MoTERNN is implemented in Python and is publicly available at https://github.com/NakhlehLab/MoTERNN.

cancer biology↗

bioRxiv2022

"Correcting'' gene trees to be more like species trees increases topological error when incomplete lineage sorting is high

The evolutionary histories of individual loci in a genome can be estimated independently, but this approach is error-prone due to the limited amount of sequence data available for each gene, which has led to the development of a diverse array of gene tree error correction methods which reduce distance to the species tree. We investigate the performance of two representatives of these methods: TRACTION and TreeFix, in the case where incomplete lineage sorting is high. We found that gene tree error correction only increases the level of error in gene tree topologies by "correcting" them to be closer to the species tree, even when the true gene and species trees are discordant. We confirm that full Bayesian inference of the gene trees under the multispecies coalescent model is more accurate than independent inference. Gene tree correction must be considered a multi-locus task where the gene tree distribution is taken into account, rather than treating gene trees independently. Significance statementGene tree information is essential for studying elucidating gene, genome, species, and phenotypic evolution, and a wide array of phylogenetic methods have been developed for gene tree estimation. Given that gene tree estimates are often inaccurate, several methods for "correcting" gene tree estimates have been devised. Here we show that correction methods that neglect the distribution of gene trees that is induced by the species phylogeny could produce poor results, calling for the development of species phylogeny-aware gene tree correction.

evolutionary biology↗

bioRxiv2022

NestedBD: Bayesian Inference of Phylogenetic Trees From Single-Cell DNA Copy Number Profile Data Under a Birth-Death Model

Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via "bulk sequencing", the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution. In this paper, we introduce a new birth-death evolutionary model of CNAs as well as a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data under this model. We assessed the accuracy of our method on both simulated and biological data and compared it to the accuracy of two standard phylogenetic tools, namely neighbor-joining and maximum parsimony (MP). We show through simulations that our method infers more accurate topologies and branch lengths. We also studied the ancestral state reconstruction accuracy with the birth-death evolutionary model and found it outperformed MP. Finally, running all three methods on a colorectal cancer data set, we observed that among all three methods, only the phylogeny inferred by NestedBD clearly separated the primary tumor cells from the metastatic ones, providing a more plausible history of the tumor cells.

cancer biology↗

bioRxiv2022

Phylovar: Towards scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data

Single-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing (scDNAseq) data, such as SCI{Phi} and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data. Here we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCI{Phi} in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases. We implemented Phylovar and made it publicly available at https://github.com/mae6/Phylovar.git.

cancer biology↗

bioRxiv2022

Supporting per-locus substitution rates improves the accuracy of species networks and avoids spurious reticulations

The development of statistical methods to infer species phylogenies with reticulation (species networks) has led to many discoveries of gene flow between distinct species. However, because the dimensionality of species networks is not fixed, these methods may compensate for kinds of model misspecification, such as assuming a single substitution rate for all genomic loci, by increasing the number of dimensions beyond the true value. The popular full Bayesian species network method MCMC_SEQ has previously made this assumption, so we have added support for the proven Dirichlet model for per-locus rates to enhance its accuracy and avoid spurious results. We studied the effects of this model using simulation and an empirical dataset from Heliconius butterflies. We found that assuming a single substitution rate applies to all loci leads to the inference of spurious reticulation in simulated and empirical datasets when a full Bayesian method is used, however, the summary method InferNetwork_ML is robust to per-locus variation in substitution rates when set to ignore gene tree branch lengths. Our implementation of the model resolves this misspecification and successfully converges to the true species networks. It also infers far more accurate gene trees than assuming a single rate, or independent inference of gene trees. Our implementation of the Dirichlet per-locus rates model is now available in PhyloNet, a software package for phylogenetic inference, open source on GitHub https://github.com/NakhlehLab/PhyloNet.

evolutionary biology↗

bioRxiv2020

Hidden genomic diversity of SARS-CoV-2: implications for qRT-PCR diagnostics and transmission

The COVID-19 pandemic has sparked an urgent need to uncover the underlying biology of this devastating disease. Though RNA viruses mutate more rapidly than DNA viruses, there are a relatively small number of single nucleotide polymorphisms (SNPs) that differentiate the main SARS-CoV-2 clades that have spread throughout the world. In this study, we investigated over 7,000 SARS-CoV-2 datasets to unveil both intrahost and interhost diversity. Our intrahost and interhost diversity analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights iSNV and SNP similarity, albeit with high variability in C>T changes. Second, iSNV and SNP patterns in SARS-CoV-2 are more similar to MERS-CoV than SARS-CoV-1. Third, a significant fraction of small indels fuel the genetic diversity of SARS-CoV-2. Altogether, our findings provide insight into SARS-CoV-2 genomic diversity, inform the design of detection tests, and highlight the potential of iSNVs for tracking the transmission of SARS-CoV-2.

genomics↗

bioRxiv2019

Unifying Gene Duplication, Loss, and Coalescence on Phylogenetic Networks

Statistical methods were recently introduced for inferring phylogenetic networks under the multispecies network coalescent, thus accounting for both reticulation and incomplete lineage sorting. Two evolutionary processes that are ubiquitous across all three domains of life, but are not accounted for by those methods, are gene duplication and loss (GDL).\n\nIn this work, we devise a three-piece model--phylogenetic network, locus network, and gene tree--that unifies all the aforementioned processes into a single model of how genes evolve in the presence of ILS, GDL, and introgression within the branches of a phylogenetic network. To illustrate the power of this model, we develop an algorithm for estimating the parameters of a phylogenetic network topology under this unified model. The algorithm consists of a set of moves that allow for stochastic search through the parameter space. The challenges with developing such moves stem from the intricate dependencies among the three pieces of the model. We demonstrate the application of the model and the accuracy of the algorithm on simulated as well as biological data.\n\nOur work adds to the biologists toolbox of methods for phylogenomic inference by accounting for more complex evolutionary processes.

evolutionary biology↗

bioRxiv2019

A Divide-and-Conquer Method for Scalable Phylogenetic Network Inference from Multi-locus Data

Reticulate evolutionary histories, such as those arising in the presence of hybridization, are best modeled as phylogenetic networks. Recently developed methods allow for statistical inference of phylogenetic networks while also accounting for other processes, such as incomplete lineage sorting (ILS). However, these methods can only handle a small number of loci from a handful of genomes.\n\nIn this paper, we introduce a novel two-step method for scalable inference of phylogenetic networks from the sequence alignments of multiple, unlinked loci. The method infers networks on subproblems and then merges them into a network on the full set of taxa. To reduce the number of trinets to infer, we formulate a Hitting Set version of the problem of finding a small number of subsets, and implement a simple heuristic to solve it. We studied their performance, in terms of both running time and accuracy, on simulated as well as on biological data sets. The two-step method accurately infers phylogenetic networks at a scale that is infeasible with existing methods. The results are a significant and promising step towards accurate, large-scale phylogenetic network inference.\n\nWe implemented the algorithms in the publicly available software package PhyloNet (https://bioinfocs.rice.edu/PhyloNet).\n\nContactnakhleh@rice.edu

evolutionary biology↗

bioRxiv2018

Inference of species histories in the presence of gene flow

When populations become isolated, members of these populations can diverge genetically over time. This leads to genetic differences between these populations that increase over time if the isolation persists. This process can be counteracted by gene flow, i.e. when genes are exchanged between populations. In order to study the speciation processes when gene flow is present, isolation-with-migration methods have been developed. These methods typically assume that the ranked topology of the species history is already known. However, this is often not the case and the species tree is therefore of interest itself. For the inference of species trees, it is in turn often necessary to assume that there is no gene flow between co-existing species. This assumption, however, can lead to wrongly inferred speciation times and species tree topologies. We here introduce a new method that allows inference of the species tree while explicitly modelling the flow of genes between coexisting species. By using Markov chain Monte Carlo sampling, we co-infer the species tree alongside evolutionary parameters of interest. By using simulations, we show that our newly introduced approach is able to reliably infer the species trees and parameters of the isolation-with-migration model from genetic sequence data. We then use this approach to infer the species history of the mosquitoes from the Anopheles gambiae species complex. Accounting for gene flow when inferring the species history suggests a slightly different speciation order and gene flow than previously suggested.

evolutionary biology↗