Biology subjects

Caley, K.

Publications and source records attributed to Caley, K..

4 recordsLinked to original sources

bioRxiv2025

Neutral mutation disequilibrium is common in prokaryotes and animals

Widely used models of sequence evolution assume DNA sequences are at equilibrium with respect to the processes that drive divergence, mutation and natural selection. Violations of this assumption have been shown to affect the conclusions from analyses of genetic variation. However, there is currently a lack of statistical methods to assess whether this phenomenon occurs with sufficient frequency and magnitude to warrant concern. Here, we introduce such techniques and apply them to cases where genetically encoded changes to mutation are expected to induce neutral mutation disequilibrium (NMD) across an entire genome (loss of the hypermutable base 5-methyl-cytosine in Drosophila melanogaster), or in a limited genomic region (translocation of the Fxy gene to the pseudo-autosomal region in Mus musculus). We find that the proportion of 9,237 genes in NMD in the D. melanogaster genome ([~]0.81) is markedly elevated compared to its sister taxa D. simulans ([~]0.31) which retains 5-methyl-cytosine. We further establish that the translocation of Fxy has resulted in a [~] 3x increase in the magnitude of NMD. These empirical case studies validate the robustness of our methods. Applying the methods to human evolution, from 613 genes, we estimated the proportion of NMD for the exons as [~]0.64 and introns as [~]0.22. These results establish NMD as a common phenomenon and suggest that conclusions from studies that assumed equilibrium in order to understand biological mechanisms may misattribute to biology what should be credited to measurement error. SignificanceIt has been famously said that all models are wrong, but some are useful. Statistical models of sequence divergence provide the fundamental quantities from which we derive meaning about the causative mechanisms responsible for patterns in molecular evolution. Published work has established current models are not just wrong, but misleading - incorrectly assuming neutral mutation equilibrium leads to errors in the inference of the mode of natural selection and the existence of the molecular clock. In this work, we demonstrate that neutral mutation disequilibrium is common, suggesting that these errors may be widespread. Our results raise the strong likelihood that detected patterns in molecular evolution are being misattributed to biological mechanisms when, in fact, they reflect systematic measurement error.

genetics↗

bioRxiv2025

piqtree: A Python Package for Seamless Phylogenetic Inference with IQ-TREE

piqtree (pronounced pie-cue-tree) is an easy to use, open-source Python package that provides Python script based control of IQ-TREEs phylogenetic inference engine. piqtree builds IQ-TREE as a Python package, presenting a library of Python functions for performing many of IQ-TREEs capabilities including phylogenetic reconstruction, ultrafast bootstrapping, branch length optimization, model selection, rapid neighbor-joining, alignment simulation, and more. As piqtree explicitly uses IQ-TREEs phylogenetic algorithms, the computational and statistical performance of piqtree equal that of IQ-TREE. Modestly higher memory usage may be expected owing to the Python runtime and the need to load the alignment in Python. By exposing IQ-TREEs algorithms within Python, piqtree offers users a greatly simplified experience in development of phylogenetic workflows through seamless interoperability with other Python libraries and tools mediated by the cogent3 package. It enables users to perform interactive phylogenetic analyses and visualization using, for instance, Jupyter notebooks. We present the key features available in the piqtree library and a small case study that showcases its interoperability. piqtree is distributed for use as a standard Python package at https://pypi.org/project/piqtree/, documentation is available at https://piqtree.readthedocs.io and source code at https://github.com/iqtree/piqtree.

bioinformatics↗

bioRxiv2024

diverse-seq: an application for alignment-free selecting and clustering biological sequences

The algorithms required for phylogenetics -- multiple sequence alignment and phylogeny estimation -- are both compute intensive. As the size of DNA sequence datasets continues to increase, there is a need for a tool that can effectively lessen the computational burden associated with this widely used analysis. diverse-seq implements computationally efficient alignment-free algorithms that enable efficient prototyping for phylogenetic workflows. It can accelerate parameter selection searches for sequence alignment and phylogeny estimation by identifying a subset of sequences that are representative of the diversity in a collection. We show that selecting representative sequences with an entropy measure of k-mer frequencies correspond well to sampling via conventional genetic distances. The computational performance is linear with respect to the number of sequences and can be run in parallel. Applied to a collection of 10.5k whole microbial genomes on a laptop took [~]8 minutes to prepare the data and 4 minutes to select 100 representatives. diverse-seq can further boost the performance of phylogenetic estimation by providing a seed phylogeny that can be further refined by a more sophisticated algorithm. For [~]1k whole microbial genomes on a laptop, it takes [~]1.8 minutes to estimate a bifurcating tree from mash distances. The diverse-seq algorithms are not limited to homologous sequences. As such, they can improve the performance of other workflows. For instance, machine learning projects that involve non-homologous sequences can benefit as representative sampling can mitigate biases from imbalanced groups. diverse-seq is a BSD-3 licensed Python package that provides both a command-line interface and cogent3 plugins. The latter simplifies integration by users into their own analyses. It is available via the Python Package Index and GitHub. Statement of needAccurately selecting a representative subset of biological sequences can improve the statistical accuracy and computational performance of data sampling workflows. In many cases, the reliability of such analyses is contingent on the sample capturing the full diversity of the original collection (e.g. estimating large phylogenies Parks et al., 2018; Zhu et al., 2019). Additionally, the computation time of algorithms reliant on numerical optimisation, such as phylogenetic estimation, can be markedly reduced by having a good initial estimate. Existing tools to the data sampling problem require input data in formats that themselves can be computationally costly to acquire. For instance, tree-based sequence selection procedures can be efficient, but they rely on a phylogenetic tree or a pairwise genetic distance matrix, both of which require alignment of homologous sequences (Balaban et al., 2019; e.g. Widmann et al., 2006). Adding both the time for sequence alignment and tree estimation presents a barrier to their use. The diverse-seq sequence selection algorithms are linear in time for the number of sequences and more flexible than published approaches. While the algorithms do not require sequences to be homologous, when applied to homologous sequences, the set selected is comparable to what would be expected based on genetic distance. The diverse-seq clustering algorithm is linear in time for the combined sequence length. For homologous sequences, the estimated trees are approximations to that estimated from an alignment by IQ-TREE2 (Minh et al., 2020).

bioinformatics↗

bioRxiv2022

Synthetic lethality-based prediction of cancer treatment response from histopathology images

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Importantly, ENLIGHT-DeepPT successfully predicts true responders in five independent patients cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, trained and tested on the same cohort in cross validation. Its future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries. Statement of SignificanceENLIGHT-DeepPT is the first approach shown to successfully predict response to multiple targeted and immune cancer therapies from H&E slides. In distinction from all previous H&E slides prediction approaches, it does not require supervised training on a specific cohort for each drug/indication treatment but is trained to predict expression on the TCGA cohort and then can predict response to an array of treatments without any further training. ENLIGHT-DeepPT can provide rapid treatment recommendations to oncologists and help advance precision oncology in underserved regions and low-income countries.

cancer biology↗