Biology subjects

Huttley, G. A.

Publications and source records attributed to Huttley, G. A..

12 recordsLinked to original sources

bioRxiv2025

Improved gene tree inference from removing alignment errors both from focal genes and when training substitution models

Multiple Sequence Alignment (MSA) is a key step in phylogenetic analysis, and is prone to error. Unfortunately, algorithms that remove likely alignment errors from MSAs sometimes also remove informative residues, making phylogenetic tree inference worse. Here we present a novel MSA cleaning algorithm based on consensus between MSAs using a range of Hidden Markov Models and guide trees, named CLOAK (CLeaning On the basis of Alignment C(K)onsensus). CLOAK is a gentle filter, with a low false positive rate for removal from MSAs according to the BALiBASE benchmarks, while still removing a significant fraction of likely alignment errors. Gentle vs. stringent MSA filtering methods are appropriate for different tasks. We assess methods based on their ability to bring the gene trees of single copy orthologs closer to the accepted species tree. Amino acid substitution models trained on filtered MSAs improve gene tree inference, with stricter filtering methods providing the biggest model improvements. In contrast, it is gentler filtering of single gene MSAs that provides additional improvements to gene tree inference, with CLOAK performing best.

evolutionary biology↗

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↗

bioRxiv2024

Spectral Cluster Supertree: fast and statistically robust merging of rooted phylogenetic trees

AO_SCPLOWBSTRACTC_SCPLOWThe algorithms for phylogenetic reconstruction are central to computational molecular evolution. The relentless pace of data acquisition has exposed their poor scalability and the conclusion that the conventional application of these methods is impractical and not justifiable from an energy usage perspective. Furthermore, the drive to improve the statistical performance of phylogenetic methods produces increasingly parameter-rich models of sequence evolution, which worsens the computational performance. Established theoretical and algorithmic results identify supertree methods as critical to divide-and-conquer strategies for improving scalability of phylogenetic reconstruction. Of particular importance is the ability to explicitly accommodating rooted topologies. These can arise from the more biologically plausible non-stationary models of sequence evolution. We make a contribution to addressing this challenge with Spectral Cluster Supertree, a novel supertree method for merging a set of overlapping rooted phylogenetic trees. It offers significant improvements over Min-Cut supertree and previous state-of-the-art methods in terms of both time complexity and overall topological accuracy, particularly for problems of large size. We perform comparisons against Min-Cut supertree and Bad Clade Deletion. Leveraging two tree topology distance metrics, we demonstrate that while Bad Clade Deletion generates more correct clades in its resulting supertree, Spectral Cluster Supertrees generated tree is generally more topologically close to the true model tree. Over large datasets containing 10000 taxa and -500 source trees, where Bad Clade Deletion usually takes -2 hours to run, our method generates a supertree in on average 20 seconds. Spectral Cluster Supertree is released under an open source license and is available on the python package index as sc-supertree. This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government.

bioinformatics↗

bioRxiv2022

The Inherited Rate Matrix algorithm for phylogenetic model selection for non-stationary Markov processes

In phylogenetic reconstruction, substitution models that assume nucleotide frequencies do not change through time are in very widespread use. DNA sequences can exhibit marked compositional difference. Such compositional heterogeneity implies sequence divergence has arisen from substitution processes that differ between the lineages. Accordingly, there has been considerable interest in exploring the non-stationary model class, which is capable of generating divergent sequence composition. These models are typically parameter rich and this richness is further compounded by the use of time-heterogeneous variants in order to capture compositional heterogeneity. This illuminates two barriers limiting the widespread application of the non-stationary model class: both the compute-time required to fit a model and the risk of model over-fitting increase with the number of parameters and the extent of time-heterogeneity. In this study, we address these issues with a novel model selection algorithm we term the Inherited Rate Matrix algorithm (hereafter IRM). This approach is based on the notion that a species inherits the substitution tendencies of its ancestor. We further present the non-stationary heterogeneous across lineages model (hereafter ns-HAL algorithm) which extends the HAL algorithm of Jayaswal et al. [2014] to the general nucleotide Markov process. The IRM algorithm substantially reduces the complexity of identifying a sufficient solution to the problem of time-heterogeneous substitution processes across lineages. We also address the issue of reducing the computing time with development of a constrained-optimisation approach for the IRM algorithm (fast-IRM). Our algorithms are implemented in Python 3. From a simulation study based on 2nd codon position genome sequences of yeast, we establish that IRM is significantly more accurate than both ns-HAL and heterogeneity in the substitution process across lineages (hereafter HAL) for close and dispersed sequences. IRM is up to 70x faster than ns-HAL while fast-IRM is around 140x faster. fast-IRM also showed a marked speed improvement over a C++ implementation of HAL. Our comparison of the accuracy of IRM with fast-IRM showed no difference with identical inferences made for all data sets. These two algorithms greatly improve the compute time for model selection of a non-stationary process, increasing the suite of problems to which this important substitution model class can be applied.

bioinformatics↗

bioRxiv2021

Bayesian Inference of Joint Coalescence Times for Sampled Sequences

The site frequency spectrum (SFS) is a commonly used statistic to summarize genetic variation in a sample of genomic sequences from a population. Such a genomic sample is associated with an imputed genealogical history with attributes such as branch lengths, coalescence times and the time to the most recent common ancestor (TMRCA) as well as topological and combinatorial properties. We present a Bayesian model for sampling from the joint posterior distribution of coalescence times conditional on the SFS associated with a sample of sequences in the absence of selection. In this model, the combinatorial properties of a genealogy, which is represented as a coalescent tree, are expressed as matrices. This facilitates the calculation of likelihoods and the effective sampling of the entire space of tree structures according to the Equal Rates Markov (or Yule-type) measure. Unlike previous methods, assumptions as to the type of stochastic process that generated the genealogical tree are not required. Novel approaches to defining both uninformative and informative prior distributions are employed. The uncertainty in inference due to the stochastic nature of mutation and the unknown tree structure is expressed by the shape of the posterior distributions. The method is implemented using the general purpose Markov Chain Monte Carlo software PyMC3. From the sampled posterior distribution of coalescence times, one can also infer related quantities such as the number of ancestors of a sample at a given time in the past (ancestral distribution) and the probability of specific relationships between branch lengths (for example, that the most recent branch is longer than all the others). The performance of the method is evaluated against simulated data and is also applied to historic mitochondrial data from the Nuu-Chah-Nulth people of North America. The method can be used to obtain estimates of the TMRCA of the sample. The relationship of these estimates to those given by "Thomsons estimator" is explored.

genetics↗

bioRxiv2021

A New Likelihood-based Test for Natural Selection

We present a new statistic for testing for neutral evolution from allele frequency data summarised as a site frequency spectrum, which we call the relative likelihood neutrality test or{rho} . Classical methods of testing for natural selection, such as Tajimas D and its relatives, require the null model to have constant population size over time and therefore can confound demographic change with natural selection.{rho} can directly incorporate a null hypothesis reflecting general demographic histories. It has a natural Bayesian interpretation as an approximation to the log-probability of the null model, given the data. We use simulations to show that{rho} has greater power than Tajimas D to detect departure from neutrality for a range of scenarios of positive and negative selection. We also show how{rho} can be adapted to account for sequencing error. Application to the ACKR1 (FYO) gene in humans supported previous studies inferring positive selection in sub-Saharan populations which were based on inter-population comparisons. However, we did not find the signal of selection to be maximal in the region of the FY*O or Duffy-null allele in these populations. We also applied{rho} to investigate in greater detail a region on the 2q11.1 band of the human genome that has previously been identified as showing evidence of selection. This was done for a range of populations: for the European populations we incorporated a demographic history with a bottleneck corresponding to the putative out of Africa event. We were able to localise signals of selection to some specific regions and genes. Overall, we suggest that{rho} will be a useful tool for identifying genomic regions that may be subject to natural selection.

genetics↗

bioRxiv2020

Quantifying influences on intragenomic mutation rate

We report work to quantify the impact on the probability of human genome polymorphism both of recombination and of sequence context at different scales. We use population-based analyses of data on human genetic variants obtained from the public Ensembl database. For recombination, we calculate the variance due to recombination and the probability that a recombination event causes a mutation. We employ novel statistical procedures to take account of the spatial auto-correlation of recombination and mutation rates along the genome. Our results support the view that genomic diversity in recombination hotspots arises from a direct effect of recombination on mutation rather than from the effect of selective sweeps. We also use the statistic of variance due to context to compare the effect on the probability of polymorphism of contexts of various sizes. We find that when the 12 point mutations are considered separately, variance due to context increases significantly as we move from 3-mer to 5-mer and from 5-mer to 7-mer contexts. However, when all mutations are considered in aggregate, these differences are outweighed by the effect of interaction between the central base and its immediate neighbors. This interaction is itself dominated by the transition mutations, including, but not limited to, the CpG effect. We also demonstrate strand-asymmetry of contextual influence in intronic regions, which is hypothesized to be a result of transcription coupled DNA repair. We consider the extent to which the measures we have used can be used to meaningfully compare the relative magnitudes of the impact of recombination and context on mutation.

genetics↗

bioRxiv2018

Species-level microbial sequence classification is improved by source-environment information

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate that species-level resolution is attainable.

bioinformatics↗

bioRxiv2018

Machine learning techniques for classifying the mutagenic origins of point mutations

There is increasing interest in developing diagnostics that discriminate individual mutagenic mechanisms in a range of applications that include identifying population specific mutagenesis and resolving distinct mutation signatures in cancer samples. Analyses for these applications assume that mutagenic mechanisms have a unique relationship with neighboring bases that allows them to be distinguished. Direct support for this assumption is limited to a small number of simple cases, e.g. CpG hypermutability. We have directly evaluated whether the mechanistic origin of a point mutation can be resolved using only sequence context for a more complicated case. We contrasted mutations originating from the multitude of mutagenic processes that normally operate in the mouse germline with those induced by the potent mutagen N-ethyl-N-nitrosourea (ENU). The considerable overlap in the mutation spectra of these two samples make this a challenging problem. Employing a new, robust log-linear modeling method, we demonstrate that neighboring bases contain information regarding point mutation direction that differs between the ENU-induced and spontaneous mutation classes. A logistic regression classifier proved to be substantially more powerful at discriminating between the different mutation classes than alternatives. Concordance between the feature set of the best classifier and information content analyses suggest our results can be generalized to other mutation classification problems. We conclude that machine learning can be used to build a practical classification tool to identify the mutation mechanism for individual genetic variants. Software implementing our approach is freely available under the BSD 3-clause license.

bioinformatics↗

bioRxiv2018

q2-sample-classifier: machine-learning tools for microbiome classification and regression

Microbiome studies often aim to predict outcomes or differentiate samples based on their microbial compositions, tasks that can be efficiently performed by supervised learning methods. Here we present a benchmark comparison of supervised learning classifiers and regressors implemented in scikit-learn, a Python-based machine-learning library. We additionally present q2-sample-classifier, a plugin for the QIIME 2 microbiome bioinformatics framework, that facilitates application of the scikit-learn classifiers to microbiome data. Random forest, extra trees, and gradient boosting models demonstrate the highest performance for both supervised classification and regression of microbiome data. Automated feature selection and hyperparameter tuning enhance performance of most methods but may not be necessary under all circumstances. The q2-sample-classifier plugin makes these methods more accessible and interpretable to a broad audience of microbiologists, clinicians, and others who wish to utilize supervised learning methods for predicting sample characteristics based on microbiome composition. The q2-sample-classifier source code is available at https://github.com/qiime2/q2-sample-classifier. It is released under a BSD-3-Clause license, and is freely available including for commercial use.

bioinformatics↗