Biology subjects

Lunter, G.

Publications and source records attributed to Lunter, G..

10 recordsLinked to original sources

bioRxiv2021

Benchmarking small-variant genotyping in polyploids

Genotyping from sequencing is the basis of emerging strategies in the molecular breeding of polyploid plants. However, compared with the situation for diploids, where genotyping accuracies are confidently determined with comprehensive benchmarks, polyploids have been neglected; there are no benchmarks measuring genotyping error rates for small variants using real sequencing reads. We previously introduced a variant calling method - Octopus - that accurately calls germline variants in diploids and somatic mutations in tumors. Here, we evaluate Octopus and other popular tools on whole-genome tetraploid and hexaploid datasets created using in silico mixtures of diploid Genome In a Bottle samples. We find that genotyping errors are abundant for typical sequencing depths, but that Octopus makes 25% fewer errors than other methods on average. We supplement our benchmarks with concordance analysis in real autotriploid banana datasets.

bioinformatics↗

bioRxiv2020

Ancient admixture into Africa from the ancestors of non-Africans

Genetic diversity across human populations has been shaped by demographic history, making it possible to infer past demographic events from extant genomes. However, demographic inference in the ancient past is difficult, particularly around the out-of-Africa event in the Late Middle Paleolithic, a period of profound importance to our species history. Here we present SMCSMC, a Bayesian method for inference of time-varying population sizes and directional migration rates under the coalescent-with-recombination model, to study ancient demographic events. We find evidence for substantial migration from the ancestors of present-day Eurasians into African groups between 40 and 70 thousand years ago, predating the divergence of Eastern and Western Eurasian lineages. This event accounts for previously unexplained genetic diversity in African populations, and supports the existence of novel population substructure in the Late Middle Paleolithic. Our results indicate that our species demographic history around the out-of-Africa event is more complex than previously appreciated.

genomics↗

bioRxiv2020

Short and long-read genome sequencing methodologies for somatic variant detection; genomic analysis of a patient with diffuse large B-cell lymphoma

Recent advances in throughput and accuracy mean that the Oxford Nanopore Technologies (ONT) PromethION platform is a now a viable solution for genome sequencing. Much of the validation of bioinformatic tools for this long-read data has focussed on calling germline variants (including structural variants). Somatic variants are outnumbered many-fold by germline variants and their detection is further complicated by the effects of tumour purity/subclonality. Here, we evaluate the extent to which Nanopore sequencing enables genome-wide detection and analysis of somatic variation. We do this through sequencing tumour and germline genomes for a patient with diffuse B-cell lymphoma and comparing results with 150bp short-read sequencing of the same samples. Calling germline single nucleotide variants (SNVs) from the long-read data achieved good specificity and sensitivity. However, results of somatic SNV calling highlight the need for the development of specialized joint calling algorithms. We find the comparative performance of different tools varies significantly between structural variant types, and suggest long reads are especially advantageous for calling large somatic deletions and duplications. Finally, we highlight the utility of long reads for phasing clinically relevant variants, confirming that a somatic 1.6Mb deletion and a p.(Arg249Met) mutation involving TP53 are oriented in trans.

genetics↗

bioRxiv2019

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning.

Understanding 3D genome structure requires high throughput, genome-wide approaches. However, assays for all vs. all chromatin interaction mapping are expensive and time consuming, which severely restricts their usage for large-scale mutagenesis screens or for mapping the impact of sequence variants. Computational models sophisticated enough to grasp the determinants of chromatin folding provide a unique window into the functional determinants of 3D genome structure as well as the effects of genome variation.\n\nA chromatin interaction predictor should work at the base pair level but also incorporate large-scale genomic context to simultaneously capture the large scale and intricate structures of chromatin architecture. Similarly, to be a flexible and generalisable approach it should also be applicable to data it has not been explicitly trained on. To develop a model with these properties, we designed a deep neuronal network (deepC) that utilizes transfer learning to accurately predict chromatin interactions from DNA sequence at megabase scale. The model generalizes well to unseen chromosomes and works across cell types, Hi-C data resolutions and a range of sequencing depths. DeepC integrates DNA sequence context on an unprecedented scale, bridging the different levels of resolution from base pairs to TADs. We demonstrate how this model allows us to investigate sequence determinants of chromatin folding at genome-wide scale and to predict the importance of regulatory elements and the impact of sequence variations.

genomics↗

bioRxiv2019

Repertoire-wide phylogenetic models of B cell molecular evolution reveal evolutionary signatures of aging and vaccination

In order to produce effective antibodies, B cells undergo rapid somatic hypermutation (SHM) and selection for binding affinity to antigen via a process called affinity maturation. The similarities between this process and evolution by natural selection have led many groups to use evolutionary and phylogenetic methods to characterize the development of immunological memory, vaccination, and other processes that depend on affinity maturation. However, these applications are limited by several features of affinity maturation that violate assumptions in standard phylogenetic models. Further, most phylogenetic models are designed to be applied to individual lineages comprising genetically diverse sequences, while B cell repertoires often consist of hundreds to thousands of separate low-diversity lineages. Here, we introduce a hierarchical phylogenetic framework that incorporates the unique features of SHM, and integrates information from all lineages in a repertoire to more precisely estimate model parameters. We demonstrate the power of this approach by characterizing previously un-described phenomena in affinity maturation. First, we find evidence consistent with age related changes in SHM hot-and cold-spot motifs. Second, we identify a consistent relationship between increased tree length and signs of increased negative selection, apparent in the repertoires of both healthy subjects and those undergoing active immune responses. This suggests that B cell lineages shift towards negative selection over time as a general feature of affinity maturation. Our study provides a framework for undertaking repertoire-wide phylogenetic testing of SHM hypotheses, and provides a new means of charactering the process of mutation and selection during affinity maturation.

immunology↗

bioRxiv2018

Inferring B cell specificity for vaccines using a mixture model

Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B Cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in the serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCR sequences. We use data from two studies to assess the model and estimate that we can identify vaccine-specific sequences with 69% sensitivity.

genomics↗

bioRxiv2018

A unified haplotype-based method for accurate and comprehensive variant calling

Haplotype-based variant callers, which consider physical linkage between variant sites, are currently among the best tools for germline variation discovery and genotyping from short-read sequencing data. However, almost all such tools were designed specifically for detecting common germline variation in diploid populations, and give sub-optimal results in other scenarios. Here we present Octopus, a versatile haplotype-based variant caller that uses a polymorphic Bayesian genotyping model capable of modeling sequencing data from a range of experimental designs within a unified haplotype-aware framework. We show that Octopus accurately calls de novo mutations in parent-offspring trios and germline variants in individuals, including SNVs, indels, and small complex replacements such as microinversions. In addition, using a carefully designed synthetic-tumour data set derived from clean sequencing data from a sample with known germline haplotypes, and observed mutations in large cohort of tumour samples, we show that Octopus accurately characterizes germline and somatic variation in tumours, both with and without a paired normal sample. Sequencing reads and prior information are combined to phase called genotypes of arbitrary ploidy, including those with somatic mutations. Octopus also outputs realigned evidence BAMs to aid validation and interpretation.

bioinformatics↗

bioRxiv2018

Demographic inference using particle filters for continuous Markov jump processes

Demographic events shape a populations genetic diversity, a process described by the coalescent-with-recombination (CwR) model that relates demography and genetics by an unobserved sequence of genealogies. The space of genealogies over genomes is large and complex, making inference under this model challenging. We approximate the CwR with a continuous-time and -space Markov jump process. We develop a particle filter for such processes, using way-points to reduce the problem to the discrete-time case, and generalising the Auxiliary Particle Filter for discrete-time models. We use Variational Bayes for parameter inference to model the uncertainty in parameter estimates for rare events, avoiding biases seen with Expectation Maximization. Using real and simulated genomes, we show that past population sizes can be accurately inferred over a larger range of epochs than was previously possible, opening the possibility of jointly analyzing multiple genomes under complex demographic models. Code is available at https://github.com/luntergroup/smcsmc MSC 2010 subject classificationsPrimary 60G55, 62M05, 62M20, 62F15; secondary 92D25.

evolutionary biology↗

bioRxiv2018

An Equivariant Bayesian Convolutional Network predicts recombination hotspots and accurately resolves binding motifs

MotivationConvolutional neural networks (CNNs) have been trememdously successful in many contexts, particularly where training data is abundant and signal-to-noise ratios are large. However, when predicting noisily observed biological phenotypes from DNA sequence, each training instance is only weakly informative, and the amount of training data is often fundamentally limited, emphasizing the need for methods that make optimal use of training data and any structure inherent in the model.\n\nResultsHere we show how to combine equivariant networks, a general mathematical framework for handling exact symmetries in CNNs, with Bayesian dropout, a version of MC dropout suggested by a reinterpretation of dropout as a variational Bayesian approximation, to develop a model that exhibits exact reverse-complement symmetry and is more resistant to overtraining. We find that this model has increased power and generalizability, resulting in significantly better predictive accuracy compared to standard CNN implementations and state-of-art deep-learning-based motif finders. We use our network to predict recombination hotspots from sequence, and identify high-resolution binding motifs for the recombination-initiation protein PRDM9, which were recently validated by high-resolution assays. The network achieves a predictive accuracy comparable to that attainable by a direct assay of the H3K4me3 histone mark, a proxy for PRDM9 binding.\n\nAvailabilityhttps://github.com/luntergroup/EquivariantNetworks\n\nContactrichard.brown@well.ox.ac.uk, gerton.lunter@well.ox.ac.uk\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

genomics↗

bioRxiv2017

A high throughput screen for active human transposable elements

Transposable elements (TEs) are mobile genetic sequences that randomly propagate within their hosts genome. This mobility has the potential to affect gene transcription and cause disease. However, TEs are technically challenging to identify, which complicates efforts to assess the impact of TE insertions on disease. Here we present a targeted sequencing protocol and computational pipeline to identify polymorphic and novel TE insertions using next-generation sequencing: TE-NGS. The method simultaneously targets the three subfamilies that are responsible for the majority of recent TE activity (L1HS, AluYa5/8, and AluYb8/9) thereby obviating the need for multiple experiments and reducing the amount of input material required. Here we describe the laboratory protocol and detection algorithm, and a benchmark experiment for the reference genome NA12878. We demonstrate a substantial enrichment for on-target fragments, and high sensitivity and precision to both reference and NA12878-specific insertions. We report 17 previously unreported loci for this individual which are supported by orthogonal long-read evidence, and we identify 1,471 polymorphic and novel TEs in 12 additional samples that were previously undocumented in databases of insertion polymorphisms. We anticipate that future applications of TE-NGS alongside exome sequencing of patients with sporadic disease will reduce the number of unresolved cases, and improve estimates of the contribution of TEs to human genetic disease.

genomics↗