Biology subjects

Bennett, M. F.

Publications and source records attributed to Bennett, M. F..

9 recordsLinked to original sources

bioRxiv2026

nf-cavalier: A Nextflow Pipeline for Rare Disease Variant Prioritization and Reporting

nf-cavalier is a Nextflow pipeline that automates genomic variant annotation, filtering, and reporting for individuals with rare Mendelian diseases. The pipeline takes as input variant callsets for an individual, family, or rare disease cohort, together with a target gene panel or a phenotype of interest. Variants are then filtered using various customisable criteria, including predicted gene consequence, computational pathogenicity predictions, population frequency, and familial segregation. The sequencing data for candidate variants is then visualised for human review. Candidate variant results are returned in user-friendly output formats, including interactive HTML reports and PowerPoint slide decks, with embedded links to external resources that enable rapid review by clinical research teams. nf-cavalier is maintained on GitHub (bahlolab/nf-cavalier) and licensed under the permissive MIT open-source licence.

bioinformatics↗

bioRxiv2026

Knowledge Inclusive Machine Learning for Disease Gene Prioritisation

The predictive performance of machine learning models depends on the context available to them. In disease gene prioritisation, this context comprises two forms: specific context from sample-level experimental data, such as gene expression and protein-protein interaction networks, and general context from accumulated and curated biological knowledge capturing established relationships among genes, diseases, and pathways. Neither is sufficient alone: experimental data are sensitive to dataset-specific noise and lack broader biological grounding, while curated knowledge lacks the resolution required for gene-level discrimination. Consequently, most machine learning approaches relying solely on experimental data risk learning spurious correlations rather than underlying biology. Here we introduce Knowledge Inclusive Machine Learning (KIML), a paradigm that integrates both context types within a unified analytical pipeline. KIML combines experimental data with two types of general context: literature-derived representations from PubMed and structured biomedical knowledge graphs. We evaluate the approach on Developmental and Epileptic Encephalopathy and benchmark it against recent methods using publicly available datasets. Performance is assessed using temporal-split evaluation and biological evaluations, including ontology enrichment analysis. KIML consistently outperforms existing approaches, providing improved predictive accuracy and biologically meaningful insights. Furthermore, the framework generates interpretable explanations of gene prioritisation and demonstrates strong generalisability across six additional diseases.

genetics↗

bioRxiv2026

A computational model for quantifying instability of tandem repeats across the genome

Tandem repeats (TRs) exhibit high levels of somatic mosaicism, which is increasingly recognized as an important modifier of repeat expansion disorders. Long-read sequencing can capture full-length repeat alleles, yet robust frameworks for quantifying instability across TRs genome-wide are still needed. Here, we introduce a general-purpose model for quantifying TR instability in a given long-read sequencing dataset, without explicitly distinguishing biological mosaicism from technical noise, and which is broadly applicable to both simple and structurally complex loci. This model accurately characterizes allelic instability at each TR locus by representing the distribution of read-to-consensus deviations for each allele. Using HiFi sequencing data from 256 HPRC cell line samples, we fitted models for 617,007 TR loci, including known pathogenic repeats. We observe that instability levels are generally low, but vary substantially across individual TRs, and are driven more strongly by repeat composition than overall repeat length. Furthermore, we applied our method to targeted PureTarget long-read data from samples with known repeat expansions and identified significant mosaicism in the majority of expanded alleles. Our model offers a practical way to quantify instability of tandem repeats across the genome and to detect unusually unstable repeat alleles.

bioinformatics↗

bioRxiv2024

Defining a tandem repeat catalog and variation clusters for genome-wide analyses and population databases

Tandem repeat (TR) catalogs are important components of repeat genotyping studies as they define the genomic coordinates and expected motifs of all TR loci being analyzed. In recent years, genome-wide studies have used catalogs ranging in size from fewer than 200,000 to over 7 million loci. Where these catalogs overlapped, they often disagreed on locus boundaries, hindering the comparison and reuse of results across studies. Now, with multiple groups developing public databases of TR variation in large population cohorts, there is a risk that, without sufficient consensus in the choice of locus definitions, the use of divergent repeat catalogs will lead to confusion, fragmentation, and incompatibility across resources. In this paper, we compare existing TR catalogs and discuss desirable features of a comprehensive genome-wide catalog. We then present a new, richly annotated catalog designed for large-scale analyses and population databases. This new catalog, which we call the TRExplorer catalog v1.0, contains 4.86 million TR loci and, unlike most catalogs, is designed to be useful for both short-read and long-read analyses. It consists of 4,803,366 STRs and 59,675 VNTRs, of which 780,607 STRs and 21,888 VNTRs are both polymorphic and entirely absent from widely-used catalogs previously developed for short-read analyses. Additionally, our catalog stratifies TRs into two groups: 1) isolated TRs suitable for repeat copy number analysis using short-read or long-read data and 2) so-called variation clusters that contain TRs within wider polymorphic regions that are best studied through sequence-level analysis. To define variation clusters, we present a novel algorithm that leverages long-read HiFi sequencing data to group repeats with surrounding polymorphisms. We show that the human genome contains at least 25,000 complex variation clusters, most of which span over 120 bp and contain five or more TRs. Resolving the sequence of entire variation clusters instead of individually genotyping constituent TRs leads to a more accurate analysis of these regions and enables us to profile variation that would have been missed otherwise. We also share the trexplorer.broadinstitute.org portal which allows anyone to search, visualize, and download the catalog along with variation clusters and annotations.

genomics↗

bioRxiv2021

REViewer: Haplotype-resolved visualization of read alignments in and around tandem repeats

BackgroundExpansions of short tandem repeats are the cause of many neurogenetic disorders including familial amyotrophic lateral sclerosis, Huntington disease, and many others. Multiple methods have been recently developed that can identify repeat expansions in whole genome or exome sequencing data. Despite the widely-recognized need for visual assessment of variant calls in clinical settings, current computational tools lack the ability to produce such visualizations for repeat expansions. Expanded repeats are difficult to visualize because they correspond to large insertions relative to the reference genome and involve many misaligning and ambiguously aligning reads. ResultsWe implemented REViewer, a computational method for visualization of sequencing data in genomic regions containing long repeat expansions. To generate a read pileup, REViewer reconstructs local haplotype sequences and distributes reads to these haplotypes in a way that is most consistent with the fragment lengths and evenness of read coverage. To create appropriate training materials for onboarding new users, we performed a concordance study involving 12 scientists involved in STR research. We used the results of this study to create a user guide that describes the basic principles of using REViewer as well as a guide to the typical features of read pileups that correspond to low confidence repeat genotype calls. Additionally, we demonstrated that REViewer can be used to annotate clinically-relevant repeat interruptions by comparing visual assessment results of 44 FMR1 repeat alleles with the results of triplet repeat primed PCR. For 38 of these alleles, the results of visual assessment were consistent with triplet repeat primed PCR. ConclusionsRead pileup plots generated by REViewer offer an intuitive way to visualize sequencing data in regions containing long repeat expansions. Laboratories can use REViewer to assess the quality of repeat genotype calls as well as to visually detect interruptions or other imperfections in the repeat sequence and the surrounding flanking regions.

bioinformatics↗

bioRxiv2021

Ultrafast, alignment-free detection of repeat expansions in NGS and RNAseq data

Short tandem repeat expansions are an established cause of diseases such as Huntingtons disease. Bioinformatic methods for detecting repeat expansions in short-read sequencing have revealed new repeat expansions in humans. Current bioinformatic methods to detect repeat expansions require alignment information to identify repetitive motif enrichment at genomic locations. We present superSTR, an ultrafast method that does not require alignment. We demonstrate superSTRs ability to efficiently process both whole-genome and whole-exome sequencing data. Using superSTR we perform the first analysis of the UK Biobank to efficiently screen the exomes of 49,953 biobank participants for repeat expansions. We identify known mutations, as well as diseases not previously associated with REs. We further demonstrate the first bioinformatic screening of RNA sequencing data to detect repeat expansions in patients with spinocerebellar ataxia and Fuchs endothelial corneal dystrophy, and mouse models of myotonic dystrophy. superSTR is a highly computationally-efficient repeat expansion tool screening and detection tool for genomewide novel repeat expansion analysis, significantly outperforming existing methods. superSTR is available from https://github.com/bahlolab/superSTR.

bioinformatics↗

bioRxiv2021

Loss-of-function variants in the cardiac Kv11.1 channel as a genetic biomarker for SUDEP

ObjectiveTo compare the frequency and impact on channel function of KCNH2 variants in SUDEP patients with epilepsy controls comprising patients older than 50 years, a group with low SUDEP risk, and establish loss-of-function KCNH2 variants as predictive biomarkers of SUDEP risk. MethodsWe searched for KCNH2 variants with a minor allele frequency of < 5%. Functional analysis in Xenopus laevis oocytes was performed for all KCNH2 variants identified. ResultsKCNH2 variants were found in 11.1% (10/90) of SUDEP individuals compared to 6.0% (20/332) of epilepsy controls (p = 0.11). Loss-of-function KCNH2 variants, defined as causing > 20% reduction in maximal amplitude, were observed in 8.9% (8/90) SUDEP patients compared to 3.3% (11/332) epilepsy controls suggesting about three-fold enrichment (nominal p = 0.04). KCNH2 variants that did not change channel function occurred at a similar frequency in SUDEP (2.2%; 2/90) and epilepsy control (2.7%; 9/332) cohorts (p > 0.99). Rare KCNH2 variants (< 1% allele frequency) associated with greater loss of function and an [~]11-fold enrichment in the SUDEP cohort (nominal p = 0.03). In silico tools were unable to predict the impact of a variant on function highlighting the need for electrophysiological analysis. ConclusionsThese data show that loss-of-function KCNH2 variants are enriched in SUDEP patients and suggest that cardiac mechanisms contribute to SUDEP risk. We propose that genetic screening in combination with functional analysis can identify loss-of-function KCNH2 variants that could act as biomarkers of an individuals SUDEP risk.

neuroscience↗

bioRxiv2019

ExpansionHunter Denovo: A computational method for locating known and novel repeat expansions in short-read sequencing data

Expansions of short tandem repeats are responsible for over 40 monogenic disorders, and undoubtedly many more pathogenic repeat expansions (REs) remain to be discovered. Existing methods for detecting REs in short-read sequencing data require predefined repeat catalogs. However recent discoveries have emphasized the need for detection methods that do not require candidate repeats to be specified in advance. To address this need, we introduce ExpansionHunter Denovo, an efficient catalog-free method for genome-wide detection of REs. Analysis of real and simulated data shows that our method can identify large expansions of 41 out of 44 pathogenic repeats, including nine recently reported non-reference REs not discoverable via existing methods. ExpansionHunter Denovo is freely available at https://github.com/Illumina/ExpansionHunterDenovo

bioinformatics↗

bioRxiv2019

Connecting omics signatures of diseases, drugs, and mechanisms of actions with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Among these, iLINCS is unique in scope and versatility of the data provided and the analytics facilitated. iLINCS (http://ilincs.org) is an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates analysis of user-submitted omics signatures of diseases and cellular perturbations in the context of a large compendium of pre-computed signatures (>200,000), as well as mining and re-analysis of the large collection of omics datasets (>12,000), pre-computed signatures, and their connections. Analytics workflows driven by user-friendly interfaces enable users with only conceptual understanding of the analysis strategy to execute sophisticated analyses of omics signatures, such as systems biology analyses and interpretation of signatures, mechanism of action analysis, and signature-driven drug repositioning. In summary, iLINCS workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures.

bioinformatics↗