Biology subjects

Pinese, M.

Publications and source records attributed to Pinese, M..

6 recordsLinked to original sources

bioRxiv2024

AEon: A global genetic ancestry estimation tool

BackgroundGenetic ancestry is a major confounding factor in genetic association studies, and it is essential to estimate and account for ancestry in genomic research. Despite this importance, accurate ancestry information is difficult to obtain, and existing methods to estimate ancestry are not designed for modern sequencing data. This methodological gap hinders the integration of genetic ancestry information in modern research, and limits progress in finding the genetic determinants of disease in diverse populations. To address this gap we present AEon, a probabilistic model-based global ancestry estimation tool, ready for use on modern genomic data. ResultsAEon predicts an individuals fractional ancestral population membership, accounting for possible admixture. Unlike previous global ancestry estimation tools, AEon takes input directly from a VCF/BCF, includes default training data based on the 26 reference populations of the 1000 Genomes Project, and produces visualisation aids and diagnostics to complement data output. ConclusionsAEons turnkey design significantly reduces the time taken to estimate ancestry from VCFs without compromising accuracy compared to ADMIXTURE analysis, with a 4.8-fold decrease in runtime for a file with 10,000 sites and 100 individuals. AEon is available at github.com/GenomicRisk/aeon.

bioinformatics↗

bioRxiv2019

mity: A highly sensitive mitochondrial variant analysis pipeline for whole genome sequencing data

MotivationMitochondrial diseases (MDs) are the most common group of inherited metabolic disorders and are often challenging to diagnose due to extensive genotype-phenotype heterogeneity. MDs are caused by mutations in the nuclear or mitochondrial genome, where pathogenic mitochondrial variants are usually heteroplasmic and typically at much lower allelic fraction in the blood than affected tissues. Both genomes can now be readily analysed using unbiased whole genome sequencing (WGS), but most nuclear variant detection methods fail to detect low heteroplasmy variants in the mitochondrial genome. ResultsWe present mity, a bioinformatics pipeline for detecting and interpreting heteroplasmic SNVs and INDELs in the mitochondrial genome using WGS data. In 2,980 healthy controls, we observed on average 3,166x coverage in the mitochondrial genome using WGS from blood. mity utilises this high depth to detect pathogenic mitochondrial variants, even at low heteroplasmy. mity enables easy interpretation of mitochondrial variants and can be incorporated into existing diagnostic WGS pipelines. This could simplify the diagnostic pathway, avoid invasive tissue biopsies and increase the diagnostic rate for MDs and other conditions caused by impaired mitochondrial function. Availabilitymity is available from https://github.com/KCCG/mityunder an MIT license. Contactclare.puttick@crick.ac.uk, carolyn.sue@sydney.edu.au, MCowley@ccia.org.au

bioinformatics↗

bioRxiv2018

The Medical Genome Reference Bank: Whole genomes and phenotype of 2,570 healthy elderly

Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2,570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analysed the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. Individuals in the MGRB had fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK BioBank cohorts, consistent with risk depletion. Pervasive age-related somatic changes were correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing. This research has been conducted using the UK Biobank Resource under Application Number 17984.

genomics↗

bioRxiv2018

Network-aware mutation clustering of cancer

The grouping of cancers across tissue boundaries is central to precision oncology, but remains a difficult problem. Here we present EPICC (Experimental Protein Interaction Clustering of Cancer), a novel technique to cluster cancer patients based on DNA mutation profile, that leverages knowledge of protein-protein interactions to reduce noise and amplify biological signal. We applied EPICC to data from The Cancer Genome Atlas (TCGA), and both recapitulated known cancer clusterings, and identified new cross-tissue cancer groups that may indicate novel cancer molecular subtypes. Investigation of EPICC clusters revealed new protein modules which were recurrently mutated across cancers, and indicate new avenues for research into cancer biology. EPICC leveraged the Vodafone DreamLab citizen science platform, and we provide our results as a resource for researchers to investigate the role of protein modules in cancer.

bioinformatics↗

bioRxiv2018

The Medical Genome Reference Bank: a whole-genome data resource of 4,000 healthy elderly individuals. Rationale and cohort design

Allele frequency data from human reference populations is of increasing value for filtering and assignment of pathogenicity to genetic variants. Aged and healthy populations are more likely to be selectively depleted of pathogenic alleles, and therefore particularly suitable as a reference populations for the major diseases of clinical and public health importance. However, reference studies of the healthy elderly have remained under-represented in human genetics. We have developed the Medical Genome Reference Bank (MGRB), a large-scale comprehensive whole-genome dataset of confirmed healthy elderly individuals, to provide a publicly accessible resource for health-related research, and for clinical genetics. It also represents a useful resource for studying the genetics of healthy aging. The MGRB comprises 4,000 healthy, older individuals with no reported history of cancer, cardiovascular disease or dementia, recruited from two Australian community-based cohorts. DNA derived from blood samples will be subject to whole genome sequencing. The MGRB will measure genome-wide genetic variation in 4,000 individuals, mostly of European decent, aged 60-95 years (mean age [≥] 75 years). The MGRB has committed to a policy of data sharing, employing a hierarchical data management system to maintain participant privacy and confidentiality, whilst maximizing research and clinical usage of the database. The MGRB will represent a dataset of international significance, broadly accessible to the clinical and genetic research community.

genomics↗

bioRxiv2017

blkbox: Integration Of Multiple Machine Learning Approaches To Identify Disease Biomarkers

MotivationMachine learning (ML) is a powerful tool to create supervised models that can distinguish between classes and facilitate biomarker selection in high-dimensional datasets, including RNA Sequencing (RNA-Seq). However, it is variable as to which is the best performing ML algorithm(s) for a specific dataset, and identifying the optimal match is time consuming. blkbox is a software package including a shiny frontend, that integrates nine ML algorithms to select the best performing classifier for a specific dataset. blkbox accepts a simple abundance matrix as input, includes extensive visualization, and also provides an easy to use feature selection step to enable convenient and rapid potential biomarker selection, all without requiring parameter optimization.\n\nResultsFeature selection makes blkbox computationally inexpensive while multi-functionality, including nested cross-fold validation (NCV), ensures robust results. blkbox identified algorithms that outperformed prior published ML results. Applying NCV identifies features, which are utilized to gain high accuracy.\n\nAvailabilityThe software is available as a CRAN R package and as a developer version with extended functionality on github (https://github.com/gboris/blkbox).\n\nContactb.guennewig@garvan.org.au

bioinformatics↗