Biology subjects

Szul, P.

Publications and source records attributed to Szul, P..

3 recordsLinked to original sources

bioRxiv2019

VariantSpark, A Random Forest Machine Learning Implementation for Ultra High Dimensional Data

The demands on machine learning methods to cater for ultra high dimensional datasets, datasets with millions of features, have been increasing in domains like life sciences and the Internet of Things (IoT). While Random Forests are suitable for \"wide\" datasets, current implementations such as Googles PLANET lack the ability to scale to such dimensions. Recent improvements by Yggdrasil begin to address these limitations but do not extend to Random Forest. This paper introduces CursedForest, a novel Random Forest implementation on top of Apache Spark and part of the VariantSpark platform, which parallelises processing of all nodes over the entire forest. CursedForest is 9 and up to 89 times faster than Googles PLANET and Yggdrasil, respectively, and is the first method capable of scaling to millions of features.

bioinformatics↗

bioRxiv2019

TRIBES: A user-friendly pipeline for relatedness detection and disease gene discovery

SummaryTRIBES is a user-friendly pipeline for relatedness detection in genomic data. TRIBES is the first tool which is both accurate up to 7th degree relatives (e.g. third cousins) and combines essential data processing steps into a single user-friendly pipeline. Furthermore, using a proof-of-principle cohort comprising amyotrophic lateral sclerosis cases with known relationship structures and a known causal mutation in SOD1, we demonstrated that TRIBES can successfully uncover disease susceptibility loci. TRIBES has multiple applications in addition to disease gene mapping, including sample quality control in genome wide association studies and avoiding consanguineous unions in family planning.\n\nAvailabilityTRIBES is freely available on GitHub: https://github.com/aehrc/TRIBES/\n\nContactnatalie.twine@csiro.au\n\nSupplementary informationXXXX

bioinformatics↗

bioRxiv2019

IBD analysis of Australian amyotrophic lateral sclerosis SOD1-mutation carriers identifies five founder events and links sporadic cases to existing ALS families

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterised by the loss of upper and lower motor neurons resulting in paralysis and eventual death. Approximately 10% of ALS cases have a family history of disease, while the remaining cases present as apparently sporadic. Heritability studies suggest a significant genetic component to sporadic ALS, and although most sporadic cases have an unknown genetic etiology, some familial ALS mutations have also been found in sporadic cases. This suggests that some sporadic cases may be unrecognised familial cases with reduced disease penetrance. Identifying a familial basis of disease in apparently sporadic ALS cases has significant genetic counselling implications for immediate relatives. A powerful strategy to uncover a familial link is identity-by-descent (IBD) analysis which detects genomic regions that have been inherited from a common ancestor. We performed IBD analysis on 90 Australian familial ALS cases from 25 families and three sporadic ALS cases, each of whom carried one of three SOD1 mutations (p.I114T, p.V149G and p.E101G). We identified five unique haplotypes that carry these mutations in our cohort, indicative of five founder events. This included two different haplotypes that carry SOD1 p.I114T, where one haplotype was present in one sporadic case and 20 families, while the second haplotype was found in the remaining two sporadic cases and one family, thus linking these familial and sporadic cases. Furthermore, we linked two families that carry SOD1 p.V149G and found that SOD1 p.E101G arose independently in each family that carries this mutation.

genetics↗