Biology subjects

Pirkl, M.

Publications and source records attributed to Pirkl, M..

3 recordsLinked to original sources

bioRxiv2025

Geno2pheno: Interpretable and accurate HIV antibody resistance prediction

BackgroundAntiretroviral therapy (ART) is a life saving option for people living with HIV-1 (PLWH) and is effective against many viral strains. The most common ARTs involve combinations of drugs targeting viral or cellular proteins. Most of these drugs have to be taken daily. An alternative to ARTs with established inhibitors comprises broadly neutralizing antibodies (bNAbs). However, bNAbs share the problem of viral resistance with protein inhibitors. We developed a web service geno2pheno[bNAbs] that allows users to upload viral genotypes and estimates the respective resistance to many common bNAbs. The service uses trained statistical models to classify the virus into sensitive and resistant, respectively or to regress the IC50. MethodsWe used two linear models as well as two neural nets for each task and multi-task (MT) learning to train both models for IC50 prediction and classification simultaneously. During multi-task learning we penalize divergence of class and IC50 score in addition to the loss individual to each of the models. FindingsWe compared the linear models of geno2pheno[bNAbs] to other state-of-the-art methods like recurrent neural nets and self-attention, and found them to be competitive in regard to accuracy and have the benefit of fast computation and being easily interpretable in regard to features, i.e., positions on the envelope. InterpretationWe developed a web service for the prediction of antibody resistance (geno2pheno[bNAbs]) to HIV-1, which is free to use and can be extended to other viruses, like Sars-Cov2, in the future.

bioinformatics↗

bioRxiv2020

Within-patient genetic diversity of SARS-CoV-2

SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity. Author SummarySince it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.

genomics↗

bioRxiv2018

Single cell network analysis with a mixture of Nested Effects Models

MotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.\n\nResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.\n\nAvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/.\n\nContactmartin.pirkl@bsse.ethz.ch, niko.beerenwinkel@bsse.ethz.ch\n\nSupplementary informationSupplementary data are available.online.

systems biology↗