Making the most of genomic data with OMA.

F1000Res

Department of Computational Biology, University of Lausanne, Lausanne, 1015, Switzerland.

Published: April 2021

The OMA Collection is a resource for users of Orthologous Matrix. In this collection, we provide tutorials and protocols on how to leverage the tools provided by OMA to analyse your data. Here, I explain the motivation for this collection and its published works thus far.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331100PMC
http://dx.doi.org/10.12688/f1000research.24904.1DOI Listing

Publication Analysis

Top Keywords

making genomic
4
genomic data
4
data oma
4
oma oma
4
oma collection
4
collection resource
4
resource users
4
users orthologous
4
orthologous matrix
4
matrix collection
4

Similar Publications

Background: Breast cancer had been the most frequently diagnosed cancer among women, making up nearly one-third of all female cancers. Hormone receptor-positive breast cancer (HR+BC) was the most prevalent subtype of breast cancer and exhibited significant heterogeneity. Despite advancements in endocrine therapies, patients with advanced HR+BC often faced poor outcomes due to the development of resistance to treatment.

View Article and Find Full Text PDF

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) is a diverse family of variant surface antigens, encoded by var genes, that mediates binding of infected erythrocytes to human cells and plays a key role in parasite immune evasion and malaria pathology. The increased availability of parasite genome sequence data has revolutionised the study of PfEMP1 diversity across multiple P. falciparum isolates.

View Article and Find Full Text PDF

Microbiome profiling tools rely on reference catalogues, which significantly affect their performance. Comparing them is, however, challenging, mainly due to differences in their native catalogues. In this study, we present a novel standardized benchmarking framework that makes such comparisons more accurate.

View Article and Find Full Text PDF

Human immunodeficiency virus (HIV) is an exemplar virus, still the most studied and best understood and a model for mechanisms of viral replication, immune evasion and pathogenesis. In this review, we consider the earliest stages of HIV infection from transport of the virion contents through the cytoplasm to integration of the viral genome into host chromatin. We present a holistic model for the virus-host interaction during this pivotal stage of infection.

View Article and Find Full Text PDF

Unveiling the ghost: machine learning's impact on the landscape of virology.

J Gen Virol

January 2025

Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.

The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years, machine learning has become an increasingly capable technology for addressing this challenge, as advances in methods and computational power have dramatically improved the performance of models and led to their widespread adoption across industries and disciplines. Nascent applications of machine learning technology to virus research have now expanded, providing new tools for handling large-scale datasets and leading to a reshaping of existing workflows for phenotype prediction, phylogenetic analysis, drug discovery and more.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!