bioRxiv Science⌕ Search

Biology subjects

Cokol, M.

Publications and source records attributed to Cokol, M..

3 recordsLinked to original sources

Genomic Annotation Infrastructure (GAIn): Pipelines and Resource Repositories for Annotating Variants, Positions, and Regions

Interpretation of genomic variants, positions, and regions depends on reliable annotation--adding evidence such as predicted effect, conservation, population frequency, and gene-level context--yet the underlying resources are numerous, versioned, and assembly-specific. We present the Genomic Annotation Infrastructure (GAIn), a platform that generates transparent, reproducible annotations via declarative pipelines that define annotation tasks as ordered lists of components, called annotators, that produce annotation attributes using genomic resources from Genomic Resource Repositories (GRRs). We provide two public GRRs: a main repository containing more than 250 heterogeneous genomic resources, and a separate GRR-ENCODE repository containing resources derived from thousands of ENCODE (Encyclopedia of DNA Elements) project experiments. Users can use the annotation pipelines we made available, author custom annotation pipelines, and execute annotation tasks with these pipelines via GAIns web and command-line interfaces. The web interface can be used without any setup, but it relies on shared computational infrastructure and imposes limits on the size of annotation tasks. The command-line interface requires setup but supports arbitrarily large annotation tasks through simple-to-use parallelization and offers a broader set of features. For example, command-line GAIn can be extended by using custom GRRs or creating custom annotators via its plugin architecture. In addition, GAIns re-annotation feature, which updates annotations as they evolve, substantially simplifies maintaining annotations in a large genomics analysis project. GAIns resource management, explicit versioning, and pipeline abstraction provide an auditable, maintainable, and efficient foundation for modern genomic annotation across reference assemblies and use cases.

bioinformatics↗

The Genotype and Phenotypes in Families (GPF) platform manages the large and complex data at SFARI

The exploration of genotypic variants impacting phenotypes is a cornerstone in genetics research. The emergence of vast collections containing deeply genotyped and phenotyped families has made it possible to pursue the search for variants associated with complex diseases. However, managing these large-scale datasets requires specialized computational tools tailored to organize and analyze the extensive data. GPF (Genotypes and Phenotypes in Families) is an open-source platform (https://github.com/iossifovlab/gpf) that manages genotypes and phenotypes derived from collections of families. The GPF interface allows interactive exploration of genetic variants, enrichment analysis for de novo mutations, and phenotype/genotype association tools. In addition, GPF allows researchers to share their data securely with the broader scientific community. GPF is used to disseminate two large-scale family collection datasets (SSC, SPARK) for the study of autism funded by the SFARI foundation. However, GPF is versatile and can manage genotypic data from other small or large family collections. Our GPF-SFARI GPF instance (https://gpf.sfari.org/) provides protected access to comprehensive genotypic and phenotypic data for the SSC and SPARK. In addition, GPF-SFARI provides public access to an extensive collection of de novo mutations identified in individuals with autism and related disorders and to gene-level statistics of the protected datasets characterizing the genes roles in autism. Here, we highlight the primary features of GPF within the context of GPF-SFARI.

bioinformatics↗

Antibiotic hypersensitivity signatures identify targets for attack in the Acinetobacter baumannii cell envelope

Acinetobacter baumannii is an opportunistic pathogen that is a critical, high-priority target for new antibiotic development. Clearing of A. baumannii requires relatively high doses of antibiotics across the spectrum, primarily due to its protective cell envelope. Many of the proteins that support envelope integrity and modulate drug action are uncharacterized, largely because there is an absence of orthologs for several proteins that perform essential envelope-associated processes, impeding progress on this front. To identify targets that can synergize with current antibiotics, we performed an exhaustive analysis of A. baumannii mutants causing hypersensitivity to a multitude of antibiotic treatments. By examining mutants with antibiotic hypersensitivity profiles that parallel mutations in proteins of known function, we show that the function of poorly annotated proteins can be predicted and used to identify candidate missing link proteins in essential A. baumannii processes. Using this strategy, we uncovered multiple uncharacterized proteins with critical roles in cell division or cell elongation, and revealed that a predicted cell wall D,D-endopeptidase has an unappreciated function in lipooligosaccharide synthesis. Moreover, we provide a genetic strategy that uses hypersensitivity signatures to predict drug synergies, allowing the identification of {beta}-lactams that work cooperatively based on the cell wall assembly machineries that they preferentially target. These data reveal multiple pathways critical for envelope growth in A. baumannii that can be targeted in combination strategies for attacking the pathogen.

microbiology↗