Publications by authors named "Alexander Ostrovsky"
An important unmet need revealed by the COVID-19 pandemic is the near-real-time identification of potentially fitness-altering mutations within rapidly growing SARS-CoV-2 lineages. Although powerful molecular sequence analysis methods are available to detect and characterize patterns of natural selection within modestly sized gene-sequence datasets, the computational complexity of these methods and their sensitivity to sequencing errors render them effectively inapplicable in large-scale genomic surveillance contexts. Motivated by the need to analyze new lineage evolution in near-real time using large numbers of genomes, we developed the Rapid Assessment of Selection within CLades (RASCL) pipeline.
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- - Modern biology is increasingly reliant on computational methods to handle the large and complex datasets that are emerging, posing a challenge for experimental biologists who may lack computational skills.
- - Galaxy is a web-based platform that provides access to a variety of computational biology tools and public biological data repositories, allowing users to blend private and public datasets.
- - The article offers detailed protocols for using Galaxy to conduct specific biological analyses, including finding human coding exons, analyzing ChIP-seq data, comparing datasets, and working with RNA-seq.
Background: The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets.
Results: Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology.