RASCL: Rapid Assessment of Selection in CLades through molecular sequence analysis.

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. RASCL applies state of the art phylogenetic comparative methods to evaluate selective processes acting at individual codon sites and across whole genes. RASCL is scalable and produces automatically updated regular lineage-specific selection analysis reports: even for lineages that include tens or hundreds of thousands of sampled genome sequences. Key to this performance is (i) generation of automatically subsampled high quality datasets of gene/ORF sequences drawn from a selected "query" viral lineage; (ii) contextualization of these query sequences in codon alignments that include high-quality "background" sequences representative of global SARS-CoV-2 diversity; and (iii) the extensive parallelization of a suite of computationally intensive selection analysis tests. Within hours of being deployed to analyze a novel rapidly growing lineage of interest, RASCL will begin yielding JavaScript Object Notation (JSON)-formatted reports that can be either imported into third-party analysis software or explored in standard web-browsers using the premade RASCL interactive data visualization dashboard. By enabling the rapid detection of genome sites evolving under different selective regimes, RASCL is well-suited for near-real-time monitoring of the population-level selective processes that will likely underlie the emergence of future variants of concern in measurably evolving pathogens with extensive genomic surveillance.

Using Galaxy to Perform Large-Scale Interactive Data Analyses-An Update.

Modern biology continues to become increasingly computational. Datasets are becoming progressively larger, more complex, and more abundant. The computational savviness necessary to analyze these data creates an ongoing obstacle for experimental biologists. Galaxy (galaxyproject.org) provides access to computational biology tools in a web-based interface. It also provides access to major public biological data repositories, allowing private data to be combined with public datasets. Galaxy is hosted on high-capacity servers worldwide and is accessible for free, with an option to be installed locally. This article demonstrates how to employ Galaxy to perform biologically relevant analyses on publicly available datasets. These protocols use both standard and custom tools, serving as a tutorial and jumping-off point for more intensive and/or more specific analyses using Galaxy. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Finding human coding exons with highest SNP density Basic Protocol 2: Calling peaks for ChIP-seq data Basic Protocol 3: Compare datasets using genomic coordinates Basic Protocol 4: Working with multiple alignments Basic Protocol 5: Single cell RNA-seq.

A single-cell RNA-sequencing training and analysis suite using the Galaxy framework.

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. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. CONCLUSIONS: The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.