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Silverstein, M.

Publications and source records attributed to Silverstein, M..

2 recordsLinked to original sources

The activity, divergence, and evolutionary degradation of modern-day homing endonucleases and their reconstructed ancestors

Homing endonucleases (HEs) are selfish genetic elements that drive the mobilization of their own coding sequences, often in concert with surrounding introns. Homing endonuclease genes (HEGs) usually display life cycles in which they accumulate inactivating mutations after invading a host genomic target site, leading to eventual removal from the genome. We identified several hundred novel HEGs and determined the distribution of their proteins behaviors and activities. Approximately 10% are expressed as properly folded functional proteins that cleave predictable DNA target sites. Another [~]20% display significant expression but little to no cleavage activity; the remainder display severely reduced expression. Despite the presence of debilitating mutations throughout most HEGs, ancestral reconstructions yielded endonucleases with improved expression and stability. One such reconstruction, at a hypothetical node preceding highly diverged HEs that cleave unique target sites, binds (but does not cleave) their individual targets. It instead cleaves a DNA sequence that represents a hybrid of those modern-day DNA targets, while displaying a specificity profile that resembled those of previously characterized HEs. Its DNA-bound crystal structure adds detail to our understanding of how homing endonuclease DNA contacting surfaces and residues shift and rearrange during evolution, ultimately leading to their action at new target sites.

biochemistry↗

Reproducible Tools and Enhanced Computational Workflows for Batch Effect Evaluation of High-Throughput Data Using BatchQC

Batch effect correction is a common and often necessary step in data analysis to reduce bias due to technical and experimental factors when combining multiple batches of data. The severity of the batch effects dictates the correction strategy; therefore, a careful assessment of each datasets batch effects is necessary. BatchQC is an R package that provides reproducible tools and visualizations for quantitatively and qualitatively addressing batch effects across a broad range of data types. BatchQC integrates with standardized Bioconductor data structures and features an object-oriented design, enabling the application of workflows that can freely evaluate and process data within and outside the package tools. Common batch evaluation methods, along with novel quantitative metrics, help determine the benefits of batch correction for each dataset and enable direct comparisons between methods. Here, we present BatchQC as the first comprehensive batch-correction R package, with independent tools, reproducible workflows, visualization, and novel statistics.

bioinformatics↗