Biology subjects

Becker, S. A.

Publications and source records attributed to Becker, S. A..

2 recordsLinked to original sources

bioRxiv2022

Universal Amplicon Sequencing of North Imperial Valley Wetlands Microbiomes

DNA sequencing of complex microbial communities allows for the classification and quantification of thousands of distinct organisms in diverse environmental niches. We present a three domain "Universal Amplicon" (UA) method to simultaneously amplify DNA from the ribosomal small subunit locus from bacteria, archaea, and eukaryotes (and their organelles) using a single pair of amplification primers. We demonstrate the amenability of the UA to multiplexed Illumina library preparation and MiSeq-based sequencing. We validate the UA by sequencing a commercially available microbial community of known quantitative composition and through direct comparison to a shotgun metagenomics dataset. Following validation, we apply the UA to a time-course study of the wetlands of the Northern Imperial Valley in California and show substantial and variable microbial life in the Salton Sea and nearby waters. We demonstrate that the microbial ecology of the Salton Sea varies on at least a monthly basis and is distinct from the surrounding area. Finally, we contribute an open-source Shiny app for real-time analysis of complex metagenomic communities, with application to this study and far beyond.

ecology↗

bioRxiv2018

Deep Semantic Protein Representation for Annotation, Discovery, and Engineering

Computational assignment of function to proteins with no known homologs is still an unsolved problem. We have created a novel, function-based approach to protein annotation and discovery called D-SPACE (Deep Semantic Protein Annotation Classification and Exploration), comprised of a multi-task, multi-label deep neural network trained on over 70 million proteins. Distinct from homology and motif-based methods, D-SPACE encodes proteins in high-dimensional representations (embeddings), allowing the accurate assignment of over 180,000 labels for 13 distinct tasks. The embedding representation enables fast searches for functionally related proteins, including homologs undetectable by traditional approaches. D-SPACE annotates all 109 million proteins in UniProt in under 35 hours on a single computer and searches the entirety of these in seconds. D-SPACE further quantifies the relative functional effect of mutations, facilitating rapid in silico mutagenesis for protein engineering applications. D-SPACE incorporates protein annotation, search, and other exploratory efforts into a single cohesive model.

bioinformatics↗