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Riesselman, A.

Publications and source records attributed to Riesselman, A..

2 recordsLinked to original sources

The EVcouplings Python framework for coevolutionary sequence analysis

SummaryCoevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. This approach requires extensive computational pipelines that integrate multiple tools, databases, and data processing steps. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The application has an easy to use command line interface to run workflows with user control over all analysis parameters, while the underlying modular Python package allows interactive data analysis and rapid development of new workflows. Through this multi-layered approach, the EVcouplings framework makes the full power of coevolutionary analyses available to entry-level and advanced users.\n\nAvailabilityhttps://github.com/debbiemarkslab/evcouplings\n\nContactsander.research@gmail.com, debbie@hms.harvard.edu

bioinformatics

Rapid genome recoding by iterative recombineering of synthetic DNA

Genome recoding will provide a deeper understanding of genetics and transform biotechnology. We bypass the reliance of previous genome recoding methods on site-specific enzymes and demonstrate a rapid recombineering based strategy for writing genomes by Stepwise Integration of Rolling Circle Amplified Segments (SIRCAS). We installed the largest number of codon substitutions in a single organism yet published, creating a strain of Salmonella typhimurium with 1557 leucine codon changes across 200 kb of the genome.

synthetic biology