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Schubert, B.

Publications and source records attributed to Schubert, B..

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

Genome-wide discovery of epistatic loci affecting antibiotic resistance using evolutionary couplings

The analysis of whole genome sequencing data should, in theory, allow the discovery of interdependent loci that cause antibiotic resistance. In practice, however, identifying this epistasis remains a challenge as the vast number of possible interactions erodes statistical power. To solve this problem, we extend a method that has been successfully used to identify epistatic residues in proteins to infer genomic loci that are strongly coupled and associated with antibiotic resistance. Our method reduces the number of tests required for an epistatic genome-wide association study and increases the likelihood of identifying causal epistasis. We discovered 38 loci and 250 epistatic pairs that influence the dose needed to inhibit growth for five different antibiotics in 1,102 isolates of Neisseria gonorrhoeae that were confirmed in an independent dataset of 495 isolates. Many known resistance-affecting loci were recovered; however, the majority of loci occurred in unreported genes, including murE which was associated with cefixime. About half of the novel epistasis we report involved at least one locus previously associated with antibiotic resistance, including interactions between gyrA and parC associated with ciprofloxacin. Still, many combinations involved unreported loci and genes. Our work provides a systematic identification of epistasis pairs affecting antibiotic resistance in N. gonorrhoeae and a generalizable method for epistatic genome-wide association studies.

genomics