BioinformaticsBioinformaticsPeer-reviewed journal article
pydca v1.0: a comprehensive software for Direct Coupling Analysis of RNA and Protein Sequences
Mehari B. Zerihun, Fabrizio PucciiD, Emanuel K. PeteriD, Alexander H. SchugiD
Abstract
The ongoing advances in sequencing technologies have provided a massive increase in the availability of sequence data. This made it possible to study the patterns of correlated substitution between residues in families of homologous proteins or RNAs and to retrieve structural and stability information. Direct coupling Analysis (DCA) infers coevolutionary couplings between pairs of residues indicating their spatial proximity, making such information a valuable input for subsequent structure prediction. Here we present pydca, a standalone Python-based software package for the DCA of protein- and RNA-homologous families. It is based on two popular inverse statistical approaches, namely, the mean-field and the pseudo-likelihood maximization and is equipped with a series of functionalities that range from multiple sequence alignment trimming to contact map visualization. Thanks to its efficient implementation, features and user-friendly command line interface, pydca is a modular and easy-to-use tool that can be used by researchers with a wide range of backgrounds.\n\nAvailabilityhttps://github.com/KIT-MBS/pydca
- assay: protein sequence
- assay: GWAS summary statistics
- research focus: software / tool
- approach: statistical model
- Citations
- 44
- FWCI
- 1.49
- Field percentile
- top 18%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- RADI (Reduced Alphabet Direct Information): Improving execution time for direct-coupling analysis
Bernat Anton, Mireia Besalú, … Baldomero Oliva·NAR Genomics and BioinformaticsPeer-reviewed·Apr 9, 2021
- Statistical Investigations of Protein Residue Direct Couplings
Andrew F. Neuwald, Stephen F. Altschul·PLoS Computational BiologyPeer-reviewed·Dec 31, 2018
- adabmDCA 2.0 - a flexible but easy-to-use package for Direct Coupling Analysis
Lorenzo Rosset, Roberto Netti, … Francesco Zamponi·Methods in molecular biologyPeer-reviewed·Feb 24, 2012
- A Reproducibility Analysis-based Statistical Framework for Residue-Residue Evolutionary Coupling Detection
Yunda Si, Yi Zhang, Chengfei Yan·Briefings in BioinformaticsPeer-reviewed·Dec 15, 2021
- Structure-informed direct coupling analysis improves protein mutational landscape predictions
Matsvei Tsishyn, Hugo Talibart, … Fabrizio Pucci·bioRxiv·Mar 28, 2026
- Patch-DCA: Improved Protein Interface Prediction by utilizing Structural Information and Clustering DCA scores
Amir Vajdi, Kourosh Zarringhalam, Nurit Haspel·BioinformaticsPeer-reviewed·Oct 15, 2019