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Sackett, P. W.

Publications and source records attributed to Sackett, P. W..

2 recordsLinked to original sources

HaploCart: Human mtDNA Haplogroup Classification Using a Pangenomic Reference Graph

Current mitochondrial DNA (mtDNA) haplogroup classification tools map reads to a single reference genome and perform inference based on the detected mutations to this reference. This approach biases haplogroup assignments towards the reference and prohibits accurate calculations of the uncertainty in assignment. We present HaploCart, an mtDNA haplogroup classifier which uses VGs pangenomic reference graph framework together with principles of Bayesian inference. We demonstrate that our approach significantly outperforms available tools by being more robust to lower coverage or incomplete consensus sequences and producing phylogenetically-aware confidence scores that are unbiased towards any haplogroup. HaploCart is available both as a command-line tool and through a user-friendly web interface. The program written in C++ accepts as input consensus FASTA, FASTQ, or GAM files, and outputs a text file with the haplogroup assignments along with confidence estimates. Our work considerably reduces the amount of data required to obtain a confident mitochondrial haplogroup assignment. HaploCart is available as a command-line tool at https://github.com/grenaud/vgan and as a web server at https://services.healthtech.dtu.dk/service. php?HaploCart.

bioinformatics↗

RosettaDDGPrediction for high-throughput mutational scans: from stability to binding

Reliable prediction of free energy changes upon amino acidic substitutions ({Delta}{Delta}Gs) is crucial to investigate their impact on protein stability and protein-protein interaction. Moreover, advances in experimental mutational scans allow high-throughput studies thanks to sophisticated multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput calculations of {Delta}{Delta}Gs. In this context, the Rosetta modeling suite implements effective approaches to predict the change in the folding free energy in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. Their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for {Delta}{Delta}G prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. RosettaDDGPrediction assists with checking whether the runs are completed successfully aggregates raw data for multiple variants, and generates publication-ready graphics. We showed the potential of the tool in selected case studies, including variants of unknown significance found in children who developed cancer, proteins with known experimental unfolding {Delta}{Delta}Gs values, interactions between target proteins and a disordered functional motif, and phospho-mimetic variants. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

bioinformatics↗