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1. CHESPA/CHESCA-SPARKY: automated NMR data analysis plugins for SPARKY to map protein allostery

   https://doi.org/10.1093/bioinformatics/btaa781  

   Shao, Hongzhao; Boulton, Stephen; Olivieri, Cristina; Mohamed, Hebatallah; Akimoto, Madoka; Subrahmanian, Manu Veliparambil; Veglia, Gianluigi; Markley, John L; Melacini, Giuseppe; Lee, Woonghee (September 2020, Bioinformatics)

   Yann, Ponty (Ed.)

   Abstract Motivation Correlated Nuclear Magnetic Resonance (NMR) chemical shift changes identified through the CHEmical Shift Projection Analysis (CHESPA) and CHEmical Shift Covariance Analysis (CHESCA) reveal pathways of allosteric transitions in biological macromolecules. To address the need for an automated platform that implements CHESPA and CHESCA and integrates them with other NMR analysis software packages, we introduce here integrated plugins for NMRFAM-SPARKY that implement the seamless detection and visualization of allosteric networks. Availability and implementation CHESCA-SPARKY and CHESPA-SPARKY are available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (http://pine.nmrfam.wisc.edu/download_packages.html), the NMRbox Project (https://nmrbox.org) and to subscribers to the SBGrid (https://sbgrid.org). The assigned spectra involved in this study and tutorial videos using this dataset are available at https://sites.google.com/view/chescachespa-sparky. Supplementary information Supplementary data are available at Bioinformatics Online. 

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2. TieBrush: an efficient method for aggregating and summarizing mapped reads across large datasets

   https://doi.org/10.1093/bioinformatics/btab342  

   Varabyou, Ales; Pertea, Geo; Pockrandt, Christopher; Pertea, Mihaela (May 2021, Bioinformatics)

   Ponty, Yann (Ed.)

   Abstract Summary Although the ability to programmatically summarize and visually inspect sequencing data is an integral part of genome analysis, currently available methods are not capable of handling large numbers of samples. In particular, making a visual comparison of transcriptional landscapes between two sets of thousands of RNA-seq samples is limited by available computational resources, which can be overwhelmed due to the sheer size of the data. In this work, we present TieBrush, a software package designed to process very large sequencing datasets (RNA, whole-genome, exome, etc.) into a form that enables quick visual and computational inspection. TieBrush can also be used as a method for aggregating data for downstream computational analysis, and is compatible with most software tools that take aligned reads as input. Availability and implementation TieBrush is provided as a C++ package under the MIT License. Precompiled binaries, source code and example data are available on GitHub (https://github.com/alevar/tiebrush). Supplementary information Supplementary data are available at Bioinformatics online. 

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3. POKY: a software suite for multidimensional NMR and 3D structure calculation of biomolecules

   https://doi.org/10.1093/bioinformatics/btab180  

   Lee, Woonghee; Rahimi, Mehdi; Lee, Yeongjoon; Chiu, Abigail (March 2021, Bioinformatics)

   Gorodkin, Jan (Ed.)

   Abstract Summary The need for an efficient and cost-effective method is compelling in biomolecular NMR. To tackle this problem, we have developed the Poky suite, the revolutionized platform with boundless possibilities for advancing research and technology development in signal detection, resonance assignment, structure calculation, and relaxation studies with the help of many automation and user interface tools. This software is extensible and scalable by scripting and batching as well as providing modern graphical user interfaces and a diverse range of modules right out of the box. Availability Poky is freely available to non-commercial users at https://poky.clas.ucdenver.edu. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. eCOMPASS: evaluative comparison of multiple protein alignments by statistical score

   https://doi.org/10.1093/bioinformatics/btab374  

   Neuwald, Andrew F; Kolaczkowski, Bryan D; Altschul, Stephen F (May 2021, Bioinformatics)

   Ponty, Yann (Ed.)

   Abstract Motivation Detecting subtle biologically relevant patterns in protein sequences often requires the construction of a large and accurate multiple sequence alignment (MSA). Methods for constructing MSAs are usually evaluated using benchmark alignments, which, however, typically contain very few sequences and are therefore inappropriate when dealing with large numbers of proteins. Results eCOMPASS addresses this problem using a statistical measure of relative alignment quality based on direct coupling analysis (DCA): To maintain protein structural integrity over evolutionary time, substitutions at one residue position typically result in compensating substitutions at other positions. eCOMPASS computes the statistical significance of the congruence between high scoring directly coupled pairs and 3D contacts in corresponding structures, which depends upon properly aligned homologous residues. We illustrate eCOMPASS using both simulated and real MSAs. Availability and Implementation The eCOMPASS executable, C ++ open source code and input data sets are available at https://www.igs.umaryland.edu/labs/neuwald/software/compass. Supplementary information Supplementary data are available at Bioinformatics online. 

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5. Estimating error models for whole genome sequencing using mixtures of Dirichlet-multinomial distributions

   https://doi.org/10.1093/bioinformatics/btx133  

   Wu, Steven H.; Schwartz, Rachel S.; Winter, David J.; Conrad, Donald F.; Cartwright, Reed A.; Stegle, ed., Oliver (March 2017, Bioinformatics)

   Abstract MotivationAccurate identification of genotypes is an essential part of the analysis of genomic data, including in identification of sequence polymorphisms, linking mutations with disease and determining mutation rates. Biological and technical processes that adversely affect genotyping include copy-number-variation, paralogous sequences, library preparation, sequencing error and reference-mapping biases, among others. ResultsWe modeled the read depth for all data as a mixture of Dirichlet-multinomial distributions, resulting in significant improvements over previously used models. In most cases the best model was comprised of two distributions. The major-component distribution is similar to a binomial distribution with low error and low reference bias. The minor-component distribution is overdispersed with higher error and reference bias. We also found that sites fitting the minor component are enriched for copy number variants and low complexity regions, which can produce erroneous genotype calls. By removing sites that do not fit the major component, we can improve the accuracy of genotype calls. Availability and ImplementationMethods and data files are available at https://github.com/CartwrightLab/WuEtAl2017/ (doi:10.5281/zenodo.256858). Supplementary informationSupplementary data is available at Bioinformatics online. 

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