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1. modSaRa: a computationally efficient R package for CNV identification

   https://doi.org/10.1093/bioinformatics/btx212  

   Xiao, Feifei; Niu, Yue; Hao, Ning; Xu, Yanxun; Jin, Zhilin; Zhang, Heping (August 2017, Bioinformatics)

   Hancock, John (Ed.)

   Abstract SummaryChromosomal copy number variation (CNV) refers to a polymorphism that a DNA segment presents deletion or duplication in the population. The computational algorithms developed to identify this type of variation are usually of high computational complexity. Here we present a user-friendly R package, modSaRa, designed to perform copy number variants identification. The package is developed based on a change-point based method with optimal computational complexity and desirable accuracy. The current version of modSaRa package is a comprehensive tool with integration of preprocessing steps and main CNV calling steps. Availability and ImplementationmodSaRa is an R package written in R, C ++ and Rcpp and is now freely available for download at http://c2s2.yale.edu/software/modSaRa. Supplementary informationSupplementary data are available at Bioinformatics online. 

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2. CREPE: a Shiny app for transcription factor cataloguing

   https://doi.org/10.1093/bioadv/vbad055  

   Rosado-Tristani, Diego A.; Rodríguez-Martínez, José A.; Bateman, ed., Alex (April 2023, Bioinformatics Advances)

   Abstract SummaryTranscription factors (TFs) are proteins that directly interpret the genome to regulate gene expression and determine cellular phenotypes. TF identification is a common first step in unraveling gene regulatory networks. We present CREPE, an R Shiny app to catalogue and annotate TFs. CREPE was benchmarked against curated human TF datasets. Next, we use CREPE to explore the TF repertoires of Heliconius erato and Heliconius melpomene butterflies. Availability and implementationCREPE is available as a Shiny app package available at GitHub (github.com/dirostri/CREPE). Supplementary informationSupplementary data are available at Bioinformatics Advances online. 

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3. 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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4. A data-adaptive Bayesian regression approach for polygenic risk prediction

   https://doi.org/10.1093/bioinformatics/btac024  

   Song, Shuang; Hou, Lin; Liu, Jun S.; Schwartz, ed., Russell (January 2022, Bioinformatics)

   Abstract MotivationPolygenic risk score (PRS) has been widely exploited for genetic risk prediction due to its accuracy and conceptual simplicity. We introduce a unified Bayesian regression framework, NeuPred, for PRS construction, which accommodates varying genetic architectures and improves overall prediction accuracy for complex diseases by allowing for a wide class of prior choices. To take full advantage of the framework, we propose a summary-statistics-based cross-validation strategy to automatically select suitable chromosome-level priors, which demonstrates a striking variability of the prior preference of each chromosome, for the same complex disease, and further significantly improves the prediction accuracy. ResultsSimulation studies and real data applications with seven disease datasets from the Wellcome Trust Case Control Consortium cohort and eight groups of large-scale genome-wide association studies demonstrate that NeuPred achieves substantial and consistent improvements in terms of predictive r2 over existing methods. In addition, NeuPred has similar or advantageous computational efficiency compared with the state-of-the-art Bayesian methods. Availability and implementationThe R package implementing NeuPred is available at https://github.com/shuangsong0110/NeuPred. Supplementary informationSupplementary data are available at Bioinformatics online. 

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5. RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB

   https://doi.org/10.1093/bioinformatics/btab813  

   Bittrich, Sebastian; Rose, Yana; Segura, Joan; Lowe, Robert; Westbrook, John D.; Duarte, Jose M.; Burley, Stephen K.; Valencia, ed., Alfonso (December 2021, Bioinformatics)

   Abstract MotivationMembrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task. ResultsWe present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D. Availability and implementationAnnotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar). Supplementary informationSupplementary data are available at Bioinformatics online. 

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