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   https://doi.org/10.1093/bioinformatics/btaa884  

   Adams, Richard H ; Castoe, Todd A ; DeGiorgio, Michael ( December 2020 , Bioinformatics)

   Ponty, Yann (Ed.)

   Abstract Summary Here, we present PhyloWGA, an open source R package for conducting phylogenetic analysis and investigation of whole genome data. Availabilityand implementation Available at Github (https://github.com/radamsRHA/PhyloWGA). Supplementary information Supplementary data are available at Bioinformatics online. 

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2. U-PASS: unified power analysis and forensics for qualitative traits in genetic association studies

   https://doi.org/10.1093/bioinformatics/btz637  

   Gao, Zheng ; Terhorst, Jonathan ; Van Hout, Cristopher V. ; Stoev, Stilian ; Schwartz, ed., Russell ( August 2019 , Bioinformatics)

   Despite the availability of existing calculators for statistical power analysis in genetic association studies, there has not been a model-invariant and test-independent tool that allows for both planning of prospective studies and systematic review of reported findings. In this work, we develop a web-based application U-PASS (Unified Power analysis of ASsociation Studies), implementing a unified framework for the analysis of common association tests for binary qualitative traits. The application quantifies the shared asymptotic power limits of the common association tests, and visualizes the fundamental statistical trade-off between risk allele frequency and odds ratio. The application also addresses the applicability of asymptotics-based power calculations in finite samples, and provides guidelines for single-SNP-based association tests. In addition to designing prospective studies, U-PASS enables researchers to retrospectively assess the statistical validity of previously reported associations.

   U-PASS is an open-source R Shiny application. A live instance is hosted at https://power.stat.lsa.umich.edu. Source is available on https://github.com/Pill-GZ/U-PASS.

   Supplementary data are available at Bioinformatics online.

    

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3. eMPRess: a systematic cophylogeny reconciliation tool

   https://doi.org/10.1093/bioinformatics/btaa978  

   Santichaivekin, Santi ; Yang, Qing ; Liu, Jingyi ; Mawhorter, Ross ; Jiang, Justin ; Wesley, Trenton ; Wu, Yi-Chieh ; Libeskind-Hadas, Ran ( November 2020 , Bioinformatics)

   Russell, Schwartz (Ed.)

   Abstract Summary We describe eMPRess, a software program for phylogenetic tree reconciliation under the duplication-transfer-loss model that systematically addresses the problems of choosing event costs and selecting representative solutions, enabling users to make more robust inferences. Availability and implementation eMPRess is freely available at http://www.cs.hmc.edu/empress. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. Grouper: graph-based clustering and annotation for improved de novo transcriptome analysis

   https://doi.org/10.1093/bioinformatics/bty378  

   Malik, Laraib ; Almodaresi, Fatemeh ; Patro, Rob ; Birol, ed., Inanc ( May 2018 , Bioinformatics)

   De novo transcriptome analysis using RNA-seq offers a promising means to study gene expression in non-model organisms. Yet, the difficulty of transcriptome assembly means that the contigs provided by the assembler often represent a fractured and incomplete view of the transcriptome, complicating downstream analysis. We introduce Grouper, a new method for clustering contigs from de novo assemblies that are likely to belong to the same transcripts and genes; these groups can subsequently be analyzed more robustly. When provided with access to the genome of a related organism, Grouper can transfer annotations to the de novo assembly, further improving the clustering.

   On de novo assemblies from four different species, we show that Grouper is able to accurately cluster a larger number of contigs than the existing state-of-the-art method. The Grouper pipeline is able to map greater than 10% more reads against the contigs, leading to accurate downstream differential expression analyses. The labeling module, in the presence of a closely related annotated genome, can efficiently transfer annotations to the contigs and use this information to further improve clustering. Overall, Grouper provides a complete and efficient pipeline for processing de novo transcriptomic assemblies.

   The Grouper software is freely available at https://github.com/COMBINE-lab/grouper under the 2-clause BSD license.

   Supplementary data are available at Bioinformatics online.

    

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5. Microbench: automated metadata management for systems biology benchmarking and reproducibility in Python

   https://doi.org/10.1093/bioinformatics/btac580  

   Lubbock, Alexander L. R. ; Lopez, Carlos F. ; Kelso, ed., Janet ( August 2022 , Bioinformatics)

   Computational systems biology analyses typically make use of multiple software and their dependencies, which are often run across heterogeneous compute environments. This can introduce differences in performance and reproducibility. Capturing metadata (e.g. package versions, GPU model) currently requires repetitious code and is difficult to store centrally for analysis. Even where virtual environments and containers are used, updates over time mean that versioning metadata should still be captured within analysis pipelines to guarantee reproducibility.

   Microbench is a simple and extensible Python package to automate metadata capture to a file or Redis database. Captured metadata can include execution time, software package versions, environment variables, hardware information, Python version and more, with plugins. We present three case studies demonstrating Microbench usage to benchmark code execution and examine environment metadata for reproducibility purposes.

   Install from the Python Package Index using pip install microbench. Source code is available from https://github.com/alubbock/microbench.

   Supplementary data are available at Bioinformatics online.

    

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