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1. medna-metadata: an open-source data management system for tracking environmental DNA samples and metadata

   https://doi.org/10.1093/bioinformatics/btac556  

   Kimble, M. ; Allers, S. ; Campbell, K. ; Chen, C. ; Jackson, L. M. ; King, B. L. ; Silverbrand, S. ; York, G. ; Beard, K. ; Robinson, ed., Peter ( August 2022 , Bioinformatics)

   Environmental DNA (eDNA), as a rapidly expanding research field, stands to benefit from shared resources including sampling protocols, study designs, discovered sequences, and taxonomic assignments to sequences. High-quality community shareable eDNA resources rely heavily on comprehensive metadata documentation that captures the complex workflows covering field sampling, molecular biology lab work, and bioinformatic analyses. There are limited sources that provide documentation of database development on comprehensive metadata for eDNA and these workflows and no open-source software.

   We present medna-metadata, an open-source, modular system that aligns with Findable, Accessible, Interoperable, and Reusable guiding principles that support scholarly data reuse and the database and application development of a standardized metadata collection structure that encapsulates critical aspects of field data collection, wet lab processing, and bioinformatic analysis. Medna-metadata is showcased with metabarcoding data from the Gulf of Maine (Polinski et al., 2019).

   The source code of the medna-metadata web application is hosted on GitHub (https://github.com/Maine-eDNA/medna-metadata). Medna-metadata is a docker-compose installable package. Documentation can be found at https://medna-metadata.readthedocs.io/en/latest/?badge=latest. The application is implemented in Python, PostgreSQL and PostGIS, RabbitMQ, and NGINX, with all major browsers supported. A demo can be found at https://demo.metadata.maine-edna.org/.

   Supplementary data are available at Bioinformatics online.

    

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2. GPU-powered model analysis with PySB/cupSODA

   https://doi.org/10.1093/bioinformatics/btx420  

   Harris, Leonard A. ; Nobile, Marco S. ; Pino, James C. ; Lubbock, Alexander L. R. ; Besozzi, Daniela ; Mauri, Giancarlo ; Cazzaniga, Paolo ; Lopez, Carlos F. ; Wren, ed., Jonathan ( June 2017 , Bioinformatics)

   A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator.

   The PySB/cupSODA interface has been integrated into the PySB modeling framework (version 1.4.0), which can be installed from the Python Package Index (PyPI) using a Python package manager such as pip. cupSODA source code and precompiled binaries (Linux, Mac OS/X, Windows) are available at github.com/aresio/cupSODA (requires an Nvidia GPU; developer.nvidia.com/cuda-gpus). Additional information about PySB is available at pysb.org.

   Supplementary data are available at Bioinformatics online.

    

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3. scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data

   https://doi.org/10.1093/bioinformatics/btac271  

   Song, Dongyuan ; Xi, Nan_Miles ; Li, Jingyi_Jessica ; Wang, Lin ; Vitek, ed., Olga ( April 2022 , Bioinformatics)

   The number of cells measured in single-cell transcriptomic data has grown fast in recent years. For such large-scale data, subsampling is a powerful and often necessary tool for exploratory data analysis. However, the easiest random subsampling is not ideal from the perspective of preserving rare cell types. Therefore, diversity-preserving subsampling is required for fast exploration of cell types in a large-scale dataset. Here, we propose scSampler, an algorithm for fast diversity-preserving subsampling of single-cell transcriptomic data.

   scSampler is implemented in Python and is published under the MIT source license. It can be installed by “pip install scsampler” and used with the Scanpy pipline. The code is available on GitHub: https://github.com/SONGDONGYUAN1994/scsampler. An R interface is available at: https://github.com/SONGDONGYUAN1994/rscsampler.

   Supplementary data are available at Bioinformatics online.

    

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4. RANGER-DTL 2.0: rigorous reconstruction of gene-family evolution by duplication, transfer and loss

   https://doi.org/10.1093/bioinformatics/bty314  

   Bansal, Mukul S. ; Kellis, Manolis ; Kordi, Misagh ; Kundu, Soumya ; Kelso, ed., Janet ( April 2018 , Bioinformatics)

   RANGER-DTL 2.0 is a software program for inferring gene family evolution using Duplication-Transfer-Loss reconciliation. This new software is highly scalable and easy to use, and offers many new features not currently available in any other reconciliation program. RANGER-DTL 2.0 has a particular focus on reconciliation accuracy and can account for many sources of reconciliation uncertainty including uncertain gene tree rooting, gene tree topological uncertainty, multiple optimal reconciliations and alternative event cost assignments. RANGER-DTL 2.0 is open-source and written in C++ and Python.

   Pre-compiled executables, source code (open-source under GNU GPL) and a detailed manual are freely available from http://compbio.engr.uconn.edu/software/RANGER-DTL/.

   Supplementary data are available at Bioinformatics online.

    

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5. gmxapi: a high-level interface for advanced control and extension of molecular dynamics simulations

   https://doi.org/10.1093/bioinformatics/bty484  

   Irrgang, M. Eric ; Hays, Jennifer M. ; Kasson, Peter M. ; Valencia, ed., Alfonso ( June 2018 , Bioinformatics)

   Molecular dynamics simulations have found use in a wide variety of biomolecular applications, from protein folding kinetics to computational drug design to refinement of molecular structures. Two areas where users and developers frequently need to extend the built-in capabilities of most software packages are implementing custom interactions, for instance biases derived from experimental data, and running ensembles of simulations. We present a Python high-level interface for the popular simulation package GROMACS that i) allows custom potential functions without modifying the simulation package code, ii) maintains the optimized performance of GROMACS and iii) presents an abstract interface to building and executing computational graphs that allows transparent low-level optimization of data flow and task placement. Minimal dependencies make this integrated API for the GROMACS simulation engine simple, portable and maintainable. We demonstrate this API for experimentally-driven refinement of protein conformational ensembles.

   LGPLv2.1 source and instructions are available at https://github.com/kassonlab/gmxapi.

   Supplementary data are available at Bioinformatics online.

    

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