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1. UbE3-APA: a bioinformatic strategy to elucidate ubiquitin E3 ligase activities in quantitative proteomics study

   https://doi.org/10.1093/bioinformatics/btac069  

   Gong, Yao ; Chen, Yue ; Vitek, ed., Olga ( February 2022 , Bioinformatics)

   Ubiquitination is widely involved in protein homeostasis and cell signaling. Ubiquitin E3 ligases are critical regulators of ubiquitination that recognize and recruit specific ubiquitination targets for the final rate-limiting step of ubiquitin transfer reactions. Understanding the ubiquitin E3 ligase activities will provide knowledge in the upstream regulator of the ubiquitination pathway and reveal potential mechanisms in biological processes and disease progression. Recent advances in mass spectrometry-based proteomics have enabled deep profiling of ubiquitylome in a quantitative manner. Yet, functional analysis of ubiquitylome dynamics and pathway activity remains challenging.

   Here, we developed a UbE3-APA, a computational algorithm and stand-alone python-based software for Ub E3 ligase Activity Profiling Analysis. Combining an integrated annotation database with statistical analysis, UbE3-APA identifies significantly activated or suppressed E3 ligases based on quantitative ubiquitylome proteomics datasets. Benchmarking the software with published quantitative ubiquitylome analysis confirms the genetic manipulation of SPOP enzyme activity through overexpression and mutation. Application of the algorithm in the re-analysis of a large cohort of ubiquitination proteomics study revealed the activation of PARKIN and the co-activation of other E3 ligases in mitochondria depolarization-induced mitophagy process. We further demonstrated the application of the algorithm in the DIA (data-independent acquisition)-based quantitative ubiquitylome analysis.

   Source code and binaries are freely available for download at URL: https://github.com/Chenlab-UMN/Ub-E3-ligase-Activity-Profiling-Analysis, implemented in python and supported on Linux and MS Windows.

   Supplementary data are available at Bioinformatics online.

    

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2. MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data

   https://doi.org/10.1186/s40168-023-01476-3  

   Ayala-Ortiz, Christian ; Graf-Grachet, Nathalia ; Freire-Zapata, Viviana ; Fudyma, Jane ; Hildebrand, Gina ; AminiTabrizi, Roya ; Howard-Varona, Cristina ; Corilo, Yuri E. ; Hess, Nancy ; Duhaime, Melissa B. ; et al ( February 2023 , Microbiome)

   Microbiomes are now recognized as the main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e., metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking.

   Here, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line-based pipeline for the analysis (e.g., chemodiversity analysis, multivariate statistics), visualization (e.g., Van Krevelen diagrams, elemental and molecular class composition plots), and presentation of direct injection high-resolution FT-ICR MS data sets after molecular formula assignment has been performed. When compared to other available FT-ICR MS software, MetaboDirect is superior in that it requires a single line of code to launch a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network-based approach) that provide an experimental assessment of metabolite connections within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses.

   Application of MetaboDirect to FT-ICR MS-based metabolomic data sets from a marine phage-bacterial infection experiment and aSphagnumleachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. The source code and User’s guide of MetaboDirect are freely available through (https://github.com/Coayala/MetaboDirect) and (https://metabodirect.readthedocs.io/en/latest/), respectively.

    

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3. scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data

   https://doi.org/10.1093/bioinformatics/btac684  

   Gu, Haocheng ; Cheng, Hao ; Ma, Anjun ; Li, Yang ; Wang, Juexin ; Xu, Dong ; Ma, Qin ; Birol, ed., Inanc ( October 2022 , Bioinformatics)

   Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep-learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell–cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized.

   The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks and interpret the cell–cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms.

   scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https://github.com/OSU-BMBL/scGNN2.0.

   Supplementary data are available at Bioinformatics online.

    

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4. 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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5. Tandem‐trapped ion mobility spectrometry/mass spectrometry coupled with ultraviolet photodissociation

   https://doi.org/10.1002/rcm.9192  

   Liu, Fanny C. ; Ridgeway, Mark E. ; Winfred, J. S. Raaj Vellore ; Polfer, Nicolas C. ; Lee, Jusung ; Theisen, Alina ; Wootton, Christopher A. ; Park, Melvin A. ; Bleiholder, Christian ( September 2021 , Rapid Communications in Mass Spectrometry)

   Tandem‐ion mobility spectrometry/mass spectrometry methods have recently gained traction for the structural characterization of proteins and protein complexes. However, ion activation techniques currently coupled with tandem‐ion mobility spectrometry/mass spectrometry methods are limited in their ability to characterize structures of proteins and protein complexes.

   Here, we describe the coupling of the separation capabilities of tandem‐trapped ion mobility spectrometry/mass spectrometry (tTIMS/MS) with the dissociation capabilities of ultraviolet photodissociation (UVPD) for protein structure analysis.

   We establish the feasibility of dissociating intact proteins by UV irradiation at 213 nm between the two TIMS devices in tTIMS/MS and at pressure conditions compatible with ion mobility spectrometry (2–3 mbar). We validate that the fragments produced by UVPD under these conditions result from a radical‐based mechanism in accordance with prior literature on UVPD. The data suggest stabilization of fragment ions produced from UVPD by collisional cooling due to the elevated pressures used here (“UVnoD2”), which otherwise do not survive to detection. The data account for a sequence coverage for the protein ubiquitin comparable to recent reports, demonstrating the analytical utility of our instrument in mobility‐separating fragment ions produced from UVPD.

   The data demonstrate that UVPD carried out at elevated pressures of 2–3 mbar yields extensive fragment ions rich in information about the protein and that their exhaustive analysis requires IMS separation post‐UVPD. Therefore, because UVPD and tTIMS/MS each have been shown to be valuable techniques on their own merit in proteomics, our contribution here underscores the potential of combining tTIMS/MS with UVPD for structural proteomics.

    

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