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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)

   Abstract MotivationUbiquitination 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. ResultsHere, 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. Availability and implementationSource 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 informationSupplementary data are available at Bioinformatics online. 

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2. 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)

   RationaleTandem‐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. MethodsHere, 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. ResultsWe 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. ConclusionsThe 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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3. ExplorATE: a new pipeline to explore active transposable elements from RNA-seq data

   https://doi.org/10.1093/bioinformatics/btac354  

   Femenias, Martin M.; Santos, Juan C.; Sites, Jr, Jack W.; Avila, Luciano J.; Morando, Mariana; Kendziorski, ed., Christina (May 2022, Bioinformatics)

   Abstract MotivationTransposable elements (TEs) are ubiquitous in genomes and many remain active. TEs comprise an important fraction of the transcriptomes with potential effects on the host genome, either by generating deleterious mutations or promoting evolutionary novelties. However, their functional study is limited by the difficulty in their identification and quantification, particularly in non-model organisms. ResultsWe developed a new pipeline [explore active transposable elements (ExplorATE)] implemented in R and bash that allows the quantification of active TEs in both model and non-model organisms. ExplorATE creates TE-specific indexes and uses the Selective Alignment (SA) to filter out co-transcribed transposons within genes based on alignment scores. Moreover, our software incorporates a Wicker-like criteria to refine a set of target TEs and avoid spurious mapping. Based on simulated and real data, we show that the SA strategy adopted by ExplorATE achieved better estimates of non-co-transcribed elements than other available alignment-based or mapping-based software. ExplorATE results showed high congruence with alignment-based tools with and without a reference genome, yet ExplorATE required less execution time. Likewise, ExplorATE expands and complements most previous TE analyses by incorporating the co-transcription and multi-mapping effects during quantification, and provides a seamless integration with other downstream tools within the R environment. Availability and implementationSource code is available at https://github.com/FemeniasM/ExplorATEproject and https://github.com/FemeniasM/ExplorATE_shell_script. Data available on request. Supplementary informationSupplementary 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)

   Abstract MotivationEnvironmental 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. ResultsWe 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). Availability and implementationThe 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 informationSupplementary data are available at Bioinformatics online. 

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5. 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)

   Abstract SummaryThe 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. Availability and implementationscSampler 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 informationSupplementary data are available at Bioinformatics online. 

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