Koina: Democratizing machine learning for proteomics research.

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

CRISPR/Cas9 gene targeting plus nanopore DNA sequencing with the plasmid pBR322 in the classroom.

Both nanopore-based DNA sequencing and CRISPR/Cas-based gene editing represent groundbreaking innovations in molecular biology and genomics, offering unprecedented insights into and tools for working with genetic information. For students, reading, editing, and even writing DNA will be part of their everyday life. We have developed a laboratory procedure that includes (i) the biosynthesis of a guide RNA for, (ii) targeting Cas9 to specifically linearize the pBR322 plasmid, and (iii) the identification of the cutting site through nanopore DNA sequencing. The protocol is intentionally kept simple and requires neither living organisms nor biosafety laboratories. We divided the experimental procedures into separate activities to facilitate customization. Assuming access to a well-equipped molecular biology laboratory, an initial investment of approximately $2,700 is necessary. The material costs for each experiment group amount to around $130. Furthermore, we have developed a freely accessible website (https://dnalesen.hs-mittweida.de) for sequence read analysis and visualization, lowering the required computational skills to a minimum. For those with strong computational skills, we provide instructions for terminal-based data processing. With the presented activities, we aim to provide a hands-on experiment that engages students in modern molecular genetics and motivates them to discuss potential implications. The complete experiment can be accomplished within half a day and has been successfully implemented by us at high schools, in teacher training, and at universities. Our tip is to combine CRISPR/Cas gene targeting with nanopore-based DNA sequencing. As a tool, we provide a website that facilitates sequence data analysis and visualization.

NeuronBridge: an intuitive web application for neuronal morphology search across large data sets.

BACKGROUND: Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome's structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities. RESULTS: Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine. CONCLUSIONS: NeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org .

Eclipse web service application programming interface.

Objective.Low-coupling seamless integration of multiple systems is the core foundation of smart radiotherapy. Following Service-Oriented Architecture style, a set of named operations (Eclipse Web Service API, EWSAPI) was developed for realizing network call of Eclipse.Approach.Under the guidance of Vertical Slice Architecture, EWSAPI was implemented in the C# language and based on ASP .Net Core 6.0. Each operation consists of three components: Request, Endpoint and Response. Depending on the function, the exchanged data for each operation, as input or output parameters, is the empty or a predefined JSON data. These operations were realized and enriched gradually, layer by layer, with reference to the clinical business classification. The business logic of each operation was developed and maintained independently. In situations where Eclipse Scripting API(ESAPI) was required, constraints of ESAPI were followed.Main results.Selected features of Eclipse TPS were encapsulated as standard web services, which can be invocated by other software through network. Several processes for data quality control and planning were encapsulated into interfaces, thereby extending the functionality of Eclipse. Currently, EWSAPI already covers testing of service interface, quality control of radiotherapy data, automation tasks for plan designing and DICOM RT files' transmission. All the interfaces support asynchronous invocation. A separate Eclipse context will be created for each invocation, and is released in the end.Significance.EWSAPI which is a set of standard web services for calling Eclipse features through network is flexible and extensible. It is an efficient way to integration of Eclipse and other systems and will be gradually enriched with the deepening of clinical applications.

MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer.

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.

MRMPro: a web-based tool to improve the speed of manual calibration for multiple reaction monitoring data analysis by mass spectrometry.

BACKGROUND: As a gold-standard quantitative technique based on mass spectrometry, multiple reaction monitoring (MRM) has been widely used in proteomics and metabolomics. In the analysis of MRM data, as no peak picking algorithm can achieve perfect accuracy, manual inspection is necessary to correct the errors. In large cohort analysis scenarios, the time required for manual inspection is often considerable. Apart from the commercial software that comes with mass spectrometers, the open-source and free software Skyline is the most popular software for quantitative omics. However, this software is not optimized for manual inspection of hundreds of samples, the interactive experience also needs to be improved. RESULTS: Here we introduce MRMPro, a web-based MRM data analysis platform for efficient manual inspection. MRMPro supports data analysis of MRM and schedule MRM data acquired by mass spectrometers of mainstream vendors. With the goal of improving the speed of manual inspection, we implemented a collaborative review system based on cloud architecture, allowing multiple users to review through browsers. To reduce bandwidth usage and improve data retrieval speed, we proposed a MRM data compression algorithm, which reduced data volume by more than 60% and 80% respectively compared to vendor and mzML format. To improve the efficiency of manual inspection, we proposed a retention time drift estimation algorithm based on similarity of chromatograms. The estimated retention time drifts were then used for peak alignment and automatic EIC grouping. Compared with Skyline, MRMPro has higher quantification accuracy and better manual inspection support. CONCLUSIONS: In this study, we proposed MRMPro to improve the usability of manual calibration for MRM data analysis. MRMPro is free for non-commercial use. Researchers can access MRMPro through http://mrmpro.csibio.com/ . All major mass spectrometry formats (wiff, raw, mzML, etc.) can be analyzed on the platform. The final identification results can be exported to a common.xlsx format for subsequent analysis.

PMart Web Application: Marketplace for Interactive Analysis of Panomics Data.

PMart is a web-based tool for reproducible quality control, exploratory data analysis, statistical analysis, and interactive visualization of 'omics data, based on the functionality of the pmartR R package. The newly improved user interface supports more 'omics data types, additional statistical capabilities, and enhanced options for creating downloadable graphics. PMart supports the analysis of label-free and isobaric-labeled (e.g., TMT, iTRAQ) proteomics, nuclear magnetic resonance (NMR) and mass-spectrometry (MS)-based metabolomics, MS-based lipidomics, and ribonucleic acid sequencing (RNA-seq) transcriptomics data. At the end of a PMart session, a report is available that summarizes the processing steps performed and includes the pmartR R package functions used to execute the data processing. In addition, built-in safeguards in the backend code prevent users from utilizing methods that are inappropriate based on omics data type. PMart is a user-friendly interface for conducting exploratory data analysis and statistical comparisons of omics data without programming.

Injectiondesign: web service of plate design with optimized stratified block randomization for modern GC/LC-MS-based sample preparation.

BACKGROUND: Plate design is a necessary and time-consuming operation for GC/LC-MS-based sample preparation. The implementation of the inter-batch balancing algorithm and the intra-batch randomization algorithm can have a significant impact on the final results. For researchers without programming skills, a stable and efficient online service for plate design is necessary. RESULTS: Here we describe InjectionDesign, a free online plate design service focused on GC/LC-MS-based multi-omics experiment design. It offers the ability to separate the position design from the sequence design, making the output more compatible with the requirements of a modern mass spectrometer-based laboratory. In addition, it has implemented an optimized block randomization algorithm, which can be better applied to sample stratification with block randomization for an unbalanced distribution. It is easy to use, with built-in support for common instrument models and quick export to a worksheet. CONCLUSIONS: InjectionDesign is an open-source project based on Java. Researchers can get the source code for the project from Github: https://github.com/CSi-Studio/InjectionDesign . A free web service is also provided: http://www.injection.design .

Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets.

Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR (HIV drug Resistance), for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.

FexSplice: A LightGBM-Based Model for Predicting the Splicing Effect of a Single Nucleotide Variant Affecting the First Nucleotide G of an Exon.

Single nucleotide variants (SNVs) affecting the first nucleotide G of an exon (Fex-SNVs) identified in various diseases are mostly recognized as missense or nonsense variants. Their effect on pre-mRNA splicing has been seldom analyzed, and no curated database is available. We previously reported that Fex-SNVs affect splicing when the length of the polypyrimidine tract is short or degenerate. However, we cannot readily predict the splicing effects of Fex-SNVs. We here scrutinized the available literature and identified 106 splicing-affecting Fex-SNVs based on experimental evidence. We similarly identified 106 neutral Fex-SNVs in the dbSNP database with a global minor allele frequency (MAF) of more than 0.01 and less than 0.50. We extracted 115 features representing the strength of splicing cis-elements and developed machine-learning models with support vector machine, random forest, and gradient boosting to discriminate splicing-affecting and neutral Fex-SNVs. Gradient boosting-based LightGBM outperformed the other two models, and the length and nucleotide compositions of the polypyrimidine tract played critical roles in the discrimination. Recursive feature elimination showed that the LightGBM model using 15 features achieved the best performance with an accuracy of 0.80 ± 0.12 (mean and SD), a Matthews Correlation Coefficient (MCC) of 0.57 ± 0.15, an area under the curve of the receiver operating characteristics curve (AUROC) of 0.86 ± 0.08, and an area under the curve of the precision-recall curve (AUPRC) of 0.87 ± 0.09 using a 10-fold cross-validation. We developed a web service program, named FexSplice that accepts a genomic coordinate either on GRCh37/hg19 or GRCh38/hg38 and returns a predicted probability of aberrant splicing of A, C, and T variants.

The VEGA web service: multipurpose online tools for molecular modelling and docking analyses.

The paper presents the VEGA Online web service, which includes a set of freely available tools deriving from the development of the VEGA suite of programs. In detail, the paper is focused on two tools: the VEGA Web Edition (WE) and the Score tool. The former is a versatile file format converter including relevant features for 2D/3D conversion, for surface mapping and for editing/preparing input files. The Score application allows rescoring docking poses and in particular includes the MLP Interactions Scores (MLPInS) for describing hydrophobic interactions. To the best of our knowledge, this web service is the only available resource by which one can calculate both the virtual log P of a given input molecule according to the MLP approach plus the corresponding MLP surface.

A centralized resource for bacterial-fungal interactions research.

Research on bacterial-fungal interactions (BFIs) has revealed that fungi and bacteria frequently interact with one another within diverse ecosystems and microbiomes. Assessing the current state of knowledge within the field of BFI research, particularly with respect to what interactions between bacteria and fungi have been previously described, is very challenging and time consuming. This is largely due to a lack of any centralized resource, with reports of BFIs being spread across publications in numerous journals using non-standardized text to describe the relationships. To address this issue, we have developed the BFI Research Portal, a publicly accessible database of previously reported interactions between bacterial and fungal taxa to serve as a centralized resource for the field. Users can query bacterial or fungal taxa to see what members from the other kingdom have been observed as interaction partners. Search results are accompanied by interactive and intuitive visual outputs, and the database is a dynamic resource that will be updated as new BFIs are reported.

NetGO 3.0: Protein Language Model Improves Large-scale Functional Annotations.

As one of the state-of-the-art automated function prediction (AFP) methods, NetGO 2.0 integrates multi-source information to improve the performance. However, it mainly utilizes the proteins with experimentally supported functional annotations without leveraging valuable information from a vast number of unannotated proteins. Recently, protein language models have been proposed to learn informative representations [e.g., Evolutionary Scale Modeling (ESM)-1b embedding] from protein sequences based on self-supervision. Here, we represented each protein by ESM-1b and used logistic regression (LR) to train a new model, LR-ESM, for AFP. The experimental results showed that LR-ESM achieved comparable performance with the best-performing component of NetGO 2.0. Therefore, by incorporating LR-ESM into NetGO 2.0, we developed NetGO 3.0 to improve the performance of AFP extensively. NetGO 3.0 is freely accessible at https://dmiip.sjtu.edu.cn/ng3.0.

MitoFish, MitoAnnotator, and MiFish Pipeline: Updates in 10 Years.

MitoFish, MitoAnnotator, and MiFish Pipeline are comprehensive databases of fish mitochondrial genomes (mitogenomes), accurate annotation software of fish mitogenomes, and a web platform for metabarcoding analysis of fish mitochondrial environmental DNA (eDNA), respectively. The MitoFish Suite currently receives over 48,000 visits worldwide every year; however, the performance and usefulness of the online platforms can still be improved. Here, we present essential updates on these platforms, including an enrichment of the reference data sets, an enhanced searching function, substantially faster genome annotation and eDNA analysis with the denoising of sequencing errors, and a multisample comparative analysis function. These updates have made our platform more intuitive, effective, and reliable. These updated platforms are freely available at http://mitofish.aori.u-tokyo.ac.jp/.

Detection and Mitigation of IoT-Based Attacks Using SNMP and Moving Target Defense Techniques.

This paper proposes a solution for ensuring the security of IoT devices in the cloud environment by protecting against distributed denial-of-service (DDoS) and false data injection attacks. The proposed solution is based on the integration of simple network management protocol (SNMP), Kullback-Leibler distance (KLD), access control rules (ACL), and moving target defense (MTD) techniques. The SNMP and KLD techniques are used to detect DDoS and false data sharing attacks, while the ACL and MTD techniques are applied to mitigate these attacks by hardening the target and reducing the attack surface. The effectiveness of the proposed framework is validated through experimental simulations on the Amazon Web Service (AWS) platform, which shows a significant reduction in attack probabilities and delays. The integration of IoT and cloud technologies is a powerful combination that can deliver customized and critical solutions to major business vendors. However, ensuring the confidentiality and security of data among IoT devices, storage, and access to the cloud is crucial to maintaining trust among internet users. This paper demonstrates the importance of implementing robust security measures to protect IoT devices in the cloud environment and highlights the potential of the proposed solution in protecting against DDoS and false data injection attacks.

Web Services for RNA-RNA Interaction Prediction.

Non-coding RNAs have various biological functions such as translational regulation, and RNA-RNA interactions play essential roles in the mechanisms of action of these RNAs. Therefore, RNA-RNA interaction prediction is an important problem in bioinformatics, and many tools have been developed for the computational prediction of RNA-RNA interactions. In addition to the development of novel algorithms with high accuracy, the development and maintenance of web services is essential for enhancing usability by experimental biologists. In this review, we survey web services for RNA-RNA interaction predictions and introduce how to use primary web services. We present various prediction tools, including general interaction prediction tools, prediction tools for specific RNA classes, and RNA-RNA interaction-based RNA design tools. Additionally, we discuss the future perspectives of the development of RNA-RNA interaction prediction tools and the sustainability of web services.

An Integrated Framework for Fault Resolution in Business Processes.

Cloud and edge-computing based platforms have enabled rapid development of distributed business process (BP) applications in a plug and play manner. However, these platforms do not provide the needed capabilities for identifying or repairing faults in BPs. Faults in BP may occur due to errors made by BP designers because of their lack of understanding of the underlying component services, misconfiguration of these services, or incorrect/incomplete BP workflow specifications. Such faults may not be discovered at design or development stage and may occur at runtime. In this paper, we present a unified framework for automated fault resolution in BPs. The proposed framework employs a novel and efficient fault resolution approach that extends the generate-and-validate program repair approach. In addition, we propose a hybrid approach that performs fault resolution by analyzing a faulty BP in isolation as well as by comparing with other BPs using similar services. This hybrid approach results in improved accuracy and broader coverage of fault types. We also perform an extensive experimental evaluation to compare the effectiveness of the proposed approach using a dataset of 208 faulty BPs.

Plant_SNP_TATA_Z-Tester: A Web Service That Unequivocally Estimates the Impact of Proximal Promoter Mutations on Plant Gene Expression.

Synthetic targeted optimization of plant promoters is becoming a part of progress in mainstream postgenomic agriculture along with hybridization of cultivated plants with wild congeners, as well as marker-assisted breeding. Therefore, here, for the first time, we compiled all the experimental data-on mutational effects in plant proximal promoters on gene expression-that we could find in PubMed. Some of these datasets cast doubt on both the existence and the uniqueness of the sought solution, which could unequivocally estimate effects of proximal promoter mutation on gene expression when plants are grown under various environmental conditions during their development. This means that the inverse problem under study is ill-posed. Furthermore, we found experimental data on in vitro interchangeability of plant and human TATA-binding proteins allowing the application of Tikhonov's regularization, making this problem well-posed. Within these frameworks, we created our Web service Plant_SNP_TATA_Z-tester and then determined the limits of its applicability using those data that cast doubt on both the existence and the uniqueness of the sought solution. We confirmed that the effects (of proximal promoter mutations on gene expression) predicted by Plant_SNP_TATA_Z-tester correlate statistically significantly with all the experimental data under study. Lastly, we exemplified an application of Plant_SNP_TATA_Z-tester to agriculturally valuable mutations in plant promoters.

Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization.

Complex biomedical data generated during clinical, omics and mechanism-based experiments have increasingly been exploited through cloud- and visualization-based data mining techniques. However, the scientific community still lacks an easy-to-use web service for the comprehensive visualization of biomedical data, particularly high-quality and publication-ready graphics that allow easy scaling and updatability according to user demands. Therefore, we propose a community-driven modern web service, Hiplot (https://hiplot.org), with concise and top-quality data visualization applications for the life sciences and biomedical fields. This web service permits users to conveniently and interactively complete a few specialized visualization tasks that previously could only be conducted by senior bioinformatics or biostatistics researchers. It covers most of the daily demands of biomedical researchers with its equipped 240+ biomedical data visualization functions, involving basic statistics, multi-omics, regression, clustering, dimensional reduction, meta-analysis, survival analysis, risk modelling, etc. Moreover, to improve the efficiency in use and development of plugins, we introduced some core advantages on the client-/server-side of the website, such as spreadsheet-based data importing, cross-platform command-line controller (Hctl), multi-user plumber workers, JavaScript Object Notation-based plugin system, easy data/parameters, results and errors reproduction and real-time updates mode. Meanwhile, using demo/real data sets and benchmark tests, we explored statistical parameters, cancer genomic landscapes, disease risk factors and the performance of website based on selected native plugins. The statistics of visits and user numbers could further reflect the potential impact of this web service on relevant fields. Thus, researchers devoted to life and data sciences would benefit from this emerging and free web service.

Corrigendum: A Web Server for GPCR-GPCR Interaction Pair Prediction.

[This corrects the article DOI: 10.3389/fendo.2022.825195.].