Web Server Interface Research Articles

INTREPPPID-an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction.

An overwhelming majority of protein-protein interaction (PPI) studies are conducted in a select few model organisms largely due to constraints in time and cost of the associated 'wet lab' experiments. In silico PPI inference methods are ideal tools to overcome these limitations, but often struggle with cross-species predictions. We present INTREPPPID, a method that incorporates orthology data using a new 'quintuplet' neural network, which is constructed with five parallel encoders with shared parameters. INTREPPPID incorporates both a PPI classification task and an orthologous locality task. The latter learns embeddings of orthologues that have small Euclidean distances between them and large distances between embeddings of all other proteins. INTREPPPID outperforms all other leading PPI inference methods tested on both the intraspecies and cross-species tasks using strict evaluation datasets. We show that INTREPPPID's orthologous locality loss increases performance because of the biological relevance of the orthologue data and not due to some other specious aspect of the architecture. Finally, we introduce PPI.bio and PPI Origami, a web server interface for INTREPPPID and a software tool for creating strict evaluation datasets, respectively. Together, these two initiatives aim to make both the use and development of PPI inference tools more accessible to the community.

CORDAX web server: an online platform for the prediction and 3D visualization of aggregation motifs in protein sequences.

Proteins, the molecular workhorses of biological systems, execute a multitude of critical functions dictated by their precise three-dimensional structures. In a complex and dynamic cellular environment, proteins can undergo misfolding, leading to the formation of aggregates that take up various forms, including amorphous and ordered aggregation in the shape of amyloid fibrils. This phenomenon is closely linked to a spectrum of widespread debilitating pathologies, such as Alzheimer's disease, Parkinson's disease, type-II diabetes, and several other proteinopathies, but also hampers the engineering of soluble agents, as in the case of antibody development. As such, the accurate prediction of aggregation propensity within protein sequences has become pivotal due to profound implications in understanding disease mechanisms, as well as in improving biotechnological and therapeutic applications. We previously developed Cordax, a structure-based predictor that utilizes logistic regression to detect aggregation motifs in protein sequences based on their structural complementarity to the amyloid cross-beta architecture. Here, we present a dedicated web server interface for Cordax. This online platform combines several features including detailed scoring of sequence aggregation propensity, as well as 3D visualization with several customization options for topology models of the structural cores formed by predicted aggregation motifs. In addition, information is provided on experimentally determined aggregation-prone regions that exhibit sequence similarity to predicted motifs, scores, and links to other predictor outputs, as well as simultaneous predictions of relevant sequence propensities, such as solubility, hydrophobicity, and secondary structure propensity. The Cordax webserver is freely accessible at https://cordax.switchlab.org/.

Bacmethy: A novel and convenient tool for investigating bacterial DNA methylation pattern and their transcriptional regulation effects

AbstractDNA methylation serves as the primary mode of epigenetic regulation in prokaryotes, particularly through transcriptional regulation. With the rapid implementation of third‐generation sequencing technology, we are currently experiencing a golden age of bacterial epigenomics. However, there has been a lack of comprehensive research exploring the versatility and consequential impact of bacterial DNA methylome on cellular and physiological functions. There is a critical need for a user‐friendly bioinformatics tool that can effectively characterize DNA methylation modification features and predict the regulation patterns. To address this gap, the current study introduces Bacmethy, an innovative tool that utilizes SMRT‐seq data and offers a range of analytical modules. First, the tool classifies methylation sites in the genome, highlighting the distinct regulations present under varying modification fractions and location enrichment. Furthermore, this tool enables us to identify regulatory region methylation and potential cis and trans interactions between methylation sites and regulatory effectors. Using benchmark data sets and our data, we show that our tool facilitates the understanding of the distinctive traits of DNA methylation modifications and predicts transcriptional regulation effects on important physiological and pathological functions. Bacmethy code is freely available, and the Docker image is downloadable. Bacmethy has been made available as a user‐friendly web server interface at https://bacmethy.med.sustech.edu.cn.

LED Control System Using Arduino Wemos D1 R1 Based on Web Server Communication Via Internet of Things (IoT)

The Internet of Things (IoT) is a concept that connects physical devices to an Internet network, enabling data exchange and remote control. In this study, an LED control system was developed using the Arduino Wemos D1 R1, which is based on communication via a web server using IoT. This system uses a web server as a user interface to remotely control the LEDs. Users can access the web server via internet-connected devices, such as smartphones or laptops. They can set the LED status (on or off) by clicking the button provided on the web server interface. This research can be used as a basis for developing other device control systems using the Arduino Wemos D1 R1 and the IoT concept. In further development, this system can be improved by adding sensors or other additional features for more complex applications.

Epitope1D: accurate taxonomy-aware B-cell linear epitope prediction.

The ability to identify B-cell epitopes is an essential step in vaccine design, immunodiagnostic tests and antibody production. Several computational approaches have been proposed to identify, from an antigen protein or peptide sequence, which residues are more likely to be part of an epitope, but have limited performance on relatively homogeneous data sets and lack interpretability, limiting biological insights that could otherwise be obtained. To address these limitations, we have developed epitope1D, an explainable machine learning method capable of accurately identifying linear B-cell epitopes, leveraging two new descriptors: a graph-based signature representation of protein sequences, based on our well-established Cutoff Scanning Matrix algorithm and Organism Ontology information. Our model achieved Areas Under the ROC curve of up to 0.935 on cross-validation and blind tests, demonstrating robust performance. A comprehensive comparison to alternative methods using distinct benchmark data sets was also employed, with our model outperforming state-of-the-art tools. epitope1D represents not only a significant advance in predictive performance, but also allows biologically meaningful features to be combined and used for model interpretation. epitope1D has been made available as a user-friendly web server interface and application programming interface at https://biosig.lab.uq.edu.au/epitope1d/.

Developing a Laser Induced Liquid Beam Ion Desorption Spectral Database as Reference for Spaceborne Mass Spectrometers

AbstractSpaceborne impact ionization mass spectrometers, such as the Cosmic Dust Analyzer on board the past Cassini spacecraft or the SUrface Dust Analyzer being built for NASA's upcoming Europa Clipper mission, are of crucial importance for the exploration of icy moons in the Solar System, such as Saturn's moon Enceladus or Jupiter's moon Europa. For the interpretation of data produced by these instruments, analogue experiments on Earth are essential. To date, thousands of laboratory mass spectra have been recorded with an analogue experiment for impact ionization mass spectrometers. Simulation of mass spectra of ice grains in space is achieved by a Laser Induced Liquid Beam Ion Desorption (LILBID) approach. The desorbed cations or anions are analyzed in a time‐of‐flight mass spectrometer. The amount of unstructured raw data is increasingly challenging to sort, process, interpret and compare with data from space. Thus far this has been achieved manually for individual mass spectra because no database containing the recorded reference spectra was available. Here we describe the development of a comprehensive, extendable database containing cation and anion mass spectra from the laboratory LILBID facility. The database is based on a Relational Database Management System with a web server interface and enables filtering of the laboratory data using a wide range of parameters. The mass spectra can be compared not only with data from past and future space missions but also mass spectral data generated by other, terrestrial, techniques. The validated and approved subset of the database is available for general public (https://lilbid-db.planet.fu-berlin.de).

PreBINDS: An Interactive Web Tool to Create Appropriate Datasets for Predicting Compound–Protein Interactions

Given the abundant computational resources and the huge amount of data of compound–protein interactions (CPIs), constructing appropriate datasets for learning and evaluating prediction models for CPIs is not always easy. For this study, we have developed a web server to facilitate the development and evaluation of prediction models by providing an appropriate dataset according to the task. Our web server provides an environment and dataset that aid model developers and evaluators in obtaining a suitable dataset for both proteins and compounds, in addition to attributes necessary for deep learning. With the web server interface, users can customize the CPI dataset derived from ChEMBL by setting positive and negative thresholds to be adjusted according to the user’s definitions. We have also implemented a function for graphic display of the distribution of activity values in the dataset as a histogram to set appropriate thresholds for positive and negative examples. These functions enable effective development and evaluation of models. Furthermore, users can prepare their task-specific datasets by selecting a set of target proteins based on various criteria such as Pfam families, ChEMBL’s classification, and sequence similarities. The accuracy and efficiency of in silico screening and drug design using machine learning including deep learning can therefore be improved by facilitating access to an appropriate dataset prepared using our web server (https://binds.lifematics.work/).

ModFOLD8: accurate global and local quality estimates for 3D protein models.

Methods for estimating the quality of 3D models of proteins are vital tools for driving the acceptance and utility of predicted tertiary structures by the wider bioscience community. Here we describe the significant major updates to ModFOLD, which has maintained its position as a leading server for the prediction of global and local quality of 3D protein models, over the past decade (>20 000 unique external users). ModFOLD8 is the latest version of the server, which combines the strengths of multiple pure-single and quasi-single model methods. Improvements have been made to the web server interface and there has been successive increases in prediction accuracy, which were achieved through integration of newly developed scoring methods and advanced deep learning-based residue contact predictions. Each version of the ModFOLD server has been independently blind tested in the biennial CASP experiments, as well as being continuously evaluated via the CAMEO project. In CASP13 and CASP14, the ModFOLD7 and ModFOLD8 variants ranked among the top 10 quality estimation methods according to almost every official analysis. Prior to CASP14, ModFOLD8 was also applied for the evaluation of SARS-CoV-2 protein models as part of CASP Commons 2020 initiative. The ModFOLD8 server is freely available at: https://www.reading.ac.uk/bioinf/ModFOLD/.

A Classification Javanese Letters Model using a Convolutional Neural Network with KERAS Framework

One of the essential things in research engaged in the field of Computer Vision is image classification, wherein previous studies models were used to classify an image. Javanese Letters, in this case, is a basis of a sentence that uses the Javanese language. The problem is that Javanese sentences are often found in Yogyakarta, especially the use of name tourist attractions, making it difficult for tourists to translate these Javanese sentences. Therefore, in this study, we try to create a Javanese character classification model hoping that this model will later be used as a basis for developing research into the next stage. One of the most popular methods lately for dealing with image classification problems is to use Deep Learning techniques, namely using the Convolutional Neural Network (CNN) method using the KERAS framework. The simplicity of the training model and dataset used in this work brings the advantage of computation weight and time. The model has an accuracy of 86.68% using 1000 datasets and conducted for 50 epochs based on the results. The average inference time with the same specification mentioned above is 0.57 seconds, and again the fast inference time is because of the simplicity of the model and dataset toolbar. This model's advantages with fast and light computation time bring the possibility to use this model on devices with limited computation resources such as mobile devices, familiar web server interface, and internet-of-things devices.

Long-read amplicon denoising.

Long-read next-generation amplicon sequencing shows promise for studying complete genes or genomes from complex and diverse populations. Current long-read sequencing technologies have challenging error profiles, hindering data processing and incorporation into downstream analyses. Here we consider the problem of how to reconstruct, free of sequencing error, the true sequence variants and their associated frequencies from PacBio reads. Called ‘amplicon denoising’, this problem has been extensively studied for short-read sequencing technologies, but current solutions do not always successfully generalize to long reads with high indel error rates. We introduce two methods: one that runs nearly instantly and is very accurate for medium length reads and high template coverage, and another, slower method that is more robust when reads are very long or coverage is lower. On two Mock Virus Community datasets with ground truth, each sequenced on a different PacBio instrument, and on a number of simulated datasets, we compare our two approaches to each other and to existing algorithms. We outperform all tested methods in accuracy, with competitive run times even for our slower method, successfully discriminating templates that differ by a just single nucleotide. Julia implementations of Fast Amplicon Denoising (FAD) and Robust Amplicon Denoising (RAD), and a webserver interface, are freely available.

SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions.

Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here, we present a webserver that implements this method efficiently. With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than 10-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry. Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/. Supplementary data are available at Bioinformatics online.

Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility.

Protein aggregation is a hallmark of a growing number of human disorders and constitutes a major bottleneck in the manufacturing of therapeutic proteins. Therefore, there is a strong need of in-silico methods that can anticipate the aggregative properties of protein variants linked to disease and assist the engineering of soluble protein-based drugs. A few years ago, we developed a method for structure-based prediction of aggregation properties that takes into account the dynamic fluctuations of proteins. The method has been made available as the Aggrescan3D (A3D) web server and applied in numerous studies of protein structure-aggregation relationship. Here, we present a major update of the A3D web server to version 2.0. The new features include: extension of dynamic calculations to significantly larger and multimeric proteins, simultaneous prediction of changes in protein solubility and stability upon mutation, rapid screening for functional protein variants with improved solubility, a REST-ful service to incorporate A3D calculations in automatic pipelines, and a new, enhanced web server interface. A3D 2.0 is freely available at: http://biocomp.chem.uw.edu.pl/A3D2/

Patient health monitoring system based on zigbee communication protocol

This study presents a review of ZigBee communication protocol and a wireless sensor network (WSN) for monitoring patients' physiological conditions continuously using ZigBee communication protocol. The physiological conditions monitored are body temperature and pulse rate. The body temperature and pulse rate are monitored by ZigBee sensor nodes for each patient, and the output is transmitted via ZigBee communication protocol to a ZigBee base node. A peer-to-peer network topology is implemented with three ZigBee sensor nodes and a ZigBee base node. The observed physiological signals are then sent from the ZigBee base node to a remote wireless monitor hosted in a personal computer via a universal serial bus. The remote wireless monitor is made of a web server and web interface. The medical attendant can then simultaneously monitor remotely multiple patients from one patient monitoring station.

CABS-flex 2.0: a web server for fast simulations of flexibility of protein structures.

Classical simulations of protein flexibility remain computationally expensive, especially for large proteins. A few years ago, we developed a fast method for predicting protein structure fluctuations that uses a single protein model as the input. The method has been made available as the CABS-flex web server and applied in numerous studies of protein structure-function relationships. Here, we present a major update of the CABS-flex web server to version 2.0. The new features include: extension of the method to significantly larger and multimeric proteins, customizable distance restraints and simulation parameters, contact maps and a new, enhanced web server interface. CABS-flex 2.0 is freely available at http://biocomp.chem.uw.edu.pl/CABSflex2.

OncomiR: an online resource for exploring pan-cancer microRNA dysregulation.

Dysregulation of microRNAs (miRNAs) is extensively associated with cancer development and progression. miRNAs have been shown to be biomarkers for predicting tumor formation and outcome. However, identification of the relationships between miRNA expression and tumor characteristics can be difficult and time-consuming without appropriate bioinformatics expertise. To address this issue, we present OncomiR, an online resource for exploring miRNA dysregulation in cancer. Using combined miRNA-seq, RNA-seq and clinical data from The Cancer Genome Atlas, we systematically performed statistical analyses to identify dysregulated miRNAs that are associated with tumor development and progression in most major cancer types. Additional analyses further identified potential miRNA-gene target interactions in tumors. These results are stored in a backend database and presented through a web server interface. Moreover, through a backend bioinformatics pipeline, OncomiR can also perform dynamic analysis with custom miRNA selections for in-depth characterization of miRNAs in cancer. The OncomiR website is freely accessible at www.oncomir.org. xiaowei.wang@wustl.edu. Supplementary data are available at Bioinformatics online.

Open Polar Server (OPS)—An Open Source Infrastructure for the Cryosphere Community

The Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas has collected approximately 1000 terabytes (TB) of radar depth sounding data over the Arctic and Antarctic ice sheets since 1993 in an effort to map the thickness of the ice sheets and ultimately understand the impacts of climate change and sea level rise. In addition to data collection, the storage, management, and public distribution of the dataset are also primary roles of the CReSIS. The Open Polar Server (OPS) project developed a free and open source infrastructure to store, manage, analyze, and distribute the data collected by CReSIS in an effort to replace its current data storage and distribution approach. The OPS infrastructure includes a spatial database management system (DBMS), map and web server, JavaScript geoportal, and MATLAB application programming interface (API) for the inclusion of data created by the cryosphere community. Open source software including GeoServer, PostgreSQL, PostGIS, OpenLayers, ExtJS, GeoEXT and others are used to build a system that modernizes the CReSIS data distribution for the entire cryosphere community and creates a flexible platform for future development. Usability analysis demonstrates the OPS infrastructure provides an improved end user experience. In addition, interpolating glacier topography is provided as an application example of the system.

SG-ADVISER CNV: copy-number variant annotation and interpretation.

Copy-number variants have been associated with a variety of diseases, especially cancer, autism, schizophrenia, and developmental delay. The majority of clinically relevant events occur de novo, necessitating the interpretation of novel events. In this light, we present the Scripps Genome ADVISER CNV annotation pipeline and Web server, which aims to fill the gap between copy number variant detection and interpretation by performing in-depth annotations and functional predictions for copy number variants. The Scripps Genome ADVISER CNV suite includes a Web server interface to a high-performance computing environment for calculations of annotations and a table-based user interface that allows for the execution of numerous annotation-based variant filtration strategies and statistics. The annotation results include details regarding location, impact on the coding portion of genes, allele frequency information (including allele frequencies from the Scripps Wellderly cohort), and overlap information with other reference data sets (including ClinVar, DGV, DECIPHER). A summary variant classification is produced (ADVISER score) based on the American College of Medical Genetics and Genomics scoring guidelines. We demonstrate >90% sensitivity/specificity for detection of pathogenic events. Scripps Genome ADVISER CNV is designed to allow users with no prior bioinformatics expertise to manipulate large volumes of copy-number variant data. Scripps Genome ADVISER CNV is available at http://genomics.scripps.edu/ADVISER/.

MiRDB: an online resource for microRNA target prediction and functional annotations.

MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in many physiological and disease processes. One major challenge in miRNA studies is the identification of genes regulated by miRNAs. To this end, we have developed an online resource, miRDB (http://mirdb.org), for miRNA target prediction and functional annotations. Here, we describe recently updated features of miRDB, including 2.1 million predicted gene targets regulated by 6709 miRNAs. In addition to presenting precompiled prediction data, a new feature is the web server interface that allows submission of user-provided sequences for miRNA target prediction. In this way, users have the flexibility to study any custom miRNAs or target genes of interest. Another major update of miRDB is related to functional miRNA annotations. Although thousands of miRNAs have been identified, many of the reported miRNAs are not likely to play active functional roles or may even have been falsely identified as miRNAs from high-throughput studies. To address this issue, we have performed combined computational analyses and literature mining, and identified 568 and 452 functional miRNAs in humans and mice, respectively. These miRNAs, as well as associated functional annotations, are presented in the FuncMir Collection in miRDB.

SeAMotE: a method for high-throughput motif discovery in nucleic acid sequences.

BackgroundThe large amount of data produced by high-throughput sequencing poses new computational challenges. In the last decade, several tools have been developed for the identification of transcription and splicing factor binding sites.ResultsHere, we introduce the SeAMotE (Sequence Analysis of Motifs Enrichment) algorithm for discovery of regulatory regions in nucleic acid sequences. SeAMotE provides (i) a robust analysis of high-throughput sequence sets, (ii) a motif search based on pattern occurrences and (iii) an easy-to-use web-server interface. We applied our method to recently published data including 351 chromatin immunoprecipitation (ChIP) and 13 crosslinking immunoprecipitation (CLIP) experiments and compared our results with those of other well-established motif discovery tools. SeAMotE shows an average accuracy of 80% in finding discriminative motifs and outperforms other methods available in literature.ConclusionsSeAMotE is a fast, accurate and flexible algorithm for the identification of sequence patterns involved in protein-DNA and protein-RNA recognition. The server can be freely accessed at http://s.tartaglialab.com/new_submission/seamote.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-925) contains supplementary material, which is available to authorized users.

Performance Evaluation for VoIP on Campus

The VoIP Campus implementation is to make the existing VoIP technology become more beneficial for campus stake holder. This VoIP on Campus (VoC) technology make use of a web server, facilitating users to carry out VoIP registration, get and changing account, and also to see others who have register and active in this VoIP network. Basically, this VoC infrastructure uses asterisk as VoIP server and playVoIP as web server interface, those programs included in a server computer. Furthermore, the server interconnected with several servers, such as, PBX, SMS gateway, ENUM server, softphone and smartphone. At this moment, VoC network serve locally, but next time it will be developed so that it could be served in public network, and further VoC network could be connected to VoIP Rakyat, the biggest VoIP network in Indonesia. In this research, VoC network have been tested for several QoS parameters, such as throughput, delay, jitter, packet loss, and MOS. Average value for each parameter, are : 27 kbps throughput, 20.08 ms delay, 3.54 ms jitter, 0.08% packet loss, and 3.3 MOS. Those results indicates that VoC network have a good performance. Â