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PLCOjs, a FAIR GWAS web SDK for the NCI Prostate, Lung, Colorectal, and Ovarian Cancer Genetic Atlas Project

Bioinformatics Oxford Journals - Thu, 28/07/2022 - 5:30am
AbstractMotivationThe Division of Cancer Epidemiology and Genetics (DCEG) and the Division of Cancer Prevention (DCP) at the National Cancer Institute (NCI) have recently generated genome-wide association study (GWAS) data for multiple traits in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Genomic Atlas project. The GWAS included 110,000 participants. The dissemination of the genetic association data through a data portal called GWAS Explorer, in a manner that addresses the modern expectations of FAIR reusability by data scientists and engineers, is the main motivation for the development of the open-source JavaScript Software Development Kit (SDK) reported here.ResultsThe PLCO GWAS Explorer resource relies on a public stateless HTTP API deployed as the sole backend service for both the landing page’s web application and third-party analytical workflows. The core PLCOjs SDK is mapped to each of the API methods, and also to each of the reference graphic visualizations in the GWAS Explorer. A few additional visualization methods extend it. As is the norm with Web SDKs, no download or installation is needed and modularization supports targeted code injection for web applications, reactive notebooks (Observable) and node-based Web services.Availabilitycode at https://github.com/episphere/plco; project page at https://episphere.github.io/plcoSupplementary informationTutorial at https://youtu.be/87dXT9YtbfY (17 mins).
Categories: Bioinformatics Trends

ToxSTAR: drug-induced liver injury prediction tool for the web environment

Bioinformatics Oxford Journals - Thu, 28/07/2022 - 5:30am
AbstractSummaryDrug-induced liver injury (DILI) is a challenging endpoint in predictive toxicology because of the complex reactive metabolites that cause liver damage and the wide range of mechanisms involved in the development of the disease. ToxSTAR provides structural similarity-based DILI analysis and in-house DILI prediction models that predict four DILI subtypes (cholestasis, cirrhosis, hepatitis, and steatosis) based on drug and drug metabolite molecules.AvailabilityToxSTAR is freely available at https://toxstar.kitox.re.kr/.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

BioCaster in 2021: Automatic Disease Outbreaks Detection from Global News Media

Bioinformatics Oxford Journals - Thu, 28/07/2022 - 5:30am
AbstractSummaryBioCaster was launched in 2008 to provide an ontology-based text mining system for early disease detection from open news sources. Following a six-year break, we have re-launched the system in 2021. Our goal is to systematically upgrade the methodology using state-of-the-art neural network language models, whilst retaining the original benefits that the system provided in terms of logical reasoning and automated early detection of infectious disease outbreaks. Here we present recent extensions such as neural machine translation in 10 languages, neural classification of disease outbreak reports, and a new cloud-based visualisation dashboard. Furthermore, we discuss our vision for further improvements, including combining risk assessment with event semantics and assessing the risk of outbreaks with multi-granularity. We hope that these efforts will benefit the global public health community.Availability and implementationBioCaster web-portal is freely accessible at http://biocaster.org.
Categories: Bioinformatics Trends

Best templates outperform homology models in predicting the impact of mutations on protein stability

Bioinformatics Oxford Journals - Wed, 27/07/2022 - 5:30am
AbstractMotivationPrediction of protein stability change upon mutation (ΔΔG) is crucial for facilitating protein engineering and understanding of protein folding principles. Robust prediction of protein folding free energy change requires the knowledge of protein three-dimensional (3D) structure. In case protein 3D structure is not available, one can predict the structure from protein sequence; however, the perspectives of ΔΔG predictions for predicted protein structures are unknown. The accuracy of using 3D structures of the best templates for the ΔΔG prediction is also unclear.ResultsTo investigate these questions, we used a representative set of seven diverse and accurate publicly available tools (FoldX, Eris, Rosetta, DDGun, ACDC-NN, ThermoNet, and DynaMut) for stability change prediction combined with AlphaFold or I-Tasser for protein 3D structure prediction. We found that best templates perform consistently better than (or similar to) homology models for all ΔΔG predictors. Our findings imply using the best template structure for the prediction of protein stability change upon mutation if the protein 3D structure is not available.AvailabilityThe data are available at https://github.com/ivankovlab/template-vs-model.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

ColocQuiaL: A QTL-GWAS colocalization pipeline

Bioinformatics Oxford Journals - Wed, 27/07/2022 - 5:30am
AbstractSummaryIdentifying genomic features responsible for genome-wide association study (GWAS) signals has proven to be a difficult challenge; many researchers have turned to colocalization analysis of GWAS signals with expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) to connect GWAS signals to candidate causal genes. The ColocQuiaL pipeline provides a framework to perform these colocalization analyses at scale across the genome and returns summary files and locus visualization plots to allow for detailed review of the results. As an example, we used ColocQuiaL to perform colocalization between the latest type 2 diabetes GWAS data and Genotype-Tissue Expression (GTEx) v8 single-tissue eQTL and sQTL data.Availability and ImplementationColocQuiaL is primarily written in R and is freely available on GitHub: https://github.com/bvoightlab/ColocQuiaL.
Categories: Bioinformatics Trends

TargetMine 2022: A new vision into drug target analysis

Bioinformatics Oxford Journals - Wed, 27/07/2022 - 5:30am
AbstractSummaryWe introduce the newest version of TargetMine, which includes the addition of new visualization options; integration of previously disaggregated functionality; and the migration of the front-end to the newly available Bluegenes service.Availability and ImplementationTargeteMine is accessible online at https://targetmine.mizuguchilab.org/bluegenes. Users do not need to register to use the software. Source code for the different components listed in the article is available from TargetMine’s organizational account at http://github.com/targetmine.Supplementary informationA brief reference user guide is available as Supplementary dataSupplementary data at Bioinformatics online.
Categories: Bioinformatics Trends

Automatic DNA replication tract measurement to assess replication and repair dynamics at the single molecule level

Bioinformatics Oxford Journals - Tue, 26/07/2022 - 5:30am
AbstractMotivationDNA fibre assay has a potential application in genomic medicine, cancer and stem cell research at the single-molecule level. A major challenge for the clinical and research implementation of DNA fibre assays is the slow speed in which manual analysis takes place as it limits the clinical actionability. While automatic detection of DNA fibres speeds up this process considerably, current publicly available software have limited features in terms of their user interface for manual correction of results which in turn limit their accuracy and ability to account for atypical structures that may be important in diagnosis or investigative studies. We recognise that core improvements can be made to the GUI to allow for direct interaction with automatic results to preserve accuracy as well as enhance the versatility of automatic DNA fibre detection for use in variety of situations.ResultsTo address the unmet needs of diverse DNA fibre analysis investigations, we propose DNA Stranding, an open source software that is able to perform accurate fibre length quantification (13.22% mean relative error) and fibre pattern recognition (R > 0.93) with up to 6 fibre patterns supported. With the graphical interface we developed, user can conduct semi-automatic analyses which benefits from the advantages of both automatic and manual processes to improve workflow efficiency without compromising accuracy.AvailabilityThe software package is available at https://github.com/lgole/DNAStranding.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling

Bioinformatics Oxford Journals - Tue, 26/07/2022 - 5:30am
AbstractMotivationRecent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3-dimensional protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions and modeling protein complexes at the proteome level.ResultsWe applied RoseTTAFold and AlphaFold, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death, and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to modeling of the complex structures by AlphaFold, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most top ranked pairs with high contact probability were supported by known protein-protein interactions and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2, and ECSIT-NDUFAF1. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in.AvailabilityData of mitochondrial proteins and their interactions are available at: http://conglab.swmed.edu/mitochondriaSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Overcoming Selection Bias In Synthetic Lethality Prediction

Bioinformatics Oxford Journals - Mon, 25/07/2022 - 5:30am
AbstractMotivationSynthetic lethality (SL) between two genes occurs when simultaneous loss-of-function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalisability in the presence of selection bias in SL data.ResultsWe show that SL data exhibit considerable gene selection bias. Our experiments designed to assess robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorisation) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularised logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue, and healthy donor tissue samples. SBSL models are built and tested using ∼8000 experimentally derived SL pairs across breast, colon, lung, and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalisability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue, and differential expression in tumour samples.Availability and Implementationhttps://github.com/joanagoncalveslab/sbsl
Categories: Bioinformatics Trends

TEspeX: consensus-specific quantification of transposable element expression preventing biases from exonized fragments

Bioinformatics Oxford Journals - Mon, 25/07/2022 - 5:30am
AbstractSummaryTransposable Elements (TEs) play key roles in crucial biological pathways. Therefore, several tools enabling the quantification of their expression were recently developed. However, many of the existing tools lack the capability to distinguish between the transcription of autonomously expressed TEs and TE fragments embedded in canonical coding/non-coding non-TE transcripts. Consequently, an apparent change in the expression of a given TE may simply reflect the variation in the expression of the transcripts containing TE-derived sequences. To overcome this issue, we have developed TEspeX, a pipeline for the quantification of TE expression at the consensus level. TEspeX uses Illumina RNA-seq short reads to quantify TE expression avoiding counting reads deriving from inactive TE fragments embedded in canonical transcripts.Availability and ImplementationThe tool is implemented in python3, distributed under the GNU General Public License (GPL) and available on Github at https://github.com/fansalon/TEspeX (Zenodo URL: https://doi.org/10.5281/zenodo.6800331).Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Prediction of HIV Sensitivity to Monoclonal Antibodies Using Aminoacid Sequences and Deep Learning

Bioinformatics Oxford Journals - Mon, 25/07/2022 - 5:30am
AbstractMotivationKnowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivity can be used to find potent antibody combinations that broadly neutralize multiple and diverse HIV strains. Sensitivity prediction can be combined with other methods such as generative algorithms to design novel antibodies in silico or with feature selection to uncover the sites of interest in the sequence. However, these tools are limited in the absence of in silico accurate prediction methods.ResultsOur method leverages the CATNAP dataset, probably the most comprehensive collection of HIV-antibodies assays and predicts the antibody-virus sensitivity in the form of binary classification. The methods proposed by others focus primarily on analysing the virus sequences. However, our paper demonstrates the advantages gained by modelling the antibody-virus sensitivity as a function of both virus and antibody sequences. The input is formed by the virus envelope and the antibody variable region aminoacid sequences. No structural features are required, which makes our system very practical, given that sequence data is more common than structures. We compare with two other state of the art methods that leverage the same dataset and use sequence data only. Our approach, based on neuronal networks and transfer learning, measures increased predictive performance as measured on a set of 31 specific broadly neutralizing antibodies.Availabilityhttps://github.com/vlad-danaila/deep_hiv_ab_pred/tree/fc-att-fix
Categories: Bioinformatics Trends

Incorporating family disease history and controlling case–control imbalance for population-based genetic association studies

Bioinformatics Oxford Journals - Mon, 25/07/2022 - 5:30am
ABSTRACTMotivationIn the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution.ResultsWe propose a new method for genetic association test with family disease history, mixed-model-based Test with Adjusted Phenotype and Empirical saddlepoint approximation, which controls for increased phenotype correlation by adopting a two-variance-component mixed model, accounts for case–control imbalance by using empirical saddlepoint approximation, and is flexible to incorporate any existing adjusted phenotypes, such as phenotypes from the LT-FH method. We show through simulation studies and analysis of UK Biobank data of white British samples and the Korean Genome and Epidemiology Study of Korean samples that the proposed method is robust and yields better calibration compared to existing methods while gaining power for detection of variant–phenotype associations.Availability and implementationThe summary statistics and code generated in this study are available at https://github.com/styvon/TAPE.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Mocafe: a comprehensive Python library for simulating cancer development with Phase Field Models

Bioinformatics Oxford Journals - Mon, 25/07/2022 - 5:30am
AbstractSummaryMathematical models are effective in studying cancer development at different scales from metabolism to tissue. Phase Field Models (PFMs) have been shown to reproduce accurately cancer growth and other related phenomena, including expression of relevant molecules, extracellular matrix remodeling, and angiogenesis. However, implementations of such models are rarely published, reducing access to these techniques. To reduce this gap, we developed Mocafe, a modular open-source Python package that implements some of the most important PFMs reported in the literature. Mocafe is designed to handle both PFMs purely based on differential equations and hybrid agent-based PFMs. Moreover, Mocafe is meant to be extensible, allowing the inclusion of new models in future releases.Availability and ImplementationMocafe is a Python package based on FEniCS, a popular computing platform for solving Partial Differential Equations. The source code, extensive documentation and demos are provided on GitHub at URL: https://github.com/BioComputingUP/mocafe. Moreover, we uploaded on Zenodo an archive of the package, which is available at DOI: https://doi.org/10.5281/zenodo.6366052.
Categories: Bioinformatics Trends

BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task

Bioinformatics Oxford Journals - Mon, 25/07/2022 - 5:30am
AbstractMotivationBiomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model’s performance.MethodsWe present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model.ResultsWe extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets—BioASQ-7b, BioASQ-8b, and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets.AvailabilityBioADAPT-MRC is freely available as an open-source project at https://github.com/mmahbub/BioADAPT-MRC.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Detecting Retinal Neural and Stromal Cell Classes and Ganglion Cell Subtypes Based on Transcriptome Data with Deep Transfer Learning

Bioinformatics Oxford Journals - Mon, 25/07/2022 - 5:30am
AbstractMotivationTo develop and assess the accuracy of deep learning models that identify different retinal cell types, as well as different retinal ganglion cell (RGC) subtypes, based on patterns of single-cell RNA sequencing (scRNA-seq) in multiple data sets.ResultsDeep domain adaptation models were developed and tested using three different datasets. The first dataset included 44,808 single retinal cells from mice (39 cell types) with 24,658 genes, the second dataset included 6,225 single RGCs from mice (41 subtypes) with 13,616 genes, and the third dataset included 35,699 single RGCs from mice (45 subtypes) with 18,222 genes. We used four loss functions in the learning process to align the source and target distributions, reduce misclassification errors, and maximize robustness. Models were evaluated based on classification accuracy and confusion matrix. The accuracy of the model for correctly classifying 39 different retinal cell types in the first dataset was ∼92%. Accuracy in the second and third datasets reached ∼97% and 97% in correctly classifying 40 and 45 different RGCs subtypes, respectively. Across a range of seven different batches in the first dataset, the accuracy of the lead model ranged from 74% to nearly 100%. The lead model provided high accuracy in identifying retinal cell types and RGC subtypes based on scRNA-seq data. The performance was reasonable based on data from different batches as well. The validated model could be readily applied to scRNA-seq data to identify different retinal cell types and subtypes.AvailabilityThe code and datasets are available on https://github.com/DM2LL/Detecting-Retinal-Cell-Classes-and-Ganglion-Cell-Subtypes. We have also added the class labels of all samples to the datasets. Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

MOCHI, a comprehensive cross-platform tool for amplicon-based microbiota analysis

Bioinformatics Oxford Journals - Mon, 25/07/2022 - 5:30am
AbstractMotivationMicrobiota analyses have important implications for health and science. These analyses make use of 16S/18S rRNA gene sequencing to identify taxa and predict species diversity. However, most available tools for analyzing microbiota data require adept programming skills and in-depth statistical knowledge for proper implementation. While long-read amplicon sequencing can lead to more accurate taxa predictions and is quickly becoming more common, practitioners have no easily accessible tools with which to perform their analyses.ResultsWe present MOCHI, a GUI tool for microbiota amplicon sequencing analysis. MOCHI preprocesses sequences, assigns taxonomy, identifies different abundant species and predicts species diversity and function. It takes either taxonomic count table or FASTQ of partial 16S/18S rRNA or full-length 16S rRNA gene as input. It performs analyses in real-time and visualizes data in both tabular and graphical formats.AvailabilityMOCHI can be installed to run locally or accessed as a web tool at https://mochi.life.nctu.edu.tw.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

An improved residual network using deep fusion for identifying RNA 5-Methylcytosine sites

Bioinformatics Oxford Journals - Fri, 22/07/2022 - 5:30am
AbstractMotivation5-Methylcytosine (m5C) is a crucial post-transcriptional modification. With the development of technology, it is widely found in various RNAs. Numerous studies have indicated that m5C plays an essential role in various activities of organisms, such as tRNA recognition, stabilization of RNA structure, RNA metabolism, and so on. Traditional identification is costly and time-consuming by wet biological experiments. Therefore, computational models are commonly used to identify the m5C sites. Due to the vast computing advantages of deep learning, it is feasible to construct the predictive model through deep learning algorithms.ResultsIn this study, we construct a model to identify m5C based on a deep fusion approach with an improved residual network. Firstly, sequence features are extracted from the RNA sequences using Kmer, K-tuple nucleotide frequency component (KNFC), Pseudo dinucleotide composition (PseDNC), and Physical and chemical property (PCP). Kmer and KNFC extract information from a statistical point of view. PseDNC and PCP extract information from the physicochemical properties of RNA sequences. Then, two parts of information are fused with new features using bidirectional long and short-term memory and attention mechanisms, respectively. Immediately after, the fused features are fed into the improved residual network for classification. Finally, 10-fold cross-validation and independent set testing are used to verify the credibility of the model. The results show that the accuracy reaches 91.87%, 95.55%, 92.27%, and 95.60% on the training sets and independent test sets of A. thaliana and M. musculus, respectively. This is a considerable improvement compared to previous studies and demonstrates the robust performance of our model.AvailabilityThe data and code related to the study are available at https://github.com/alivelxj/m5c-DFRESG.Supplementary informationSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

LanceOtron: a deep learning peak caller for genome sequencing experiments

Bioinformatics Oxford Journals - Fri, 22/07/2022 - 5:30am
AbstractMotivationGenome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome-wide. Regions where these elements are found appear as peaks in the analog signal of an assay’s coverage track, and despite the ease with which humans can visually categorize these patterns, the size of many genomes necessitates algorithmic implementations. Commonly used methods focus on statistical tests to classify peaks, discounting that background signal does not completely follow any known probability distribution and reducing the information-dense peak shapes to simply maximum height. Deep learning has been shown to be highly accurate for many pattern recognition tasks, on par or even exceeding human capabilities, providing an opportunity to reimagine and improve peak calling.ResultsWe present the peak calling framework LanceOtron, which combines deep learning for recognizing peak shape with multifaceted enrichment calculations for assessing significance. In benchmarking ATAC-seq, ChIP-seq, and DNase-seq, LanceOtron outperforms long-standing, gold-standard peak callers through its improved selectivity and near perfect sensitivity.AvailabilityA fully featured web application is freely available from LanceOtron.molbiol.ox.ac.uk, command line interface via python is pip installable from PyPI at https://pypi.org/project/lanceotron/, and source code and benchmarking tests available at https://github.com/LHentges/LanceOtron.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

ShinySyn: a Shiny/R application for the interactive visualization and integration of macro- and micro-synteny data

Bioinformatics Oxford Journals - Fri, 22/07/2022 - 5:30am
AbstractMotivationSynteny analysis is a widely used framework in comparative genomics studies, which provides valuable information to reveal chromosome collinearity in both intra-species and inter-species. Most analysis pipelines, however, are command line-based, making it challenging for biologists to run the algorithms and visualize the results. Existing visualization tools either provide static plots or can only be run on web-based servers and lack efficient visualization methods for associating macro-synteny blocks with individual gene pairs in a micro-synteny region.ResultsWe developed ShinySyn, a Shiny/R application built on the MCscan framework that provides an easy-to-use graphic interface for synteny analyses without requiring any programming skills, to reduce technical barriers. ShinySyn not only provides interactive visualization for macro-synteny, micro-synteny, and genome-level dot views, but it also creates an intuitive representation with a dynamic zooming feature from macro-synteny to individual homologous genes.AvailabilityThe source code and installation instructions for ShinySyn can be accessed via https://github.com/obenno/ShinySyn. A pre-built docker image is also available at https://hub.docker.com/r/obenno/shinysyn. The application can be used locally or seamlessly integrated into any Shiny application server.
Categories: Bioinformatics Trends

REALGAR: a web app of integrated respiratory omics data

Bioinformatics Oxford Journals - Thu, 21/07/2022 - 5:30am
AbstractMotivationIn the post genome-wide association study (GWAS) era, omics techniques have characterized information beyond genomic variants to include cell and tissue type-specific gene transcription, transcription factor binding sites, expression quantitative trait loci (eQTL), and many other biological layers. Analysis of omics data and its integration has in turn improved the functional interpretation of disease-associated genetic variants. Over 170,000 transcriptomic and epigenomic datasets corresponding to studies of various cell and tissue types under specific disease, treatment, and exposure conditions are available in the Gene Expression Omnibus resource. Although these datasets are valuable to guide the design of experimental validation studies to understand the function of disease-associated genetic loci, in their raw form, they are not helpful to experimental researchers who lack adequate computational resources or experience analyzing omics data. We sought to create an integrated re-source of tissue-specific results from omics studies that is guided by disease-specific knowledge to facilitate the design of experiments that can provide biologically meaningful insights into genetic as-sociations.ResultsWe designed the Reducing Associations by Linking Genes And omics Results (REALGAR) web app to provide multi-layered omics information based on results from GWAS, transcriptomic, epigenomic, and eQTL studies for gene-centric analysis and visualization. With a focus on asthma datasets, the integrated omics results it contains facilitate the formulation of hypotheses related to airways disease-associated genes and can be addressed with experimental validation studies.AvailabilityThe REALGAR web app is available at: http://realgar.org/. The source code is available at: https://github.com/HimesGroup/realgar.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

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