Disease progression associated cytokines in COVID-19 patients with deteriorating and recovering health conditions.

Understanding the immune response to COVID-19 is challenging due to its high variability among individuals. To identify differentially expressed cytokines between the deteriorating and recovering phases, we analyzed the Electronic Health Records (EHR) and cytokine profile data in a COVID-19 cohort of 444 infected patients and 145 non-infected healthy individuals. We categorized each patient's progression into Deterioration Phase (DP) and Recovery Phase (RP) using longitudinal neutrophil, lymphocyte and lactate dehydrogenase levels.

Dihydrotestosterone Enhances MICA-Mediated Immune Responses to Epstein-Barr Virus-Associated Gastric Carcinoma.

Background: Epstein-Barr virus-associated gastric carcinoma (EBVaGC) is a subset of gastric cancers linked to EBV infection. While the role of male hormones in cancers such as prostate, breast, and ovarian cancers is well-studied, their impact on EBVaGC remains less understood. This study aims to examine the effect of dihydrotestosterone (DHT) on EBVaGC, particularly focusing on its influence on the immune response.

Denoiseit: denoising gene expression data using rank based isolation trees.

Background: Selecting informative genes or eliminating uninformative ones before any downstream gene expression analysis is a standard task with great impact on the results. A carefully curated gene set significantly enhances the likelihood of identifying meaningful biomarkers.

Method: In contrast to the conventional forward gene search methods that focus on selecting highly informative genes, we propose a backward search method, DenoiseIt, that aims to remove potential outlier genes yielding a robust gene set with reduced noise.

Exosome from IFN-γ-Primed Induced Pluripotent Stem Cell-Derived Mesenchymal Stem Cells Improved Skin Inflammation and Barrier Function.

The pathogenesis of atopic dermatitis (AD) is multifactorial, including immune dysregulation and epidermal barrier defects, and a novel therapeutic modality that can simultaneously target multiple pathways is needed. We investigated the therapeutic effects of exosomes (IFN-γ-iExo) secreted from IFN-γ-primed induced pluripotent stem cell-derived mesenchymal stem cells (iMSC) in mice with -induced AD. IFN-γ-iExo was epicutaneously administered to mice with AD-like skin lesions.

MOPA: An integrative multi-omics pathway analysis method for measuring omics activity.

Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score).

Characterization of a new CCCTC-binding factor binding site as a dual regulator of Epstein-Barr virus latent infection.

Distinct viral gene expression characterizes Epstein-Barr virus (EBV) infection in EBV-producing marmoset B-cell (B95-8) and EBV-associated gastric carcinoma (SNU719) cell lines. CCCTC-binding factor (CTCF) is a structural chromatin factor that coordinates chromatin interactions in the EBV genome. Chromatin immunoprecipitation followed by sequencing against CTCF revealed 16 CTCF binding sites in the B95-8 and SNU719 EBV genomes.

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective.

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps.

TimesVector-Web: A Web Service for Analysing Time Course Transcriptome Data with Multiple Conditions.

From time course gene expression data, we may identify genes that modulate in a certain pattern across time. Such patterns are advantageous to investigate the transcriptomic response to a certain condition. Especially, it is of interest to compare two or more conditions to detect gene expression patterns that significantly differ between them.

Corrigendum: MONTI: A Multi-Omics Non-Negative Tensor Decomposition Framework for Gene-Level Integrative Analysis.

[This corrects the article DOI: 10.3389/fgene.2021.

MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative Analysis.

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data.

DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration.

Pharmacogenomics is the study of how genes affect a person's response to drugs. Thus, understanding the effect of drug at the molecular level can be helpful in both drug discovery and personalized medicine. Over the years, transcriptome data upon drug treatment has been collected and several databases compiled before drug treatment cancer cell multi-omics data with drug sensitivity ( , AUC) or time-series transcriptomic data after drug treatment.

Development of genetic quality tests for good manufacturing practice-compliant induced pluripotent stem cells and their derivatives.

Although human induced pluripotent stem cell (hiPSC) lines are karyotypically normal, they retain the potential for mutation in the genome. Accordingly, intensive and relevant quality controls for clinical-grade hiPSCs remain imperative. As a conceptual approach, we performed RNA-seq-based broad-range genetic quality tests on GMP-compliant human leucocyte antigen (HLA)-homozygous hiPSCs and their derivatives under postdistribution conditions to investigate whether sequencing data could provide a basis for future quality control.

HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis.

Background: Integrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples.

Results: HTRgene is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition.

Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data.

Motivation: Biological pathways are extensively used for the analysis of transcriptome data to characterize biological mechanisms underlying various phenotypes. There are a number of computational tools that summarize transcriptome data at the pathway level. However, there is no comparative study on how well these tools produce useful information at the cohort level, enabling comparison of many samples or patients.

The mRNA and miRNA transcriptomic landscape of Panax ginseng under the high ambient temperature.

Background: Ginseng is a popular traditional herbal medicine in north-eastern Asia. It has been used for human health for over thousands of years. With the rise in global temperature, the production of Korean ginseng (Panax ginseng C.

Clustering and evolutionary analysis of small RNAs identify regulatory siRNA clusters induced under drought stress in rice.

Motivation: Drought tolerance is an important trait related to growth and yield in crop. Until now, drought related research has focused on coding genes. However, non-coding RNAs also respond significantly to environmental stimuli such as drought stress.

TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

Motivation: Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.

Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways.

Motivation: To understand the dynamic nature of the biological process, it is crucial to identify perturbed pathways in an altered environment and also to infer regulators that trigger the response. Current time-series analysis methods, however, are not powerful enough to identify perturbed pathways and regulators simultaneously. Widely used methods include methods to determine gene sets such as differentially expressed genes or gene clusters and these genes sets need to be further interpreted in terms of biological pathways using other tools.

piClust: a density based piRNA clustering algorithm.

Piwi-interacting RNAs (piRNAs) are recently discovered, endogenous small non-coding RNAs. piRNAs protect the genome from invasive transposable elements (TE) and sustain integrity of the genome in germ cell lineages. Small RNA-sequencing data can be used to detect piRNA activations in a cell under a specific condition.

Bio and health informatics meets cloud : BioVLab as an example.

The exponential increase of genomic data brought by the advent of the next or the third generation sequencing (NGS) technologies and the dramatic drop in sequencing cost have driven biological and medical sciences to data-driven sciences. This revolutionary paradigm shift comes with challenges in terms of data transfer, storage, computation, and analysis of big bio/medical data. Cloud computing is a service model sharing a pool of configurable resources, which is a suitable workbench to address these challenges.