Genomics and Informatics最新文献

Cancer of the stomach is the second most frequent cancer-related death worldwide. The survival rate of patients with gastric cancer (GC) remains fragile. There is a requirement to discover biomarkers for prognosis approaches. Helicobacter pylori in the stomach is closely associated with the progression of GC. We identified the genes associated with poor/favorable prognosis in H. pylori-induced GC. Multivariate statistical analysis was applied on the Gene Expression Omnibus (GEO) dataset GSE54397 to identify differentially expressed miRNAs (DEMs) in gastric tissues with H. pylori-induced cancer compared with the H. pylori-positive with non-cancerous tissue. A protein interaction map (PIM) was built and subjected to DEMs targets. The enriched pathways and biological processes within the PIM were identified based on substantial clusters. Thereafter, the most critical genes in the PIM were illustrated, and their prognostic impact in GC was investigated. Considering p-value less than 0.01 and |Log2 fold change| as >1, five microRNAs demonstrated significant changes among the two groups. Gene functional analysis revealed that the ubiquitination system, neddylation pathway, and ciliary process are primarily involved in H. pylori-induced GC. Survival analysis illustrated that the overexpression of DOCK4, GNAS, CTGF, TGF-b1, ESR1, SELE, TIMP3, SMARCE1, and TXNIP was associated with poor prognosis, while increased MRPS5 expression was related to a favorable prognosis in GC patients. DOCK4, GNAS, CTGF, TGF-b1, ESR1, SELE, TIMP3, SMARCE1, TXNIP, and MRPS5 may be considered prognostic biomarkers for H. pylori-induced GC. However, experimental validation is necessary in the future.

A wave of new technologies has created opportunities for the cost-effective generation of high-throughput profiles of biological systems, foreshadowing a "data-driven science" era. The large variety of data available from biological research is also a rich resource that can be used for innovative endeavors. However, we are facing considerable challenges in big data deposition, integration, and translation due to the complexity of biological data and its production at unprecedented exponential rates. To address these problems, in 2020, the Korean government officially announced a national strategy to collect and manage the biological data produced through national R&D fund allocations and provide the collected data to researchers. To this end, the Korea Bioinformation Center (KOBIC) developed a new biological data repository, the Korea BioData Station (K-BDS), for sharing data from individual researchers and research programs to create a data-driven biological study environment. The K-BDS is dedicated to providing free open access to a suite of featured data resources in support of worldwide activities in both academia and industry.

OncoPrint, the plot to visualize an overview of genetic variants in sequencing data, has been widely used in the field of cancer genomics. However, still, there have been no Python libraries capable to generate OncoPrint yet, a big hassle to plot OncoPrints within Python-based genetic variants analysis pipelines. This paper introduces a new Python package PyOncoPrint, which can be easily used to plot OncoPrints in Python. The package is based on the existing widely used scientific plotting library Matplotlib, the resulting plots are easy to be adjusted for various needs.

Microglia, similar to peripheral macrophages, are the primary immune cells of the central nervous system (CNS). Microglia exist in the resting state in the healthy CNS, but can be activated and polarized into either M1 or M2 subtypes for immune defense and the maintenance of CNS homeostasis by multiple stimuli. Several long noncoding RNAs (lncRNAs) mediate human inflammatory diseases and neuropathologies by regulating their target genes. However, the function of common lncRNAs that contribute to microglial activation remains unclear. Thus, we used bioinformatic approaches to identify common lncRNAs involved in microglial activation in vitro. Our study identified several lncRNAs as common regulators of microglial activation. We identified 283 common mRNAs and 53 common lncRNAs during mouse M1 microglial activation processes, whereas 26 common mRNAs and five common lncRNAs were identified during mouse M2 microglial activation processes. A total of 648 common mRNAs and 274 common lncRNAs were identified during the activation of human M1 microglia. In addition, we identified 1,920 common co-expressed pairs in mouse M1 activation processes and 25 common co-expressed pairs in mouse M2 activation processes. Our study provides a comprehensive understanding of common lncRNA expression profiles in microglial activation processes in vitro. The list of common lncRNAs identified in this study provides novel evidence and clues regarding the molecular mechanisms underlying microglial activation.

The fishes of the Chiasmodontidae family, known as swallower fishes, are species adapted to live in deep seas. Several studies have shown the proximity of this family to Tetragonuridae and Amarsipidae. However, the phylogenetic position of this clade related to other Pelagiaria groups remains uncertain even when phylogenomic studies are employed. Since the low number of published mitogenomes, our study aimed to assemble six new mitochondrial genomes of Chiasmodontidae from database libraries to expand the discussion regarding the phylogeny of this group within Scombriformes. As expected, the composition and organization of mitogenomes were stable among the analyzed species, although we detected repetitive sequences in the D-loop of species of the genus Kali not seen in Chiasmodon, Dysalotus, and Pseudoscopelus. Our phylogeny incorporating 51 mitogenomes from several families of Scombriformes, including nine chiasmodontids, recovered interfamilial relationships well established in previous studies, including a clade containing Chiasmodontidae, Amarsipidae, and Tetragonuridae. However, phylogenetic relationships between larger clades remain unclear, with disagreements between different phylogenomic studies. We argue that such inconsistencies are not only due to biases and limitations in the data but mainly to complex biological events in the adaptive irradiation of Scombriformes after the Cretaceous-Paleogene extinction event.

Breast cancer is the most common cancer worldwide, and advanced breast cancer with metastases is incurable mainly with currently available therapies. Therefore, it is essential to understand molecular characteristics during the progression of breast carcinogenesis. Here, we report a dataset of whole genomes from the human mammary epithelial cell system derived from a reduction mammoplasty specimen. This system comprises pre-stasis 184D cells, considered normal, and seven cell lines along cancer progression series that are immortalized or additionally acquired anchorage-independent growth. Our analysis of the whole-genome sequencing (WGS) data indicates that those seven cancer progression series cells have somatic mutations whose number ranges from 8,393 to 39,564 (with an average of 30,591) compared to 184D cells. These WGS data and our mutation analysis will provide helpful information to identify driver mutations and elucidate molecular mechanisms for breast carcinogenesis.

Bioinformatics education can be defined as the teaching and learning of how to use software tools, along with mathematical and statistical analysis, to solve biological problems. Although many resources are available, most students still struggle to understand even the simplest sequence alignment algorithms. Applying visualizations to these topics benefits both lecturers and students. Unfortunately, educational software for visualizing step-bystep processes in the user experience of sequence alignment algorithms is rare. In this article, an educational visualization tool for biological sequence alignment is presented, and the source code is released in order to encourage the collaborative power of open-source software, with the expectation of further contributions from the community in the future. Two different modules are integrated to enable a student to investigate the characteristics of alignment algorithms.

Many packages for a meta-analysis of genome-wide association studies (GWAS) have beendeveloped to discover genetic variants. Although variations across studies must be considered, there are not many currently-accessible packages that estimate between-study heterogeneity. Thus, we propose a python based application called Beta-Meta which can easilyprocess a meta-analysis by automatically selecting between a fixed effects and a randomeffects model based on heterogeneity. Beta-Meta implements flexible input data manipulation to allow multiple meta-analyses of different genotype-phenotype associations in asingle process. It provides a step-by-step meta-analysis of GWAS for each association inthe following order: heterogeneity test, two different calculations of an effect size and ap-value based on heterogeneity, and the Benjamini-Hochberg p-value adjustment. Thesemethods enable users to validate the results of individual studies with greater statisticalpower and better estimation precision. We elaborate on these and illustrate them with examples from several studies of infertility-related disorders.

The pathogen Gallibacterium anatis has caused heavy economic losses for commercial poultry farms around the world. However, despite its importance, the functions of its hypothetical proteins (HPs) have been poorly characterized. The present study analyzed the functions and structures of HPs obtained from Gallibacterium anatis (NCTC11413) using various bioinformatics tools. Initially, all the functions of HPs were predicted using the VICMpred tool, and the physicochemical properties of the identified virulence proteins were then analyzed using Expasy's ProtParam server. A virulence protein (WP_013745346.1) that can act as a potential drug target was further analyzed for its secondary structure, followed by homology modeling and three-dimensional (3D) structure determination using the Swiss-Model and Phyre2 servers. The quality assessment and validation of the 3D model were conducted using ERRAT, Verify3D, and PROCHECK programs. The functional and phylogenetic analysis was conducted using ProFunc, STRING, KEGG servers, and MEGA software. The bioinformatics analysis revealed 201 HPs related to cellular processes (n = 119), metabolism (n = 61), virulence (n = 11), and information/storage molecules (n = 10). Among the virulence proteins, three were detected as drug targets and six as vaccine targets. The characterized virulence protein WP_013745346.1 is proven to be stable, a drug target, and an enzyme related to the citrate cycle in the present pathogen. This enzyme was also found to facilitate other metabolic pathways, the biosynthesis of secondary metabolites, and the biosynthesis of amino acids.

Brugada syndrome (BS) is an autosomal dominant inheritance cardiac arrhythmia disorder associated with sudden death in young adults. Thailand has the highest prevalence of BS worldwide, and over 60% of patients with BS still have unclear disease etiology. Here, we performeda new viral metagenome analysis pipeline called VIRIN and validated it with whole genome sequencing (WGS) data of HeLa cell lines and hepatocellular carcinoma. Then the VIRIN pipelinewas applied to identify viral integration positions from unmapped WGS data of Thai males, including 100 BS patients (case) and 100 controls. Even though the sample preparation had noviral enrichment step, we can identify several virus genes from our analysis pipeline. The predominance of human endogenous retrovirus K (HERV-K) viruses was found in both cases andcontrols by blastn and blastx analysis. This study is the first report on the full-length HERV-Kassembled genomes in the Thai population. Furthermore, the HERV-K integration breakpointpositions were validated and compared between the case and control datasets. Interestingly,Brugada cases contained HERV-K integration breakpoints at promoters five times more oftenthan controls. Overall, the highlight of this study is the BS-specific HERV-K breakpoint positionsthat were found at the gene coding region "NBPF11" (n = 9), "NBPF12" (n = 8) and longnon-coding RNA (lncRNA) "PCAT14" (n = 4) region. The genes and the lncRNA have been reported to be associated with congenital heart and arterial diseases. These findings provide another aspect of the BS etiology associated with viral genome integrations within the humangenome.