Content of the Bioinformatics for Dentistry, with its respective primary sources.

The Actinobacteria are a large diverse phylum of bacteria, often with large genomes with a high G+C content. There is great variation in the sequence quality, equivalence of annotation and phylogenetic representation in the sequence databases meaning that evolutionary and phylogenetic studies may be challenging. To address this, we have assembled a curated, high-level, taxa specific, non-redundant database to aid detailed comparative analysis of Actinobacteria. ActDES constitutes a novel resource for the community of Actinobacterial researchers that will be useful primarily for two types of analyses: (i) comparative genomic studies - facilitated by reliable orthologs identification across a set of defined, phylogenetically representative genomes, and (ii) phylogenomic studies which will be improved by identification of gene subsets at specified taxonomic level. These studies can then act as a springboard for the study of the evolution of virulence genes, studying the evolution of metabolism and metabolic engineering target identification. Data summary All genome sequences used in this study can be found in the NCBI taxonomy browser https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi and are summarised along with Accession numbers in Table S11. All other data is available on Figshare.a. Perl script filesb. List of genomes from NCBI (Actinobacteria database.xlsx) Table S1c. CVS genome annotation files including the FASTA files of nucleotide and amino acids sequences (612 individual .cvs files – folder cvs)d. BLAST nucleotide database (.fasta file)e. BLAST protein database (.fasta file)f. Table S2 Expansion table genus level (Expansion table.xlsx Tab Genus level)g. Table S2 Expansion table species level (Expansion table.xlsx Tab species level)h. All data for GlcP and Glk data – blast hits from ActDES database, MUSCLE Alignment files and .nwk tree files

Global Bioinformatics market size was USD 12.76 Billion in 2022 and it is forecasted to reach USD 29.32 Billion by 2030. Bioinformatics Industry's Compound Annual Growth Rate will be 10.4% from 2023 to 2030. What are the driving factors for the Bioinformatics market?

The primary factors propelling the global bioinformatics industry are advances in genomics, rising demand for protein sequencing, and rising public-private sector investment in bioinformatics. Large volumes of data are being produced by the expanding use of next-generation sequencing (NGS) and other genomic technologies; these data must be analyzed using advanced bioinformatics tools. Furthermore, the global bioinformatics industry may benefit from the development of emerging advanced technologies. However, the bioinformatics discipline contains intricate algorithms and massive amounts of data, which can be difficult for researchers and demand a lot of processing power. What is Bioinformatics?

Bioinformatics is related to genetics and genomics, which involves the use of computer technology to store, collect, analyze, and disseminate biological information, and data, such as DNA and amino acid sequences or annotations about these sequences. Researchers and medical professionals use databases that organize and index this biological data to better understand health and disease, and in some circumstances, as a component of patient care. Through the creation of software and algorithms, bioinformatics is primarily used to extract knowledge from biological data. Bioinformatics is frequently used in the analysis of genomics, proteomics, 3D protein structure modeling, image analysis, drug creation, and many other fields.

Primary osteoporosis has long been underdiagnosed and undertreated. Currently, ferroptosis may be a promising research direction in the prevention and treatment of primary osteoporosis. However, the specific mechanism of ferroptosis in primary osteoporosis remains a mystery. Differentially expressed genes (DEGs) were identified in bone mesenchymal stromal cells (BMSCs) of primary osteoporosis and heathy patients from the GEO databases with the help of bioinformatics analysis. Then, we intersected these DEGs with the ferroptosis dataset and obtained 80 Ferr-DEGs. Several bioinformatics algorithms (PCA, RLE, Limma, BC, MCC, etc.) were adopted to integrate the results. Additionally, we explored the potential functional roles of the Ferr-DEGs via GO and KEGG. Protein–protein interactions (PPI) were used to predict potential interactive networks. Finally, 80 Ferr-DEGs and 5 key Ferr-DEGs were calculated. The 5 key Ferr-DEGs were further verified in the OVX mouse model. In conclusion, through a variety of bioinformatics methods, our research successfully identified 5 key Ferr-DEGs associated with primary osteoporosis and ferroptosis, namely, sirtuin 1(SIRT1), heat shock protein family A (Hsp70) member 5 (HSPA5), mechanistic target of rapamycin kinase (MTOR), hypoxia inducible factor 1 subunit alpha (HIF1A) and beclin 1 (BECN1), which were verified in an animal model.

Model organism database that serves as central repository and web-based resource for zebrafish genetic, genomic, phenotypic and developmental data. Data represented are derived from three primary sources: curation of zebrafish publications, individual research laboratories and collaborations with bioinformatics organizations. Data formats include text, images and graphical representations.Serves as primary community database resource for laboratory use of zebrafish. Developed and supports integrated zebrafish genetic, genomic, developmental and physiological information and link this information extensively to corresponding data in other model organism and human databases.

Structural Bioinformatics Software Market Outlook

As per our latest research, the global Structural Bioinformatics Software market size reached USD 1.48 billion in 2024, demonstrating robust demand across biopharmaceutical research, drug discovery, and academic sectors. The market is experiencing a healthy compound annual growth rate (CAGR) of 10.2% and is forecasted to attain a value of USD 3.58 billion by 2033. This growth can be attributed to the rapid advancements in computational biology, the increasing adoption of artificial intelligence and machine learning in protein structure prediction, and the surge in drug development activities globally.

One of the primary growth drivers for the Structural Bioinformatics Software market is the intensifying focus on precision medicine and personalized therapeutics. With the global pharmaceutical industry placing increasing emphasis on developing targeted therapies, there is a critical need for advanced software tools that can model, predict, and analyze complex biomolecular structures. These tools are pivotal for understanding protein-ligand interactions, predicting the effects of mutations, and identifying novel druggable targets. The integration of high-throughput sequencing data with structural bioinformatics platforms has further accelerated the pace of discovery, enabling researchers to move from raw data to actionable insights with unprecedented speed and accuracy.

Another significant factor propelling the market is the evolution of computational power and cloud-based infrastructure. The exponential increase in available biological data, coupled with the complexity of protein folding and molecular dynamics simulations, demands scalable and high-performance computing resources. Cloud-based structural bioinformatics solutions have democratized access to sophisticated algorithms and databases, making them available to a broader range of users, including smaller biotech firms and academic labs. This shift has not only reduced the barriers to entry but also fostered greater collaboration and innovation in the field, as researchers can now share data, workflows, and results seamlessly across geographies.

The market is also benefiting from heightened collaboration between academia, research organizations, and industry players. Public-private partnerships, government funding initiatives, and global consortia are fueling the development of next-generation structural bioinformatics platforms. These collaborations are focused on addressing critical challenges such as protein structure prediction, functional annotation, and molecular modeling. The emergence of open-source software and community-driven databases has further enriched the ecosystem, providing researchers with access to a wealth of curated data and cutting-edge analytical tools. As the field continues to evolve, the synergy between computational advancements and experimental validation is expected to drive the adoption of structural bioinformatics software across diverse end-user segments.

Structure-Based Drug Design is an integral component of the drug discovery process, leveraging the detailed knowledge of the three-dimensional structure of biological targets to design more effective therapeutic agents. This approach utilizes advanced computational tools to model the interactions between drug candidates and their targets, allowing researchers to optimize binding affinity and selectivity. By focusing on the structural aspects of drug-target interactions, Structure-Based Drug Design enhances the precision and efficiency of the drug development pipeline, ultimately leading to the creation of more targeted and effective treatments. The integration of this methodology with structural bioinformatics software is revolutionizing the way researchers approach complex biological challenges, offering new avenues for innovation and discovery.

From a regional perspective, North America remains the dominant market for structural bioinformatics software, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific region. The robust presence of leading pharmaceutical and biotechnology companies, coupled with significant investments in research and development, has established North America as a global innovation hub. Meanwhi

MicroRNAs (miRNAs) are ubiquitous regulators of gene expression, evolutionarily conserved in plants and mammals. In recent years, although a growing number of papers debate the role of plant miRNAs on human gene expression, the molecular mechanisms through which this effect is achieved are still not completely elucidated. Some evidence suggest that this interaction might be sequence specific, and in this work, we investigated this possibility by transcriptomic and bioinformatics approaches. Plant and human miRNA sequences from primary databases were collected and compared for their similarities (global or local alignments). Out of 2,588 human miRNAs, 1,606 showed a perfect match of their seed sequence with the 5′ end of 3,172 plant miRNAs. Further selections were applied based on the role of the human target genes or of the miRNA in cell cycle regulation (as an oncogene, tumor suppressor, or a biomarker for prognosis, or diagnosis in cancer). Based on these criteria, 20 human miRNAs were selected as potential functional analogous of 7 plant miRNAs, which were in turn transfected in different cell lines to evaluate their effect on cell proliferation. A significant decrease was observed in colorectal carcinoma HCT116 cell line. RNA-Seq demonstrated that 446 genes were differentially expressed 72 h after transfection. Noteworthy, we demonstrated that the plant mtr-miR-5754 and gma-miR4995 directly target the tumor-associated long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and nuclear paraspeckle assembly transcript 1 (NEAT1) in a sequence-specific manner. In conclusion, according to other recent discoveries, our study strengthens and expands the hypothesis that plant miRNAs can have a regulatory effect in mammals by targeting both protein-coding and non-coding RNA, thus suggesting new biotechnological applications.

Different computational approaches are employed to efficiently identify novel repositioning possibilities utilizing different sources of information and algorithms. It is critical to propose high-valued candidate-repositioning possibilities before conducting lengthy in vivo validation studies that consume significant resources. Here we report a novel multi-methodological approach to identify opportunities for drug repositioning. We performed analyses of real-world data (RWD) acquired from the United States Food and Drug Administration’s Adverse Event Reporting System (FAERS) and the claims database maintained by the Japan Medical Data Center (JMDC). These analyses were followed by cross-validation through bioinformatics analyses of gene expression data. Inverse associations revealed using disproportionality analysis (DPA) and sequence symmetry analysis (SSA) were used to detect potential drug-repositioning signals. To evaluate the validity of the approach, we conducted a feasibility study to identify marketed drugs with the potential for treating inflammatory bowel disease (IBD). Primary analyses of the FAERS and JMDC claims databases identified psycholeptics such as haloperidol, diazepam, and hydroxyzine as candidates that may improve the treatment of IBD. To further investigate the mechanistic relevance between hit compounds and disease pathology, we conducted bioinformatics analyses of the associations of the gene expression profiles of these compounds with disease. We identified common biological features among genes differentially expressed with or without compound treatment as well as disease-perturbation data available from open sources, which strengthened the mechanistic rationale of our initial findings. We further identified pathways such as cytokine signaling that are influenced by these drugs. These pathways are relevant to pathologies and can serve as alternative targets of therapy. Integrative analysis of RWD such as those available from adverse-event databases, claims databases, and transcriptome analyses represent an effective approach that adds value to efficiently identifying potential novel therapeutic opportunities.

MicroRNAs (miRNAs) are ubiquitous regulators of gene expression, evolutionarily conserved in plants and mammals. In recent years, although a growing number of papers debate the role of plant miRNAs on human gene expression, the molecular mechanisms through which this effect is achieved are still not completely elucidated. Some evidence suggest that this interaction might be sequence specific, and in this work, we investigated this possibility by transcriptomic and bioinformatics approaches. Plant and human miRNA sequences from primary databases were collected and compared for their similarities (global or local alignments). Out of 2,588 human miRNAs, 1,606 showed a perfect match of their seed sequence with the 5′ end of 3,172 plant miRNAs. Further selections were applied based on the role of the human target genes or of the miRNA in cell cycle regulation (as an oncogene, tumor suppressor, or a biomarker for prognosis, or diagnosis in cancer). Based on these criteria, 20 human miRNAs were selected as potential functional analogous of 7 plant miRNAs, which were in turn transfected in different cell lines to evaluate their effect on cell proliferation. A significant decrease was observed in colorectal carcinoma HCT116 cell line. RNA-Seq demonstrated that 446 genes were differentially expressed 72 h after transfection. Noteworthy, we demonstrated that the plant mtr-miR-5754 and gma-miR4995 directly target the tumor-associated long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and nuclear paraspeckle assembly transcript 1 (NEAT1) in a sequence-specific manner. In conclusion, according to other recent discoveries, our study strengthens and expands the hypothesis that plant miRNAs can have a regulatory effect in mammals by targeting both protein-coding and non-coding RNA, thus suggesting new biotechnological applications.

Protein-Protein, Genetic, and Chemical Interactions for Kathiresan T (2009):A protein interaction network for the large conductance Ca(2+)-activated K(+) channel in the mouse cochlea. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The large conductance Ca(2+)-activated K(+) or BK channel has a role in sensory/neuronal excitation, intracellular signaling, and metabolism. In the non-mammalian cochlea, the onset of BK during development correlates with increased hearing sensitivity and underlies frequency tuning in non-mammals, whereas its role is less clear in mammalian hearing. To gain insights into BK function in mammals, coimmunoprecipitation and two-dimensional PAGE, combined with mass spectrometry, were used to reveal 174 putative BKAPs from cytoplasmic and membrane/cytoskeletal fractions of mouse cochlea. Eleven BKAPs were verified using reciprocal coimmunoprecipitation, including annexin, apolipoprotein, calmodulin, hippocalcin, and myelin P0, among others. These proteins were immunocolocalized with BK in sensory and neuronal cells. A bioinformatics approach was used to mine databases to reveal binary partners and the resultant protein network, as well as to determine previous ion channel affiliations, subcellular localization, and cellular processes. The search for binary partners using the IntAct molecular interaction database produced a putative global network of 160 nodes connected with 188 edges that contained 12 major hubs. Additional mining of databases revealed that more than 50% of primary BKAPs had prior affiliations with K(+) and Ca(2+) channels. Although a majority of BKAPs are found in either the cytoplasm or membrane and contribute to cellular processes that primarily involve metabolism (30.5%) and trafficking/scaffolding (23.6%), at least 20% are mitochondrial-related. Among the BKAPs are chaperonins such as calreticulin, GRP78, and HSP60 that, when reduced with siRNAs, alter BKalpha expression in CHO cells. Studies of BKalpha in mitochondria revealed compartmentalization in sensory cells, whereas heterologous expression of a BK-DEC splice variant cloned from cochlea revealed a BK mitochondrial candidate. The studies described herein provide insights into BK-related functions that include not only cell excitation, but also cell signaling and apoptosis, and involve proteins concerned with Ca(2+) regulation, structure, and hearing loss.

BackgroundDespite the remarkable progress of bioinformatics, how the primary structure of a protein leads to a three-dimensional fold, and in turn determines its function remains an elusive question. Alignments of sequences with known function can be used to identify proteins with the same or similar function with high success. However, identification of function-related and structure-related amino acid positions is only possible after a detailed study of every protein. Folding pattern diversity seems to be much narrower than sequence diversity, and the amino acid sequences of natural proteins have evolved under a selective pressure comprising structural and functional requirements acting in parallel.Principal FindingsThe approach described in this work begins by generating a large number of amino acid sequences using ROSETTA [Dantas G et al. (2003) J Mol Biol 332:449–460], a program with notable robustness in the assignment of amino acids to a known three-dimensional structure. The resulting sequence-sets showed no conservation of amino acids at active sites, or protein-protein interfaces. Hidden Markov models built from the resulting sequence sets were used to search sequence databases. Surprisingly, the models retrieved from the database sequences belonged to proteins with the same or a very similar function. Given an appropriate cutoff, the rate of false positives was zero. According to our results, this protocol, here referred to as Rd.HMM, detects fine structural details on the folding patterns, that seem to be tightly linked to the fitness of a structural framework for a specific biological function.ConclusionBecause the sequence of the native protein used to create the Rd.HMM model was always amongst the top hits, the procedure is a reliable tool to score, very accurately, the quality and appropriateness of computer-modeled 3D-structures, without the need for spectroscopy data. However, Rd.HMM is very sensitive to the conformational features of the models' backbone.

BackgroundLeukemia recurrence continues to be the primary cause of treatment failure, it is meaningful to find new biomarkers for its treatment. In this study, we aim to use cell line study to assess the expression and prognostic value of ARHGAP6 in acute myeloid leukemia.MethodsWe applied two acute myeloid leukemia cell lines in the research. Expression level, proliferation assay and apoptosis assay for ARHGAP6 in those cell lines were involved for the study. Then, GEO, TCGA data and bioinformatics analysis were evaluated.ResultsTHP-1 and U937 cell lines both had higher expression levels of ARHGAP6 than control.The cell proliferation of THP-1 and U937 transfected with ARHGAP6 siRNA was significantly reduced. Knock down the gene ARHGAP6 increases AML cell apoptosis. The overall survival (OS) and disease-free survival (DFS) was assessed against the expression of ARHGAP 6 using the KM plotter databases. High expression ARHGAP6 was associated poor OS and DFS in AML. Enrichment analysis suggested that ARHGAP6 mainly mediated the function of growth factor binding, immunoglobulin binding, mRNA binding. Involved in LCK proto-oncogene, Src family tyrosine kinase, tyrosine kinase non receptor 2, platelet derived growth factor receptor beta and Rho associated coiled-coil containing protein kinase 1.ConclusionFrom cell lines-based functional assays to bioinformatic analysis, this study demonstrated that clinical potential of ARHGAP6 as a novel biomarker of AML.

Ovarian cancer is one of the most common malignant tumours affecting the female reproductive organs. CD147 (BSG) and CD98hc (SLC3A2) are oncogenes that form the CD98hc-CD147 complex, which regulates the proliferation, metastasis, metabolism, and cell cycle of cancer cells. The roles of the CD98hc-CD147 complex in ovarian cancer remain unclear. We analysed the expression and prognostic value of CD147 and CD98hc in ovarian cancer using the TCGA and ICGC databases. The effect of CD147 and CD98hc on the tumour immune response was analysed using the TIMER database. CD98hc was more highly expressed in normal tissues than primary tumour tissues, while CD147 was more highly expressed in primary tumour tissues than normal tissues. CD98hc expression was significantly associated with neutrophil and dendritic cell levels. CD147 and CD98hc were correlated with DNA repair, the cell cycle, and DNA replication. The CD98hc-CD147 complex could serve as a target for ovarian cancer treatment. What is already known on this subject? CD98hc and CD147 are oncogenes that induce the proliferation and metastasis of cancer cells. The CD98hc-CD147 complex has been identified as a risk factor for cancer patients and causes resistance to cancer treatment.What do the results of this study add? We confirmed the expression levels of CD98hc and CD147 in ovarian cancer tissues and the effects of these oncogenes on the tumour immune response.What are the implications of these findings for clinical practice and/or further research? The CD98hc-CD147 complex may serve as a new target for ovarian cancer therapy. What is already known on this subject? CD98hc and CD147 are oncogenes that induce the proliferation and metastasis of cancer cells. The CD98hc-CD147 complex has been identified as a risk factor for cancer patients and causes resistance to cancer treatment. What do the results of this study add? We confirmed the expression levels of CD98hc and CD147 in ovarian cancer tissues and the effects of these oncogenes on the tumour immune response. What are the implications of these findings for clinical practice and/or further research? The CD98hc-CD147 complex may serve as a new target for ovarian cancer therapy.