Familiarity with genome-scale data and the bioinformatic skills to analyze it have become essential for understanding and advancing modern biology and human health, yet many undergraduate biology majors are never exposed to hands-on bioinformatics. This paper presents a module that introduces students to applied bioinformatic analysis within the context of a research-based microbiology lab course. One of the most commonly used genomic analyses in biology is resequencing: determining the sequence of DNA bases in a derived strain of some organism, and comparing it to the known ancestral genome of that organism to better understand the phenotypic differences between them. Many existing CUREs — Course Based Undergraduate Research Experiences — evolve or select new strains of bacteria and compare them phenotypically to ancestral strains. This paper covers standardized strategies and procedures, accessible to undergraduates, for preparing and analyzing microbial whole-genome resequencing data to examine the genotypic differences between such strains. Wet-lab protocols and computational tutorials are provided, along with additional guidelines for educators, providing instructors without a next-generation sequencing or bioinformatics background the necessary information to incorporate whole-genome sequencing and command-line analysis into their class. This module introduces novice students to running software at the command-line, giving them exposure and familiarity with the types of tools that make up the vast majority of open-source scientific software used in contemporary biology. Completion of the module improves student attitudes toward computing, which may make them more likely to pursue further bioinformatics study.

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Bioinformatics Market Size 2025-2029

The bioinformatics market size is valued to increase by USD 15.98 billion, at a CAGR of 17.4% from 2024 to 2029. Reduction in cost of genetic sequencing will drive the bioinformatics market.

Market Insights

North America dominated the market and accounted for a 43% growth during the 2025-2029.
By Application - Molecular phylogenetics segment was valued at USD 4.48 billion in 2023
By Product - Platforms segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 309.88 million 
Market Future Opportunities 2024: USD 15978.00 million
CAGR from 2024 to 2029 : 17.4%

Market Summary

The market is a dynamic and evolving field that plays a pivotal role in advancing scientific research and innovation in various industries, including healthcare, agriculture, and academia. One of the primary drivers of this market's growth is the rapid reduction in the cost of genetic sequencing, making it increasingly accessible to researchers and organizations worldwide. This affordability has led to an influx of large-scale genomic data, necessitating the development of sophisticated bioinformatics tools for Next-Generation Sequencing (NGS) data analysis. Another significant trend in the market is the shortage of trained laboratory professionals capable of handling and interpreting complex genomic data. This skills gap creates a demand for user-friendly bioinformatics software and services that can streamline data analysis and interpretation, enabling researchers to focus on scientific discovery rather than data processing. For instance, a leading pharmaceutical company could leverage bioinformatics tools to optimize its drug discovery pipeline by analyzing large genomic datasets to identify potential drug targets and predict their efficacy. By integrating these tools into its workflow, the company can reduce the time and cost associated with traditional drug discovery methods, ultimately bringing new therapies to market more efficiently. Despite its numerous benefits, the market faces challenges such as data security and privacy concerns, data standardization, and the need for interoperability between different software platforms. Addressing these challenges will require collaboration between industry stakeholders, regulatory bodies, and academic institutions to establish best practices and develop standardized protocols for data sharing and analysis.

What will be the size of the Bioinformatics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleBioinformatics, a dynamic and evolving market, is witnessing significant growth as businesses increasingly rely on high-performance computing, gene annotation, and bioinformatics software to decipher regulatory elements, gene expression regulation, and genomic variation. Machine learning algorithms, phylogenetic trees, and ontology development are integral tools for disease modeling and protein interactions. cloud computing platforms facilitate the storage and analysis of vast biological databases and sequence datas, enabling data mining techniques and statistical modeling for sequence assembly and drug discovery pipelines. Proteomic analysis, protein folding, and computational biology are crucial components of this domain, with biomedical ontologies and data integration platforms enhancing research efficiency. The integration of gene annotation and machine learning algorithms, for instance, has led to a 25% increase in accurate disease diagnosis within leading healthcare organizations. This trend underscores the importance of investing in advanced bioinformatics solutions for improved regulatory compliance, budgeting, and product strategy.

Unpacking the Bioinformatics Market Landscape

Bioinformatics, an essential discipline at the intersection of biology and computer science, continues to revolutionize the scientific landscape. Evolutionary bioinformatics, with its molecular dynamics simulation and systems biology approaches, enables a deeper understanding of biological processes, leading to improved ROI in research and development. For instance, next-generation sequencing technologies have reduced sequencing costs by a factor of ten, enabling genome-wide association studies and transcriptome sequencing on a previously unimaginable scale. In clinical bioinformatics, homology modeling techniques and protein-protein interaction analysis facilitate drug target identification, enhancing compliance with regulatory requirements. Phylogenetic analysis tools and comparative genomics studies contribute to the discovery of novel biomarkers and the development of personalized treatments. Bioimage informatics and proteomic data integration employ advanced sequence alignment algorithms and functional genomics tools to unlock new insights from complex

The Molecular Biology Information Service (MBIS) of the Health Sciences Library System (HSLS) at the University of Pittsburgh conducted a 33-question online survey to evaluate the effectiveness of services provided by the MBIS. The survey was administered via Qualtrics. Questions were organized into 6 categories: Demographics, Software, Instruction, Website, Service, and Outreach. Questions were a mix of multiple choice, ranking, and free text. Participants were recruited during a six-week period in early 2018. The survey was advertised via numerous methods: MBIS blog post, HSLS website post, MBIS listserv notifications, direct email invitations, and during MBIS workshops. The survey did not require oversight by the University of Pittsburgh IRB.The CSV file contains de-identifed survey responses--identifying information for Q6.7 was redacted.
Also included is a PDF of the survey questions and a PDF of the Qualtrics survey response report.

The Bioinformatics Services Market will grow from $4.3B in 2025 to $15.7B by 2035, at a CAGR of 12.6%, driven by rising demand for biologics and biosimilars.

Despite being a well-established research method, the use of whole-genome sequencing (WGS) for routine molecular typing and pathogen characterization remains a substantial challenge due to the required bioinformatics resources and/or expertise. Moreover, many national reference laboratories and centers, as well as other laboratories working under a quality system, require extensive validation to demonstrate that employed methods are “fit-for-purpose” and provide high-quality results. A harmonized framework with guidelines for the validation of WGS workflows does currently, however, not exist yet, despite several recent case studies highlighting the urgent need thereof. We present a validation strategy focusing specifically on the exhaustive characterization of the bioinformatics analysis of a WGS workflow designed to replace conventionally employed molecular typing methods for microbial isolates in a representative small-scale laboratory, using the pathogen Neisseria meningitidis as a proof-of-concept. We adapted several classically employed performance metrics specifically toward three different bioinformatics assays: resistance gene characterization (based on the ARG-ANNOT, ResFinder, CARD, and NDARO databases), several commonly employed typing schemas (including, among others, core genome multilocus sequence typing), and serogroup determination. We analyzed a core validation dataset of 67 well-characterized samples typed by means of classical genotypic and/or phenotypic methods that were sequenced in-house, allowing to evaluate repeatability, reproducibility, accuracy, precision, sensitivity, and specificity of the different bioinformatics assays. We also analyzed an extended validation dataset composed of publicly available WGS data for 64 samples by comparing results of the different bioinformatics assays against results obtained from commonly used bioinformatics tools. We demonstrate high performance, with values for all performance metrics >87%, >97%, and >90% for the resistance gene characterization, sequence typing, and serogroup determination assays, respectively, for both validation datasets. Our WGS workflow has been made publicly available as a “push-button” pipeline for Illumina data at https://galaxy.sciensano.be to showcase its implementation for non-profit and/or academic usage. Our validation strategy can be adapted to other WGS workflows for other pathogens of interest and demonstrates the added value and feasibility of employing WGS with the aim of being integrated into routine use in an applied public health setting.

IntroductionEarly-stage accurate diagnosis of malignant pleural mesothelioma (MPM) has always been a formidable challenge. DNA and protein as biomarkers for the diagnosis of MPM have received considerable attention, and yet the outcomes are inconsistent.MethodsIn this study, a systematic search employing PubMed, EMBASE, and Cochrane Library to identify relevant studies from the first day of databases to October 2021. Moreover, we adopt the QUADAS-2 to evaluate the quality of eligible studies and Stata 15.0 and Review Manager 5.4 software programs to perform the meta-analysis. Additionally, bioinformatics analysis was performed at GEPIA for the purpose of exploring relationship between related genes and the survival time of MPM patients.ResultsWe included 15 studies at the DNA level and 31studies at the protein level in this meta-analysis. All results demonstrated that the diagnostic accuracy of the combination of MTAP + Fibulin-3 was the highest with the SEN 0.81 (95% CI: 0.67, 0.89) and the SPE 0.95 (95% CI: 0.90, 0.97). And the bioinformatics analysis indicated that the higher MTAP gene expression level was beneficial to enhance the survival time of MPM patients.DiscussionNonetheless, as a result of the limitations of the included samples, it may be necessary to conduct additional research before drawing conclusions.Systematic review registrationhttps://inplasy.com/inplasy-2022-10-0043/, identifier INPLASY2022100043.

IntroductionIschemic stroke (IS) is a cerebrovascular disease that can be disabling and fatal, and there are limitations in the clinical treatment and prognosis of IS. It has been reported that changes in the expression profile of circRNAs have been found during injury in ischemic stroke, and circRNAs play an important role in the IS cascade response. However, the specific mechanisms involved in the pathogenesis of IS are not yet fully understood, and thus in-depth studies are needed.MethodsIn this study, one circRNA dataset (GSE161913), one miRNA dataset (GSE60319) and one mRNA dataset (GSE180470) were retrieved from the Gene Expression Omnibus (GEO) database and included, and the datasets were differentially expressed analyzed by GEO2R and easyGEO to get the DEcircRNA, DEmiRNA and DEmRNA, and DEmRNA was enriched using ImageGP, binding sites were predicted in the ENCORI database, respectively, and the competitive endogenous RNA (ceRNA) regulatory network was visualized by the cytoscape software, and then selected by MCC scoring in the cytoHubba plugin Hub genes. In addition, this study conducted a case–control study in which blood samples were collected from stroke patients and healthy medical examiners to validate the core network of ceRNAs constructed by biosignature analysis by real-time fluorescence quantitative qRT-PCR experiments.ResultsA total of 233 DEcircRNAs, 132 DEmiRNAs and 72 DEmRNAs were screened by bioinformatics analysis. circRNA-mediated ceRNA regulatory network was constructed, including 148 circRNAs, 43 miRNAs and 44 mRNAs. Finally, CLEC16A|miR-654-5p|RARA competitive endogenous regulatory axis was selected for validation by qRT-PCR, and the validation results were consistent with the bioinformatics analysis.DiscussionIn conclusion, the present study establishes a new axis of regulation associated with IS, providing new insights into the pathogenesis of IS.

The investigation of microbial proteins by mass spectrometry (metaproteomics) is a key technology for simultaneously assessing the taxonomic composition and the functionality of microbial communities in medical, environmental, and biotechnological applications. We present an improved metaproteomics workflow using an updated sample preparation and a new version of the MetaProteomeAnalyzer software for data analysis. High resolution by multidimensional separation (GeLC, MudPIT) was sacrificed to aim at fast analysis of a broad range of different samples in less than 24 h. The improved workflow generated at least two times as many protein identifications than our previous workflow, and a drastic increase of taxonomic and functional annotations. Improvements of all aspects of the workflow, particularly the speed, are first steps toward potential routine clinical diagnostics (i.e., fecal samples) and analysis of technical and environmental samples. The MetaProteomeAnalyzer is provided to the scientific community as a central remote server solution at www.mpa.ovgu.de.

Online biomedical repositories contain a wealth of freely available data submitted by the research community, but to reuse this data in further studies requires well-annotated associated metadata. There is a growing set of community-developed standards for creating this metadata, often in the form of templates; still, the difficulties of working with these standards are significant. The Center for Expanded Data Annotation and Retrieval (CEDAR) is building an end-to-end system to ease the authoring of metadata and the templates. This system targets the creation of higher quality metadata to facilitate data discovery, interoperability, and reuse. With our public release in September 2016, we now support many new features which make authoring easier.Template and Metadata Repository: We developed a standardized representation of metadata and the templates that describe them, together with Web-based services to store, search, and share these resources. Templates created using CEDAR technology are stored in our openly accessible community repository, and can now be shared with other people and groups. Researchers can search for templates to annotate their studies, and share their metadata with others. We’ve now added Web-based interfaces and REST APIs to facilitate access to templates, and all the metadata collected using those templates.Template and Metadata Editor: We developed highly interactive Web-based tools to simplify the process of authoring metadata and templates. The Template Editor allows users to create, search, and author templates. An upgraded feature provides interoperation with ontologies: interactive look-up services linked to NCBO’s BioPortal (bioportal.bioontology.org) let template authors find ontology terms to annotate fields in their templates and to define possible values of fields, including creating new terms and value sets. The Metadata Editor, which creates a forms-based acquisition interface from a template, has been redesigned so users can more easily populate metadata based on the template fields.

BackgroundDespite the positive effects of endurance training on the cardiovascular (CV) system, excessive exercise induces not only physiological adaptations but also adverse changes in CV system, including the heart. We aimed to evaluate the selected miRNAs expression based on bioinformatic analysis and their changes before and after an ultramarathon run.Materials and MethodsCardiac tissue-specific targets were identified with the Tissue 2.0 database. Gene-gene interaction data were retrieved from the STRING app for Cytoscape. Twenty-three endurance athletes were recruited to the study. Athletes ran to completion (100 km) or exhaustion (52–91 km, median 74 km). All participants completed pre- and post-run testing. miRNAs expressions were measured both before and after the race.ResultsEnrichment analysis of the signaling pathways associated with the genes targeted by miRNAs selected for qRT-PCR validation (miR-1-3p, miR-126, miR-223, miR-125a-5p, miR-106a-5p, and miR-15a/b). All selected miRNAs showed overlap in regulation in pathways associated with cancer, IL-2 signaling, TGF-β signaling as well as BDNF signaling pathway. Analysis of metabolites revealed significant regulation of magnesium and guanosine triphosphate across analyzed miRNA targets. MiR-1-3p, miR-125a-5p, miR-126, and miR-223 expressions were measured in 23 experienced endurance athletes, before and after an ultramarathon wherein athletes ran to completion (100 km) or exhaustion (52–91 km, median 74 km). The expressions of miR-125a-5p, miR-126, and miR-223 were significantly increased after the race (p = 0.007, p = 0.001, p = 0.014, respectively). MiR-1-3p expression post-run showed a negative correlation with the post-run levels of high-sensitivity C-reactive protein (hs-CRP) (r = −0.632, p = 0.003). Higher miR-1-3p expression was found in runners, who finished the race under 10 h compared to runners who finished over 10 h (p = 0.001). Post-run miR-125a-5p expression showed a negative correlation with the peak lactate during the run (r = −0.576, p = 0.019).ConclusionExtreme physical activity, as exemplified by an ultramarathon, is associated with changes in circulating miRNAs’ expression related to inflammation, fibrosis, and cardiac muscle function. In particular, the negative correlations between miR-125a-5p and lactate concentrations, and miR-1-3p and hs-CRP, support their role in specific exercise-induced adaptation. Further studies are essential to validate the long-term effect of these observations.

IntroductionPharmacogenomic (PGx) testing improves drug efficacy and reduces risk of toxicity for commonly prescribed medications, with most pharmacogenomic studies largely focused on individuals of European descent to date. The impact of pharmacogenomic testing in a racially diverse population is still emerging, especially for Admixed American patients.MethodsIn this study, we assessed the frequency of actionable PGx variants by analyzing anonymized exome sequencing data of a racially diverse cohort of 1777 pediatric patients, collected for routine clinical genetic diagnosis at Children‘s Hospital Los Angeles (CHLA). Utilizing exome data, we used the Illumina DRAGEN germline pipeline v4.2, to determine the predicted phenotypes of 25 pharmacogenes including HLA‐A and HLA‐B, including CPIC Level A genes and genes recommended for PGx testing by the U.S. Food and Drug Administration. To assess cross-platform consistency, we compared our results to those generated by PyPGx, a pharmacogenomic genotyping tool developed by the same author as Stargazer. As the distribution of PGx alleles is ancestry specific, we estimated genetic ancestry bioinformatically using the Somalier tool.ResultsGenetic ancestry analysis demonstrated that 62% of our cohort was Admixed American, followed by 23% European, 8% East Asian, 5% African American, and 2% South East Asian. Actionability analysis showed that: 1) 93% of all exome cases had at least one actionable PGx phenotype, 2) one in five cases (22%) had at least three actionable PGx phenotypes, and 3) CYP2B6 (54%) and CYP2D6 (33%) had the highest number of actionable phenotypes. Further analysis revealed notable differences, including higher rates of poor metabolizers for CYP2B6 and variations in CYP2D6 metabolizer statuses, in PGx phenotypes compared to previously collated frequencies in the PharmGKB database, especially within the Admixed American population.DiscussionIn conclusion, our study reinforces the importance of PGx testing, underscores the diversity of PGx variation in ancestral backgrounds, and supports the clinical utility of preemptive PGx testing using exome or genome sequencing approaches.

Since early 2020, the world suffers from a new beta-coronavirus, called SARS-CoV-2, that has devastating effects globally due to its associated disease, Covid-19. Until today, Covid-19, which not only causes life-threatening lung infections but also impairs various other organs and tissues, has killed hundreds of thousands of people and caused irreparable damage to many others. Since the very onset of the pandemic, huge efforts were made worldwide to fully understand this virus and numerous studies were, and still are, published. Many of these deal with structural analyses of the viral spike glycoprotein and with vaccine development, antibodies and antiviral molecules or immunomodulators that are assumed to become essential tools in the struggle against the virus. This paper summarizes knowledge on the properties of the four structural proteins (spike protein S, membrane protein M, envelope protein E and nucleocapsid protein N) of the SARS-CoV-2 virus and its relatives, SARS-CoV and MERS-CoV, that emerged few years earlier. Moreover, attention is paid to ways to analyze such proteins using freely available bioinformatic tools and, more importantly, to bring these proteins alive by looking at them on a computer/laptop screen with the easy-to-use but highly performant and interactive molecular graphics program DeepView. It is hoped that this paper will stimulate non-bioinformaticians and non-specialists in structural biology to scrutinize these and other macromolecules and as such will contribute to establishing procedures to fight these and maybe other forthcoming viruses.

BackgroundPulmonary fibrosis (PF) is a progressive and fatal interstitial lung disease with limited treatment options. Premature senescence of alveolar epithelial type II cells (AT2 cells) plays a critical role in PF pathogenesis. This study aimed to identify natural compounds targeting senescence-related pathways for PF treatment.MethodsAn integrated approach was implemented, combining bioinformatics, artificial intelligence (AI)-assisted molecular docking, ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling, and experimental validation. Core targets associated with aging-related pulmonary fibrosis (PF) were identified via database mining (GeneCards and AgingAtlas) and protein-protein interaction (PPI) network analysis. Natural compounds were screened using the HERB database, and resveratrol (RES) was selected due to its multi-target activity and favorable ADMET characteristics. The efficacy of RES was evaluated through in vitro experiments using bleomycin (BLM)-induced senescent A549 alveolar epithelial cells and in vivo studies in a BLM-induced PF mouse model (C57BL/6J). Molecular docking simulations were performed to predict the binding affinity between RES and key targets, including SERPINE1, MMP2, and IL-6.ResultsBioinformatics identified 322 aging-related PF targets, with TP53, AKT1, STAT3, JUN, and NFKB1 as core regulators. Resveratrol was selected as a top candidate modulating all five core targets and exhibiting optimal drug-likeness. Molecular docking and dynamics simulations confirmed strong binding affinity between RES and key senescence-associated proteins (SERPINE1: −8 kcal/mol; MMP2: −7.5 kcal/mol; IL-6: −7.1 kcal/mol). In vitro, RES (10–40 μM) significantly suppressed bleomycin-induced senescence in A549 cells, reducing SA-β-Gal activity and downregulating SERPINE1, MMP2, and IL6 expression. In vivo, RES treatment (20–80 mg/kg, 21 days) attenuated bleomycin-induced PF in mice, improving weight loss, reducing alveolar damage, inflammation, and collagen deposition (Masson’s trichrome) in a dose-dependent manner.ConclusionResveratrol effectively inhibits alveolar epithelial cell senescence and ameliorates pulmonary fibrosis, likely by targeting key senescence-associated pathways (e.g., SERPINE1, MMP2, IL-6). This study provides a promising transdisciplinary strategy for anti-fibrotic drug discovery and highlights RES as a potential therapeutic candidate for PF.

BackgroundPulmonary fibrosis (PF) is a progressive and fatal interstitial lung disease with limited treatment options. Premature senescence of alveolar epithelial type II cells (AT2 cells) plays a critical role in PF pathogenesis. This study aimed to identify natural compounds targeting senescence-related pathways for PF treatment.MethodsAn integrated approach was implemented, combining bioinformatics, artificial intelligence (AI)-assisted molecular docking, ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling, and experimental validation. Core targets associated with aging-related pulmonary fibrosis (PF) were identified via database mining (GeneCards and AgingAtlas) and protein-protein interaction (PPI) network analysis. Natural compounds were screened using the HERB database, and resveratrol (RES) was selected due to its multi-target activity and favorable ADMET characteristics. The efficacy of RES was evaluated through in vitro experiments using bleomycin (BLM)-induced senescent A549 alveolar epithelial cells and in vivo studies in a BLM-induced PF mouse model (C57BL/6J). Molecular docking simulations were performed to predict the binding affinity between RES and key targets, including SERPINE1, MMP2, and IL-6.ResultsBioinformatics identified 322 aging-related PF targets, with TP53, AKT1, STAT3, JUN, and NFKB1 as core regulators. Resveratrol was selected as a top candidate modulating all five core targets and exhibiting optimal drug-likeness. Molecular docking and dynamics simulations confirmed strong binding affinity between RES and key senescence-associated proteins (SERPINE1: −8 kcal/mol; MMP2: −7.5 kcal/mol; IL-6: −7.1 kcal/mol). In vitro, RES (10–40 μM) significantly suppressed bleomycin-induced senescence in A549 cells, reducing SA-β-Gal activity and downregulating SERPINE1, MMP2, and IL6 expression. In vivo, RES treatment (20–80 mg/kg, 21 days) attenuated bleomycin-induced PF in mice, improving weight loss, reducing alveolar damage, inflammation, and collagen deposition (Masson’s trichrome) in a dose-dependent manner.ConclusionResveratrol effectively inhibits alveolar epithelial cell senescence and ameliorates pulmonary fibrosis, likely by targeting key senescence-associated pathways (e.g., SERPINE1, MMP2, IL-6). This study provides a promising transdisciplinary strategy for anti-fibrotic drug discovery and highlights RES as a potential therapeutic candidate for PF.

IntroductionExtensive efforts have been made to explore members of the IL-10 family as potential therapeutic strategies for various diseases; however, their biological role in chronic rhinosinusitis with nasal polyps (CRSwNP) remains underexplored.MethodsGene expression datasets GSE136825, GSE179265, and GSE196169 were retrieved from the Gene Expression Omnibus (GEO) for analysis. Candidate genes were identified by intersecting differentially expressed genes (DEGs) between the CRSwNP and control groups (DEGsall) with those between the high- and low-score groups within the CRSwNP cohort (DEGsNP). Biomarker selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and the Boruta algorithm. Further refinement of biomarkers was carried out using receiver operating characteristic (ROC) analysis, with genes demonstrating an area under the curve (AUC) greater than 0.7 being considered significant. Genes exhibiting consistent expression trends and significant differences across both GSE136825 and GSE179265 were selected as potential biomarkers. Cell-type annotation was performed on GSE196169, and the expression profiles of the biomarkers across various cell types were analyzed. A competing endogenous RNA (ceRNA) network and a biomarker-drug interaction network were also established. Additionally, the mRNALocater database was utilized to determine the cellular localization of the identified biomarkers.ResultsThe intersection of 1817 DEGsall and 24 DEGsNP yielded 15 candidate genes. Further filtering through LASSO, SVM-RFE, and Boruta led to the identification of seven candidate biomarkers: PRB3, KRT16, MUC6, SPAG4, FGFBP1, NR4A1, and GSTA2. Six of these genes demonstrated strong diagnostic performance in GSE179265, while four biomarkers, showing both significant differences and consistent expression trends, were validated in both GSE179265 and GSE136825. Single-cell sequencing analysis of GSE196169 revealed seven distinct cell types, including endothelial cells, with the biomarkers predominantly expressed in epithelial cells. The ceRNA network comprised nine nodes and eleven edges, with only FGFBP1 exhibiting a complete lncRNA-miRNA-mRNA interaction.DiscussionThis study identifies several novel biomarkers and their associated drugs for CRSwNP therapy, as well as potential therapeutic targets, such as spiperone and arnenous acid, identified through molecular docking. Ultimately, this work underscores the identification of four IL-10 family-related biomarkers, providing a theoretical foundation for future clinical research in CRSwNP.

The formyltetrahydrofolate synthetase (FTHFS) gene is a molecular marker of choice to study the diversity of acetogenic communities. However, current analyses are limited due to lack of a high-throughput sequencing approach for FTHFS gene amplicons and a dedicated bioinformatics pipeline for data analysis, including taxonomic annotation and visualization of the sequence data. In the present study, we combined the barcode approach for multiplexed sequencing with unsupervised data analysis to visualize acetogenic community structure. We used samples from a biogas digester to develop proof-of-principle for our combined approach. We successfully generated high-throughput sequence data for the partial FTHFS gene and performed unsupervised data analysis using the novel bioinformatics pipeline “AcetoScan” presented in this study, which resulted in taxonomically annotated OTUs, phylogenetic tree, abundance plots and diversity indices. The results demonstrated that high-throughput sequencing can be used to sequence the FTHFS amplicons from a pool of samples, while the analysis pipeline AcetoScan can be reliably used to process the raw sequence data and visualize acetogenic community structure. The method and analysis pipeline described in this paper can assist in the identification and quantification of known or potentially new acetogens. The AcetoScan pipeline is freely available at https://github.com/abhijeetsingh1704/AcetoScan.

Genome-wide Association Studies (GWAS) identify genome variations related to specific phenotypes using Single Nucleotide Polymorphism (SNP) markers. Genotyping platforms like SNP-Array or sequencing-based techniques (GBS) can genotype samples with many SNPs. These approaches may bias tropical maize analyses due to reliance on the temperate line B73 as the reference genome. An alternative is a simulated genome called “Mock,” adapted to the population using bioinformatics. Recent studies show SNP-Array, GBS, and Mock yield similar results for population structure, heterotic groups definition, tester selection, and genomic hybrid prediction. However, no studies have examined the results generated by these different genotyping approaches for GWAS. This study aims to test the equivalence among the three genotyping scenarios in identifying significant effect genes in GWAS. To achieve this, maize was used as the model species, where SNP-Array genotyped 360 inbred lines from a public panel via the Affymetrix platform and GBS. The GBS data were used to perform SNP calling using the temperate inbred line B73 as the reference genome (GBS-B73) and a simulated genome “Mock” obtained in-silico (GBS-Mock). The study encompassed four above-ground traits with plants grown under two levels of water supply: well-watered (WW) and water-stressed (WS). In total, 46, 34, and 31 SNP were identified in the SNP-Array, GBS-B73, and GBS-Mock scenarios, respectively, across the two water levels, associated with the evaluated traits following the comparative analysis of each genotyping method individually. Overall, the identified candidate genes varied along the various scenarios but had the same functionality. Regarding SNP-Array and GBS-B73, genes with functional similarity were identified even without coincidence in the physical position of the SNPs. These genes and regions are involved in various processes and responses with applications in plant breeding. In terms of accuracy, the combination of genotyping scenarios compared to those isolated is feasible and recommended, as it increased all traits under both water conditions. In this sense, it is worth highlighting the combination of GBS-B73 and GBS-Mock scenarios, not only due to the increase in the resolution of GWAS results but also the reduction of costs associated with genotyping and the possibility of conducting genomic breeding methods.

The selection of transcription terminators (TTs) for pairing with high expressing constitutive promoters in chimeric constructs is crucial to deliver optimal transgene expression in plants. In this study, the use of the native combinations of four polyubiquitin gene promoters and corresponding TTs resulted in up to >3-fold increase in transgene expression in maize. Of the eight polyubiquitin promoter and TT regulatory elements utilized, seven were novel and identified from the polyubiquitin genes of Brachypodium distachyon, Setaria italica, and Zea mays. Furthermore, gene expression driven by the Cassava mosaic virus promoter was studied by pairing the promoter with distinct TTs derived from the high expressing genes of Arabidopsis. Of the three TTs studied, the polyubiquitin10 gene TT produced the highest transgene expression in maize. Polyadenylation patterns and mRNA abundance from eight distinct TTs were analyzed using 3′-RACE and next-generation sequencing. The results exhibited one to three unique polyadenylation sites in the TTs. The poly(A) site patterns for the StPinII TT were consistent when the same TT was deployed in chimeric constructs irrespective of the reporter gene and promoter used. Distal to the poly(A) sites, putative polyadenylation signals were identified in the near-upstream regions of the TTs based on previously reported mutagenesis and bioinformatics studies in rice and Arabidopsis. The putative polyadenylation signals were 9 to 11 nucleotides in length. Six of the eight TTs contained the putative polyadenylation signals that were overlaps of either canonical AAUAAA or AAUAAA-like polyadenylation signals and AUGAAU, a top-ranking-hexamer of rice and Arabidopsis gene near-upstream regions. Three of the polyubiquitin gene TTs contained the identical 9-nucleotide overlap, AUGAAUAAG, underscoring the functional significance of such overlaps in mRNA 3′ end processing. In addition to identifying new combinations of regulatory elements for high constitutive trait gene expression in maize, this study demonstrated the importance of TTs for optimizing gene expression in plants. Learning from this study could be applied to other dicotyledonous and monocotyledonous plant species for transgene expression. Research on TTs is not limited to transgene expression but could be extended to the introduction of appropriate mutations into TTs via genome editing, paving the way for expression modulation of endogenous genes.

In this study, we used a bioinformatic approach to construct a miRNA-target gene interaction network potentially involved in the anabolic effect of parathyroid hormone analogue teriparatide [PTH (1–34)] on osteoblasts. We extracted a dataset of 26 microRNAs (miRNAs) from previously published studies and predicted miRNA target interactions (MTIs) using four software tools: DIANA, miRWalk, miRDB, and TargetScan. By constructing an interactome of PTH-regulated miRNAs and their predicted target genes, we elucidated signaling pathways regulating pluripotency of stem cells, the Hippo signaling pathway, and the TGF-beta signaling pathway as the most significant pathways in the effects of PTH on osteoblasts. Furthermore, we constructed intersection of MTI networks for these three pathways and added validated interactions. There are 8 genes present in all three selected pathways and a set of 18 miRNAs are predicted to target these genes, according to literature data. The most important genes in all three pathways were BMPR1A, BMPR2 and SMAD2 having the most interactions with miRNAs. Among these miRNAs, only miR-146a-5p and miR-346 have validated interactions in these pathways and were shown to be important regulators of these pathways. In addition, we also propose miR-551b-5p and miR-338–5p for further experimental validation, as they have been predicted to target important genes in these pathways but none of their target interactions have yet been verified. Our wet-lab experiment on miRNAs differentially expressed between PTH (1–34) treated and untreated mesenchymal stem cells supports miR-186–5p from the literature obtained data as another prominent miRNA. The meticulous selection of miRNAs outlined will significantly support and guide future research aimed at discovering and understanding the crucial pathways of osteoanabolic PTH-epigenetic effects on osteoblasts. Additionally, they hold potential for the discovery of new PTH target genes, innovative biomarkers for the effectiveness and safety of osteoporosis-affected treatment, as well as novel therapeutic targets.