Data for sequence comparison of commamox genomes and genes identified. This dataset is associated with the following publication: Camejo, P., J. Santodomingo, K. McMahon, and D. Noguera. Genome-enabled insights into the ecophysiology of the comammox bacterium Ca. Nitrospira nitrosa. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 2(5): 1-16, (2017).

The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.

Over the past year, biology educators and staff at the U.S. Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free web-based platform where anyone can conduct sophisticated and reproducible bioinformatic analyses via a graphical user interface. Here, we demonstrate the utility of KBase as a platform for bioinformatics education, and present a set of modular, adaptable, and customizable instructional units for teaching concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. Each module contains teaching resources, publicly available data, analysis tools, and Markdown capability, enabling instructors to modify the lesson as appropriate for their specific course. We present initial student survey data on the effectiveness of using KBase for teaching bioinformatic concepts, provide an example case study, and detail the utility of the platform from an instructor’s perspective. Even as in-person teaching returns, KBase will continue to work with instructors, supporting the development of new active learning curriculum modules. For anyone utilizing the platform, the growing KBase Educators Organization provides an educators network, accompanied by community-sourced guidelines, instructional templates, and peer support, for instructors wishing to use KBase within a classroom at any educational level–whether virtual or in-person.

Leveraging prior viral genome sequencing data to make predictions on whether an unknown, emergent virus harbors a ‘phenotype-of-concern’ has been a long-sought goal of genomic epidemiology. A predictive phenotype model built from nucleotide-level information alone is challenging with respect to RNA viruses due to the ultra-high intra-sequence variance of their genomes, even within closely related clades. We developed a degenerate k-mer method to accommodate this high intra-sequence variation of RNA virus genomes for modeling frameworks. By leveraging a taxonomy-guided ‘group-shuffle-split’ cross validation paradigm on complete coronavirus assemblies from prior to October 2018, we trained multiple regularized logistic regression classifiers at the nucleotide k-mer level. We demonstrate the feasibility of this method by finding models accurately predicting withheld SARS-CoV-2 genome sequences as human pathogens and accurately predicting withheld Swine Acute Diarrhea Syndrome coronavirus (...

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.

The global biological software market is experiencing robust growth, driven by the increasing adoption of advanced technologies in life sciences research and healthcare. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of approximately 12% from 2025 to 2033, reaching an estimated market value of $7 billion by 2033. This expansion is fueled by several key factors: the escalating demand for high-throughput data analysis in genomics and proteomics, the rising prevalence of chronic diseases necessitating advanced diagnostic tools, and the growing adoption of cloud-based solutions for enhanced collaboration and accessibility. Furthermore, the continuous development of sophisticated algorithms and user-friendly interfaces is making biological software more accessible to a wider range of researchers and clinicians. The segment encompassing experimental design and data analysis software holds a significant market share, reflecting the crucial role of computational tools in optimizing research workflows and extracting meaningful insights from complex biological datasets. North America currently dominates the market, owing to the robust presence of established biotechnology companies and a well-funded research infrastructure. However, Asia-Pacific is expected to witness significant growth in the coming years due to the expanding healthcare sector and increasing government investments in research and development. Market restraints include the high cost of software licenses, the requirement for specialized training to effectively utilize these tools, and the potential challenges associated with data security and integration across different platforms. Nevertheless, the ongoing innovation in software capabilities, coupled with the increasing adoption of subscription-based models and cloud-based solutions, is expected to mitigate these constraints. The competitive landscape is characterized by a mix of established players like Thermo Fisher Scientific and DNASTAR, along with smaller specialized companies offering niche solutions. This dynamic competitive environment fosters innovation and drives the development of advanced biological software solutions tailored to the specific needs of diverse research and clinical applications. Future growth will be influenced by factors such as advancements in artificial intelligence and machine learning within the software, integration with laboratory automation systems, and increasing collaboration between software providers and research institutions.

The Life Science IT Analytics Software market is booming, projected to reach $15 billion by 2033, driven by genomic data growth and personalized medicine. Learn about key trends, top companies (Illumina, Thermo Fisher, Qiagen), and market forecasts in our comprehensive analysis.

This research addresses the pressing issue of antibiotic resistance, a global health challenge that undermines the efficacy of treatments against infectious diseases. Focusing on Pseudomonas aeruginosa—a Gram-negative bacterium known for causing opportunistic infections—this study emphasizes its prioritization by the World Health Organization (WHO) as a critical-level pathogen requiring new therapeutic approaches.

To identify antibiotics associated with P. aeruginosa, the study employed text mining techniques on the Scielo database. The resulting dataset comprises 98 antibiotics, each documented with detailed textual information and referencing data. Additionally, the dataset includes structural files of the antibiotics in several formats suitable for computational modeling and simulations. These formats encompass Protein Data Bank, Partial Charge & Atom Type (PDBQT), Simplified Molecular Input Line Entry System (SMI), IUPAC International Chemical Identifier (INCHI), Molecular Design Limited Molfile (MOL2), Structure-Data File (SDF), Chemical Markup Language (CML), Cartesian Coordinates File (XYZ), Scalable Vector Graphics (SVG), Molecular File (MOL) and Protein Data Bank (PDB) files, with molecular models generated via OpenBabel to facilitate advanced studies in drug development and resistance mechanisms.

Cost-effective next-generation sequencing has made unbiased gene expression investigations possible. Gene expression studies at the level of single neurons may be especially important for understanding nervous system structure and function because of neuron-specific functionality and plasticity. While cellular dissociation is a prerequisite technical manipulation for such single-cell studies, the extent to which the process of dissociating cells affects neural gene expression has not been determined. Here, we examine the effect of cellular dissociation on gene expression in the mouse hippocampus. We also determine to which extent such changes might confound studies on the behavioral and physiological functions of hippocampus.

This dataset contains the data, software, and results the accompany a manuscript that is in the process of submission to the journal Hippocampus.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for master of science in bioinformatics in the U.S.

The Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE) seeks to promote the use of bioinformatics and data science as a way to teach biology.

This dataset comprises 1000 hypothetical patient or sample entries, each detailing gene expression profiles and relevant clinical characteristics. It includes a mix of both numerical and categorical data types, allowing for the application of diverse machine learning and statistical analysis methods

Column Descriptions: PatientID (Categorical/Numerical): A unique identification number assigned to each patient. Age (Numerical): The patient's age. Can be used to investigate potential correlations between age and gene expression profiles. Gender (Categorical): The patient's gender (0: Female, 1: Male). Effects of gender on gene expression or disease status can be analyzed. Gene_X_Expression (Numerical): The relative expression level of a specific gene, "Gene X". This represents a hypothetical gene that might play a role in disease progression or treatment response. Gene_Y_Expression (Numerical): The relative expression level of another specific gene, "Gene Y". Can be studied in conjunction with or independently of Gene X. SmokingStatus (Categorical): The patient's smoking status (0: Non-smoker, 1: Ex-smoker, 2: Current smoker). Environmental factors' impact on gene expression and disease can be assessed. DiseaseStatus (Categorical): The patient's status for the target disease (0: Healthy, 1: Disease A, 2: Disease B). This can serve as the primary target variable for your predictive models.

TreatmentResponse (Categorical/Numerical): The degree of response to applied treatment (0: No Response, 1: Partial Response, 2: Full Response). The role of gene expression profiles in predicting treatment success can be explored. Use Cases and Potential Projects This dataset serves as an excellent starting point for students, researchers, and enthusiasts in bioinformatics, computational biology, data science, and machine learning, enabling various projects such as: Disease Diagnosis/Classification: Building models to predict HastalıkDurumu using gene expression levels and other clinical factors. Treatment Response Prediction: Forecasting how patients with specific gene expression profiles might respond to treatment (TedaviYanıtı). Biomarker Discovery: Identifying gene expression levels (e.g., Gen_X_İfadesi, Gen_Y_İfadesi) that show strong correlations with disease or treatment response. Feature Engineering and Selection: Evaluating the importance of various features in the dataset and creating new ones to enhance model performance. Data Visualization: Generating visualizations to explore relationships between gene expression data and demographic/clinical factors. Regression and Correlation Analyses: Quantitatively examining the effects of factors like age and smoking status on gene expression levels.

Why Use This Dataset? Privacy Secure: Being entirely synthetic, it carries no privacy or ethical concerns associated with real patient data. Diversity: The mix of both numerical and categorical variables offers a rich ground for experimenting with different analytical techniques. Predictive Potential: Clear target variables like HastalıkDurumu and TedaviYanıtı make it ideal for developing classification and regression models. Educational and Learning: Perfect for applying fundamental data science and machine learning concepts for anyone interested in the bioinformatics domain.

This dataset is a dedicated resource for learning how to parse core bioinformatics file formats. It contains representative samples of FASTA and GenBank files. The goal is to provide raw data for practicing essential data extraction skills. FASTA files contain sequence data, such as DNA, RNA, or protein, in a simple text format. GenBank files include detailed sequence annotations, features, and metadata. This is an ideal starting point for anyone learning Biopython or general sequence manipulation in genomics.

Global Bioinformatics Services Market is segmented by Application (Pharmaceutical Companies_ Biotech Companies_ Research Institutions), Type (Biotechnology_ Life Sciences_ Genomics_ Bioinformatics_ Data Science), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Figures and survey data from forthcoming pre-print:

In a 2016 survey of 704 National Science Foundation (NSF) Biological Sciences Directorate principle investigators (BIO PIs), nearly 90% indicated they are currently or will soon be analyzing large data sets. BIO PIs considered a range of computational needs important to their work—including high performance computing (HPC), bioinformatics support, multi-step workflows, updated analysis software, and the ability to store, share, and publish data. Previous studies in the U.S. and Canada emphasized infrastructure needs. However, BIO PIs said the most pressing unmet needs are training in data integration, data management, and scaling analyses for HPC – acknowledging that data science skills will be required to build a deeper understanding of life.

The dataset consists of 1763 observations, each representing a unique patient, and 12 different attributes associated with heart disease. This dataset is a critical resource for researchers focusing on predictive analytics in cardiovascular diseases.

Variables Overview: 1. Age: A continuous variable indicating the age of the patient. 2. Sex: A categorical variable with two levels ('Male', 'Female'), indicating the gender of the patient. 3. CP (Chest Pain type): A categorical variable describing the type of chest pain experienced by the patient, with categories such as 'Asymptomatic', 'Atypical Angina', 'Typical Angina', and 'Non-Angina'. 4. TRTBPS (Resting Blood Pressure): A continuous variable indicating the resting blood pressure (in mm Hg) on admission to the hospital. 5. Chol (Serum Cholesterol): A continuous variable measuring the serum cholesterol in mg/dl. 6. FBS (Fasting Blood Sugar): A binary variable where 1 represents fasting blood sugar > 120 mg/dl, and 0 otherwise. 7. Rest ECG (Resting Electrocardiographic Results): Categorizes the resting electrocardiographic results of the patient into 'Normal', 'ST Elevation', and other categories. 8. Thalachh (Maximum Heart Rate Achieved): A continuous variable indicating the maximum heart rate achieved by the patient. 9. Exng (Exercise Induced Angina): A binary variable where 1 indicates the presence of exercise-induced angina, and 0 otherwise. 10. Oldpeak (ST Depression Induced by Exercise Relative to Rest): A continuous variable indicating the ST depression induced by exercise relative to rest. 11. Slope (Slope of the Peak Exercise ST Segment): A categorical variable with levels such as 'Flat', 'Up Sloping', representing the slope of the peak exercise ST segment. 14. Target: A binary target variable indicating the presence (1) or absence (0) of heart disease.

Descriptive Statistics: The patients' age ranges from 29 to 77 years, with a mean age of approximately 54 years. The resting blood pressure spans from 94 to 200 mm Hg, and the average cholesterol level is about 246 mg/dl. The maximum heart rate achieved varies widely among patients, from 71 to 202 beats per minute.

Importance for Research: This dataset provides a comprehensive view of various factors that could potentially be linked to heart disease, making it an invaluable resource for developing predictive models. By analyzing relationships and patterns within these variables, researchers can identify key predictors of heart disease and enhance the accuracy of diagnostic tools. This could lead to better preventive measures and treatment strategies, ultimately improving patient outcomes in the realm of cardiovascular health

QIIME 2 (pronounced “chime two”) is a microbiome multi-omics bioinformatics and data science platform that is trusted, free, open source, extensible, and community developed and supported.

Question Paper Solutions of chapter Biological Data of Bioinformatics, 7th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

The bioinformatics market has emerged as a pivotal domain at the intersection of biology and data science, playing an essential role in the analysis and interpretation of complex biological data. As the demand for genomic and proteomic data analysis continues to rise, bioinformatics offers innovative solutions for d

1. SARS-CoV-2SpikeProteinMutations.docx contains data on mutations found in aligned SARS-CoV-2 surface glycoproteins.
2. SARS-CoV-2SpikeProteinVariants.docx contains data on computed SARS-CoV-2 surface glycoprotein variants in Europe.