Articles tagged with Bioinformatics
A UTHSC team developed a unique community-focused COVID-19 data registry, MEMPHI-SYS, to guide public health policies and interventions nationwide. The registry collects demographic information, geographic locations, medical history, and risk factors, providing insights into the spread and presentation of COVID.
A study by the University of Tsukuba found that Siebold's beech populations on Okushiri Island have high genetic diversity, suggesting they may have persisted there since before the last glacial maximum. This challenges the assumption that island populations are less genetically diverse due to geographical isolation.
The IU Simon Comprehensive Cancer Center has created an endowed chair in cancer informatics with a $3 million gift from the Walther and Regenstrief foundations. This initiative aims to analyze large amounts of data to identify those at risk of developing cancer, optimize prevention and detection, and improve outcomes.
Hiroshima University researchers have generated a high-quality genome assembly of red perilla, allowing scientists to harness its abundance of potentially useful bioactive chemicals. The study enables targeted gene editing for enhanced phytochemical production, paving the way for new medical applications.
Heidelberg researchers decoded genetic regulation of sperm formation across various mammal species, including humans. The study revealed a time-related pattern of gene expression differences, highlighting the rapid evolution of spermatogenesis.
A meta-analysis of transcriptome data from aphids and locusts reveals that DNA replication, metabolic processes, and the mitotic cell cycle are enriched in response to crowded conditions. The study also identifies discrepancies with previous research findings, suggesting alternative explanations for gene expression patterns.
A new undergraduate research program at the University of Arizona aims to address local health issues in Arizona using a community-based approach. The PHIRE program recruits 60 students annually for training in health informatics and places them in research projects relevant to their communities.
Researchers successfully reconstructed a two-billion-year-old enzyme, shedding light on an ancient evolutionary mechanism that enables modern enzymes to work efficiently despite interruptions. The study reveals the ancestral enzyme is processive, removing correctly added nucleotide building blocks, and shows how bioinformatics and bioc...
Research reveals that brain function networks are affected differently by aging, gender, and blood immune factors. The study found correlations between cytokine clock, brain shrinkage, and gender, with females having a faster ticking cytokine clock.
Researchers developed an integrated approach to accelerate drug discovery by combining complex datasets from two screening platforms and next-generation metabolomics analysis. The new framework identified known compounds, confirmed mechanisms of action, and discovered novel compounds with unique biological signatures.
A Collaborative Research Centre investigates animal navigation using the Earth's magnetic field. The study focuses on vertebrates, including birds and fish, aiming to protect endangered migratory species.
Researchers identified genes associated with aggressive clear cell renal carcinoma, a subtype of kidney cancer. Anticoagulant therapies may enhance cancer treatment efficacy. Specific genes involved in blood clotting and insulin transport were found to be linked to disease progression.
MU researchers, including Jay J. Thelen and Dong Xu, are exploring genetic modification to increase seed oil production in camelina and pennycress for biofuel use in the aviation industry. The team aims to create a sustainable 'green energy' source as an alternative to petroleum-based fossil fuels.
Researchers developed STAARpipeline to analyze rare genetic variants, enabling comprehensive analysis of noncoding variants. The tool found significant associations in gene-centric and non-gene-centric analyses, advancing genetic research.
The use of Frictionless Data enhances both machine readability and human engagement with scientific outputs, turning articles into dynamic entities. Interactive figures enable readers to directly interact with data points, making the publication process more efficient and reproducible.
Researchers discovered a pattern of DNA mutations that links bladder cancer to tobacco smoking using a powerful new machine learning tool. The tool identified four mutational signatures, including one tied to tobacco smoking, which could lead to more customized treatments for patients with specific cancers.
The Bioteque computational tool integrates vast amounts of biological data, providing insights into gene functions, protein interactions, and disease connections. By analyzing over 30 million functional interactions, the tool can predict drug sensitivity and accelerate biomedical research.
Researchers analyzed proteins from eight species and found that low-complexity regions (LCRs) share similar roles across species, helping proteins join larger-scale assemblies. They also discovered species-specific LCR sequences corresponding to unique functions, such as forming plant cell walls.
Researchers from Hiroshima University have identified two types of genes that are upregulated in both rice and thale cress when exposed to oxygen deprivation. The study suggests distinct molecular mechanisms under hypoxia in plants and animals, with potential applications for creating flood-tolerant crops.
A research team analyzed germ cells in chickens using RNA-sequencing to predict the mechanism that determines sex. The study reveals a mix of cell-autonomous and somatic-cell regulation, with sex determination occurring earlier in females than males.
A team from Ruhr-Universität Bochum developed a novel neural network that can classify tissue samples as containing tumors or not. The AI also generates an activation map showing where the tumor is detected, based on falsifiable hypotheses.
Researchers found 87 genes affected by deletions or short insertions in the woolly mammoth genome, potentially impacting its adaptability to cold environments. These changes may have contributed to unique traits such as fur growth and hair shape.
A new study at the University of Missouri found that prenatal opioid exposure can trigger long-term neurological or behavioral effects in children. The research suggests that opioids impact the developing fetus' gut microbiome, leading to changes in metabolic pathways and potentially affecting overall health and wellness.
Researchers have developed an algorithm to identify the protozoan Trypanosoma cruzi in photographs of blood samples taken with a mobile phone camera. The low-cost method can be reproduced and adapted for other purposes that depend on images, such as analyzing feces, skin, or colposcopies.
Researchers have developed Fanflow4Insects, a functional annotation workflow that annotates gene functions in insects. The team has annotated the Japanese stick insect and silkworm, providing valuable information for entomological research and genome editing.
Researchers developed a new machine learning method called MotifBoost that can predict infections based on limited T-cell receptor data. By focusing on short sequences of amino acids in the receptors, the approach achieved more accurate results with smaller datasets, shedding light on the human immune system's recognition of germs.
Researchers developed a mathematical model to predict the efficiency of nanoparticle delivery into cells, particularly in stem cells. They found that nanoparticles become trapped in bubble-like vesicles, preventing them from reaching their targets.
The Mount Sinai Hospital has been awarded $4.2 million over five years to establish a Proteogenomic Data Analysis Center, which will help identify potential biomarkers and drug targets for cancer and new insights into cancer biology.
A UC Riverside study reports that switching from combustible cigarettes to electronic cigarettes does not restore the nasal epithelium to that of a non-smoker. The researchers found alterations in gene expression profiles associated with cigarette smokers, indicating potential harm from prolonged EC use.
Rensselaer researchers will use a five-year grant to develop novel inhibitors of the SARS-CoV-2 virus's CLpro and PLpro proteases. The team aims to create an orally bioavailable drug that can be administered at home, with the potential for improved antiviral activity when combined with other drugs like remdesivir.
A team of scientists has developed a novel computational approach to analyze DNA sequences of thousands of bacteria, revealing previously unknown gene clusters responsible for producing metabolites of interest. The study highlights the potential of these bacterial compounds in treating colon cancer and improving treatment options.
A new machine learning algorithm called 'ikarus' has found a gene signature characteristic of tumors, distinguishing between healthy and tumor cells in various types of cancer. The algorithm was trained on single-cell sequencing data sets and demonstrated an extraordinarily high success rate in distinguishing between different cell types.
Researchers developed a machine learning model to predict NAFLD development based on gut microbiome data, showing 90% of subjects who developed the disease had subtle differences in their samples. The model combines easily measurable information from blood and microbiome data with high accuracy.
Researchers developed an algorithm to classify patients into disease subtypes based on molecular changes, enabling more targeted treatments. The tool has shown potential in a study on non-alcoholic fatty liver disease, identifying lipid biomarkers for disease progression.
A new computer-aided diagnostic tool, Deep-Lung Parenchyma-Enhancing (DLPE), uses artificial intelligence to reveal signs of pulmonary fibrosis in COVID long-haulers, helping to explain respiratory symptoms and improve disease management.
Researchers at Gwangju Institute of Science and Technology have developed a new bioinformatics pipeline, CRESSP, to investigate the mechanism underlying autoimmune diseases following SARS-CoV-2 infection. The tool identified potential epitopes responsible for COVID-related autoimmune diseases and predicted cross-reactive epitopes of di...
Researchers developed an algorithm to quickly identify two cancer drugs that work well together, reducing testing time from three years to eight weeks. The new method uses gene expression data to prioritize potential combinations and has been confirmed in lab tests.
A recent study found that boosting the pink pigeon's numbers is not sufficient to prevent its extinction in the future. The species has a high genetic load of bad mutations, which puts it at risk of collapse if conservation efforts do not continue.
Researchers have developed a refined tool to identify potentially druggable gene amplifications in tumors using epigenetic data. The new methodology improves the characterization of somatic copy number alterations up to 20% with high accuracy, reaching 100% specificity and 93.3% sensitivity.
A team of researchers used a CRISPR-based approach to study the evolution of lung cancer cells, tracking their history from the first activation of cancer-causing mutations. The study revealed significant diversity between subpopulations of cells within the same tumor, with certain groups becoming more fit and aggressive over time.
Researchers developed a new software tool, called 'meta-transcriptome detector,' that integrates genetic sequencing analysis of hosts and their microbiomes. The tool enables analysis of gene expression activity in both microbes and hosts simultaneously, allowing researchers to spot relationships between them.
Researchers have identified potential biomarkers in lupus nephritis through large-scale protein analysis, which may replace invasive tests. The study's findings could lead to more accurate diagnosis and treatment of the disease.
The study found that T-cell immunity was effective against Omicron variants in approximately 90% of vaccinated Europeans. The researchers identified a specific HLA-DRB1*03:01 variant that allowed the virus to evade immune recognition, particularly affecting individuals with this genetic variation.
A team of researchers developed a prognostic model using automated brain scans and machine learning to predict survival and recovery in severe TBI patients. The model accurately predicted risk of death and unfavorable outcomes at six months after the injury, potentially improving clinical decision-making and patient care.
A recent study by Japanese researchers found that Staphylococcus argenteus, a bacterial organism causing food poisoning, was present in 13.9% of tested chicken samples. The study also revealed cross-contamination among retail foods and slaughterhouses, suggesting a potential link between the two.
Researchers developed a bioinformatics platform called REFLECT to predict optimal combination therapies based on co-occurring tumor alterations. The tool selected combinations that resulted in improved patient outcomes across pre-clinical and clinical studies, leading to increased durable responses.
Researchers found that mutation frequencies in mitochondrial DNA of developing egg cells are lower and increase less with age compared to non-reproductive cells. This suggests a protective mechanism may keep reproductive cells relatively stable, potentially related to human propensity to reproduce at later ages.
The SAVE program, a NIH initiative, provides real-time risk assessment of SARS-CoV-2 variants on immune protection. It uses a coordinated approach to identify and curate data about these variants, their impact on immunity, and their effects on vaccine protection.
A recent study identified two main subtypes of tuberculosis based on immune response to the infection. One subtype has a better prognosis and could improve treatment options, while another has a higher risk of treatment failure and death.
Autophagy is induced when ER-bound ribosomes are stalled, rescuing them from cellular harm. The ERC grant aims to decipher the role of Autophagy in rescuing stalled ER-bound ribosomes.
The African BioGenome Project aims to sequence the genomes of Africa's endemic plants, animals, fungi, and protists. This will build resilience in breeding, sustainable food systems, and biodiversity conservation, aligning with the post-2020 Global Biodiversity Framework and the United Nations Sustainable Development Goals.
A team of researchers found that evolution has an overwhelming preference for simple algorithms, leading to the emergence of highly symmetric structures in biology. This is because simple recipes are more efficient and easier to follow than complex ones, resulting in a higher probability of producing symmetrical outcomes.
Researchers created a comprehensive genomic regulatory map of a 24-hour-old zebrafish embryo, identifying millions of regulatory segments that control gene transcription. The study used single-cell technologies and machine learning algorithms to analyze genome data from over 23,000 nuclei.
A recent study reveals that parthenogenesis, a type of asexual reproduction, leads to a loss of efficiency in selecting beneficial mutations, reduced genetic diversity, and decreased heterozygosity in stick insects. This has significant implications for species' ability to adapt to changing environments.
University of Illinois researchers found a key gene responsible for increasing soybean protein content by approximately 2%. The discovery could lead to significant increases in protein production, addressing global food security issues. However, the gene's function is unclear and may involve the plant's circadian machinery.
Researchers developed an AI system called GestaltMatcher that uses facial characteristics to detect rare diseases with high accuracy. The system considers similarities between patients and can even suggest diagnoses for previously unknown diseases.
A recent study by KU Leuven and The Francis Crick Institute analyzed 2,658 tumor samples and found that 21% exhibited double mutations in both maternal and paternal DNA copies. This discovery sheds new light on the origin of mutations and their role in cancer development.
Researchers have developed a machine learning framework called dynamo that can predict a cell's path over time, including its fate and genetic changes. The tool uses data from individual cells to create mathematical equations describing the cell's trajectory.
A study by Michigan Medicine researchers found that people with diabetes have more macrophages in their fat tissue, particularly the anti-inflammatory CD206+ subtype. This subtype is unique in terms of gene expression and may play a role in the development of diabetes.
Researchers have identified a potential novel mechanism of action against drug-resistant tuberculosis (TB) caused by mutations in the EmbR protein, which binds to transmembrane kinase PknH. This study proposes a new approach for computer-aided drug discovery and has important implications for treating other critical diseases.