Articles tagged with Gene Prediction
G-Quadruplex DNA structures play a crucial role in regulating genes and cell processes, but their visualization is challenging due to the dynamic nature of double standard DNA. Fluorescence-active small molecule probes have emerged as a real-time visualization method, enabling researchers to detect G-quadruplexes with high selectivity.
Researchers at the Salk Institute have identified mechanisms that activate oncogenes in cancer cells, providing insights into predicting and treating the disease. The study found that structural variants in DNA can impact gene expression, leading to cancer, but most variants have no effect.
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.
A gene signature of four specific genes (SAA1, SAA2, APOL1, and MET) predicts the risk of tumour spreading and survival in kidney cancer patients. The study identified a link between the microenvironment and immune system inhibition.
Researchers found that horizontal gene transfer can accelerate bacterial adaptation under most growth conditions, but not all. By analyzing the fitness effects of gene transfer, they can predict when it will provide an advantage.
A mutation in the TMEM163 zinc transporter gene has been definitively linked to hypomyelinating leukodystrophy, a rare and often fatal neurological disorder. The study's findings provide new insights into the role of zinc in normal brain development, injury, and disease.
Researchers at Cedars-Sinai created complex computer models of individual brain cells using artificial intelligence. These models capture the electrical signals that neurons fire to communicate with each other, allowing researchers to replicate brain activity at the single-cell level.
Researchers identified a three-gene signature in multiple myeloma tumors that predicts a positive response to selinexor-based therapy. The discovery could improve patient selection for targeted agents and expand the use of the drug into patients who haven't failed other therapies.
Researchers identified specific genetic signals in people with severe coronavirus infection using machine learning. They found that more than 1,000 genes linked to the development of severe COVID-19 cases account for three-quarters of the genetic drivers for severe illness.
Biologists at the University of Rochester have identified two key systems controlling gene expression related to longevity: circadian networks regulating negative lifespan genes and the pluripotency network controlling positive lifespan genes. This research provides new insights into understanding how longevity evolves and may lead to ...
A new study reveals that most mutations in biologically essential genes change steadily and randomly across 700 million years of evolution. This constant drift makes it impossible to reliably predict the effects of most mutations into the future or back into the past.
A team of researchers identified a stem rust resistance gene from wild goat grass species Aegilops sharonensis, which can be cross-bred into wheat for immunity against deadly crop pathogens. The genetic potential of this hardy relative has been largely unexplored and holds promise for reducing the threat of the stem rust disease.
A new study from Mayo Clinic and Baylor College of Medicine found that targeted genomic information can significantly impact drug prescribing practices. By applying drug-gene testing, clinicians can identify nearly every patient as a potential candidate for preemptive testing, particularly for drugs with unknown genetic influences.
Researchers developed a novel genetic barcode system to mark cancer cells with different gene modifications and image their characteristics. The Perturb-map platform identified specific genes controlling lung tumor growth, immune composition, and response to immunotherapy, offering new approaches for targeting anti-cancer drugs.
A new mathematical framework has been created to study fitness landscapes of regulatory DNA, enabling the prediction of gene expression changes. The framework uses a neural network model trained on millions of experimental measurements to decipher the evolutionary past and future of non-coding sequences.
Annual MRI screenings starting at ages 30-35 may reduce breast-cancer mortality by more than 50% among women who carry certain genetic changes in three genes. The predictions involve pathogenic variants in ATM, CHEK2 and PALB2 genes – which collectively are as prevalent as the much-reported BRCA1/2 gene mutations.
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.
Research found that mothers with metabolic syndrome can switch on an imprinted gene in their mice offspring, leading to liver disease. The study suggests a key role for imprinted genes in the development of non-alcoholic fatty liver disease (NAFLD).
Researchers identified seven rare structural variants affecting 31 genes in severely ill COVID-19 patients, shedding light on individual responses to the virus. These genetic variations may help explain differences in illness severity and suggest potential targets for early intervention.
Researchers have discovered a new method to predict heart attacks by analyzing the gene expression of foamy macrophages, revealing a person's cardiovascular health. The study found that foamy cells can be both beneficial and detrimental depending on their behavior in individuals with certain conditions.
A team of researchers at Harvard's Wyss Institute and ETH Zurich have developed a computational approach to identify genomic safe harbors (GSHs) with high potential for safe insertion of therapeutic genes. The study validated two GSH sites in adoptive T cell therapies and in vivo gene therapies for skin diseases.
Researchers found patches of low mutation rates in the genome with essential gene over-representation, suggesting a protective mechanism. This discovery could lead to advances in plant breeding and human genetics, potentially helping breed better crops or fight cancer.
Researchers found that plants have evolved a way to protect their most important genes from mutation, which has significant implications for understanding crop domestication and cancer. The study discovered non-random patterns in DNA mutations, with essential genes overrepresented in regions where mutations are rare.
New research enables regionally relevant eating-quality traits to be selected early in breeding programs, saving time and effort. Genetic markers associated with 10 grain-quality traits have been identified, which can now be used by rice breeders in Latin America and potentially worldwide.
Researchers at Cornell University used machine learning to predict the spread of antibiotic resistance genes among bacteria, identifying potential networks of exchange and driving factors. The approach could help control the spread of antibiotic resistance and develop new targets for novel antibiotics.
The American Heart Association scientific statement highlights the importance of pre-test and post-test counseling for families with children undergoing CVD genetic testing. The statement emphasizes the need for a multidisciplinary approach to address family concerns and provide appropriate follow-up care.
A study by the Center for Cell-Based Therapy in São Paulo, Brazil, has discovered a set of biomarkers that can be used to predict which patients with glioblastoma may have tumors resistant to radiation therapy. The genetic signature helps doctors choose the best treatment option for these patients.
A previously validated blood gene profile that predicts idiopathic pulmonary fibrosis mortality has been repurposed to assess the likelihood of COVID-19 survival. The study found a high-risk gene profile associated with greater ICU admission, mechanical ventilation, and in-hospital death.
Biologists have identified 71 new 'imprinted' genes in the mouse genome, which tend to be more active in one parental version than the other. The discovery sheds new light on epigenetics and its importance in health and disease.
A new computational approach identifies genes most likely linked to autism spectrum disorders (ASD) and predicts patient IQ using rare mutations. Researchers analyzed de novo missense mutations in a cohort of patients with ASD and their siblings, revealing that most genes are mutated only once.
A new algorithm called Meta-Apo reduces the need for expensive whole-genome sequencing to understand microbial function, improving consistency with 16S rRNA gene amplicon results. This approach enables accurate diagnoses like gingivitis improvement from 65% to 95% using low-cost 16S-amplicon sequencing.
Researchers have identified key genes responsible for male sexual organs in two problematic agricultural weeds. Understanding these genes could lead to new ways to control the weeds' spread. Dioecy, a rare plant reproductive trait, allows the weeds to evade multiple herbicides and adapt to environments.
A new prediction algorithm called DORGE has identified novel tumor suppressor genes and oncogenes by integrating comprehensive genetic and epigenetic data. The study found that the algorithm successfully predicted both known and novel cancer driver genes, including dual-functional genes involved in protein-protein interaction networks.
A recent study identifies two genes, SNRNP35 and ZNF140, that may affect PTSD susceptibility. Individuals with PTSD tend to have lower expression of SNRNP35 in the brain and higher expression of ZNF140 in the blood. This research could lead to new treatment and prevention strategies for PTSD.
Researchers developed a model to predict changes in tree genes, reducing unintended consequences of genetic modification. The model captures cross-regulatory influences between genes, enabling more efficient gene-modification strategies.
A recent study from the University of Alberta suggests that many cancers, diabetes, and Alzheimer's diseases have a genetic contribution of no more than 5-10%. The research highlights the importance of metabolites, chemicals, proteins, or the microbiome in determining disease risk rather than genes.
A study in Scientific Reports presents a genetic model that accurately estimates lifespans of different vertebrate species by analyzing 42 selected genes and their CpG sites, which are correlated with lifespan. The 'lifespan clock' reveals high predictive power for various species, including extinct ones.
A study found that clownfish population persistence depends on high-quality habitats, not shared genes. The researchers identified individual fish and their DNA over five generations to assess reproductive success.
A Rutgers discovery identified a siesta-suppressing gene in fruit flies that helps creatures balance the benefits of napping against getting important activities done during the day. The 'daywake' gene regulates behavioral flexibility, allowing flies to seek food or mates when temperatures are cool.
A new fact-checking computer program has identified sequence errors in 25% of biomedical research papers, including identity errors and typographic errors. The program, Seek & Blastn, could help deter fraudulent publications and re-evaluate existing conclusions.
A Stanford study has identified a set of 20 genes that predict the severity of dengue fever, allowing for more accurate diagnosis and treatment. The researchers used gene expression data from hundreds of patients to develop a predictive model that can identify patients at risk of severe disease.
Researchers solve decades-long mystery of red pigments in corn by discovering a new gene, Ufo1, which controls the expression of a pigment pathway. The study's findings have significant implications for plant breeding, biofuel production, and crop protection from pests.
Virginia Tech researchers are developing a new algorithm to predict the effects of novel gene mutations in living cells. They will apply this framework to models of cell growth and division in budding yeast, paving the way for faster drug development and improved cellular understanding.
A team of scientists discovered a gene signature linked to spinal cord injury severity, which can predict functional recovery. The study identified key genes that are switched on or off in response to injury, potentially informing the development of biomarkers for treatment.
A team at Princeton's Lewis-Sigler Institute for Integrative Genomics has produced new resources for studying the biology of multicellular organisms. The researchers analyzed gene activity patterns in four major tissues and developed a tool to predict gene expression across cell types.
Researchers developed a novel approach to predict genes susceptible to Alu/Alu-mediated rearrangements, which can cause disease. The model analyzed sequence features of Alu pairs and identified hotspots of genomic instability associated with these elements.
A team of researchers has developed an analytical tool to predict genes that can cause disease due to the production of truncated or altered proteins. The tool identified 252 candidate 'disease genes,' some of which have already been linked to disease in previous studies, supporting its effectiveness.
An international team of researchers developed a new computational method to predict the functions of thousands of microbial genes. The method, based on machine learning algorithms, analyzes 'big data' from human microbiomes and other environments to identify evolutionary signals that can assign biological roles to unknown genes.
Researchers developed a logic-based model incorporating information about developmental signaling pathways to predict disease progression and outcome in neuroblastoma. The model proved accurate in predicting outcomes in children less than 2 years old with 91% accuracy.
Researchers identified four TRAF7 mutations in seven patients with a similar multisystem disorder, associated with developmental delay, congenital heart defects and limb anomalies. The mutations reduced ERK1/2 pathway activity, suggesting a possible genetic link to the condition.
A team of scientists has created a map of the molecular network in the aging brain, revealing two new Alzheimer's disease target genes that can be deleted or modified to treat the disease. The study provides a unique resource linking molecular changes in nearly 500 older brains to cognition and brain pathology.
Researchers developed a genomic blood test that uses gene activity data to predict survival rates for patients with advanced heart failure who undergo mechanical circulatory support devices. The test was 93% accurate in predicting outcomes and could improve survival rates and treatment decisions.
Scientists used an evolutionary medicine approach to estimate Alzheimer's disease causative genes by focusing on ohnologs, duplicated genes vulnerable to change in number. The study successfully identified a group of genes related to the nervous system and high brain expression levels similar to known AD-causative genes.
Researchers developed a computer method called ZeitZeiger that uses genome-wide expression data to predict a person's 'internal' time of day. The method demonstrates accurate predictions using 15 genes from across the human genome, including non-core clock genes.
Researchers at Uppsala University found that genes frequently collaborate in large clusters or networks to regulate traits. This study highlights the importance of considering gene interactions when predicting genetic effects on individuals.
A study by researchers at UNC Lineberger Comprehensive Cancer Center identified biomarkers that predict how patients with HER2-positive breast cancer will respond to targeted treatment trastuzumab and chemotherapy. The biomarkers are based on gene signatures, genetic mutations, and immune responses.
Researchers found nearly all common obesity-related genes lack properties that suggest an evolutionary advantage, contradicting the 'thrifty gene' hypothesis. The study's results suggest a more complex evolution of human health and disease.
A recent study by Princeton University and Simons Foundation researchers has identified over 2,500 candidate genes that may contribute to autism spectrum disorder. The machine-learning program analyzed the human genome to predict which genes are at risk of causing the condition.
The National Institutes of Health (NIH) has awarded $28.3 million to The Jackson Laboratory over five years to fund phase 2 of the Knockout Mouse Production and Phenotyping Project (KOMP2). This project aims to create targeted knockout mutations for every gene in the mouse genome, providing valuable clues to their function.
A team of Princeton researchers has developed a machine-learning approach that analyzes the entire human genome to predict which genes may cause autism spectrum disorder. By identifying 2,500 potential autism genes, this new method provides a significant breakthrough in understanding the genetic basis of autism.