Bio Saga Blog - A Chronicle of Life Sciences & Informatics

By considering sequence data for individuals assessed through the 1000 Genomes Project, a team led by researchers from Yale University and Wellcome Trust Sanger Institute came up with a computational method for prioritizing potential disease culprits — including those in non-protein-coding parts of the genome. As they reported online today in Science , the researchers sifted through SNP profiles in coding and non-coding sequences in 1,092 genomes, focusing on functionally annotated areas. With the help of information from the ENCODE project, mutation databases, and other data sources, they narrowed in on sequences that seem especially sensitive to change. The group tapped these mutation-sensitive sites to develop an approach called FunSeq, which proved useful for uncovering new apparent driver mutations using sequences from around 90 cancer genomes. These included almost 100 driver candidates in non-coding sequences, according to study authors, who noted that FunSeq is expected...

Avesthagen launches commercial Whole Genome Scanning Bringing Personalized Genomics to your doorstep. Avesthagen will use Affymetrix technology to decipher the association between single nucleotide polymorphisms (SNPs)/ mutation and predisposition to the disease. The DNA chip used by Avesthagen for whole genome scanning features more than 1.8 million markers of genetic variation that include SNPs as well as probes for copy number variation (CNV). A whole genome scan of an individual would provide information to understand his/her own genetic make-up that would lead to an increased awareness about the predisposition to a disease(s). The diseases, which are offered by Avesthagen, include major types of cancer, cardiovascular diseases, diabetes, schizophrenia, Alzheimer’s, asthma, anemia, arthritis and others. The technology platform at Avesthagen is able to interrogate the genetic markers (SNPs and CNVs) across an individual’s genome...

Genome-wide association studies (GWAS) of late-onset Alzheimer disease (LOAD) have consistently observed strong evidence of association with polymorphisms in   APOE . However, until recently, variants at few other loci with statistically significant associations have replicated across studies. The present study combines data on 483,399 single nucleotide polymorphisms (SNPs) from a previously reported GWAS of 492 LOAD cases and 496 controls and from an independent set of 439 LOAD cases and 608 controls to strengthen power to identify novel genetic association signals.   Studies looking for genetic variants across the genome that affect late-onset Alzheimer disease (LOAD) have had little success identifying genes other than  APOE . Here, we use an expanded set of AD cases and controls to improve our power to detect genetic variants driving LOAD risk. Analyzing 483,399 genetic variants across the genome in a discovery dataset of 931 cases and 1,104 controls, we found a s...

Last week the XTractor Premium Knowledgebase has clocked 150,000 manually categorized biomedical facts. The knowledgebase currently grows at a rate of more than 700 facts every single day from PubMed and covers information on Biomarkers , Disease mechanisms, Clinical Trials, polymorphisms , mutations , knockouts and pathways . The knowledgebase recently crossed landmark of more 10000 relationships on knockout and mutation studies alone in XTractor. Knockout/Knockdown related facts: 10132 Mutation related facts: 14331 So not only you get the most relavant scientific facts everyday but also individual uptodate knowledge on knockouts, mutations, biomarkers , SNP and so on… XTractor Premium - A Platform for discovery , analysis and modelling of published biomedical facts. The only Knowledgebase which provides "manually" annotated facts from PubMed on a weekly basis Innovative Features like Semantic Search , Concept Linking , Bibliographic search , Save, Export a...