Sissy Bassani, PhD’s Post

A Cutting-Edge Machine Learning Innovation Enhances Genetic Risk Assessment for Diverse Populations #AI #artificialintelligence #Biotechnology #CTSLEBmethod #datasources #Geneticriskassessment #healthcaredisparities #llm #machinelearning #minoritypopulations #racialbiases

Check out my new post which discusses how we changed our understanding of the genetic code and translation in general and how can this help dealing with some genetic diseases. #genetics #translation #biology #RNA

Unlock the power of structural variant analysis with our latest blog post! Discover how the Breakends Table in VarSeq simplifies the identification and interpretation of structural variants, making your research more efficient and accurate. Don't miss out on this deep dive into innovative genetic research tools! Read more here: https://bit.ly/3VLrYBn #Genetics #Genomics #StructuralVariants #VarSeq #GoldenHelix #GeneticResearch #Bioinformatics

HEALTH STRING by SugarStrings.ai is a superior product globally in providing absolute genetic health reports, rather than the population-relative risk analysis scores offered by other genetic screening tests. Our method is proprietary and not restricted by the outdated predetermined marker-based approaches commonly used by others in this space. We decode every letter of targeted gene stretches in your DNA, resulting in comprehensive coverage and in-depth analysis. Our innovative population-agnostic approach, achieved through machine learning algorithms, makes HEALTH STRING one-of-a-kind as it is independent of genetic frequencies present in public databases which are biassed towards European populations.

Today, the #gnomAD Production Team is proud to announce the release of gnomAD v4! The v4 data set includes data from 807,162 individuals including 730,947 exomes and 76,215 genomes, all mapped to the GRCh38 reference sequence. This release is nearly 5x larger than the combined v2/v3 releases and adds additional global diversity including nearly 138,000 individuals of non-European genetic ancestry (see broad.io/gnomad_stats for more details). Over 1 billion variants were analyzed with the following passing QC: - 786,500,648 SNVs & 122,583,462 InDels available in the browser (broad.io/gnomad) and in our downloads (broad.io/gnomad_downloads) - 1,199,117 genome structural variants - 66,903 rare (< 1% site frequency) exome copy number variants The v4 release is a minimum viable product (MVP) release, which allows us to get the most critical piece of the v4 gnomAD dataset, high quality aggregate allele frequencies and updated constraint metrics, to our users as soon as possible. It also means that a few of the existing features found in v2 or v3 are not yet included in v4 but will be coming soon. This includes updated gene constraint, pext scores, sub-genetic ancestry groups, and variant co-occurrence! However, we are also excited to have gotten in a few new features including combined exome and genome filtering allele frequencies, and structural variants in GRCh38. Further details about the production of the v4 release, are described in the following blog posts: - gnomAD v4.0: broad.io/gnomad_v4 - Genetic Ancestry: https://lnkd.in/edhZ7iN6 - SVs in gnomAD v4: broad.io/gnomad_v4_sv - CNVs in gnomAD v4: broad.io/gnomad_v4_cnv As part of v4, the gnomAD team is happy to announce the launch of the gnomAD forum (broad.io/gnomad_forum). This will be a place for users to help each other, with support from the gnomAD team, discuss the data and ask questions. They look forward to seeing you all there, though keep in mind they are a small team, we are hoping this forum will enable our users to assist each other. The gnomAD team hopes this new dataset will prove useful to their users, and welcome feedback on the latest release at their email address: gnomad@broadinstitute.org.

Software tools identify forgotten genes ________________________________________ Find My Understudied Genes and the Unknome database highlight interesting genes that have been neglected by science #GeneticaHMV #MedicinaGenomica #Genetics

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries https://lnkd.in/diJ3jVYA

📃Scientific paper: Variation in haplotypes in single cysts of assemblages C and D, but not of assemblage E of Giardia duodenalis Abstract: Background Giardia duodenalis, a single-celled intestinal parasite, is divided into eight assemblages (A-H), with differences in host specificity. Giardia duodenalis reproduces asexually and cycles between the binucleated trophozoite (4 N) and the infectious cyst with four nuclei (16 N). Interaction between the nuclei is limited. Therefore, genetic drift causes differences in genetic make-up between the non-daughter nuclei; the allelic sequence heterozygosity (ASH). The ASH is low (0.01%—0.0023%) for the related assemblages A and E, higher (0.43–0.53) for assemblage B and much higher (0.74% -0.89%) for the assemblage C and D at the root of the phylogenetic tree. The heterozygosity in assemblage F, in the same clade as assemblage A and E, was unknown. The heterozygosity in the sequences of the gdh and dis3 genes was used as proxy for the ASH and whole genome amplification of single cysts followed by cloning and Sanger sequencing of dis3 fragment could reveal the genetic variation within the cyst. The aim of the study was to determine the level of heterozygosity within pooled and single cysts of different assemblages. Results The heterozygosity in gdh and dis3 was determined in pooled cysts of the assemblages A to F. Heterozygosity in the isolates of the assemblages C ( n  = 2) and D ( n  = 1) ranged from 0.41% to 0.82% for gdh and dis3 and no heterozygosity was found in the isolates of the assemblages A ( n  = 4), E ( n  = 3) and F ( n  = 3). The heterozygosity in a... Discover the rest of the scientific article on es/iode ➡️https://etcse.fr/uZho1