Mouse Genome Database (MGD) 2019.

The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the community model organism genetic and genome resource for the laboratory mouse. MGD is the authoritative source for biological reference data sets related to mouse genes, gene functions, phenotypes, and mouse models of human disease. MGD is the primary outlet for official gene, allele and mouse strain nomenclature based on the guidelines set by the International Committee on Standardized Nomenclature for Mice. In this report we describe significant enhancements to MGD, including two new graphical user interfaces: (i) the Multi Genome Viewer for exploring the genomes of multiple mouse strains and (ii) the Phenotype-Gene Expression matrix which was developed in collaboration with the Gene Expression Database (GXD) and allows researchers to compare gene expression and phenotype annotations for mouse genes. Other recent improvements include enhanced efficiency of our literature curation processes and the incorporation of Transcriptional Start Site (TSS) annotations from RIKEN\u27s FANTOM 5 initiative

Implementation Of Forward Chaining Expert System To Identify Facial Skin Types In Determining Facial Beauty Treatments

Skin is part of the outer layer of the human body which is flexible, soft and sensitive and is the first line of defense from various germs, viruses, bacteria and what is very important and the main part is facial skin, therefore we must keep it healthy, fresh and clean by determining proper beauty facial treatment. This research was conducted to design and create an expert system capable of identifying facial skin types to determine facial beauty treatments. Making this expert system using forward chaining method. The result of this research is an application that can help make it easier for users to determine facial facial treatments without having to queue and wait for consultations with a beauty doctor, so that they can immediately determine the appropriate type of skin facial treatment through the Android application

Single cell RNA-sequencing data generated from human pluripotent stem cell-derived lens epithelial cells

Detailed transcriptomic analyses of differentiated cell populations derived from human pluripotent stem cells is routinely used to assess the identity and utility of the differentiated cells. Here we provide single cell RNA-sequencing data obtained from ROR1-expressing lens epithelial cells (ROR1e LECs), obtained via directed differentiation of CA1 human embryonic stem cells. Analysis of the data using principal component analysis, heat maps and gene ontology assessments revealed phenotypes associated with lens epithelial cells. These data provide a resource for future characterisation of both normal and cataractous human lens biology. Corresponding morphological and functional data obtained from ROR1e LECs are reported in the associated research article “A simplified method for producing human lens epithelial cells and light-focusing micro-lenses from pluripotent stem cells “ (Dewi et al., 2020)

NOVEL HOMOZYGOUS VARIANT OF TBC1 DOMAIN FAMILY MEMBER 8 GENE IN FOUR LIBYAN SIBLINGS WITH AUTISTIC SPECTRUM DISORDER AND INTELLECTUAL DISABILITY WITHOUT EPILEPSY

Objective: Recent progress in genetic analysis and investigations have enabled researchers to identify potential genetic changes that may play a role in ASD. The number of genes connected with autism is growing. Whole exome sequencing(WES) identified the homozygous TBC1D8 variant. Aim to report for the first time a TBC1D8 missense variant (c.1883G>A, p. (Arg628Gln) in 4 Libyan children (3 homozygous,1 heterozygous) with severe neurodevelopmental phenotypes ASD and intellectual disability ID . Based on the data of HGMD and ClinVar, variants in only a few autosomal recessive intellectual disability ARID genes seem to be reported frequently. Method: Molecular genetic analysis of (WES) was carried out on blood samples from these children. The outcome of the genetic investigations was interpreted within the context of clinical finding, family history, and suspected mode of inheritance. Results: The number of genes associated with autism is increasing. WES identified the TBC1D8 variant. According to the longest isoform (NM_001102426.1),the nomenclature of this variant is c.1883G>A, p. (Arg628Gln) in TBC1D8 which leads to an amino acid exchange. This variant has not previously reported or described in the literature (PubMed, HGMD). Conclusion: we have provided evidence for a connection between TBC1D8 variant and ASD and ID; however, this evidence should be considered preliminary in the context of a single case report and such findings need to be replicated to gain insight in order to determine if ASD and ID are a characteristic of this variant

Optimal Structured Matrix Approximation for Robustness to Incomplete Biosequence Data

We propose a general method for optimally approximating an arbitrary matrix $\mathbf{M}$ by a structured matrix $\mathbf{T}$ (circulant, Toeplitz/Hankel, etc.) and examine its use for estimating the spectra of genomic linkage disequilibrium matrices. This application is prototypical of a variety of genomic and proteomic problems that demand robustness to incomplete biosequence information. We perform a simulation study and corroborative test of our method using real genomic data from the Mouse Genome Database. The results confirm the predicted utility of the method and provide strong evidence of its potential value to a wide range of bioinformatics applications. Our optimal general matrix approximation method is expected to be of independent interest to an even broader range of applications in applied mathematics and engineering

SLC25A12 Missense Variant in Nova Scotia Duck Tolling Retrievers Affected by Cerebellar Degeneration—Myositis Complex (CDMC)

We investigated two litters of distantly related Nova Scotia Duck Tolling Retrievers (NSDTR), of which four puppies were affected by cerebellar signs with or without neuromuscular weakness. The phenotype was termed cerebellar degeneration—myositis complex (CDMC). We suspected a heritable condition and initiated a genetic analysis. The genome of one affected dog was sequenced and compared to 565 control genomes. This search yielded a private protein-changing SLC25A12 variant in the affected dog, XM_038584842.1:c.1337C>T, predicted to result in the amino acid change XP_038440770.1:(p.Pro446Leu). The genotypes at the variant co-segregated with the phenotype as expected for a monogenic autosomal recessive mode of inheritance in both litters. Genotyping of 533 additional NSDTR revealed variant allele frequencies of 3.6% and 1.3% in a European and a North American cohort, respectively. The available clinical and biochemical data, together with current knowledge about SLC25A12 variants and their functional impact in humans, mice, and dogs, suggest the p.Pro446Leu variant is a candidate causative defect for the observed phenotype in the affected dogs

Mouse Phenome Database: a data repository and analysis suite for curated primary mouse phenotype data.

The Mouse Phenome Database (MPD; https://phenome.jax.org) is a widely accessed and highly functional data repository housing primary phenotype data for the laboratory mouse accessible via APIs and providing tools to analyze and visualize those data. Data come from investigators around the world and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD houses rigorously curated per-animal data with detailed protocols. Public ontologies and controlled vocabularies are used for annotation. In addition to phenotype tools, genetic analysis tools enable users to integrate and interpret genome-phenome relations across the database. Strain types and populations include inbred, recombinant inbred, F1 hybrid, transgenic, targeted mutants, chromosome substitution, Collaborative Cross, Diversity Outbred and other mapping populations. Our new analysis tools allow users to apply selected data in an integrated fashion to address problems in trait associations, reproducibility, polygenic syndrome model selection and multi-trait modeling. As we refine these tools and approaches, we will continue to provide users a means to identify consistent, quality studies that have high translational relevance

Essential genes: a cross-species perspective.

Protein coding genes exhibit different degrees of intolerance to loss-of-function variation. The most intolerant genes, whose function is essential for cell or/and organism survival, inform on fundamental biological processes related to cell proliferation and organism development and provide a window on the molecular mechanisms of human disease. Here we present a brief overview of the resources and knowledge gathered around gene essentiality, from cancer cell lines to model organisms to human development. We outline the implications of using different sources of evidence and definitions to determine which genes are essential and highlight how information on the essentiality status of a gene can inform novel disease gene discovery and therapeutic target identification

Advances in Big Data Bio Analytics

Delivering effective data analytics is of crucial importance to the interpretation of the multitude of biological datasets currently generated by an ever increasing number of high throughput techniques. Logic programming has much to offer in this area. Here, we detail advances that highlight two of the strengths of logical formalisms in developing data analytic solutions in biological settings: access to large relational databases and building analytical pipelines collecting graph information from multiple sources. We present significant advances on the bio_db package which serves biological databases as Prolog facts that can be served either by in-memory loading or via database backends. These advances include modularising the underlying architecture and the incorporation of datasets from a second organism (mouse). In addition, we introduce a number of data analytics tools that operate on these datasets and are bundled in the analysis package: bio_analytics. Emphasis in both packages is on ease of installation and use. We highlight the general architecture of our components based approach. An experimental graphical user interface via SWISH for local installation is also available. Finally, we advocate that biological data analytics is a fertile area which can drive further innovation in applied logic programming