Biology subjects

Arslan, A.

Publications and source records attributed to Arslan, A..

7 recordsLinked to original sources

bioRxiv2023

PEPhub: a database, web interface, and API for editing, sharing, and validating biological sample metadata

BackgroundAs biological data increases, we need additional infrastructure to share it and promote interoperability. While major effort has been put into sharing data, relatively less emphasis is placed on sharing metadata. Yet, sharing metadata is also important, and in some ways has a wider scope than sharing data itself. ResultsHere, we present PEPhub, an approach to improve sharing and interoperability of biological metadata. PEPhub provides an API, natural language search, and user-friendly web-based sharing and editing of sample metadata tables. We used PEPhub to process more than 100,000 published biological research projects and index them with fast semantic natural language search. PEPhub thus provides a fast and user-friendly way to finding existing biological research data, or to share new data. Availabilityhttps://pephub.databio.org

bioinformatics↗

bioRxiv2022

Neuron Navigator 1 Regulates Learning, Memory, and the Response to Multiple Potentially Addictive Drugs

Genetic variation accounts for much of the risk for developing a substance use disorder (SUD). Inbred mouse strains exhibit substantial and heritable differences in the extent of voluntary cocaine intravenous self-administration (IVSA). Computational genetic analysis of IVSA data obtained from an inbred strain panel identified Nav1, a member of the neuron navigator family that regulates dendrite formation and axonal guidance, as a candidate gene. To test this hypothesis, we generated and characterized Nav1 knockout (KO) mice. Nav1 KO mice exhibited increased cocaine intake during IVSA testing. Surprisingly, Nav1 KO mice also displayed a reduced susceptibility to become opioid dependent or develop opioid-induced hyperalgesia after chronic morphine administration, and had impaired spatial learning/memory. Immunohistochemistry and electrophysiology studies revealed that inhibitory synapse density in the cortex of Nav1 KO mice was reduced, and excitatory synaptic transmission was increased in the Nav1 KO cortex and hippocampus. Transcriptomic analysis revealed that Nav1 KO mice had a marked increase in excitatory neurons in a deep cortical layer. Collectively, our results indicate that Nav1 regulates learning, memory, and the response to multiple addictive drugs, and that changes in the excitatory and inhibitory synaptic balance in the cortex and hippocampus could possibly mediate these phenotypic effects.

genetics↗

bioRxiv2021

Analysis of Structural Variation Among Inbred Mouse Strains Identifies Genetic Factors for Autism-Related Traits

The genomes of six inbred strains were analyzed using long read (LR) sequencing. The results revealed that structural variants (SV) were very abundant within the genome of inbred mouse strains (4.8 per gene), which indicates that they could impact genetic traits. Analysis of the relationship between SNP and SV alleles across 53 inbred strains indicated that we have a very limited ability to infer whether SV are present using short read sequence data, even when nearby SNP alleles are known. The benefit of having a more complete map of the pattern of genetic variation was demonstrated by identifying at least three genetic factors that could underlie the unique neuroanatomic and behavioral features of BTBR mice that resemble human Autism Spectrum Disorder (ASD). Similar to the genetic findings in human ASD cohorts, the identified BTBR-unique alleles are very rare, and they cause high impact changes in genes that play a role in neurodevelopment and brain function.

genomics↗

bioRxiv2020

A Framework to Analyse and Interpret Mouse Functional Genome by Prioritizing High Impact SNPs

The essential understanding of disease pathogenesis and enabling genetic findings to be used for developing new therapeutics, is missing in the identifications of genomic loci through whole genome association studies (GWAS). Here we describe a new computational method (mMap) that reduces this gap by characterizing the functional and regulatory impact of allelic variation. The method incorporates the precomputed annotations of 26 protein functional regions and eight regulatory regions and recover SNPs that fall/lie in these regions. After annotating SNPs to functional or regulatory data, method link them to biological functions and pathways, and predicts significantly disrupted biological regions, processes and pathways, by controlling false discovery through hypergeometric test. By doing so, the method limits data to human interpretation level by prioritizing SNPs that have the potential to mediate a biological phenotype. The method is applicable to procedures that rely on the understanding of the biological causal role of mouse SNPs and is available online. In two example mMap applications, including whole genomes SNPs data from 48 inbred mice strains, we identify biological mechanisms by which SNPs can regulate pathways to govern phenotypes by targeting different coding and regulatory regions, even in closely related strains.

bioinformatics↗

bioRxiv2020

Protein Cargo Assessment through Residue Alterations

MotivationThe transport of proteins plays a crucial role in the cellular phenotype. Changes in the protein targeting sequence can result in missing protein delivery to the right destination and at the right time, and can disrupt various cellular pathways. Given the importance of single residue change(s) in the protein targeting sequence we developed a missing computational method. ResultsBy taking into account various protein features like conservation, protein modifications, charge, isoelectric effect and biochemical properties of peptides, the method, TransSite, assess the impact of mutations on the protein transportation. We applied this method to big human (cancer proteins) and nonhuman data, and discovered, in both cases, several phenotype related proteins harbour recurring mutations in their targeting sequences. Availabilityhttps://github.com/AhmedArslan/TransSite Contactaarslan@staford.edu Supplementary informationSupplementary data are available at # online.

bioinformatics↗

bioRxiv2020

High Throughput Computational Mouse Genetic Analysis

BackgroundGenetic factors affecting multiple biomedical traits in mice have been identified when GWAS data that measured responses in panels of inbred mouse strains was analyzed using haplotype-based computational genetic mapping (HBCGM). Although this method was previously used to analyze one dataset at a time; but now, a vast amount of mouse phenotypic data is now publicly available, which could lead to many more genetic discoveries. ResultsHBCGM and a whole genome SNP map covering 53 inbred strains was used to analyze 8462 publicly available datasets of biomedical responses (1.52M individual datapoints) measured in panels of inbred mouse strains. As proof of concept, causative genetic factors affecting susceptibility for eye, metabolic and infectious diseases were identified when structured automated methods were used to analyze the output. One analysis identified a novel genetic effector mechanism; allelic differences within the mitochondrial targeting sequence affected the subcellular localization of a protein. We also found allelic differences within the mitochondrial targeting sequences of many murine and human proteins, and these could affect a wide range of biomedical phenotypes. ImplicationsThese initial results indicate that genetic factors affecting biomedical responses could be identified through analysis of very large datasets, and they provide an early indication of how this type of augmented intelligence can facilitate genetic discovery.

bioinformatics↗

bioRxiv2019

Pyntheon: A Functional Analysis Framework for Protein Modifications and Mutations of 83 Model Organisms

MotivationPosttranslational modifications (PTMs) modulate proteins activity depending on the dynamics of cellular conditions, in the highly regulated processes that control the reversible nature of these modifications and a cellular state. Due to the unique importance of PTMs, a number of resources are available to analyze the protein modification data for different organisms. These databases are quite informative on a limited number of popular organisms, mostly human and yeast. However there has not been a single database to date that makes it possible to analyze the modified protein residue data for up to 83 model organisms. Moreover, there are limited resources that rely on both protein mutations and modifications in evaluating a phenotype.\n\nResultsI am presenting a comprehensive python tool Pyntheon that enables users to analyze protein modifications and mutations data. This resource can be used in different ways to know: (i) if the proteins of interest have modifications and (ii) if the modified residues overlap with mutated sites. Additional functions include, analyzing if a PTM-site is present in a functional protein region, like domain and structural regions. In summary, Pyntheon makes it possible for a larger community of researchers to evaluate their curated proteomics data and interpret the impact of mutations on phenotypes.\n\nConclusionPyntheon has multifold functions that can help analyzing the protein mutations impact on the modified residues for a large number of popular model organisms.\n\nCode-Availabilityhttps://github.com/AhmedArslan/pyntheon\n\nContactaarslan@stanford.edu

bioinformatics↗