1,721,261 research outputs found
A review of asthma genetics: gene expression studies and recent candidates
No full textRecent evidence indicates an important role of inflammation pathways, airways remodeling and epithelium activation in asthma genetics. In particular, transcriptome studies have detected differentially expressed genes involved in eosinophil apoptosis, the arginase pathway, response to allergens or interleukins, and to inhaled corticosteroids. Candidate gene and genome wide studies have localized genetic regions involved in the disease, such as the A1AR and CLCA1 genes (chromosome 1), IL-1RN and DPP10 (2q14), HLA-G and TNF-a (6p21), GPRA (7p14), FceRI and GSTP1 (11q13), NOS1, IFNG, STAT6, VDR, and other genes (12q13-26), PHF11 and flanking genes (13q14), AACT and PTGDR (14q), and ADAM33 (20p13). The role of these and other genetic determinants has to be confirmed in future, preferably longitudinal, studie
Microbial genomes metadata on public repositories: friends or foes?
No full textMotivation: Genome sequences uploaded and stored on public repositories are of crucial importance for a plethora of microbial analyses, among them: genomes comparison, phylogenetic analysis and species identification. In particular, microbial genomic sequences are uploaded by laboratories all around the world and metadata input is left to the discretion of the submitter. Based on our experience, we want to raise attention to the fact that metadata is not always fully correct – even for relevant information like taxonomical classification, which could still prove to be difficult for some microorganisms in the wet-lab. Therefore, we encourage researchers to perform some pre-analytical quality control steps before running downstream analyses. Methods: Klebsiella michiganensis and Achromobacter xylosoxidans complete assembly sequences were downloaded from NCBI RefSeq (respectively n=350 downloaded on 05-11-2021, and n=142 downloaded on 13-11-2019). Average Nucleotide Identity (ANI), which is a measure of nucleotide-level genomic similarity between the coding regions of two genomes, was calculated using fastANI v1.33 tool. Genomes showing ANI>=95% were considered as belonging to the same species. R version 4.1.1, pheatmap and colorspace R packages were used for ANI analyses results visualization purposes. Results: We performed an in silico taxonomical analysis of publicly available microbial genomes belonging to two bacterial species: Klebsiella michiganensis and Achromobacter xylosoxidans. We chose to perform such analysis focusing on these species since they both are opportunistic pathogens – i.e. microorganisms that do not usually infect healthy hosts, but establish infections in immunodepressed individuals or patients with underlying diseases – that contribute to the spread of antibiotic resistance in nosocomial settings. The analysis results (Figure 1) indicate that 9% of K. michiganensis genomes (n=31/350) were misclassified as belonging to this species, in fact they showed ANI<95% when compared to all the other genomic sequences. Moreover, as regards A. xylosoxidans, we found that 62% of genomes (n=88/142) resulted as misclassified. In conclusion, we found discrepancies among the reported taxonomic classification at species level and ANI results, suggesting a probable misclassification of some microorganisms that might compromise downstream analyses. In light of these results, we strongly suggest to perform pre-analytical quality control steps to ensure the correctness of taxonomical information. Examples of such quality controls are ANI calculation and 16S rRNA sequence analysis (same species if identity>98%) for species evaluation, or in silico multi-locus sequence typing (also known as MLST, MLST schemes for each species are available at https://pubmlst.org) for sequence type classification
A method to target individuals with an unusual genetic background and to tag SNPs for linkage disequilibrium (LD) mapping
No full textGIARFC: un sistema per la Gestione dell’Informazione nella Attività di Ricerca su Fenotipi Complessi
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A computational test for biological relatedness in genetic association studies using probabilistically inferred haplotypes
No full textApproccio informatico nello studio di Malattie Complesse per l’individuazione di possibili parentele non note tramite l’analisi del genotipo
No full textThe BioVRPi project: a valuable and sustainable alternative for genomic analysis
Full text linkSince 2012, the Raspberry Pi Foundation has started developing pocket-sized and low-cost devices, originally meant to teach computer science in developing Countries. Its growing interest and constant improvement led Raspberry Pi devices to find different applications and to suit the needs of various research areas. In the previous years, different researchers already reported applications of Raspberry Pi devices in bioinformatics, such as basic train- ing and proteomics. In the beginning of 2021, we gave birth to BioVRPi, a project which aims to develop and offer a low-cost and stable bioinformatic environment for students and re- searchers involved in the genomics and transcriptomics fields. We evaluated performances and software compatibilities of different scenarios, focusing on Genome-Wide Association Studies for complex traits in Homo sapiens, transcriptomic analyses on RNA-seq data from Strongyloides stercoralis samples and alignment of small organisms, such as SARS-CoV-2 (virus), Escherichia Coli (bacterium) and Caenorhabditis elegans (nematode). Results from both the bioinformatic and benchmarking analyses showed that Raspberry Pi devices are capable of accomplishing different bioinformatic tasks in terms of results and performances. Moreover, they proved to be a valuable low-cost and sustainable alternative, in accordance with the United Nation 2030 Agenda, to answer the needs and the challenges of the current socio-economic situation
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