Project description:The Pacific oyster, Crassostrea gigas, was voluntarily introduced from Japan and British Columbia into Europe in the early 1970s, mainly to replace the Portuguese oyster, Crassostrea angulata, in the French shellfish industry, following a severe disease outbreak. Since then, the two species have been in contact in southern Europe and, therefore, have the potential to exchange genes. Recent evolutionary genomic works have provided empirical evidence that C. gigas and C. angulata exhibit partial reproductive isolation. Although hybridization occurs in nature, the rate of interspecific gene flow varies across the genome, resulting in highly heterogeneous genome divergence. Taking this biological property into account is important to characterize genetic ancestry and population structure in oysters. Here, we identified a subset of ancestry-informative makers from the most differentiated regions of the genome using existing genomic resources. We developed two different panels in order to (i) easily differentiate C. gigas and C. angulata, and (ii) describe the genetic diversity and structure of the cupped oyster with a particular focus on French Atlantic populations. Our results confirm high genetic homogeneity among Pacific cupped oyster populations in France and reveal several cases of introgressions between Portuguese and Japanese oysters in France and Portugal.
Project description:The study of gene regulatory network and protein-protein interaction network is believed to be fundamental to the understanding of molecular processes and functions in systems biology. In this study, the authors are interested in single nucleotide polymorphism (SNP) level and construct SNP-SNP interaction network to understand genetic characters and pathogenetic mechanisms of complex diseases. The authors employ existing methods to mine, model and evaluate a SNP sub-network from SNP-SNP interactions. In the study, the authors employ the two SNP datasets: Parkinson disease and coronary artery disease to demonstrate the procedure of construction and analysis of SNP-SNP interaction networks. Experimental results are reported to demonstrate the procedure of construction and analysis of such SNP-SNP interaction networks can recover some existing biological results and related disease genes.
Project description:Italy counts a large number of local chicken populations, some without a recognized genetic structure, such as Val Platani (VPL) and Cornuta (COS), which represent noteworthy local genetic resources. In this study, the genotype data of 34 COS and 42 VPL, obtained with the Affymetrix Axiom600KChicken Genotyping Array, were used with the aim to investigate the genetic diversity, the runs of homozygosity (ROH) pattern, as well as the population structure and relationship within the framework of other local Italian and commercial chickens. The genetic diversity indices, estimated using different approaches, displayed moderate levels of genetic diversity in both populations. The identified ROH hotspots harbored genes related to immune response and adaptation to local hot temperatures. The results on genetic relationship and population structure reported a clear clustering of the populations according to their geographic origin. The COS formed a nonoverlapping genomic cluster and clearly separated from the other populations, but showed evident proximity to the Siciliana breed (SIC). The VPL highlighted intermediate relationships between the COS-SIC group and the rest of the sample, but closer to the other Italian local chickens. Moreover, VPL showed a complex genomic structure, highlighting the presence of 2 subpopulations that match with the different source of the samples. The results obtained from the survey on genetic differentiation underline the hypothesis that Cornuta is a population with a defined genetic structure. The substructure that characterizes the Val Platani chicken is probably the consequence of the combined effects of genetic drift, small population size, reproductive isolation, and inbreeding. These findings contribute to the understanding of genetic diversity and population structure, and represent a starting point for designing programs to monitor and safeguard these local genetic resources, in order to define a possible official recognition program as breeds.
Project description:Primary Objective:
Correlation of the skin and/or eye toxicity grade secondary to Cetuximab or Panitumumab and the SNP profile of the Epidermal Growth Factor Receptor (EGFR) domain III region.
Secondary Objectives:
Correlation of SNP profile with indicators of tumour response parameters, such as radiological response, duration of response, time to progression (TTP), overall survival (OS) time, incidence of non-dermatological adverse events.
Project description:Opioid-related respiratory depression is a serious clinical problem as it can cause multiple deaths and anoxic brain injury. Genetic variations influence the safety and clinical efficacy of fentanyl. Pharmacogenetic studies help in identifying single-nucleotide polymorphisms (SNPs) associated with fentanyl causing respiratory depression and aid clinician in personalized pain medicine. This narrative review gives an insight of the common SNPs associated with fentanyl.
Project description:Establishing low-cost, high-throughput, simple, and accurate single nucleotide polymorphism (SNP) genotyping techniques is beneficial for understanding the intrinsic relationship between individual genetic variations and their biological functions on a genomic scale. Here, a straightforward and reliable single-molecule approach is demonstrated for precise SNP authentication by directly measuring the fluctuations in electrical signals in an electronic circuit, which is fabricated from a high-gain field-effect silicon nanowire decorated with a single hairpin DNA, in the presence of different target DNAs. By simply comparing the proportion difference of a probe-target duplex structure throughout the process, this study implements allele-specific and accurate SNP detection. These results are supported by the statistical analyses of different dynamic parameters such as the mean lifetime and the unwinding probability of the duplex conformation. In comparison with conventional polymerase chain reaction and optical methods, this convenient and label-free method is complementary to existing optical methods and also shows several advantages, such as simple operation and no requirement for fluorescent labeling, thus promising a futuristic route toward the next-generation genotyping technique.
Project description:We describe a rapid and easily automated phylogenetic grouping technique based on analysis of bacterial genome single-nucleotide polymorphisms (SNPs). We selected 13 SNPs derived from a complete sequence analysis of 11 essential genes previously used for multilocus sequence typing (MLST) of 30 Escherichia coli strains representing the genetic diversity of the species. The 13 SNPs were localized in five genes, trpA, trpB, putP, icdA, and polB, and were selected to allow recovery of the main phylogenetic groups (groups A, B1, E, D, and B2) and subgroups of the species. In the first step, we validated the SNP approach in silico by extracting SNP data from the complete sequences of the five genes for a panel of 65 pathogenic strains belonging to different E. coli pathovars, which were previously analyzed by MLST. In the second step, we determined these SNPs by dideoxy single-base extension of unlabeled oligonucleotide primers for a collection of 183 commensal and extraintestinal clinical E. coli isolates and compared the SNP phylotyping method to previous well-established typing methods. This SNP phylotyping method proved to be consistent with the other methods for assigning phylogenetic groups to the different E. coli strains. In contrast to the other typing methods, such as multilocus enzyme electrophoresis, ribotyping, or PCR phylotyping using the presence/absence of three genomic DNA fragments, the SNP typing method described here is derived from a solid phylogenetic analysis, and the results obtained by this method are more meaningful. Our results indicate that similar approaches may be used for a wide variety of bacterial species.
Project description:Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals.
Project description:IntroductionMango is a vital horticultural fruit crop, and breeding is an essential strategy to enhance ongoing sustainability. Knowledge regarding population structure and genetic diversity in mango germplasm is essential for crop improvement.MethodsA set of 284 mango accessions from different regions of the world were subjected to high-throughput sequencing and specific-locus amplified fragment (SLAF) library construction to generate genomic single-nucleotide polymorphism (SNP).ResultsAfter filtering, raw data containing 539.61 M reads were obtained. A total of 505,300 SLAFs were detected, of which, 205,299 were polymorphic. Finally, 29,136 SNPs were employed to dissect the population structure, genetic relationships, and genetic diversity. The 284 mango accessions were divided into two major groups: one group consisted mainly of mango accessions from Australia, the United States, Cuba, India, Caribbean, Israel, Pakistan, Guinea, Burma, China, and Sri Lanka, which belonged to the Indian type (P1); the other group contained mango accessions from the Philippines, Thailand, Indonesia, Vietnam, Cambodia, Malaysia, and Singapore, which belonged to Southeast Asian type (P2). Genetic diversity, principal component analysis (PCA), and population structure analyses revealed distinct accession clusters. Current results indicated that the proposed hybridization occurred widely between P1 and P2.DiscussionMost of the accessions (80.99%) were of mixed ancestry, perhaps including multiple hybridization events and regional selection, which merits further investigation.
Project description:Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp, https://github.com/labomics/SNPsnp). To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.