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Arkansas Regressor (AR) chickens, unlike Arkansas Progressor (AP) chickens, regress tumors induced by the v-src oncogene. To better understand the genetic factors responsible for this tumor regression property, whole genome resequencing was conducted using Illumina Hi-Seq 2 × 100 bp paired-end read method (San Diego, CA, USA) with AR (confirmed tumor regression property) and AP chickens. Sequence reads were aligned to the chicken reference genome (galgal5) and produced coverage of 11× and 14× in AR and AP, respectively. A total of 7.1 and 7.3 million single nucleotide polymorphisms (SNPs) were present in AR and AP genomes, respectively. Through a series of filtration processes, a total of 12,242 SNPs were identified in AR chickens that were associated with non-synonymous, frameshift, nonsense, no-start and no-stop mutations. Further filtering of SNPs based on read depth ≥ 10, SNP% ≥ 0.75, and non-synonymous mutations identified 63 reliable marker SNPs which were chosen for gene network analysis. The network analysis revealed that the candidate genes identified in AR chickens play roles in networks centered to ubiquitin C (UBC), phosphoinositide 3-kinases (PI3K), and nuclear factor kappa B (NF-kB) complexes suggesting that the tumor regression property in AR chickens might be associated with ubiquitylation, PI3K, and NF-kB signaling pathways. This study provides an insight into genetic factors that could be responsible for the tumor regression property.
Pubmed ID: 30347774
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Commercial vendor and service provider of laboratory reagents and antibodies. Supplier of scientific instrumentation, reagents and consumables, and software services.
View all literature mentionsA commercial organization which provides assay technologies to isolate DNA, RNA, and proteins from any biological sample. Assay technologies are then used to make specific target biomolecules, such as the DNA of a specific virus, visible for subsequent analysis.
View all literature mentionsA web-based software application that enables users to analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays, metabolomics, proteomics, and RNA-Seq experiments, and small-scale experiments that generate gene and chemical lists. Users can search for targeted information on genes, proteins, chemicals, and drugs, and build interactive models of experimental systems. IPA allows exploration of molecular, chemical, gene, protein and miRNA interactions, creation of custom molecular pathways, and the ability to view and modify metabolic, signaling, and toxicological canonical pathways. In addition to the networks and pathways that can be created, IPA can provide multiple layering of additional information, such as drugs, disease genes, expression data, cellular functions and processes, or a researchers own genes or chemicals of interest.
View all literature mentionsQuality control software that perform checks on raw sequence data coming from high throughput sequencing pipelines. This software also provides a modular set of analyses which can give a quick impression of the quality of the data prior to further analysis.
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