SNAPC2 antibody - middle region (P100961_T100)
- Known as:
- SNAPC2 (anti-) - middle region (P100961_T100)
- Catalog number:
- p100961_t100
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Aviva Systems Biology
- Gene target:
- SNAPC2 antibody - middle region (P100961_T100)
Related genes to: SNAPC2 antibody - middle region (P100961_T100)
- Gene:
- SNAPC2 NIH gene
- Name:
- small nuclear RNA activating complex polypeptide 2
- Previous symbol:
- -
- Synonyms:
- SNAP45, PTFdelta
- Chromosome:
- 19p13
- Locus Type:
- gene with protein product
- Date approved:
- 1996-04-12
- Date modifiied:
- 2015-11-09
Related products to: SNAPC2 antibody - middle region (P100961_T100)
Related articles to: SNAPC2 antibody - middle region (P100961_T100)
- This study aims to identify key genes, biomarkers, and associated signaling pathways involved in liver cancer progression by analyzing differentially expressed genes (DEGs) between normal and cancerous liver tissues, with the goal of establishing diagnostic and prognostic models for liver cancer. - Source: PubMed
Publication date: 2025/06/04
Wei BenzunZheng YaoYu ShuaijunWang AiyunLyu Xiao - RNA polymerase III (Pol III) transcribes short, essential RNAs, including the U6 small nuclear RNA (snRNA). At U6 snRNA genes, Pol III is recruited by the snRNA Activating Protein Complex (SNAPc) and a Brf2-containing TFIIIB complex, forming a pre-initiation complex (PIC). Uniquely, SNAPc also recruits Pol II at the remaining splicesosomal snRNA genes (U1, 2, 4 and 5). The mechanism of SNAPc cross-polymerase engagement and the role of the SNAPC2 and SNAPC5 subunits remain poorly defined. Here, we present cryo-EM structures of the full-length SNAPc-containing Pol III PIC assembled on the U6 snRNA promoter in the open and melting states at 3.2-4.2 Å resolution. The structural comparison revealed differences with the Saccharomyces cerevisiae Pol III PIC and the basis of selective SNAPc engagement within Pol III and Pol II PICs. Additionally, crosslinking mass spectrometry localizes SNAPC2 and SNAPC5 near the promoter DNA, expanding upon existing descriptions of snRNA Pol III PIC structure. - Source: PubMed
Publication date: 2025/01/02
Shah Syed ZawarPerry Thomas NGraziadei AndreaCecatiello ValentinaKaliyappan ThangaveluMisiaszek Agata DMüller Christoph WRamsay Ewan PVannini Alessandro - Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine, and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: a) implication of three different normalization techniques and b) implication of differential analysis using the generalized linear model (GLM). Several candidate biomarker genes were identified by multiple machine learning experiments including , , , , , and from the transcriptomics data analysis and , , , , , and from the methylomics data analysis. We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization. - Source: PubMed
Publication date: 2019/09/04
Akter SadiaXu DongNagel Susan CBromfield John JPelch KatherineWilshire Gilbert BJoshi Trupti - Panic disorder (PD) is a severe and disabling mental disorder, which is moderately heritable. In a previous study, we carried out a genome-wide association study using patients with PD and control individuals from the isolated population of the Faroe Islands and identified chromosome 19p13.2 as a candidate region. To further investigate this chromosomal region for association with PD, we analysed eight single nucleotide polymorphisms (SNPs) in three candidate genes - small-nuclear RNA activating complex, polypeptide 2 (SNAPC2), mitogen-activated protein kinase kinase 7 (MAP2K7) and leucine-rich repeat containing 8 family, member E (LRRC8E) - these genes have previously been directly or indirectly implicated in other mental disorders. A total of 511 patients with PD and 1029 healthy control individuals from the Faroe Islands, Denmark and Germany were included in the current study. SNPs covering the gene region of SNAPC2, MAP2K7 and LRRC8E were genotyped and tested for association with PD. In the Faroese cohort, rs7788 within SNAPC2 was significantly associated with PD, whereas rs3745383 within LRRC8E was nominally associated. No association was observed between the analysed SNPs and PD in the Danish cohorts. In the German women, we observed a nominal association between rs4804833 within MAP2K7 and PD. We present further evidence that chromosome 19p13.2 may harbour candidate genes that contribute towards the risk of developing PD. Moreover, the implication of the associated genes in other mental disorders may indicate shared genetic susceptibility between mental disorders. We show that associated variants may be sex specific, indicating the importance of carrying out a sex-specific association analysis of PD. - Source: PubMed
Gregersen Noomi OButtenschøn Henriette NHedemand AnneNielsen Marit NDahl Hans AKristensen Ann SJohansen OddbjørgWoldbye David P DErhardt AngelikaKruse Torben AWang August GBørglum Anders DMors Ole - To identify a specific hypermethylated molecular biomarker for human malignant glioblastoma prognosis. - Source: PubMed
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