C12orf50 Over-expression Lysate Product
- Known as:
- C12orf50 Over-expression Lysate Product
- Catalog number:
- GWB-10A44B
- Product Quantity:
- 0.1 mg
- Category:
- -
- Supplier:
- GenWay
- Gene target:
- C12orf50 Over-expression Lysate Product
Related genes to: C12orf50 Over-expression Lysate Product
- Gene:
- C12orf50 NIH gene
- Name:
- chromosome 12 open reading frame 50
- Previous symbol:
- -
- Synonyms:
- FLJ35821
- Chromosome:
- 12q21.32
- Locus Type:
- gene with protein product
- Date approved:
- 2006-01-24
- Date modifiied:
- 2016-09-30
Related products to: C12orf50 Over-expression Lysate Product
(META) Human Metapneumovirus Type 16 (A1) Lysate(META) Human Metapneumovirus Type 18 (B2) Lysate(META) Human Metapneumovirus Type 20 (A2) Lysate(META) Human Metapneumovirus Type 27 (A2) Lysate(META) Human Metapneumovirus Type 3 (B1) Lysate(META) Human Metapneumovirus Type 4 (B2) Lysate(META) Human Metapneumovirus Type 5 (B1) Lysate(META) Human Metapneumovirus Type 8 (B2) Lysate(META) Human Metapneumovirus Type 9 (A1) Lysate0 day neonate eyeball cDNA. RIKEN full-length enriched library. clone E130107M17 product hypothetical protein. full insert seque - N_A Polyclonal0 day neonate head cDNA. RIKEN full-length enriched library. clone 4831434J02 product nuclear factor of activated T-cells. cytop - N_A Polyclonal0 day neonate head cDNA. RIKEN full-length enriched library. clone 4832421E02 product myocyte enhancer factor 2C. full insert se - N_A Polyclonal1,2,3,4-Tetrahydro-1,2-dimethyl-4,6-isoquinolinediol
(Major Product) CAS: 102830-16-0 Formula: C11H15NO21,2,3,4-tetrahydro-1,2-dimethyl-4,8-isoquinolinediol
(Minor Product) CAS: 102830-20-6 Formula: C11H15NO210 days embryo whole body cDNA. RIKEN full-length enriched library. clone 2610510L15 product poly(A)-specific ribonuclease (dead - N_A Polyclonal Related articles to: C12orf50 Over-expression Lysate Product
- Male infertility is a multifaceted disorder affecting approximately 50% of male partners in infertile couples. Over the years, male infertility has been diagnosed mainly through semen analysis, hormone evaluations, medical records and physical examinations, which of course are fundamental, but yet inefficient, because 30% of male infertility cases remain idiopathic. This dilemmatic status of the unknown needs to be addressed with more sophisticated and result-driven technologies and/or techniques. Genetic alterations have been linked with male infertility, thereby unveiling the practicality of investigating this disorder from the "omics" perspective. Omics aims at analyzing the structure and functions of a whole constituent of a given biological function at different levels, including the molecular gene level (genomics), transcript level (transcriptomics), protein level (proteomics) and metabolites level (metabolomics). In the current study, an overview of the four branches of omics and their roles in male infertility are briefly discussed; the potential usefulness of assessing transcriptomic data to understand this pathology is also elucidated. After assessing the publicly obtainable transcriptomic data for datasets on male infertility, a total of 1385 datasets were retrieved, of which 10 datasets met the inclusion criteria and were used for further analysis. These datasets were classified into groups according to the disease or cause of male infertility. The groups include non-obstructive azoospermia (NOA), obstructive azoospermia (OA), non-obstructive and obstructive azoospermia (NOA and OA), spermatogenic dysfunction, sperm dysfunction, and Y chromosome microdeletion. Findings revealed that 8 genes () were commonly differentially expressed between all disease groups. Likewise, 56 genes were common between NOA versus NOA and OA (). These genes, particularly the above-mentioned 8 genes, are involved in diverse biological processes such as germ cell development, spermatid development, spermatid differentiation, regulation of proteolysis, spermatogenesis and metabolic processes. Owing to the stage-specific expression of these genes, any mal-expression can ultimately lead to male infertility. Therefore, currently available data on all branches of omics relating to male fertility can be used to identify biomarkers for diagnosing male infertility, which can potentially help in unravelling some idiopathic cases. - Source: PubMed
Publication date: 2022/02/14
Omolaoye Temidayo SOmolaoye Victor AKandasamy Richard KHachim Mahmood YaseenDu Plessis Stefan S