Key Specifications Table
|Species Reactivity||Key Applications||Host||Format||Antibody Type|
|H, Ca, Mk, M, R||WB, ChIP-seq, ICC, ELISA, ChIP||R||Purified||Monoclonal Antibody|
|Presentation||Purified rat monoclonal IgG2bκ in buffer containing 0.1 M Tris-Glycine (pH 7.4), 150 mM NaCl with 0.05% sodium azide.|
|Safety Information according to GHS|
|Storage and Shipping Information|
|Storage Conditions||Stable for 1 year at 2-8°C from date of receipt.|
|Material Size||100 µg|
|Anti-phospho RNA Pol II (Ser5), clone 1H4B6 - Q2399138||Q2399138|
|Anti-phospho RNA Pol II (Ser5), clone 1H4B6 -2683302||2683302|
|Anti-phospho RNA Pol II (Ser5), clone 1H4B6 -2779741||2779741|
|Reference overview||Pub Med ID|
|A co-localization model of paired ChIP-seq data using a large ENCODE data set enables comparison of multiple samples. |
Maehara, Kazumitsu, et al.
Nucleic Acids Res., 41: 54-62 (2013) 2013
Deep sequencing approaches, such as chromatin immunoprecipitation by sequencing (ChIP-seq), have been successful in detecting transcription factor-binding sites and histone modification in the whole genome. An approach for comparing two different ChIP-seq data would be beneficial for predicting unknown functions of a factor. We propose a model to represent co-localization of two different ChIP-seq data. We showed that a meaningful overlapping signal and a meaningless background signal can be separated by this model. We applied this model to compare ChIP-seq data of RNA polymerase II C-terminal domain (CTD) serine 2 phosphorylation with a large amount of peak-called data, including ChIP-seq and other deep sequencing data in the Encyclopedia of DNA Elements (ENCODE) project, and then extracted factors that were related to RNA polymerase II CTD serine 2 in HeLa cells. We further analyzed RNA polymerase II CTD serine 7 phosphorylation, of which their function is still unclear in HeLa cells. Our results were characterized by the similarity of localization for transcription factor/histone modification in the ENCODE data set, and this suggests that our model is appropriate for understanding ChIP-seq data for factors where their function is unknown.
|The classification of mRNA expression levels by the phosphorylation state of RNAPII CTD based on a combined genome-wide approach. |
Odawara, Jun, et al.
BMC Genomics, 12: 516 (2011) 2011
Cellular function is regulated by the balance of stringently regulated amounts of mRNA. Previous reports revealed that RNA polymerase II (RNAPII), which transcribes mRNA, can be classified into the pausing state and the active transcription state according to the phosphorylation state of RPB1, the catalytic subunit of RNAPII. However, genome-wide association between mRNA expression level and the phosphorylation state of RNAPII is unclear. While the functional importance of pausing genes is clear, such as in mouse Embryonic Stem cells for differentiation, understanding this association is critical for distinguishing pausing genes from active transcribing genes in expression profiling data, such as microarrays and RNAseq. Therefore, we examined the correlation between the phosphorylation of RNAPII and mRNA expression levels using a combined analysis by ChIPseq and RNAseq.