![]() One possibility is therefore translating your query and blasting it against a protein database. Even more importantly, if you use the nucleotide sequence, the algorithm will not be able to know which changes are neutral (synonymous) these will be counted as differences, but they are not really in terms of protein sequence. Now, identity will not consider that in one sequence indeed you have a D to E transition (likely to be evolutionary neutral most of the times) and in the other you have for instance a D to F (acidic to aromatic) change, which is much less common in evolution (and likely passed through additional, unobservable, substitutions). For instance an evolutionary common change is D to E. Using the identity percentage is a crude approximation of evolutionary relatedness, especially for nucleotide sequences: in evolution there are changes that are more or less common and consequently more or less likely to be fixed identity simply does not take these differences into account. Last but not least, there is also a "technical" problem. It helps in identifying criminal suspects by law enforcement agencies as mentioned above.For example, in genetics it helps in predicting risk of diseases based on DNA sequence of individuals. bio-informatics, medicine, genetics, education, agricultural, law enforcement, e-marketing, electrical power engineering etc. It has been used in many different areas or domains viz.amazon, Taobao, Alibaba, snapdeal, Walmart, flipkart, BestBuy, ebay etc.), search engines (google, yahoo, bing, Ask.com DuckDuckGo etc.) It helps in obtaining desired search results of queries posed to e-commerce websites (e.g.This way data mining helps in increasing revenue. It has become possible due to inputs obtained from data mining softwares. The retail malls and grocery stores arrange and keep most sellable items in the most attentive positions. ![]() ![]() This way data mining benefit both possible buyers as well as sellers of the various products. It helps advertizers push right advertisements to the internet surfer on web pages based on machine learning algorithms.My question is: why is that? Could be that H3 histone does not bind unifomly to all DNA sequences? Or - I think more likely - the efficiency of IP on that region is significantly better than that on the other enhancers? If so, this should somehow be indicated in the calculations - but should we ALSO normalize the data (let's say the "input %") to the result with H3? Should we correct all IPs to the background first, than calculate fold change our input % of our protein comparing/normalizing to H3? Has anyone run into similar problems? Any comments or suggestion would be much appreciated! Moreover, this coincides with ONE of the enhancers where our protein of interest binds to. However, when I look at the data (ChIP-qPCR) with the "positive control" H3 histone, although we see a uniform binding to all examined DNA segments, there is a high peak in one of the enhancers. The reason I am bringing up this issue is because of our recent experiments: we could prove the binding of a protein to certain enhacer DNA segments in placenta but not in embryonic stem cells - so the data looks convincing. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |