Question: Reviews On Enhancer Prediction Methods
gravatar for Diana
6.5 years ago by
Diana780 wrote:


Has anyone come across some good recent reviews on enhancer prediction tools and transcription factor binding prediction tools?

Edit: I'm looking for a review article that compares different existing tools for enhancer prediction/transcription factor binding site prediction so I can get an idea about the efficiencies of different methods


prediction • 3.3k views
ADD COMMENTlink modified 6.5 years ago by Rad800 • written 6.5 years ago by Diana780
gravatar for PoGibas
6.5 years ago by
PoGibas4.8k wrote:

There is Encode thread enhancer discovery and characterization so you might want to start there.

New method announced - STARR-seq (can be used to identify and quantify enhancer activity).
science.1232542 January 17 2013

ADD COMMENTlink modified 6.5 years ago • written 6.5 years ago by PoGibas4.8k

Added some new information to my previous answer.

ADD REPLYlink written 6.5 years ago by PoGibas4.8k
gravatar for Rad
6.5 years ago by
Rad800 wrote:

I don't think such a review exists, that said, you can still gather the papers on enhancer predictions methodologies, the most famous paper is chromHMM, but there are some other methodologies based on machine learning techniques like SVM for example. As we don't know yet everything about enhancers in term of functional properties and epigenetic ones, I think it is better to start with all review on enhancers (I guess you did). If you are developing a tool on enhancer predition, my advice is to use experimental data as foreground/background which is in term of data quantity not that much. A lot of tools especially those based on SVM are trainined on a very few amount of experimental data (known enhancers, may be a couple of handreds) which makes it a bit hard to make a solid statement on prediction efficiency. The only way to confirm the accuracy is to do a functional analysis of upstream and downstream regions for an approximation.

ADD COMMENTlink written 6.5 years ago by Rad800
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