I recently read the ENCODE paper "Architecture of the human regulatory network derived from ENCODE data", and I realized that the supplementary data will greatly help me to refine projects results, in particular those files related to the K562. Unfortunately, I found that all the supplementary data files are not available to download, since both of the following sites can’t be reached.
In particular, the second link is active, but if I try to download one of the files, it points to the first link and the download is interrupted. I am writing to ask if there are any other ways to access the files.
http://encodenets.gersteinlab.org should be back up now. Let us know
I was wondering if you could help me.
I read your interesting article " The Importance of Bottlenecks in
Protein Networks: Correlation with Gene Essentiality and Expression
I have trouble understanding the definition of hubs and bottlenecks (We
defined hubs as all proteins that are in the top 20% of the degree
distribution (i.e., proteins that have the 20% highest number of neighbors).
Accordingly, we defined
bottlenecks as the proteins that are in the top 20% in terms of
For example: if we want to calculate proteins that are in top 10% of degree
distributions, in a PPI network with 1000 nodes, we consider 100 highest
we calculate 10% of the highest degree, which is for example 700 and
proteins with degree above 630 are the hubs?
Which one of these interpretations are correct?
Your first interpretation is correct, i.e., if there are 1000 proteins in the network, we consider the top 100 proteins with the highest degrees.
I am reading your paper, and have problem about the TF-target gene network data downloaded from http://encodenets.gersteinlab.org/. I want to know which refGene and gene symbol did you use when you find the TF target gene with ChIP-seq data? I find that some symbols are not concluded in hg19 refGene I download from ucsc.
the server was down for a while, and I wasn’t sure what names were you talking about. Now, I think the names are from gencode, but I cannot recall the exact release we used. I believe the names wouldn’t change in general. you can see all the releases here, the names should be in one of the metafiles.
Recently, I have read one of your paper titled “Comparative analysis of regulatory information and circuits across distant species”. In this paper, you wrote that you used simulated annealing to reveal the organization of regulatory factors in three layers of master-regulators, intermediate regulators, and low-level regulators. However, I can’t find the program for this method or the references related to this method. I want to use this method to class the TFs in my own regulatory network. Can you kindly provided this program for me?
An initial version of the code is available from encodenets.gersteinlab.org.
The code used for the analysis can be found
more recently, our group published an updated method. the code will be released very soon.
I was intrigued by your paper about classifying the human genomic regions based on experimentally determined transcription factor binding sites. I was wondering if you can share genomic loci of the six types of regions that you were able to identify in this paper. I was also wondering if by your analysis you were able to conclude which regions are not tissue specific. I was also curious to know if you have done similar analysis on other species. It would be great if you would be able to share the scripts that you used to generate these results if they are available in some sort of a program/package.
My research focus on understanding measure trust prediction in social networks. I read your paper about A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data. I am interested in this method. Maybe I would use Bayesian Networks Approach for Predicting User-User Interactions from Social Network. So I want to ask whether I can refer to the realization of the experimental in this paper, especially for the code and data.
yes – see http://networks.gersteinlab.org/intint/
I recently have been working on constructing human regulatory networks. After reading your paper <Genomic analysis of the hierarchical structure of regulatory networks> published on PNAS, I found it very amazing and useful, which may be applied for my study. I want to construct hierarchical structure of transcription factors (TFs) in humans, and my data is the expression level of these TFs and their targets obtained by RNA sequencing. Can we use your BFS method to construct the network? As we know little about the computational algorithm of BFS, would you please provide related scripts or equations for implementing it easily?
Thank you very much for occupying your precious time reading my letter and I’m looking forward to your guidance.
Hi, see http://info.gersteinlab.org/Hierarchy
Currently, I’m working on reconstructing gene regulatory network. It’s
really an interesting topic and I would like to estimate which tools is
suitable for our experimental data. I have read your published paper
"Improved Reconstruction of In Silico Gene Regulatory Networks by
Integrating Knockout and Perturbation
Data". In this paper, I can’t understand the section of learning noise from
Step 1: Calculate the probability of regulation Pb->a for each pair of genes
(b,a). I want to know how to calculate this probability, and this value of
probability can decide potential regulation or not?
I want to ask you that how to work in this section, and I’m appreciated if
you can help me to figure out.
A: Basically we used the expression levels currently believed to be
unaffected by a deletion to form a Gaussian background. Then if a gene
has an expression level far away from the mean of this Gaussian
distribution (by calculating the probability that the expression is as
extreme or more extreme than the observed one based on the Gaussian), we
consider the gene to be affected by the deletion.
I am currently working on
a network science project studying properties of heterogenous networks and greatly intrigued by your 2011 paper in PLoS
Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data
I am planning to employ the integrated human TF-miRNA-gene regulatory network constructed in your work to verify the
utility of information flow – based techniques in understanding the mapping between network topology and function. However,
the full network does not appear to be available in the supplementary information. I am writing to kindly ask if I could obtain
a copy of the human network data (e.g. CSV format edge list) for my research. I would be more than honored to be able to use
the original dataset in my work, and my apologies if it is against your plans to disclose it or it is available somewhere else that
I am not aware of. Thank you very much!
you can certainly get the network.
The data behind it and a closely related network is available from :
(see website links)
During the last days I was reading your paper "Architecture of the human
regulatory network derived from ENCODE data".
I am doing something related and I am willing to perform your kind of
analysis in addition or to merge the two ideas somehow.
For this purpose I was looking for some program code that has been
published for the analysis of your work, but so far I just found the
workflow description in the SI.
In case it is possible, I would be delighted if you could share the
relevant code with me, which would make life much easier for me and my
analysis much quicker.
I would be primarily interested in everything that allows me to infer
the hierarchy diagrams for the TF network and the TF-miRNA network.
By the way: Is there any reason why you did not include histone
modification and DNA methylation data?
some code is associated with separate papers – eg see :