Inquiry about your article ” The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics”

Q:
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
Dynamics".

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
betweenness.. )

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
degree nodes?

or

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?

A:
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.

Questions about “Architecture of the human regulatory network derived from ENCODE data”

Q:
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.

A:
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.
http://www.gencodegenes.org/releases/

Help regarding the paper “Comparative analysis of regulatory information and circuits across distant species”

Q:
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?

A:
An initial version of the code is available from encodenets.gersteinlab.org.

The code used for the analysis can be found
http://encodenets.gersteinlab.org/enets16.hierarchy_levels.m
more recently, our group published an updated method. the code will be released very soon.
http://genomebiology.com/2015/16/1/63/abstract#

Question about Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors

Q:
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.

A:
see
funseq2.gersteinlab.org
+
metatracks.gersteinlab.org

Question about A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data

Q:
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.

A:
yes – see http://networks.gersteinlab.org/intint/

related scripts or equations for implementing analyses in paper “Genomic analysis of the hierarchical structure of regulatory networks”

Q:
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.

A:
Hi, see http://info.gersteinlab.org/Hierarchy

Ask for help about your paper (PLoS ONE, 2010)

Q:
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
deletion data.
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.