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 :
I am trying to construct a "gene co-phenotype" background network using bayesian approach which is mentioned in your paper "A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data" (science ,17th October 2003).
After reading the supplementary method related with this paper, I have a question on how to set the training data set size.
In this paper, 8250 positive /2691903 negative training gene pairs are used. It is recommended that the training data set should be balance with the true situation when we use naive bayesian method. Could you give me some instruvtions on how you set the positive/negative training dataset size. It will be very glad to hear from you.
best I can do here is point you to:
I am very interested in your work on network rewiring. I have been working on experimental validation of network rewiring approaches investigating how this can be used to reprogram regulatory networks to improve heterologous protein production in Yeast. I am now in the process of analysing transcriptional rewiring phenotypes I have identified in a combinatorial library based screen. I have noticed some very interesting enrichment criteria in the groups of rewired promoters and open reading frames with regards to network structure.
I was hoping to look at how these rewired components are natively arranged with regards to their network hierarchy. I would like to use the hierarchical network model you proposed in your paper (http://www.ncbi.nlm.nih.gov/pubmed/21045205?dopt=Abstract) but I have been having trouble reconstructing it from the pdf supplemental data. I am really keen on using your model to study my experimental data further if you have any suggestions on how I could best go about this I would be most greatful.
you might find the following links useful :
website with an earlier version of the yeast hierarchy.
information on worm & fly hierarchies
I would also direct you to the wiki page:
Under the heading "Phenotypic Effects of Network Rewiring in Transcriptional Regulatory Hierarchies", this page lists all the data in a very user-friendly format that you would need to reproduce the hierarchies with all the datasets very well described/annotated.
This page has the initial regulatory network of E. coli and Yeast and it also provides you with the original breadth-first search hierarchies. In addition, it lists all the changes in the hierarchy upon deletion of each gene. There is an extensive description of what each column in each file means.
Further, in order for you to better understand the algorithm/program we used, I am also attaching a light-weight perl script that generates the hierarchy from a given network (BFS.pl) (it is well annotated with an explanation of each step). I am also attaching another perl script that I used to list the changes the hierarchy upon deletion of each gene (count_changes_modified_hierarchy.pl). Paths will be broken for input files but it should be enough for you to get a flavor of how we quantified changes in the modified hierarchies.
I recently read your article “Construction
and Analysis of an Integrated Regulatory Network Derived from
High-Throughput Sequencing Data”. In the last year, I measured mRNA and
miRNA expression in the different types of mouse skeletal muscle fibers to
discover the different regulatory circuits activated in fast and slow
myofibers. I designed a preliminary network using the databases of miRNA –
target mRNA and protein – protein interactions, and I have started to
include my expression data in order to understand the biological meaning. I
was wondering if it is possible to use your more accurate mouse regulatory
network for my data. Is this network free to use? In the article and in the
website of your laboratory I did not find any file or link with the complete
networks that you describe. I am not a computational biologist, but the
paper is very interesting and I think that the network that you design with
your method could be very useful for the scientific community.
Hereby I attach three files for our three mouse networks. 1) how miRNAs targeting genes (This is not our calculation, but downloaded from TargetScan).
2) how TFs targeting genes, 3) how TFs targeting miRNAs based on ChIP-Seq data of 12 TFs.
The files are in plain text format. The first column is the list of regulators and the second column is the list of targets. The bracket next to a gene name gives the class of the gene, TF for transcription factors, MIR for miRNAs, and X for non-TF protein-coding genes.
Thank you for your interest of our paper. I hope this information will be useful for your work.