10

Gene names are not consistent across species. Most of the time genes are automatically annotated and the algorithms try to give names that are similar to their matches in other organisms, but not always. It's also more likely that genes will be automatically named/annotated based on their sequence, and manually named/annotated based on their function. The ...


9

There are multiple ways to do this, and multiple protein interaction databases besides the ones you mentioned, such as BioGRID or IntAct. Interaction databases are different in how interactions are defined, sometimes it can be experimental evidence of interaction, sometimes coexpression, orthology-based predictions, etc. There is no single solution to your ...


7

In matlab this can be done using the SimBiology toolbox. In mathematica this can be done using the SystemModeler. In python there are multiple packages, e.g. (in no particular order) PySB, Tellurium, PySD


7

You originally had asked a very broad question, so I'll try to demonstrate why that is such a hard question to answer. I've done two fairly large differential analysis studies (and a few smaller ones) covering very different areas of research, and the approaches that other researchers used subsequent to my differential expression calculations were ...


5

Many interaction databases now work with PSI format files. Most of the main databases can do this and the EBI has set up PSICQUIC View, a very useful page where you can query multiple databases at once. Note that it is very important to limit the results according to the detection method. There is a lot of noise in protein interaction databases. Depending ...


4

I have used STRING pretty heavily, and have compared it to various other databases of protein interactions and signaling pathways. I do feel like it has a lot of quality interaction annotations, but you have to sift through a lot of noise to get to them. The simplest method I have found for doing this is to look at the individual scores for each ...


4

This depends on what you are trying to do and whether you value specificity over sensitivity. We can't tell you since it is entirely dependent on the biological question you want to answer. However, I would recommend two things: Don't use STRING. The creators of STRING made the choice to value sensitivity over all else, so they include any interaction they ...


4

I think the easiest way is to download the graph using STRINGdb. library(STRINGdb) string_db <- STRINGdb$new(version="10", species=9606, score_threshold=400, input_directory="" ) full.graph <- string_db$get_graph() Now you can use igraph, to manipulate the graph. Let's assume you want to take 200 proteins with the highest ...


4

You could try using BioMart in Ensembl Metazoa. Filter by your list of gene names in honeybee, then get the fly homologues as attributes. The homologues are calculated by sequence comparison and clustering.


4

You can't build a network of a single cell only with the expression of a single cell. You either need previous known interactions or pathways or you need to use several cells/samples. If you use previous known information, you can use pathway information, otherwise you can group some cells and use something along the lines of WGCNA to find a scale-free ...


4

An easy way to visualize this is to make a tab-delimited table that contains edge information for both species and both networks, color the edges depending on the species, and make the thickness and transparency dependent on the interaction strength. For example, say you have a list of orthologs for the two species and just assign them a number or a name. ...


3

You can read about it on the website, the last column is the Log likelihood scores (LLS) between the genes in the first two columns. An LLS of 0 means not better than random link. On the website they show papers with fuller details about the statistics.


3

You can use exportNetworkToCytoscape or exportNetworkToVisANT to generate and egde list file with the two nodes at the ends and weight for each edge. Hopefully there's a way to import that into networkX.


3

I assume you are simulating a null distribution. Are you investigation recombination?? My main advice is to use population genetic terminology rather than geomometry to describe your simulation (e.g. vertex is a migration event between allopatric populations). Migration is investigated between populations investigated via $F_{ST}$. You appear to be assuming ...


2

If you run this Rscript using the gene name as an argument, you'll get a file with the pathway written to the directory. #!/usr/bin/Rscript args = commandArgs(trailingOnly=TRUE) library(paxtoolsr) id <- args[1] write.table(graphPc(source=id,kind='neighborhood', format='BINARY_SIF',verbose=TRUE), file=paste(id,'pathway',sep='_'), ...


2

I would be concerned about the input data that produced these numbers. This dataset has a really odd shape with a dip at the start: data.df <- read.table("soft_threshold.txt", header=TRUE); png("scale-free-194.png"); plot(data.df$SFT.R.sq); dummy <- dev.off(); Here's what I expect this to look like (from the supplementary information in this paper): ...


2

Co-expression network will give you an idea about the genes having similar expression patterns and the nodes will be decided on the basis of correlation scores and nothing much you will get from differential gene expression perspective. I would suggest you to try some other models for differential expression analysis like baySeq , DESeq, edgeR, NOIseq. ...


2

While you can use networks to find differentially expressed genes (see the WGCNA package, which does this) in my experience this ends up largely matching what you'd get using a traditional package with a looser threshold for significance. Given the time savings of traditional packages, there's rarely any gain to using networks (it won't hurt to try, just ...


2

This error came from the fact that the output of the new fimo version has an additional 'motif_alt_id' column in second position. CRCmapper has been modified to take this into account. You can find the updated version here: https://bitbucket.org/young_computation/crcmapper/src/master/ P.S.: sorry if we missed you request user1545 and thanks for using ...


2

It seems that the author of CRCmapper.py wants to extract sequence_name, start, stop columns from fimo.txt file. But the code extract motif_alt_id, sequence_name, start columns. So the index of the code should be changed. fimo.txt looks like this: motif_id motif_alt_id sequence_name start stop strand score p-value q-value matched_sequence ...


2

I am not sure if I understood correctly how you classify the drugs. But what you attempt to do is similar to what they do here but in that article they use the drugs targeted to a molecule/pathway to find combination of drugs that are better for the disease (not an alternative drug), see the first image: In that article they mention the pathway cross-talk ...


2

You can start by looking at the correlation (if data is non-parametric) of each variable with each variable of each data point you have. One thing that might change is the correlation between variables along the disease. Next you can determine which relationships are constant along the disease progression/clinical trial. Identify also the ones that don't ...


2

I have recently develop an R package (sorry not a Cytoscape solution) to deal with this kind of situations of something grouping other elements, like pathways and genes. The package is based on GSEABase of Bioconductor to store this relationships. Basically you should store a gene set for each TF and genes it is linked. I would recommend to use the name of ...


2

So, you can give a try to this Cytoscape app that will allow to perform comparison between networks. This app is not specifically designed for comparing networks across different species but it will enable you comparing any kind of networks. These are the papers they published, which can give you some hints. About your specific problem...I can't help you ...


2

You can calculate gene ontology similarity with the GOSemSim R package (paper)(disclaimer I'm a contributor to this package). You have several similarity scores implemented, some of them are across the three subontologies. In python you have the GOATOOLS (paper). I think there was another Python package but now I don't remember its name.


2

It seems like there are some files related to the databases at the ftp site: http://ftp.mcs.anl.gov/pub/WIT2/. The related files were last updated on 2002. I found it via searching at google: site:*.mcs.anl.gov/ WIT. Which reports also a page about the database. There seems to be a project to develop WIT3, because there is a mailing list for developers, ...


2

For the first part, do you mean that the file is too large to be run on your computer? For the second, if I understood correctly, you can use igraph or ggnet2 to color code the nodes based on the conditions you have and see if communities are formed based on condition. This would be a simple way to do this kind of thing. I personally prefer using ggnet2 ...


2

The problem was I should use ENTREZ rather than gene symbols


2

Don't worry about it too much. I would go with power 8 based on my general experience (also reflected in WGCNA FAQ) and on the mean connectivity around 50 and median around 20, which seems reasonable to me.


2

This answer does not rely on STRINGdb package. STRING provides supplementary identifier mapping files here: https://string-db.org/mapping_files/. It looks like you are interested in human STRING name one - https://string-db.org/mapping_files/STRING_display_names/human.name_2_string.tsv.gz. You could read it as a separate lookup.


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