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15

Well you are assuming one sequenced genome/proteome per NCBI tax id. That is no longer true. So if you click on the proteome filter it decreases by half. Which gets you into the 60,000 range. Now not all of these are "different" conceptual proteins, many are artifacts from the way GenBank/EMBL/DDBJ interact with the TrEMBL section of UniProtKB i.e. they are ...


5

Comparing some commonly used languages in bioinformatics I think that indeed Perl is losing users but there are still quite a lot of people using it. Bash (or other shell) is essential. While one can replace Python with C or Java and eventually replace R by Python or Julia, I find Bash to be really necessary. To compare Python to R, Python is much faster. ...


5

Protein-protein interactions is an area of biophysics, so certainly Biophysical Journal (published by Cell) is a nice place to look. PNAS has a broad range of areas and they pay a lot of attention to nice biophysics and modeling. But also they require (seems to me) experimental evidence. The Journal of computational chemistry certainly about a lot of ...


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There are 20+ thousand genes in the genome, but each of these can produce multiple proteins. In addition to this, you have protein fragments and cleavage products further increasing the number of entries.


4

It sounds like you have considered most of the obvious alternatives (and thank you for clarifying the question). I suppose the first question in an alternatively spliced transcript with a retained intron is whether the open reading frame of the protein is maintained. If there is a termination codon that now becomes in-frame due to the intron then the ...


4

This has been studied by some of my labmates in Why is the biological hydrophobicity scale more accurate than earlier experimental hydrophobicity scales? I am not involved in their research, but here is the gist of the paper: Different scales are, as you say, developed based on different criteria. In particular, Eisenberg scale is one of the consensus ...


3

I have found that this chapter by Lincoln Stein is a very easy, accessible, and useful introduction to writing a Perl script: Using Perl to facilitate biological analysis. It is Chapter 18 in "Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins" 3rd edition, by Baxevanis and Ouellette. I use Perl and bash for almost everything, but most ...


3

I highly recommend you to visit Pathguide to get a sense of how vast is the catalog of Pathway Databases. Looking into the category Pathway Diagrams or in Transcription Factors / Gene Regulatory Networks should help in your task. I would start by looking at these DataBases: GeneMania BioCarta WikiPathways Reactome If you are working with a species other ...


2

Interactions denote protein-protein interactions, which means physical association between proteins. By nature, these networks/graphs are undirected. Replication interactions (actually a not very good term) denote gene regulatory interactions that affect HIV replication. These sets also include the regulatory effects of HIV genes on host genes (and hence ...


2

If you already know the basic sequence i.e. the fixed regions in the primer; for e.g. the known nucleotides in the sequence - NGATWGCTSATNGC, then you can implement your algorithm like this: Fix the max length of the primers. Lets say 20nt. Generate all combinations of primers (20nt): that is pretty straightforward. You will have 4N × 2(R+Y+S+W+K+M) × ...


2

Actually there is a way to determine the initial conditions for this problem. We assume that the gating variables $r$ and $s$ are at a steady state before the membrane potential jump from$u_0$ to $u'$. If we look at the first graph(the steady state relation versus membrane potential) we can see that at the membrane potential $u_0$ that $r(t=0)\approx 0$ ...


2

My colleagues and I have recently published a toolbox of command-line utilities that are modeled after GNU Textutils (e.g. grep, cut, tr, uniq) called FAST: Fast Analysis of Sequences (cite). To install FAST you can use: perl -MCPAN -e 'install FAST' Or install the current development version from github. The FAST install comes with a cookbook, ...


2

This is somewhat outside my field, but basically it seems they're defining the independence of the three hypothetical metabolic pathways in two different ways: Mathematically the pathways are not independent since P₃ is a combination of P₁ and P₂. Biologically the pathways are independent since each requires a different combination of enzymes and ...


2

Some suggestions. For identifying function do a homology search. There is little functional annotation of lncRNAs. So homology based information can be obtained only for protein sequences. So you can try these: Check the coding potential. Find ORFs (perhaps set a minimum length cutoff). To be stringent you can also check for Kozak consensus sequences (for ...


2

As I understand, what you really asking is: What is the mathematical model one can use to fit drug lethality/efficiency data? With answer to that you can go to stackexchange and bug them for fitting solution in language of your choice. However, answer to that is quite complicated, it depends on your system and experiment. And also, how well you want to ...


1

If some exons are missing in one of the samples then the introns will, by definition, be different. What this allows is for the bounds of the outermost exons to vary a bit. This is particularly useful for exon 1, which often has lower coverage.


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Sometimes SwissProt annotations come from direct experimental evidence, but this is rare. It depends on the entry, but more often than not the annotation will have been obtained by some kind of sequence analysis or prediction based on sequence similarity.



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