I'll do your 10-day in silico experiment for free in February

#1 Mixter

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Posted 10 December 2008 - 11:20 AM

In two months, I can do some own bioinformatics-related research in a two-week
practical course, and pick my own topic. Instead of the default topics, I would love
to invest the time doing something relevant to aging research, such as something
with aging-related expression data, diabetes research, or mitochondrial genes.

I will have to work with in silico methods on existing data only, and _must_ meaningfully
apply 1-2 of the methods below.
Please suggest well-suited ideas along with existing experimental data and optionally some
the methods to mine it. Needs to done in less than 10 days by one guy.
Please make your suggestions now

- Any of the basic stuff like involving homology, orthology search with BLAST, some phylogeny, etc.

- Gene prediction and correlation (maybe a protein databank for some aging-related subset)

- Functional classification of proteins

- Regulatory Motifs/Elements (e.g. MotifSampling)

- Biological Networks (metabolomics; graphing metabolic- or other networks)

- Systems biology using existing _basic_ tools

- Correlation of studies, must involve systems biology or sequence data (not purely a meta analysis)\

- Anything involving clustering of protein or DNA sequence data

- Gene Expression analysis (< 10 days, so please don't suggest anything too huge :-) )

Edited by mixter, 10 December 2008 - 11:26 AM.

#2 Mixter

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Posted 28 December 2008 - 10:24 AM

bump. nobody wants any interesting analysis done? shrug

#3 JLL

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Posted 28 December 2008 - 11:53 AM

Sounds like a good deal, but I don't understand enough about bioinformatics to even suggest an experiment.

#4 Guest_Shinigami_*

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Posted 07 January 2009 - 03:35 AM

Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.
Taylor RC, Acquaah-Mensah G, Singhal M, Malhotra D, Biswal S. Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, U.S. Department of Energy, Richland, Washington, United States of America. ronald.taylor24@gmail.com

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein-protein and protein-DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of R elatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2(+/+) and Nrf2(-/-) mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease.

Maybe you could do the same for NF-kappaB, TNF-alpha or SIRT1?
Just an idea...

#5 maestro949

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Posted 10 January 2009 - 04:37 PM

I'm taking informatics courses and will typically use aging data sets for my projects. For all of the BLAST and genomics work I did in prior courses I would simply select genes from GenAge that worked well for the paper/project @ hand. My prof recommended looking @ Tom Kirkwood and Darren Wilkinson's work for modeling aging type projects. The 2nd 1/2 of my SB course is on dynamic modeling so I plan to spend time taking a deeper look at their stuff. Perhaps an idea might evolve from their work for you?

The biggest issue I see atm is that the existing microarray data isn't organized well to study from a dynamical perspective so any aging-related informatics projects need to be fairly small in scope, that is, unless the project is to better re-organize/aggregate/normalize the data!

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