Hi,

we are using the tool IMPaLA: Integrated Molecular Pathway Level Analysis

http://impala.molgen.mpg.de/

You can integrate all 3 omics

People have been integrating multiple level of information and showed that models are in agreement with experimental data. Genome, transcriptome and metabolome could be integrated to build and analyze metabolic models. You can follow some very good papers on these integration of genome scale metabolic models with transcriptome and/ or metabolome data.

http://www.sciencedirect.com/science/article/pii/S1096717604000242

http://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CFcQFjAE&url=http%3A%2F%2Fwww.rci.rutgers.edu%2F~yannis%2Fpublications%2FAndroulakis%2520CBIO%2520Final.pdf&ei=TzQYUtLeKMG3rgfpnYHIBg&usg=AFQjCNEPtrlDuyT23A6--wHlXhNRi27AdQ&sig2=WXyDPIElfFU2Nc32QHdUkg&bvm=bv.51156542,d.bmk&cad=rja

http://www.frontiersin.org/Computational_Physiology_and_Medicine/10.3389/fphys.2012.00299/abstract

http://www.ncbi.nlm.nih.gov/pubmed/12021803

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000859

http://www.ncbi.nlm.nih.gov/pubmed/20692823

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000082

You can integrate two different experimental data by O2PLS which will provide you common variance in the different experimental data sets.

You can follow these two articles on statistical integration for your reference:

http://pubs.acs.org/doi/abs/10.1021/pr060124w

http://pubs.acs.org/doi/abs/10.1021/ac301269r

Other than statistical integration, you can also use IPA, metacore, string etc. for pathway analysis with your different omics data.

You can use meta-analysis procedure for this. There is integration at two level 1) at the data level 2) at the result level. Though, to integrate different OMICS data you have to do accurate annotation.

1) First Annotate the different dataset and then try to integrate them using some common identifier. It may be REFSeq id, Entrez Id and GeneID.

2) These are the two important paper related to your query

a) http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2528050/

b) http://www.biomedcentral.com/1471-2105/9/118

3) In bioconductor number of packages(RankProd,GeneMeta) has been available for microarray meta-analysis. You may use the procedure implemented in that packages for your purpose

I am sure you'll get the answer of your question by reading these articles.

Hope this will help

Sadegh, what about using Grok solutions from Numenta? Not advertisement, don't worry, just take a look... It could be a good amplifier for your work as I guess... Sometimes people lack intuition in front of big amounts of raw and hardly interpretable data (read: binary and so on), then you just have to have a soft/hardware "slave" that can gather, filter and present some solutions... Good luck!

It would also be good to work as part of a multi-disciplinary team - what seems difficult in isolation becomes easier as each person contributes their expertise. Reach out to other people in your institution or find like-minded people - there are plenty out there! Use wetware (human brains) as well as hardware. Best wishes for success!

Martine's answer is probably the most relevant. We have been working in a project (www.ubiopred.eu) for that last 3.5 years aiming to do exactly what you suggest. It does seem to take the expertise of multiple different disciplines and lots of discussion.

My research takes a systems biology perspective. You can check out a free webinar I did lately for Ingenuity Systems. It should serve as a good intro.

http://www.youtube.com/watch?v=fzdc0GIdCnA

Thanks. I have also found a beautiful review of needed information and detailed protocol for this issue with an example:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2528050/

@Reema Singh

Thanks for this beautiful review

NOTES: I studied some of the introduced papers above:

There are two integration methods: 1) horizontal and 2) vertical.

In horizontal meta-analysis methods we try to integrate samples derived from similar layer. For example integrating all microarray expression data, or integrating all proteomics data. But in vertical method two different layers are integrated. For example genomics and proteomics data are fused together.

@sadegh , In simple words two kind of integration - one from Similar platforms and another integration from different platform. This is generally we talk in term of microarray. But basics are same here also.

Yes proteomics and genomics integration are fall under second categories. But remember proper annotation is the essential for this integration.

Interesting methods that combine gene regulatory networks with metabolism are for example rFBA and PROM. rFBA uses Boolean logic for genomics and flux balance analysis for metabolism, PROM comprises a probabilistic approach for high-throughput data and also flux balance analysis for metabolism.

http://www.ncbi.nlm.nih.gov/pubmed/18621757

http://www.ncbi.nlm.nih.gov/pubmed/20876091

Here is another good review that also covers those issues.

http://www.ncbi.nlm.nih.gov/pubmed/23525368

hello dear

i just want to know what u want how the s/w are developed OR mathematical basis of s/w OR you want some s/w for integrating these data. pls find the articles and follow the 30mics algorithm. it may help u.

http://www.biomedcentral.com/content/pdf/1752-0509-7-68.pdf

http://www.biomedcentral.com/content/pdf/1752-0509-7-64.pdf

http://3omics.cmdm.tw/help.php#cor_setting

Abstract:

Thanks for great introduced papers:

1- there are horizontal and vertical integration for each OMICs data: e.g. Breast cancer

a) integration of Breast cancer microarrays (horizontal),

b) integration of Breast cancer Protemoics (horizontal),

c) integration of Breast cancer GWAS data (horizontal),

finally:

d) integrating integrated data of proteomics, genomics and transcriptomics (vertical)

Mta-analysis methods for horizontal and vertical combining of data:

1) Vote counting, 2) Combining ranks, 3) Combining p-values, and 4) combining effect sizes ???????? I need a more detailed papers about these analytic methods. Are methods eaquel for a, b, c, and d (above)?

Then I have learned that there are several ways to analyze pathways including:

1) Overrepresentation methods (EASE, GOStat, DAVID, MetaCore)

2) Gene set enrichment methods (GO, GSEA, SAFE)

3) Set-based methods (PINK)

4) Modeling methods

5- Network-based methods (KEGG, GSEA, PANTHER, DAVID, GO, MSIGDB)

6- Text mining methods

Right?

Hi all,

Here are some just-published tools to co-visualize and co-analyse proteomics, transcriptomics and genomics data:

http://www.ncbi.nlm.nih.gov/pubmed/24152167

Tools to Covisualize and Coanalyze Proteomic Data with Genomes and Transcriptomes: Validation of Genes and Alternative mRNA Splicing.

Hi,

we are using the tool IMPaLA: Integrated Molecular Pathway Level Analysis

http://impala.molgen.mpg.de/

You can integrate all 3 omics

Is there a online or standalone tool for analyzing Regulatory flux balance analysis of E.coli gene knockouts.