Gene expression data analysis using

This project was awarded to veraanadi for $500 USD.

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Project Description

I have a project at data analysis stage and want to finish it for publication. Currently I have moved to another work place and do not have full time to finish the project. Besides, my dataset is of "non-standard" kind because it is not a canonical metagenome or metatranscriptome dataset. I have done some primary analysis on MG-RAST platform. So I am hoping to get one collaborator who knows well about MG-RAST tools and with good statistical experience.

The data set was generated with DeepSAGE RNA Tag method. I matched the tag libraries to our corresponding metagenome by Perl hash table lookup with 0 mismatch. After this step I got subpools of metagenome reads in which each read has at least one tag matched to it. I uploaded all these subpools to MG-RAST to annotate them. My goal is to find out identities (qualitative) and distribution (quantitative) of genes and bacterial species that has been captured during our RNA expression experiment.

Work with me in collaborative manner for one or two week time, each day about 1 hours. We decide tasks and sub-tasks during discussion. You then carry out the sub-tasks which involves writing Python programs for statistic data analysis of the data or metadata. Both will discuss about final results and result visualization for publications. The visualization step can be done with either Python programs or Excel. Python programs you write will be GPL licensed, and must follow good practices for future reuse and improvement. They would be published together with resulting article(s).

One MUST have experience with:
1. Advanced biological data analysis, especially high throughput sequencing data.
2. MG-RAST and KEGG.
3. Statistics concepts such as data correction and data normalization.
4. Writing python programs to analyze biological data.

PLUS points if one posses:
1. Experience with other gene and pathway annotation platforms.
2. Experience with SAGE/DeepSAGE, or Illumina data.

1. Co-authorship in articles where you involved directly to generate results of analysis.
2. Opportunities in my future projects. I will offer more projects in future if a mutual appreciation is formed from this project.

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