User talk:Xinghua Shi

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Project Ideas (Mindy, Oct 22, 2009):

1. Genome Variation Map (GVM)

Most of current genome variation and genotype-phenotype study is focused and based on SNP analysis. Recent advances in large structural variation (SV) in genomes have produce data and knowledge of genomic variations over a segment of DNA. Given the rapid growth of genome-scale variation data for different populations and the availability of large number of whole-genome sequences(e.g.PGP), it is inspiring to see how we can integrate the diverse data and make use of it at systems-level. A set of problems can be studied in this systems biology umbrella.

1) How one or multiple structural variation(s) correlate or interact with another set of SV(s)?

Each SV includes a segment of DNA varying from dozens of base pairs to kb or even Mb. Thus one SV encompasses more genetic information than one SNP does and it's more difficult to study SV-SV relationship. And one SV normally includes not only genes but also transcription factors (TF), binding sites and other regions with unknown function. So by describing SV-SV relationship, we gain insight into not only how a set of genes interact with another set of genes (QTL, etc.), but also how non-coding regions interact with genes and other non-coding regions.

2) How one or multiple genomic variation(s) correlate with or contribute to phenotype?

We can look at how a set of genes (Epistasis, etc.) or a set of SVs coorelate or even explain certain phenotype(s). With the progress of projects like PGP, Trait-o-matic, we start to viewing the genotype-phenotype at genome-scale and via compartive analysis of many individuals. The result could also contribute to PGP by providing genotype-phenotype at a different level.

3) Can we find a set of genomic variations that can be viewed as a genomic fingerprint for one specific population?

By answering this question, we can gain information about the phenotypic differences, genomic differences(e.g. one population is more suscpectible to certain desease or has larger percentage of individuals carrying a set of viruses, etc.), environmental effects (e.g. diet), population differentiation, and evolutionary history among diverse populations.


In order to understand and finally solve the problems listed above, approaches.

1) We need to build a software infrastructure and database repository that can integrate different types (genomic data, SVs, expression data, phenotypic information and any other related information. Projects like PGP and Trait-o-matic already have this set up and we might borrow some ideas.

2) Tools to store, analyze and visualize genomic data, intermediary information and phenotypic results. Particulary, I am very intersted in analyzing and presenting the data in a way that we can visualize and navigate through genome sequence to see the crosslinks between SVs or any two genomic regions. Each crosslink between two regions is color-coded and weighted and represented different relationships. For example, the crosslink can be the relationship between trans- and cis- regions, the common compounds/metabolites that connect two reactions which are in turn catalyzed by proteins with incoded genes in two SVs, etc. The boldness of the crosslinks in the later situation could be weighted to represent the stoichiomtry of two reactions.


3) Provide web interfaces and other easy access methods to scientific and general communities.

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