1. http://bioinformatics.oxfordjournals.org/content/21/16/3448.long
2. http://www.psb.ugent.be/cbd/papers/BiNGO/Home.html
2012年11月1日星期四
2012年4月18日星期三
Analyzing Biological Data Using R: Methods for Graphs and Networks
http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-61779-361-5_19
R is a powerful language and widely used software tool for the analysis and visualization of data . Its core capabilities can be extended through many different add-on packages . Among the many packages are some which offer a broad range of facilities for analyzing statistical properties of graphs . This chapter provides a practical tutorial covering the use of R methods for graphs and networks to examine biological data and analyze their topological and statistical properties .
2012年3月28日星期三
bioinformatics and systems biology in Gent U
This division incorporates several excellent researchers.
1. bioinformatics and evolutionary genomics
2. microbial systems biology
3. evolutionary systems biology
4. comparative and integrative genomics
1. bioinformatics and evolutionary genomics
2. microbial systems biology
3. evolutionary systems biology
4. comparative and integrative genomics
2012年3月27日星期二
2012年3月23日星期五
2011年4月10日星期日
how to get the list of genes involved in a biological process
http://biostar.stackexchange.com/questions/7323/how-to-get-the-list-of-genes-involved-in-a-biological-process
how to compare metabolic pathways
http://biostar.stackexchange.com/questions/7403/how-to-compare-metabolic-pathways
here is a good guide on comparing metabolic pathways
here is a good guide on comparing metabolic pathways
2011年4月5日星期二
Evolutionary Systems Biology Lab
Jaume Bertranpetit is the leader of Evolutionary Systems Biology Lab
http://www.ibe.upf-csic.es/ibe/research/research-groups/bertranpetit.html
They have done and are doing many excellent work on human genetics/genomics.
Here is a blog of a member of this lab:
http://bioinfoblog.it/
http://www.ibe.upf-csic.es/ibe/research/research-groups/bertranpetit.html
They have done and are doing many excellent work on human genetics/genomics.
Here is a blog of a member of this lab:
http://bioinfoblog.it/
2011年4月4日星期一
Interactome Networks and its importance
Pascal Braun
http://ccsb.dfci.harvard.edu/web/www/ccsb/publications/2011_papers.html
We recently completed mapping of the first binary interactome network for the reference plant Arabidopsis thaliana. Using tools of graph theory we identify biologically relevant network communities from which a picture of the overall interactome network organization starts to emerge. Combination of interaction and comparative genomics data yielded insights into network evolution, and biological inspection resulted in many hypotheses for unknown proteins and revealed unexpected connectivity between previously studied components of phytohormone signaling pathways.
Interactome Networks and Human Disease
http://www.cell.com/abstract/S0092-8674%2811%2900130-9
Summary
Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.
http://ccsb.dfci.harvard.edu/web/www/ccsb/publications/2011_papers.html
We recently completed mapping of the first binary interactome network for the reference plant Arabidopsis thaliana. Using tools of graph theory we identify biologically relevant network communities from which a picture of the overall interactome network organization starts to emerge. Combination of interaction and comparative genomics data yielded insights into network evolution, and biological inspection resulted in many hypotheses for unknown proteins and revealed unexpected connectivity between previously studied components of phytohormone signaling pathways.
Interactome Networks and Human Disease
http://www.cell.com/abstract/S0092-8674%2811%2900130-9
Summary
Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.
2011年3月31日星期四
Understanding the Evolution of Defense Metabolites in Arabidopsis thaliana Using Genome-wide Association Mapping
http://www.genetics.org/cgi/content/full/185/3/991
With the improvement and decline in cost of high-throughput genotyping and phenotyping technologies, genome-wide association (GWA) studies are fast becoming a preferred approach for dissecting complex quantitative traits. Glucosinolate (GSL) secondary metabolites within Arabidopsis spp. can serve as a model system to understand the genomic architecture of quantitative traits. GSLs are key defenses against insects in the wild and the relatively large number of cloned quantitative trait locus (QTL) controlling GSL traits allows comparison of GWA to previous QTL analyses. To better understand the specieswide genomic architecture controlling plant-insect interactions and the relative strengths of GWA and QTL studies, we conducted a GWA mapping study using 96 A. thaliana accessions, 43 GSL phenotypes, and ~230,000 SNPs. Our GWA analysis identified the two major polymorphic loci controlling GSL variation (AOP and MAM) in natural populations within large blocks of positive associations encompassing dozens of genes. These blocks of positive associations showed extended linkage disequilibrium (LD) that we hypothesize to have arisen from balancing or fluctuating selective sweeps at both the AOP and MAM loci. These potential sweep blocks are likely linked with the formation of new defensive chemistries that alter plant fitness in natural environments. Interestingly, this GWA analysis did not identify the majority of previously identified QTL even though these polymorphisms were present in the GWA population. This may be partly explained by a nonrandom distribution of phenotypic variation across population subgroups that links population structure and GSL variation, suggesting that natural selection can hinder the detection of phenotype–genotype associations in natural populations.
With the improvement and decline in cost of high-throughput genotyping and phenotyping technologies, genome-wide association (GWA) studies are fast becoming a preferred approach for dissecting complex quantitative traits. Glucosinolate (GSL) secondary metabolites within Arabidopsis spp. can serve as a model system to understand the genomic architecture of quantitative traits. GSLs are key defenses against insects in the wild and the relatively large number of cloned quantitative trait locus (QTL) controlling GSL traits allows comparison of GWA to previous QTL analyses. To better understand the specieswide genomic architecture controlling plant-insect interactions and the relative strengths of GWA and QTL studies, we conducted a GWA mapping study using 96 A. thaliana accessions, 43 GSL phenotypes, and ~230,000 SNPs. Our GWA analysis identified the two major polymorphic loci controlling GSL variation (AOP and MAM) in natural populations within large blocks of positive associations encompassing dozens of genes. These blocks of positive associations showed extended linkage disequilibrium (LD) that we hypothesize to have arisen from balancing or fluctuating selective sweeps at both the AOP and MAM loci. These potential sweep blocks are likely linked with the formation of new defensive chemistries that alter plant fitness in natural environments. Interestingly, this GWA analysis did not identify the majority of previously identified QTL even though these polymorphisms were present in the GWA population. This may be partly explained by a nonrandom distribution of phenotypic variation across population subgroups that links population structure and GSL variation, suggesting that natural selection can hinder the detection of phenotype–genotype associations in natural populations.
The Complex Genetic Architecture of the Metabolome
The Complex Genetic Architecture of the Metabolome
http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001198
Understanding how genetic variation can control phenotypic variation is a fundamental goal of modern biology. We combined genome-wide association mapping with metabolomics in the plant Arabidopsis thaliana to explore how species-wide genetic variation controls metabolism. We identified numerous naturally-variable genes that may influence plant metabolism, often clustering in “hotspots.” These hotspots were proximal to selective sweeps, regions of the genome showing decreased diversity possibly from a strong selective advantage of specific variants within the region. This suggests that metabolism may be connected to the selective advantage. Interestingly, metabolite variation in wild Arabidopsis is highly constrained despite the significant genetic variation, thus providing the plant un-sampled metabolic space if the environment shifts. The observed structuring of genetic and metabolic variation suggests individual convergence upon similar phenotypes via different genotypes, possibly intra-specific parallel evolution. This phenotypic convergence couples with a pattern of genotype—phenotype association consistent with metabolite variation largely controlled by numerous small effect genetic variants. This supports the supposition that large magnitude variation is likely unstable in a complex and interconnected metabolism. If this pattern proves generally applicable to other species, it could present a significant hurdle to identifying genes controlling metabolic trait variation via genome-wide association studies.
http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001198
Understanding how genetic variation can control phenotypic variation is a fundamental goal of modern biology. We combined genome-wide association mapping with metabolomics in the plant Arabidopsis thaliana to explore how species-wide genetic variation controls metabolism. We identified numerous naturally-variable genes that may influence plant metabolism, often clustering in “hotspots.” These hotspots were proximal to selective sweeps, regions of the genome showing decreased diversity possibly from a strong selective advantage of specific variants within the region. This suggests that metabolism may be connected to the selective advantage. Interestingly, metabolite variation in wild Arabidopsis is highly constrained despite the significant genetic variation, thus providing the plant un-sampled metabolic space if the environment shifts. The observed structuring of genetic and metabolic variation suggests individual convergence upon similar phenotypes via different genotypes, possibly intra-specific parallel evolution. This phenotypic convergence couples with a pattern of genotype—phenotype association consistent with metabolite variation largely controlled by numerous small effect genetic variants. This supports the supposition that large magnitude variation is likely unstable in a complex and interconnected metabolism. If this pattern proves generally applicable to other species, it could present a significant hurdle to identifying genes controlling metabolic trait variation via genome-wide association studies.
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