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research [2020/02/14 09:07]
127.0.0.1 external edit 		research [2020/02/28 13:54] (current)
tischulz Added graph teams software
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 === Comparative Assembly Tools === === Comparative Assembly Tools ===
  
-{{:​comparative_assembly.png?​nolink |}}Currently,​ we are using our index-based matching algorithm [[http://​bibiserv.techfak.uni-bielefeld.de/​swift|SWIFT]] as a basic component of the recently developed [[http://​bibiserv.techfak.uni-bielefeld.de/​cg-cat/​r2cat.html|r2cat]] and [[http://​bibiserv.techfak.uni-bielefeld.de/​cg-cat/​treecat.html|treecat]] methods. These are employed in the context of whole genome sequencing for closing the gaps that remain between the contigs after a standard assembly of shotgun reads. \\+{{:​comparative_assembly.png?​nolink |}}Currently,​ we are using our index-based matching algorithm [[http://​bibiserv.techfak.uni-bielefeld.de/​swift|SWIFT]] as a basic component of the recently developed [[https://​bibiserv.cebitec.uni-bielefeld.de/​cgcat|r2cat]] and [[https://​bibiserv.cebitec.uni-bielefeld.de/​cgcat|treecat]] methods. These are employed in the context of whole genome sequencing for closing the gaps that remain between the contigs after a standard assembly of shotgun reads. \\
  
 === Detection of Repeated Regions === === Detection of Repeated Regions ===
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 === Detection of Gene Clusters === === Detection of Gene Clusters ===
  
-{{:​zellen.png?​nolink |Zellen|}}Gene clusters are sets of genetic markers that maintain close physical proximities to each other in several genomes. These genetic markers often embody protein coding genes that are functionally and/or regulatorily associated.\\ Traditional methods take into account one-dimensional distances in the genomic sequence to determine physical proximities. They do not consider any spatial information regarding the structure of the chromatid in the cell and therefore might miss spatial gene clusters within the genome. However, spatial structure and function seem to be correlated in many cases.\\ Nowadays, it is possible to gain three-dimensional information about the chromatid in the cell (e.g. by Hi-C experiments).\\ We try to include this Hi-C data to improve the gene cluster model in a spatial manner ​which - on the other hand - seems to raise the computational complexity. The aim is to develop ​a polynomial algorithm that is able to find all common spatial gene clusters between two genomes.+{{:​zellen.png?​nolink |Zellen|}}Gene clusters are sets of genetic markers that maintain close physical proximities to each other in several genomes. These genetic markers often embody protein coding genes that are functionally and/or regulatorily associated.\\ Traditional methods take into account one-dimensional distances in the genomic sequence to determine physical proximities. They do not consider any spatial information regarding the structure of the chromatid in the cell and therefore might miss spatial gene clusters within the genome. However, spatial structure and function seem to be correlated in many cases.\\ Nowadays, it is possible to gain three-dimensional information about the chromatid in the cell (e.g. by Hi-C experiments).\\ We try to include this Hi-C data to improve the gene cluster model in a spatial manner. With [[https://​bibiserv.cebitec.uni-bielefeld.de/​graphteams|GraphTeams]],​ we presented ​the the first gene cluster model capable of handling spatial data. The program implements ​a polynomial ​time algorithm that is able to find all common spatial gene clusters between two genomes.
  
 === Evolution of Gene Clusters === === Evolution of Gene Clusters ===