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2019-10-03
"Statins: Complex outcomes but increasingly helpful treatment options for patients" by Mohammadkhani, Korsching et al. has been published by European Journal of Pharmacology.
Goto
2019-10-01
Congratulations to Marten - another successful Master of Science.
2019-09-16
"An integrated genome-wide multi-omics analysis of gene expression dynamics in the preimplantation mouse embryo" by Israel, Makalowski et al. has been published by Scientific Reports.
Goto
2019-08-05
"Bioinformatics of nanopore sequencing" by Makalowski and Shabardina has been published by Journal of Human Genetics
Goto
2019-07-23
Researchers in the field of bioinformatics at Muenster University, Germany and Mr. Fujihiko Minamoto (second from left)
Goto
2019-06-18
Felix defended his master
2019-05-29
Wolfgang takes his retirement
2019-05-29
Jonas made his defense
2019-01-24
"NanoPipe - a web server for nanopore MinION sequencing data analysis" by Shabardina et al. has been published by GigaScience.
View Application
Visualizing Sequence Similarity of Protein Families

Classification of proteins into families is one of the main goals of functional analysis. Proteins are usually assigned to a family on the basis of the presence of family-specific patterns, domains, or structural elements. Whereas proteins belonging to the same family are generally similar to each other, the extent of similarity varies widely across families. Some families are characterized by short, well-defined motifs, whereas others contain longer, less-specific motifs. We present a simple method for visualizing such differences. We applied our method to the Arabidopsis thaliana families listed at The Arabidopsis Information Resource (TAIR) Web site and for 76% of the nontrivial families (families with more than one member), our method identifies simple similarity measures that are necessary and sufficient to cluster members of the family together. Our visualization method can be used as part of an annotation pipeline to identify potentially incorrectly defined families. We also describe how our method can be extended to identify novel families and to assign unclassified proteins into known families.

One result of our work is the discovery that, despite the wide variety of methods used in the construction of protein families, 76% of all analyzed Arabidopsis thaliana families are fully clusterable by the proposed simple parameter schemes. Our results also show relationships between families that shar/ members, and help identify potentially incorrect family assignments. We also show how our results could be used to identify novel families and assign unclassified proteins to known families.

Reference: Veeramachaneni V. and Makalowski W. (2004) Visualizing sequence similarity of protein families. Genome Research, 14 (6): 1160-1169.[Reprint]
2018-11-15 11:50