WWU Münster UKM
"RAB5A and TRAPPC6B are novel targets for Shiga toxin 2a inactivation in kidney epithelial cells" by Kouzel at al. has been published by Scientific Reports.
"Enhancer occlusion transcripts regulate the activity of human enhancer domains via transcriptional interference: a computational perspective" by Pande et al. has been published by Nucleic Acid Research
Congratulations Felix Manske on getting the GBM-Masterpreis!
"Emergence and evolution of ERM proteins and merlin in metazoans" by Shabardina et al. has been published by Genome Biology and Evolution
"Loss of ADAMTS19 causes progressive non-syndromic heart valve disease" by Wünnemann et al. has been published by Nature Genetics
Congratulations Matias for the "Best Poster" award at the X International Conference on Bioinformatics - Celebrating the 10th Anniversary of SoIBio and 10th Anniversary of the Master in Bioinformatics Uruguay.
"Statins: Complex outcomes but increasingly helpful treatment options for patients" by Mohammadkhani, Korsching et al. has been published by European Journal of Pharmacology.
Congratulations to Marten - another successful Master of Science.
"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.
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