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2019-01-24
"NanoPipe - a web server for nanopore MinION sequencing data analysis" by Shabardina et al. has been published by GigaScience.
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2018-12-29
"Transposable Elements Significantly Contributed to the Core Promoters in the Human Genome" by Kellner and Makalowski has been published by SCIENCE CHINA Life Sciences
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2018-12-14
A special seminar "Chironex indrasaksajiae (Cnidaria, Cubozoa), a new deadly box jellyfish from Thailand: From beachside to bench to clinical practice and to community" by Dr. Nuankanya Sathira from Mahidol University, Bangkok, Thailand.
2018-12-06
Defining a Characteristic Gene Expression Set Responsible for Cancer Stem Cell-Like Features in a Sub-Population of Ewing Sarcoma Cells CADO-ES1 - by Hotfilder, Korsching et al.
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2018-11-05
Welcome Philipp Meister to our institute!
2018-10-02
Welcome Matias Rodriguez to our institute!
2018-08
miR‐21‐5p, miR‐141‐3p, and miR‐205‐5p levels in urine - promising biomarkers for the identification of prostate and bladder cancer - by Ghorbanmehr Gharbi Korsching et al.
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2018-08
"Bioinformatics v. 3" by Makalowski et al. has been published in eLS (Encyclopedia of Life Sciences).
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2018-08-09
"Life cycle adapted upstream open reading frames (uORFs) in Trypanosoma congolense: A post-transcriptional approach to accurate gene regulation" by Fervers et al has been published by PLoS ONE
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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