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Klapproth, C.

Publications and source records attributed to Klapproth, C..

2 recordsLinked to original sources

Telomerase RNA gene duplications drive telomeric repeatdiversity and evolution in Andrena bees

Most organisms in the animal kingdom require a non-coding telomerease RNA (TR) in conjunction with the telomerase reverse transcriptase (TERT) to add telomere tandem repeats to chromosome ends to genomic instability. Recent studies reported an extensive diversity in the sequence of telomeric repeats in some insect species. Our investigation of TR genes in the Andrena genus provides convincing evidence for the presence of multiple TR gene copies with different template sequences for synthesis of distinct telomeric repeat sequences in several species. In this study we describe the structure, genomic coordinates and abundance of these TR genes, and correlate our findings with the levels of tandem repeats found in DNAseq data. Based on an analysis of the synthenic context of these newly predicted TR genes, we show evidence for the existence of multiple TR paralogs that diverged during the evolution of the genus Andrena. To our knowledge this is the first time such a phenomenon is observed in animals, although recently reported for plants. Interestingly, the comprehensive annotation of all TERT genes found in yet unannotated Andrena species shows no corresponding evolutionary changes in related TERT proteins encoded by a single copy gene. Our study suggests an evolutionary mechanism for diversification of telomeric repeat sequences in certain insect species through telomerase RNA gene duplication.

bioinformatics↗

Tailored machine learning models for functional RNA detection in genome-wide screens

The in silico prediction of non-coding and protein-coding genetic loci is an area of research that has gathered large attention in the field of comparative genomics. In the last decade, much effort has been made to investigate numerous properties of nucleotide sequences that hint at their biological role in the cell. We present here a software framework for the alignment-based training, evaluation and application of machine learning models with user-defined parameters. Instead of focusing on the one-size-fits-all approach of pervasive in silico annotation pipelines, we offer a framework for the structured generation and evaluation of models based on arbitrary features and input data, focusing on stable and explainable results. Furthermore, we showcase the usage of our software package in a full-genome screen of Drosophila melanogaster and evaluate our results against the well-known but much less flexible program RNAz.

bioinformatics↗