0000000000542851
AUTHOR
Stefan Niebler
showing 5 related works from this author
Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures
2019
Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.g. Illumina-based high-throughput sequencing data, is therefore fast growing in importance, and the field is still lacking effective tools. Here we present a versatile user-friendly graphical workflow system for modification calling based on machine learning. The workflow commences with a principal module for trimming, mapping, and postprocessing. The latter includes a quantification of mismatch and arrest rates with single-nucleotide re…
Automated detection and classification of synoptic-scale fronts from atmospheric data grids
2022
Automatic determination of fronts from atmospheric data is an important task for weather prediction as well as for research of synoptic-scale phenomena. In this paper we introduce a deep neural network to detect and classify fronts from multi-level ERA5 reanalysis data. Model training and prediction is evaluated using two different regions covering Europe and North America with data from two weather services. We apply label deformation within our loss function, which removes the need for skeleton operations or other complicated post-processing steps as used in other work, to create the final output. We obtain good prediction scores with a critical success index higher than 66.9 % and an obj…
RNACache: Fast Mapping of RNA-Seq Reads to Transcriptomes Using MinHashing
2021
The alignment of reads to a transcriptome is an important initial step in a variety of bioinformatics RNA-seq pipelines. As traditional alignment-based tools suffer from high runtimes, alternative, alignment-free methods have recently gained increasing importance. We present a novel approach to the detection of local similarities between transcriptomes and RNA-seq reads based on context-aware minhashing. We introduce RNACache, a three-step processing pipeline consisting of minhashing of k-mers, match-based (online) filtering, and coverage-based filtering in order to identify truly expressed transcript isoforms. Our performance evaluation shows that RNACache produces transcriptomic mappings …
Projection-based improvement of 3D reconstructions from motion-impaired dental cone beam CT data.
2019
Purpose Computed tomography (CT) and, in particular, cone beam CT (CBCT) have been increasingly used as a diagnostic tool in recent years. Patient motion during acquisition is common in CBCT due to long scan times. This results in degraded image quality and may potentially increase the number of retakes. Our aim was to develop a marker-free iterative motion correction algorithm that works on the projection images and is suitable for local tomography. Methods We present an iterative motion correction algorithm that allows the patient's motion to be detected and taken into account during reconstruction. The core of our method is a fast GPU-accelerated three-dimensional reconstruction algorith…
Automated detection and classification of synoptic scale fronts from atmospheric data grids
2021
<p>Automatic determination of fronts from atmospheric data is an important task for weather prediction as well as for research of synoptic scale phenomena. We developed a deep neural network to detect and classify fronts from multi-level ERA5 reanalysis data. Model training and prediction is evaluated using two different regions covering Europe and North America with data from two weather services. Due to a label deformation step performed during training we are able to directly generate frontal lines with no further thinning during post processing. Our network compares well against the weather service labels with a Critical Success Index higher than 66.9% and a Object Detecti…