Biology subjects

Stoiber, M.

Publications and source records attributed to Stoiber, M..

3 recordsLinked to original sources

bioRxiv2024

Detecting m6A RNA modification from nanopore sequencing using a semi-supervised learning framework

Direct nanopore-based RNA sequencing can be used to detect post-transcriptional base modifications, such as m6A methylation, based on the electric current signals produced by the distinct chemical structures of modified bases. A key challenge is the scarcity of adequate training data with known methylation modifications. We present Xron, a hybrid encoder-decoder framework that delivers a direct methylation-distinguishing basecaller by training on synthetic RNA data and immunoprecipitation-based experimental data in two steps. First, we generate data with more diverse modification combinations through in silico cross-linking. Second, we use this dataset to train an end-to-end neural network basecaller followed by fine-tuning on immunoprecipitation-based experimental data with label-smoothing. The trained neural network basecaller outperforms existing methylation detection methods on both read-level and site-level prediction scores. Xron is a standalone, end-to-end m6A-distinguishing basecaller capable of detecting methylated bases directly from raw sequencing signals, enabling de novo methylome assembly.

bioinformatics↗

bioRxiv2021

Integrative annotation of genomic features reveal functional elements and epigenetic landscapes in the developing zebrafish

Zebrafish, a popular model for embryonic development and for modelling human diseases, has so far lacked a systematic functional annotation programme akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created the first central repository to store and process zebrafish developmental functional genomic data. Our Data Coordination Center (https://danio-code.zfin.org) combines a total of 1,802 sets of unpublished and reanalysed published genomics data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements in development, including novel classes with distinct features dependent on their activity in time and space. We delineated the distinction between regulatory elements active during zygotic genome activation and those active during organogenesis, identifying new aspects of how they relate to each other. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predict functional relationships between them beyond sequence similarity, extending the utility of zebrafish developmental genomics to mammals.

developmental biology↗

bioRxiv2017

BasecRAWller: Streaming Nanopore Basecalling Directly from Raw Signal

All current nanopore basecalling applications begin with the segmentation of raw signal into discrete events, which are ultimately processed into called bases. We propose the basecRAWller algorithm, a pair of unidirectional recurrent neural networks that enables the calling of DNA bases in real time directly from the rawest form of nanopore output. This shift in nanopore basecalling provides a number of advantages over current processing pipelines including: 1) streaming basecalling, 2) tunable ratio of insertions to deletions, and 3) potential for streaming detection of modified bases. Key to the streaming basecalling capability is sequence prediction at a delay of less than 1/100th of a second, allowing future signal to continuously modulate sequence prediction. BasecRAWller is computationally efficient enabling basecalling at speeds faster than current nanopore instrument measurement speeds on a single core. Further, basecalling can be paused and resumed without any change in the resulting predicted sequence, transforming the potential applications for dynamic read rejection capabilities. The basecRAWller algorithm provides an alternative approach to nanopore basecalling at comparable accuracy and provides the community with the capacity to train their own basecRAWller neural networks with minimal effort.

bioinformatics↗