Biology subjects

Piechotta, M.

Publications and source records attributed to Piechotta, M..

4 recordsLinked to original sources

bioRxiv2025

QutRNA2: Robust tRNA modification discovery from Nanopore direct tRNA sequencing

Transfer RNAs (tRNAs) are essential for protein synthesis and are extensively modified to ensure their structure and function. Direct RNA sequencing with Oxford Nanopore Technologies enables positional modification analysis but is challenged by tRNAs short length, redundancy, and dense modifications. We present QutRNA2, a scalable workflow featuring GPU-accelerated local alignment, statistical filtering, pairwise error profile comparison, and customizable visualization. Achieving up to 25-fold speed gains over CPU methods, QutRNA2 identifies enzyme-dependent modifications in nuclear- and mitochondrial-encoded tRNAs, demonstrated in human and mouse samples. This open-source solution provides a comprehensive, multiplexing-compatible framework for tRNA analysis, addressing a key gap in current tools.

bioinformatics↗

bioRxiv2025

MoDorado: Enhanced detection of tRNA modifications in nanopore sequencing by off-label use of modification callers

Rapid and accurate identification of tRNA modifications is crucial for understanding their role in protein translation and disease. However, their detection on tRNAs is challenging due to their high modification density. Recently, modification calling models for nanopore direct RNA sequencing became available for pseudouridine ({Psi}), m6A, inosine and m5C, as part of the Dorado basecaller. Applying the {Psi} model to tRNAs, we have mapped both known and novel {Psi} sites in Schizosaccharomyces pombe and assigned the responsible pseudouridine synthetases. Furthermore, we have developed MoDorado, an algorithm to detect modifications beyond those used in model training ("off-label use") by measuring prediction differences of pre-trained machine learning models. By leveraging the {Psi}/m6A/inosine/m5C models, MoDorado detected seven additional modifications (ncm5U, mcm5U, mcm5s2U, m7G, queuosine, m1A, and i6A), thus generating a tRNA modification map of S. pombe. This work demonstrates the potential of pre-trained models in determining the intricate landscape of tRNA modifications.

molecular biology↗

bioRxiv2023

preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology

MotivationIn precision oncology, clinicians are aiming to find the best treatment for any patient based on their molecular characterization. A major bottleneck is the annotation and evaluation of individual variants, for which usually a range of knowledge bases are manually screened. To incorporate and integrate the vast information of different databases, fast and accurate methods for harmonization are necessary. Summarypreon is a fast and accurate library for the normalization of drug names and cancer types in large-scale data integration. Availability and Implementationpreon is implemented in Python and freely available via the PyPI repository. Source code and gold standard data sets are available at https://github.com/ermshaua/preon/. Contactmanuela.benary@bih-charite.de Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2021

RNA modification mapping with JACUSA2

A whole series of high-throughput antibody-free methods for RNA modification detection from sequencing data emerged lately. We present JACUSA2 as a versatile software solution and comprehensive analysis framework for RNA modification detection assays that are based on either the Illumina or Nanopore platform. Importantly, JACUSA2 can integrate information from multiple experiments (e.g. replicates and different conditions) and different library types (e.g. first- or secondstrand libraries). We demonstrate its utility by example, showing three analysis workflows for m6A detection on published data sets: 1) MazF m6a-sensitive RNA digestion (FTO+ vs FTO-), 2) DART-seq (YTHwt vs YTHmut) and 3) Nanopore profiling (METTL3 +/+ vs -/-). All assays have been conducted in HEK293 cells and complement one another.

bioinformatics↗