Search

Motivation: The efficient and reproducible analysis of high-throughput sequencing datasets necessitates the development of methodical and robust computational pipelines that integrate established and bespoke bioinformatics analysis tools, often written in high-level programming languages such as Python. Despite the increasing availability of programming libraries for genomics, there is a noticeable lack of tools specifically focused on transcriptomics. Key tasks in this area include the association of gene features (e.g., transcript isoforms, introns or untranslated regions) with relevant subsections of (large) genomics datasets across diverse data formats, as well as efficient querying of these data based on genomic locations and annotation attributes., Results: To address the needs of transcriptomics data analyses, we developed rnalib, a Python library designed for creating custom bioinformatics analysis methods. Built on existing Python libraries like pysam and pyBigWig, rnalib offers random access support, enabling efficient access to relevant subregions of large, genome-wide datasets. Rnalib extends the filtering and access capabilities of these libraries and includes additional checks to prevent common errors when integrating genomics datasets. The library is centred on an object-oriented Transcriptome class that provides methods for stepwise annotation of gene features with both, local and remote data sources. The rnalib API cleanly separates immutable genomic locations from associated, mutable data, and offers a wide range of methods for iterating, querying, and exporting collated datasets. Rnalib establishes a fast, readable, reproducible, and robust framework for developing novel transcriptomics data analysis tools and methods., Availability: Source code, documentation and tutorials are available at https://github.com/popitsch/rnalib., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2024. Published by Oxford University Press.)

Gene expression is controlled at multiple levels, including RNA transcription and turnover. But determining the relative contributions of RNA biogenesis and decay to the steady-state abundance of cellular transcripts remains challenging because conventional transcriptomics approaches do not provide the temporal resolution to derive the kinetic parameters underlying steady-state gene expression.Here, we describe a protocol that combines metabolic RNA labeling by 4-thiouridine with chemical nucleoside conversion and whole-transcriptome sequencing followed by bioinformatics analysis to determine RNA stability in cultured cells at a genomic scale. Time-resolved transcriptomics by thiol (SH)-linked alkylation for the metabolic sequencing of RNA (SLAMseq) provides accurate information on transcript half-lives across annotated features in the genome, including by-products of transcription, such as introns. We provide a step-by-step instruction for time-resolved transcriptomics, which enhances traditional RNA sequencing protocols to acquire the temporal resolution required to directly measure the cellular kinetics of RNA turnover under physiological conditions., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The varying rates at which mRNAs decay are tightly coordinated with transcriptional changes to shape gene expression during development and disease. But currently available RNA sequencing approaches lack the temporal information to determine the relative contribution of RNA biogenesis, processing and turnover to the establishment of steady-state gene expression profiles.Here, we describe a protocol that combines metabolic RNA labeling with chemical nucleoside conversion by thiol-linked alkylation of 4-thiouridine to determine RNA stability in cultured cells (SLAMseq). When coupled to cost-effective mRNA 3' end sequencing approaches, SLAMseq determines the half-life of polyadenylated transcripts in a global and transcript-specific manner using untargeted or targeted cDNA library preparation protocols.We provide a step-by-step instruction for time-resolved mRNA 3' end sequencing, which augments traditional RNA-seq approaches to acquire the temporal resolution necessary to study the molecular principles that control gene expression.

Non-templated 3'-uridylation of RNAs has emerged as an important mechanism for regulating the processing, stability and biological function of eukaryotic transcripts. In Drosophila, oligouridine tailing by the terminal uridylyl transferase (TUTase) Tailor of numerous RNAs induces their degradation by the exonuclease Dis3L2, which serves functional roles in RNA surveillance and mirtron RNA biogenesis. Tailor preferentially uridylates RNAs terminating in guanosine or uridine nucleotides but the structural basis underpinning its RNA substrate selectivity is unknown. Here, we report crystal structures of Tailor bound to a donor substrate analog or mono- and oligouridylated RNA products. These structures reveal specific amino acid residues involved in donor and acceptor substrate recognition, and complementary biochemical assays confirm the critical role of an active site arginine in conferring selectivity toward 3'-guanosine terminated RNAs. Notably, conservation of these active site features suggests that other eukaryotic TUTases, including mammalian TUT4 and TUT7, might exhibit similar, hitherto unknown, substrate selectivity. Together, these studies provide critical insights into the specificity of 3'-uridylation in eukaryotic post-transcriptional gene regulation.

MicroRNAs are ~22 nt small, non-coding RNAs that direct posttranscriptional silencing of gene expression to regulate animal development, physiology, and disease. An emerging mechanism that controls the biogenesis of microRNAs is the addition of non-templated nucleotides, predominantly uridine, to the 3' end of precursor-microRNAs, in a process that is commonly referred to as tailing. Here, we describe methods that enable the systematic characterization of tailing events in mature microRNAs and their precursors. We report protocols for untargeted and targeted cDNA library preparation procedures, as exemplified in the context of the model organism Drosophila melanogaster and focusing on precursor-microRNAs. We also refer to a dedicated computational framework for the subsequent analysis of untemplated nucleotide additions in cDNA libraries. The described methods for the systematic characterization of posttranscriptional modifications in gene regulatory small RNAs and their precursors will be instrumental in clarifying regulatory concepts that control posttranscriptional gene silencing.