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

MOTIVATION: In precision oncology (PO), clinicians aim to find the best treatment for any patient based on their molecular characterization. A major bottleneck is the manual annotation and evaluation of individual variants, for which usually a range of knowledge bases are screened. To incorporate and integrate the vast information of different databases, fast and accurate methods for harmonizing databases with different types of information are necessary. An essential step for harmonization in PO includes the normalization of tumor entities as well as therapy options for patients. SUMMARY: preon is a fast and accurate library for the normalization of drug names and cancer types in large-scale data integration. AVAILABILITY AND IMPLEMENTATION: preon is implemented in Python and freely available via the PyPI repository. Source code and the data underlying this article are available in GitHub at https://github.com/ermshaua/preon/.

Detection of queuosine and queuosine precursors in tRNAs by direct RNA sequencing.

Queuosine (Q) is a complex tRNA modification found in bacteria and eukaryotes at position 34 of four tRNAs with a GUN anticodon, and it regulates the translational efficiency and fidelity of the respective codons that differ at the Wobble position. In bacteria, the biosynthesis of Q involves two precursors, preQ0 and preQ1, whereas eukaryotes directly obtain Q from bacterial sources. The study of queuosine has been challenging due to the limited availability of high-throughput methods for its detection and analysis. Here, we have employed direct RNA sequencing using nanopore technology to detect the modification of tRNAs with Q and Q precursors. These modifications were detected with high accuracy on synthetic tRNAs as well as on tRNAs extracted from Schizosaccharomyces pombe and Escherichia coli by comparing unmodified to modified tRNAs using the tool JACUSA2. Furthermore, we present an improved protocol for the alignment of raw sequence reads that gives high specificity and recall for tRNAs ex cellulo that, by nature, carry multiple modifications. Altogether, our results show that 7-deazaguanine-derivatives such as queuosine are readily detectable using direct RNA sequencing. This advancement opens up new possibilities for investigating these modifications in native tRNAs, furthering our understanding of their biological function.

Comprehensive identification of diverse ribosomal RNA modifications by targeted nanopore direct RNA sequencing and JACUSA2.

Ribosomal RNAs are decorated by numerous post-transcriptional modifications whose exact roles in ribosome biogenesis, function, and human pathophysiology remain largely unknown. Here, we report a targeted direct rRNA sequencing approach involving a substrate selection step and demonstrate its suitability to identify differential modification sites in combination with the JACUSA2 software. We compared JACUSA2 to other tools designed for RNA modification detection and show that JACUSA2 outperforms other software with regard to detection of base modifications such as methylation, acetylation and aminocarboxypropylation. To illustrate its widespread usability, we applied our method to a collection of CRISPR-Cas9 engineered colon carcinoma cells lacking specific enzymatic activities responsible for particular rRNA modifications and systematically compared them to isogenic wild-type RNAs. Besides the numerous 2'-O methylated riboses and pseudouridylated residues, our approach was suitable to reliably identify differential base methylation and acetylation events. Importantly, our method does not require any prior knowledge of modification sites or the need to train complex models. We further report for the first time detection of human rRNA modifications by direct RNA-sequencing on Flongle flow cells, the smallest-scale nanopore flow cell available to date. The use of these smaller flow cells reduces RNA input requirements, making our workflow suitable for the analysis of samples with limited availability and clinical work.

RNA modification mapping with JACUSA2.

Several high-throughput antibody-free methods for RNA modification detection from sequencing data have been developed. 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, such as replicates and different conditions, and different library types, such as first- or second-strand cDNA libraries. We demonstrate its utility, showing analysis workflows for N6-methyladenosine (m6A) and pseudouridine (Ψ) detection on Illumina and Nanopore sequencing data sets. Our software and its R helper package are available as open source solutions.

JACUSA: site-specific identification of RNA editing events from replicate sequencing data.

BACKGROUND: RNA editing is a co-transcriptional modification that increases the molecular diversity, alters secondary structure and protein coding sequences by changing the sequence of transcripts. The most common RNA editing modification is the single base substitution (A→I) that is catalyzed by the members of the Adenosine deaminases that act on RNA (ADAR) family. Typically, editing sites are identified as RNA-DNA-differences (RDDs) in a comparison of genome and transcriptome data from next-generation sequencing experiments. However, a method for robust detection of site-specific editing events from replicate RNA-seq data has not been published so far. Even more surprising, condition-specific editing events, which would show up as differences in RNA-RNA comparisons (RRDs) and depend on particular cellular states, are rarely discussed in the literature. RESULTS: We present JACUSA, a versatile one-stop solution to detect single nucleotide variant positions from comparing RNA-DNA and/or RNA-RNA sequencing samples. The performance of JACUSA has been carefully evaluated and compared to other variant callers in an in silico benchmark. JACUSA outperforms other algorithms in terms of the F measure, which combines precision and recall, in all benchmark scenarios. This performance margin is highest for the RNA-RNA comparison scenario. We further validated JACUSA's performance by testing its ability to detect A→I events using sequencing data from a human cell culture experiment and publicly available RNA-seq data from Drosophila melanogaster heads. To this end, we performed whole genome and RNA sequencing of HEK-293 cells on samples with lowered activity of candidate RNA editing enzymes. JACUSA has a higher recall and comparable precision for detecting true editing sites in RDD comparisons of HEK-293 data. Intriguingly, JACUSA captures most A→I events from RRD comparisons of RNA sequencing data derived from Drosophila and HEK-293 data sets. CONCLUSION: Our software JACUSA detects single nucleotide variants by comparing data from next-generation sequencing experiments (RNA-DNA or RNA-RNA). In practice, JACUSA shows higher recall and comparable precision in detecting A→I sites from RNA-DNA comparisons, while showing higher precision and recall in RNA-RNA comparisons.

Analysis of intron sequences reveals hallmarks of circular RNA biogenesis in animals.

Circular RNAs (circRNAs) are a large class of animal RNAs. To investigate possible circRNA functions, it is important to understand circRNA biogenesis. Besides human ALU repeats, sequence features that promote exon circularization are largely unknown. We experimentally identified circRNAs in C. elegans. Reverse complementary sequences between introns bracketing circRNAs were significantly enriched in comparison to linear controls. By scoring the presence of reverse complementary sequences in human introns, we predicted and experimentally validated circRNAs. We show that introns bracketing circRNAs are highly enriched in RNA editing or hyperediting events. Knockdown of the double-strand RNA-editing enzyme ADAR1 significantly and specifically upregulated circRNA expression. Together, our data support a model of animal circRNA biogenesis in which competing RNA-RNA interactions of introns form larger structures that promote circularization of embedded exons, whereas ADAR1 antagonizes circRNA expression by melting stems within these interactions.

ACCUSA2: multi-purpose SNV calling enhanced by probabilistic integration of quality scores.

SUMMARY: Direct comparisons of assembled short-read stacks are one way to identify single-nucleotide variants. Single-nucleotide variant detection is especially challenging across samples with different read depths (e.g. RNA-Seq) and high-background levels (e.g. selection experiments). We present ACCUSA2 to identify variant positions where nucleotide frequency spectra differ between two samples. To this end, ACCUSA2 integrates quality scores for base calling and read mapping into a common framework. Our benchmarks demonstrate that ACCUSA2 is superior to a state-of-the-art SNV caller in situations of diverging read depths and reliably detects subtle differences among sample nucleotide frequency spectra. Additionally, we show that ACCUSA2 is fast and robust against base quality score deviations. AVAILABILITY: ACCUSA2 is available free of charge to academic users and may be obtained from https://bbc.mdc-berlin.de/software. CONTACT: christoph.dieterich@mdc-berlin.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.