Biology subjects

Shirokikh, N. E.

Publications and source records attributed to Shirokikh, N. E..

10 recordsLinked to original sources

bioRxiv2026

T2G: fast and accurate transcriptome to genome BAM liftover for short and long read RNA sequencing

Aligning RNA sequencing reads to the transcriptome rather than the genome recovers short and junction-spanning reads that genome alignment loses or misplaces, but it leaves the alignments in transcript coordinates, which genome browsers, variant callers and most annotation tools cannot use. Existing liftover tools translate between genome assemblies, and the few that reach transcript coordinates are tied to a single quantification model or assay type. Here we describe T2G, which converts complete BAM files from transcriptome to genome coordinates while keeping CIGAR strings, alignment tags and per-base RNA modification calls intact. Across nanopore direct RNA sequencing, ribosome footprinting and paired-end Illumina RNA sequencing, aligning to the transcriptome and lifting with T2G gave higher on-target specificity and more accurate splice junctions than direct genome alignment, removed the systematic assignment of reads to processed pseudogenes, and preserved isoform identity, revealing isoform-specific modification levels that genome alignment pools together. In a reanalysis of a published RNA-binding protein experiment, T2G reduced the number of reads attributed to pseudogenes 8.5-fold compared with unspliced genome alignment. T2G is available at https://github.com/Arnaroo/T2GD as a Python reference implementation and as t2gd, a dependency-free binary for Linux, macOS and Windows that also provides a gene-level quantifier and BAM utilities toolkit.

bioinformatics↗

bioRxiv2026

Multi-omic dissection of RNA control reveals convergent cis-regulatory programs during glucose starvation in yeast

Cells reprogram gene expression at multiple, often independently studied, regulatory layers during nutrient stress. Here we use direct RNA sequencing of polysome-fractionated transcripts that simultaneously measures transcript abundance, mRNA degradation, poly(A) tail length, and three RNA modifications (m5C, m6A, pseudouridine) to build an integrated, multi-omic view of the Saccharomyces cerevisiae response to acute glucose starvation. Lasso-penalised mixture-of-regressions clustering of stochastic translation efficiency ({delta}STE) against this combined feature set partitions 5,033 transcripts into five translational programs with distinct functional identities, from a strongly upregulated translation-machinery cluster to a strongly repressed glycosylation cluster. CDS sequence composition discriminates these extremes, while 5' and 3'UTR composition contributes only marginal, largely non-significant effects after length correction. Extending this analysis, we show that the repressed glycosylation program is distinguished by a convergent stack of independent cis-regulatory signatures: weak Kozak initiation context, high upstream open reading frame burden, excess RNA secondary structure specifically over the start codon, and depletion for the targets of major stabilising and export-associated RNA-binding proteins. In other programmes, translation efficiency tunes with initiation-context strength indicating that downstream regulatory layers coregulate translation. These results provide an integrated, feature-resolved map of post-transcriptional regulation during acute nutrient stress.

genomics↗

bioRxiv2026

A refined Saccharomyces cerevisiae reference transcriptome from Direct RNA Sequencing, with a reusable pipeline for UTR annotation updates

Untranslated regions (UTRs) flanking the coding sequence govern mRNA translation, localisation, stability, and decay, making accurate UTR boundaries essential for quantitative RNA sequencing and the study of post-transcriptional control in Saccharomyces cerevisiae and beyond. Reference transcriptomes built from short-read sequencing have been invaluable to the yeast community, yet in a genome as gene-dense as that of S. cerevisiae, short reads frequently cannot be assigned to a single transcript of origin, leaving roughly one quarter of transcripts without a confidently defined UTR. Here we use Oxford Nanopore Direct RNA Sequencing (DRS), in which each full-length polyadenylated molecule is read end to end, to resolve this ambiguity and deliver two complementary resources. First, an updated, ready-to-use S. cerevisiae S288C reference: change-point segmentation of per-gene DRS coverage defined boundaries for 5,416 of the 6,695 annotated genes, and a merge-max rule retaining the longer UTR from each source ensures no gene loses existing annotation. The result adds previously absent UTRs to 927 (5') and 896 (3') genes and extends 29.4% of 5' and 26.1% of 3' boundaries among comparable genes. Second, the complete, documented pipeline so that any laboratory can rebuild or update a transcriptome from its own DRS data. Validation on two independent datasets shows improved mapping rates, reduced soft-clipping, and metagene profiles consistent with genuine transcript signal.

genomics↗

bioRxiv2026

VX: an AI-enabled desktop genome viewer and transcriptome browser with a programmable analysis framework

BackgsroundGenome and transcriptome browsers are central to the interpretation of high-throughput sequencing data, but todays tools assume a human operator at a graphical interface and offer only limited programmability. As large-language-model assistants become routine in bioinformatics [Anthropic, 2024], this creates a bottleneck: agents cannot observe the visual state of the browser or drive it through the same interface as the human user, and analyses remain fragmented across a separate ecosystem of external tools. Transcript-coordinate data, produced by ribosome profiling [Ingolia et al., 2012] and direct RNA sequencing [Garalde et al., 2018], is also awkwardly supported in chromosome-oriented viewers. ResultsWe present VX, a desktop genome and transcriptome viewer written in D, using GTK 3 and OpenGL, that handles genome-scale and transcriptome-scale data in a unified interface. VX exposes its full functionality through an embedded HTTP API on the loopback interface and a Model Context Protocol server of currently thirty-nine tools, so that scripts and LLM agents can load data, navigate, manage tracks, run analyses, and capture figures through the same contract used by the GUI. An integrated analysis framework provides more than fifty analyses and includes signal processing and peak calling, quantification, variant analysis, alignment statistics, interaction and cross-track comparisons, all with an explicit four-level scope hierarchy running from viewport to whole dataset; results are written to disk and, where appropriate, added as new tracks. Additional features include a magnifier popup for base-resolution inspection (Alt+hover), chromosome-alias resolution across UCSC, Ensembl, and NCBI conventions, viewport video recording via an ffmpeg pipe, and INI-based configuration. ConclusionsVX complements existing desktop and web browsers by providing a native agent-control layer, an integrated analysis framework, and first-class transcriptspace handling. The binary is freely available for non-commercial use; the HTTP API and MCP protocol are fully specified in this article, so third-party clients can be written independently of the core implementation.

bioinformatics↗

bioRxiv2026

dirCLIP profiles variant-specific RNA-protein interactions via nanopore long-read sequencing

RNA binding proteins (RBPs) control gene expression through their activities in essentially all processing steps of coding and noncoding RNAs and are dysregulated in disease. With >95% of human multi-exon genes undergoing alternative splicing, the inability to assign RBP sites to distinct RNA variants fundamentally limits our understanding of RNA regulation. We present dirCLIP, a method combining UV crosslinking and immunoprecipitation (CLIP) with amplification-free direct nanopore long-read sequencing preserving full-length transcripts. dirCLIP employs two complementary strategies: direct RNA sequencing detecting amino acid adduct-induced perturbations in current signals and direct cDNA sequencing capturing binding sites as mutations. Benchmarking dirCLIP with SRSF3 demonstrated >75% concordance with short-read-based data, revealed isoform-selective RNA binding and enabled detection of co-occurring binding sites in single RNA molecules. Application to the noncanonical RBP HMGA1 uncovered new AT-hook mediated RNA variant-specific interactions. dirCLIP fundamentally transforms our ability to interrogate RNA regulation of distinct isoforms and transcript variants with direct implications to disease mechanisms and development of RNA therapeutics.

molecular biology↗

bioRxiv2025

SWARM: A Single-Molecule Workflow for High-Precision Profiling of RNA Modifications

Nanopore direct RNA sequencing promises to decode the epitranscriptome by detecting multiple modifications on individual RNA molecules, but its potential for biological discovery is hampered by high false-positive rates. We present SWARM, an AI-based framework designed to overcome this fundamental limitation. Its key innovation is a crosstalk-aware training strategy that incorporates non-target modifications and orthogonally validated cellular signals, enabling high-precision detection of m6A, pseudouridine ({Psi}), and m5C at single-nucleotide and single-molecule resolution. Using rigorous in vitro and cellular RNA benchmarks, SWARM outperforms existing tools and maintains strong agreement with orthogonal methods. Applying SWARM across mammalian tissues reveals thousands of novel modification sites with confirmed motifs and localisation patterns. Our high-resolution multi-tissue modification map revealed no evidence of widespread m6A-{Psi} interplay in predominant writer contexts, challenging models of a coordinated epitranscriptomic code. We further discovered a previously unrecognised splicing-shaped mode of {Psi} deposition, whereby TRUB1-mediated pseudouridylation preferentially occurs after exon-exon ligation, consistent with local RNA structure stabilisation. SWARM provides a robust, universally applicable tool for epitranscriptome discovery.

bioinformatics↗

bioRxiv2022

Real-time biochemical-free targeted sequencing of RNA species with RISER

The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduced their respective read counts by more than 85%, resulting in an increase in sequencing depth of up to 93% for long non-coding RNAs. We also applied RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as a significant increase in non-globin reads. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISERs modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser.

bioinformatics↗

bioRxiv2022

Language-Informed Basecalling Architecture for Nanopore Direct RNA Sequencing

Algorithms developed for basecalling Nanopore signals have primarily focused on DNA to date and utilise the raw signal as the only input. However, it is known that messenger RNA (mRNA), which dominates Nanopore direct RNA (dRNA) sequencing libraries, contains specific nucleotide patterns that are implicitly encoded in the Nanopore signals since RNA is always sequenced from the 3 to 5 direction. In this study we present an approach to exploit the sequence biases in mRNA as an additional input to dRNA basecalling. We developed a probabilistic model of mRNA language and propose a modified CTC beam search decoding algorithm to conditionally incorporate the language model during basecalling. Our findings demonstrate that inclusion of mRNA language is able to guide CTC beam search decoding towards the more probable nucleotide sequence. We also propose a time efficient approach to decoding variable length nanopore signals. This work provides the first demonstration of the potential for biological language to inform Nanopore basecalling. Code is available at: https://github.com/comprna/radian.

bioinformatics↗

bioRxiv2022

R2Dtool: Positional interpretation of RNA-centric information in the context of transcriptomic and genomic features

Long-read RNA sequencing enables the mapping of RNA modifications, structures, and protein-interaction sites at the resolution of individual transcript isoforms. To understand the functions of these RNA features, it is critical to analyze them in the context of transcriptomic and genomic annotations, such as open reading frames and splice junctions. To enable this, we have developed R2Dtool, a bioinformatics tool that integrates transcript-mapped information with transcript and genome annotations, allowing for the isoform-resolved analytics and graphical representation of RNA features in their genomic context. We illustrate R2Dtools capability to integrate and expedite RNA feature analysis using epitranscriptomics data. R2Dtool facilitates the comprehensive analysis and interpretation of alternative transcript isoforms. R2Dtool is freely available under the MIT license at: https://github.com/comprna/R2Dtool.

bioinformatics↗

bioRxiv2022

Identification of m6A and m5C RNA modifications at single-molecule resolution from Nanopore sequencing

The epitranscriptome embodies many new and largely unexplored functions of RNA. A significant roadblock hindering progress in epitranscriptomics is the identification of more than one modification in individual transcript molecules. We address this with CHEUI (CH3 (methylation) Estimation Using Ionic current). CHEUI predicts N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual molecules from the same sample, the stoichiometry at transcript reference sites, and differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals to achieve high single-molecule, transcript-site, and stoichiometry accuracies in multiple tests using synthetic RNA standards and cell line data. CHEUIs capability to identify two modification types in the same sample reveals a co-occurrence of m6A and m5C in individual mRNAs in cell line and tissue transcriptomes. CHEUI provides new avenues to discover and study the function of the epitranscriptome.

bioinformatics↗