Biology subjects

Shirokikh, N.

Publications and source records attributed to Shirokikh, N..

4 recordsLinked to original sources

bioRxiv2025

FracFixR: A compositional statistical framework for absolute proportion estimation between fractions in RNA sequencing data

MotivationRNA fractionation followed by sequencing is widely used to study RNA localization, translation, and subcellular compartmentalization. Interpreting fractionated RNA-seq data poses a fundamental compositional challenge: library preparation and sequencing depth obscure the original proportions of RNA fractions, which can bias comparisons - particularly when biological changes shift RNA distribution across fractions. This bias compromises comparisons of fraction-specific RNA profiles and limits the utility of standard differential expression methods. Existing approaches using transcript frequency ratios or standard normalization fail to account for the compositional nature of fractionated samples and cannot estimate the unrecoverable "lost" fraction. ResultsWe developed FracFixR, a statistical framework that reconstructs original fraction proportions by modeling the compositional relationship between whole and fractionated RNA samples. Using non-negative linear regression on carefully selected transcripts, FracFixR estimates global fraction weights, corrects individual transcript frequencies, and quantifies unrecoverable material. The framework includes methods for differential proportion testing between conditions using binomial GLM, logit, or beta-binomial models. We rigorously validated FracFixR using synthetic data with known ground truth and real polysome profiling data from multiple cell lines, demonstrating accurate reconstruction of fraction weights (Pearson correlation > 0.85) and enabling detection of differentially translated transcripts between cancer subtypes. Availability and implementationFracFixR is implemented as an R package freely available on GitHub at https://github.com/Arnaroo/FracFixR.

bioinformatics↗

bioRxiv2024

High-Accuracy RNA Integrity Definition for Unbiased Transcriptome Comparisons with INDEGRA

RNA sample integrity variability introduces biases and obscures natural RNA degradation, posing a significant challenge in transcriptomics. To address this, we developed the Direct Transcriptome Integrity (DTI) measure, a universal and robust RNA integrity metric based on nanopore sequencing. By accurately modeling RNA fragmentation, DTI provides a reliable assessment of sample quality. Integrated into the INDEGRA package (freely available at https://github.com/Arnaroo/INDEGRA), we provide tools to correct false discoveries and enable precise differential expression and RNA degradation analyses, even for challenging sample types. INDEGRA software can be used to accurately measure RNA DTI stability metric, isolate biological component of RNA degradation from technical biases, compare biological RNA stability transcriptome-wide and suppress false degradation-induced differential gene expression hits to allow broad comparisons across samples of different quality DTI offers a straightforward and accurate method for assessing RNA degradation, characterizing both overall sample integrity and transcript-specific degradation rates using direct RNA sequencing (DRS) data. Calculated through INDEGRA, DTI reveals inter- and intra-transcript variability in degradation, while INDEGRA separates RNA degradation from mapping inaccuracies, and connects degradation profiles to RNA fragmentation rates. By leveraging INDEGRA, researchers can minimize false differential transcript abundance findings caused by variations in overall sample integrity, while preserving genuine transcript-specific differences in stability and degradation. INDEGRA supports integration with widely used differential transcript abundance tools like DESeq2, limma-voom, and edgeR, enabling seamless analysis pipelines. INDEGRA enhances the accuracy and reliability of RNA quantification in high-throughput data and simplifies comparisons across diverse transcriptomic datasets, including those derived from different tissues, species, or experimental protocols.

bioinformatics↗

bioRxiv2024

The conserved landscape of RNA modifications and transcript diversity across mammalian evolution

Gene expression programs underpin the development of shared phenotypes, yet the importance of transcript complexity in shaping mammalian evolution remains unclear. Here we present a comprehensive long-read direct RNA sequencing atlas of full-length transcripts and their m6A modifications across six tissues (hippocampus, frontal cortex, cerebellum, testes, skeletal muscle, and liver) in five mammals (human, mouse, rat, dog, and cow) and a non-mammalian out-group (chicken). Our analysis reveals that 29% of genes have multiple mammalian-conserved alternative transcripts, with 31% of these genes showing tissue-specific switching of the major transcript isoforms. We uncover extensive conservation of coordinated splicing events, primarily driven by tissue-specific mutually associated exon splicing, particularly in neural tissues and cytoskeletal genes. At the epitranscriptome level, we find that 14.2% of m6A RNA modifications are conserved across mammals, with 39% of analysed genes containing a conserved m6A RNA modification. Our work provides unprecedented insight into the evolution of transcript complexity and the epitranscriptome, highlighting their potential roles in shaping mammalian phenotypic diversity and providing a valuable resource for understanding post-transcriptional regulation across mammalian evolutionary time.

genomics↗

bioRxiv2019

Selective Translation Complex Profiling Reveals Staged Initiation and Co-translational Assembly of Initiation Factor Complexes

Translational control targeting mainly the initiation phase is central to the regulation of gene expression. Understanding all of its aspects requires substantial technological advancements. Here we modified yeast Translational Complex Profile sequencing (TCP-seq), related to ribosome profiling, and adopted it for mammalian cells. Human TCP-seq, capable of capturing footprints of 40S subunits (40Ses) in addition to 80S ribosomes (80Ses), revealed that mammalian and yeast 40Ses distribute similarly across 5UTRs indicating considerable evolutionary conservation. We further developed a variation called Selective TCP-seq (Sel-TCP-seq) enabling selection for 40Ses and 80Ses associated with an immuno-targeted factor in yeast and human. Sel-TCP-seq demonstrated that eIF2 and eIF3 travel along 5UTRs with scanning 40Ses to successively dissociate upon start codon recognition. Manifesting the Sel-TCP-seq versatility for gene expression studies, we also identified four initiating 48S conformational intermediates, provided novel insights into ATF4 and GCN4 mRNA translational control, and demonstrated co-translational assembly of initiation factor complexes.

molecular biology↗