Biology subjects

Zytnicki, M.

Publications and source records attributed to Zytnicki, M..

12 recordsLinked to original sources

bioRxiv2026

Complete pangenome graph annotation with GrAnnoT and GrAPEx

Background: Pangenome variations graphs (PVG) are promising structures to study diversity and reduce reference bias in genomics. However, it is not currently possible include complete annotation of the PVG. In a previous work, we introduced GrAnnoT, a tool for transferring the annotation from a reference genome to a PVG, and from a PVG to its embedded genomes. However, GrAnnoT was limited to transferring one annotation to the graph, and thus could not include all genes. Results: We implemented new options to GrAnnoT, that can now handle annotations of multiple genomes, and integrate them in the graph. We also developed GrAPEx, a pipeline that identifies, extracts and annotates non-annotated regions in the graph. We tested those two tools to attest their performances. Our benchmarks revealed that both GrAnnoT and GrAPEx are reliable. GrAnnoT is now able to integrate correctly several annotations, increasing the amount of biological information available in the graph. Additionally, it offers useful options to study gene distribution in the PVG. GrAPEx allows rapid annotation of the whole PVG, with no reduction in the annotation quality. Conclusion: Overall, the two tools presented here allow to annotate thoroughly the whole PVG, and integrate biological annotation to the pangenome. It also reduces the previous reference bias, where only one annotation (often the reference one) could be included in the graph. This constitutes a precious addition to the pangenomic toolkit.

genomics↗

bioRxiv2026

A novel class of tiny box C/D-like RNAs reveals hidden complexity in the mouse box C/D RNA repertoire

In recent years, the catalog of mammalian box C/D small nucleolar RNAs (SNORDs) has expanded considerably, with several hundred members now annotated in humans. Despite being a well-characterized family of antisense small noncoding RNAs, the full diversity of mammalian box C/D SNORDs remains incompletely understood. To address this, we combined deep sequencing of Fibrillarin- and Snu13-associated RNAs with systematic mining of a large collection of publicly available small RNA-seq datasets spanning diverse mouse tissues and cell types. This integrative approach uncovered an unexpectedly large repertoire of box C/D-like RNAs, comprising more than 20 000 distinct species. These RNAs display the defining hallmarks of intronic SNORDs but are expressed at low levels and exhibit poor evolutionary conservation. Strikingly, approximately 70% are only 20-50 nucleotides long, making them substantially shorter than canonical SNORDs. We designate this previously unrecognized class of box C/D-like RNAs as tiny SNORDs (tiSNORDs). Functional characterization of a representative 36-nucleotide tiSNORD demonstrates that, despite its reduced size, it retains nucleolar 2'-O-ribose methylation guide activity. The discovery of this previously unrecognized class of box C/D-like RNAs substantially expands the known repertoire of SNORDs and raises fundamental questions regarding their biogenesis, functional potential and evolutionary significance.

genetics↗

bioRxiv2026

SPA-C: an hybrid tool to accurately scaffold genomes using Hi-C and Deep-Learning

Genome assembly is a computational pipeline designed to reconstruct chromosomes from small sequencing reads. Following their assembly, contiguous sequences (contigs) are arranged into chromosome-long sequences during scaffolding. Hi-C, a long-range linkage information between regions of the genome widely used in recent large sequencing projects, is often required to correctly order contigs. Several tools have been developed to automate this task following either statistical or deep-learning approaches. Statistical approaches summarise 2D Hi-C matrices into contact densities across sequences, thus ignoring informative visual patterns. The sole existing deep-learning tool uses a transformer-based computer vision model to correct the assembly. It has been trained on several species and uses Hi-C matrices directly. Yet it comes as a supplementary step in the scaffolding process, introducing extra computation time, and has been trained on a dataset that might contain labelling errors, which could provide sub-optimal results. We propose SPA-C, an hybrid pipeline combining the strengths of both approaches. Linkage prediction is handled with a frugal CNN-based model and a graph-solving algorithm is used to generate the scaffolds. Through our inputs design, the model is able to both correct errors within assemblies and link contigs, leveraging small, local Hi-C contact matrices. We handled low-complexity regions that might induce erroneous predictions using an external tool, improving the overall accuracy of generated assemblies. On a benchmark of six various genomes and four standard metrics, SPA-C outperformed four out of four state-of-the-art methods while achieving comparable start-to-end computation time. Python and Bash scripts are available on GitHub (github.com/SPA-C/SPA-C.git) and Zenodo (10.5281/zenodo.19000361).

bioinformatics↗

bioRxiv2025

HiCDOC: chromatin compartment prediction and differential analysis from Hi-C data with replicates

MotivationThe spatial organization of the genome plays an essential role in regulating cellular functions, with A/B chromatin compartments reflecting broad differences in transcriptional and epigenetic activity. Hi-C enables genome-wide identification of such compartments, but robust differential analysis between groups of samples remains challenging. Existing approaches largely rely on Principal Component Analysis, which, applied on Hi-C matrices separately, requires heuristic sign choices to merge results and does not naturally incorporate replicates. ResultsHere we present HiCDOC, a Bioconductor package for the prediction and differential analysis of chromatin compartments from Hi-C data with replicates. HiCDOC uses constrained k-means clustering to jointly analyze multiple Hi-C matrices, incorporating replicate information to enhance robustness, and provides empirical statistical support for predicted compartment switches. Applied to Hi-C datasets from human tissues and mouse cell lines, HiCDOC identified biologically relevant compartment changes supported by transcriptional differences. Comparisons with existing tools showed both overlap and complementarity, while a controlled benchmark with artificially introduced changes confirmed high sensitivity. Although extensively tested on pairwise comparisons, HiCDOC offers a flexible framework compatible with more complex designs and, in principle, with more than two compartment states. By combining replicate-aware clustering, automatic A/B assignment across chromosomes, extensive quality control, and statistical evaluation, HiCDOC provides an alternative and complementary approach to PCA-based methods for compartment analysis. HiCDOC thus expands the methodological toolkit for exploring 3D genome dynamics and its role in cellular processes. AvailabilityHiCDOC is implemented in R and C++, and is available on Bioconductor: https://bioconductor.org/packages/release/bioc/html/HiCDOC.html Contactsylvain.foissac@inrae.fr

bioinformatics↗

bioRxiv2025

GrAnnoT, a tool for effecient and reliable annotation transfer through pangenome graph

The increasing availability of genome sequences has highlighted the limitations of using a single reference genome to represent the diversity within a species. Pangenomes, encompassing the genomic information from multiple genomes, thus offer a more comprehensive representation of intraspecific diversity. However, pangenomes in form of a variation graph often lack annotation information and tools for, which limits their utility for downstream analyses. We introduce here GrAnnoT, a tool designed for an efficient and reliable integration of annotation information in such variation graphs. It projects existing annotations from a source genome to the variation graph and subsequently to other embedded genomes. GrAnnoT was benchmarked against state-of-the-art tools on pangenome variation graphs and linear genomes from Asian rice, and tested on human and E. coli data. The results demonstrate that GrAnnoT is consensual, conservative, and fast. It provides informative outputs, such as presence-absence matrices for genes, and alignments of transferred features between source and target genomes, helping in the study of genomic variations and evolution. GrAnnoTs robustness and replicability across different species make it a valuable tool for enhancing pangenome analyses. GrAnnoT is available under the GNU GPLv3 licence at https://forge.ird.fr/diade/dynadiv/grannot.

bioinformatics↗

bioRxiv2025

Benchmarking long-read genome assemblers for three sequencing protocols and three agricultural species

Today, long read technologies make it possible to produce telomere-to-telomere genome assemblies. These assemblies permit more accurate whole genome analyses, including on repeated regions. The genome assembly process can chain several steps such as read correction, contigs assembly, contig polishing, scaffolding and gap filling, among others. Each step usually requires at least one software package, and input sequences. For the end user, it is not necessarily clear which combination of tools, together with read types, will work best for a given genome. In this work, we will focus on contig production, which is a central and complex task of the assembly process. It aims at producing the longest, errorless, sequences, called contigs, from reads. While it is possible to produce contigs from short reads, long reads are now widely preferred, since they produce much longer contigs. In this work, we evaluate several contig producing software packages (usually named assemblers), on long reads generated by two sequencers using three protocols, for three eukaryotic, complex species with different characteristics. Our aim is twofold. First, we would like to give readers insight on the impact of sequencing technology and assembler combinations, in order to help them make their choice for a given genome. Second, we would like to present different assembly metrics and provide a critical view on their interpretation.

bioinformatics↗

bioRxiv2024

Pairwise graph edit distance characterizes the impact of the construction method on pangenome graphs

MotivationPangenome variation graphs are an increasingly used tool to perform genome analysis, aiming to replace a linear reference in a wide variety of genomic analyses. The construction of a variation graph from a collection of chromosome-size genome sequences is a difficult task that is generally addressed using a number of heuristics. The question that arises is to what extent the construction method influences the resulting graph, and the characterization of variability. ResultsWe aim to characterize the differences between variation graphs derived from the same set of genomes with a metric which expresses and pinpoint differences. We designed a pairwise variation graph comparison algorithm, which establishes an edit distance between variation graphs, threading the genomes through both graphs. We applied our method to pangenome graphs built from yeast and human chromosome collections, and demonstrate that our method effectively characterizes discordances between pangenome graph construction methods and scales to real datasets. Availabilitypancat compare is published as free Rust software under the AGPL3.0 open source license. Source code and documentation are available at https://github.com/dubssieg/rs-pancat-compare. Contactsiegfried.dubois@inria.fr Supplementary informationSupplementary data are available online at https://doi.org/10.5281/zenodo.10932490. Code to replicate figures and analysis is available online at https://github.com/dubssieg/pancat_paper.

bioinformatics↗

bioRxiv2024

Assessing genome conservation on pangenomegraphs with PanSel

1MotivationWith more and more telomere-to-telomere genomes assembled, pangenomes make it possible to capture the genomic diversity of a species. Because they introduce less biases, pangenomes, represented as graphs, tend to supplant the usual linear representation of a reference genome, augmented with variations. However, this major change requires new tools adapted to this data structure. Among the numerous questions that can be addressed to a pangenome graph is the search for conserved or divergent genes. ResultsIn this article, we present a new tool, named PanSel, which computes a conservation score for each segment of the genome, and finds genomic regions that are significantly conserved, or divergent. AvailabilityPanSel, written in C++11 with no dependency, is available at https://github.com/mzytnicki/pansel. Contactmatthias.zytnicki@inrae.fr

bioinformatics↗

bioRxiv2021

srnaMapper: an optimal mapping tool for sRNA-Seq reads

MotivationSequencing is the key method to study the impact of short RNAs, which include micro RNAs, tRNA-derived RNAs, and piwi-interacting RNA, among other. The first step to make use of these reads is to map them to a genome. Existing mapping tools have been developed for the long RNAs in mind, and, so far, no tool has been conceived for short RNAs. However, short RNAs have several distinctive features which make them different from messenger RNAs: they are shorter (not greater than 200bp), they often redundant, they can be produced by duplicated loci, and they may be edited at their ends. ResultsIn this work, we present a new tool, srnaMapper, that maps these reads with all these objectives in mind. We show on two data sets that srnaMapper is more efficient considering computation time and edition error handling: it quickly retrieves all the hits, with arbitrary number of errors. AvailabilitysrnaMapper source code is available at https://github.com/mzytnicki/srnaMapper. Contactmatthias.zytnicki@inrae.fr

bioinformatics↗

bioRxiv2019

Finding differentially expressed sRNA-Seq regions with srnadiff

Small RNAs (sRNAs) encompass a great variety of different molecules of different kinds, such as micro RNAs, small interfering RNAs, Piwi-associated RNA, among other. These sRNA have a wide range of activities, which include gene regulation, protection against virus, transposable element silencing, and have been identified as a key actor to study and understand the development of the cell. Small RNA sequencing is thus routinely used to assess the expression of the diversity of sRNAs, usually in the context of differentially expression, where two conditions are compared. Many tools have been presented to detect differentially expressed micro RNAs, because they are well documented, and the associated genes are well defined. However, tools are lacking to detect other types of sRNAs, which are less studied, and have an imprecise \"gene\" structure. We present here a new method, called srnadiff, to find all kinds of differentially expressed sRNAs. To the extent of our knowledge, srnadiff is the first tool that detects differentially expressed sRNAs without the use of external information, such as genomic annotation or reference sequence of sRNAs.\n\nAuthor summaryWe present here a new method for the ab initio discovery of differentially expressed small RNAs. The standard method, sometimes named annotate-then-identify, first finds possible genes, and tests for differential expression. In contrast, our method skips the first step and scans the genome for potential differentially expressed regions (the identify-then-annotate strategy). Since our method is the first one to use the identify-then-annotate strategy on sRNAs, we compared our method against a similar method, developed for long RNAs (derfinder), and to the annotate-then-identify strategy, where the sRNAs have been identified beforehand using a segmentation tool, on three published datasets, and a simulated one. Results show that srnadiff gives much better results than derfinder, and is also better than the annotate-then-identify strategy on many aspects. srnadiff is available as a Bioconductor package, together with a detailed manual: https://bioconductor.org/packages/release/bioc/html/srnadiff.html

bioinformatics↗

bioRxiv2018

MeCP2-E1 isoform is a dynamically expressed, weakly DNA-bound protein with different protein and DNA interactions compared to MeCP2-E2

MeCP2 - a chromatin-binding protein associated with Rett syndrome - has two main isoforms, MeCP2-E1 and MeCP2-E2, with 96% amino acid identity differing in a few N-terminal amino acid residues. Previous studies have shown brain region-specific expression of these isoforms which, in addition to their different cellular localization and differential expression during brain development, suggest they may also have non-overlapping molecular mechanisms. However, differential functions of MeCP2-E1 and E2 remain largely unexplored. Here, we show that the N-terminal domains (NTD) of MeCP2-E1 and E2 modulate the ability of the methyl binding domain (MBD) to interact with DNA as well as influencing the turnover rates, binding dynamics, response to nuclear depolarization, and circadian oscillations of the two isoforms. Our proteomics data indicate that both isoforms exhibit unique interacting protein partners. Moreover, genome-wide analysis using ChIP-seq provide evidence for a shared as well as a specific regulation of different sets of genes. Our findings provide insight into the functional complexity of MeCP2 by dissecting differential aspects of its two isoforms.\n\nSignificanceWhether the two E1 and E2 isoforms of MeCP2 have different structural and/or functional implications has been highly controversial and is not well known. Here we show that the relatively short N-terminal sequence variation between the two isoforms impinges them with an important DNA binding difference. Moreover, MeCP2-E1 and E2 exhibit a different cellular dynamic behavior and have some distinctive interacting partners. In addition, while sharing genome occupancy they specifically bind to several distinctive genes.

biochemistry↗

bioRxiv2018

Livestock genome annotation: transcriptome and chromatin structure profiling in cattle, goat, chicken and pig.

BackgroundFunctional annotation of livestock genomes is a critical step to decipher the genotype-to-phenotype relationship underlying complex traits. As part of the Functional Annotation of Animal Genomes (FAANG) action, the FR-AgENCODE project (http://www.fragencode.org) aimed to profile the landscape of transcription (RNA-seq), chromatin accessibility (ATAC-seq) and conformation (Hi-C) in four livestock species representing ruminants (cattle, goat), monogastrics (pig) and birds (chicken), using three target samples related to metabolism (liver) and immunity (CD4+ and CD8+ T cells).\n\nResultsRNA-seq assays considerably extended the available catalog of annotated transcripts and identified differentially expressed genes with unknown function, including new syntenic lncRNAs. ATAC-seq highlighted an enrichment for transcription factor binding sites in differentially accessible regions of the chromatin. Comparative analyses revealed a core set of conserved regulatory regions across species. Topologically Associating Domains (TADs) and epigenetic A/B compartments annotated from Hi-C data were consistent with RNA-seq and ATAC-seq data. Multi-species comparisons showed that conserved TAD boundaries had stronger insulation properties than species-specific ones and that the genomic distribution of orthologous genes in A/B compartments was significantly conserved across species.\n\nConclusionsWe report the first multi-species and multi-assay genome annotation results obtained by a FAANG project. Beyond the generation of reference annotations and the confirmation of previous findings on model animals, the integrative analysis of data from multiple assays and species sheds a new light on the multi-scale selective pressure shaping genome organization from birds to mammals. Overall, these results emphasize the value of FAANG for research on domesticated animals and reinforces the importance of future meta-analyses of the reference datasets being generated by this community on different species.

genomics↗