bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/189431

Redkmer: An assembly-free pipeline for the identification of abundant and specific X-chromosome target sequences for X-shredding by CRISPR endonucleases

Papathanos, P. A.·Windbichler, N.
View original source ↗Export metadata ↓

Abstract

CRISPR-based synthetic sex ratio distorters, that operate by shredding the X-chromosome during male meiosis, are promising tools for the area-wide control of harmful insect pest or disease vector species. However, the selection of gRNA targets, in the form of high-copy sequence repeats on the X chromosome of a given species, is difficult since such repeats are not accurately resolved in genome assemblies and cant be assigned to chromosomes with confidence. We have therefore developed the redkmer computational pipeline, designed to identify short and highly-abundant sequence elements occurring uniquely on the X-chromosome. Redkmer was designed to use as input exclusively raw WGS data from males and females. We tested redkmer with suitable short and long read WGS data of An. gambiae, the major vector of human malaria, in which the X-shredding paradigm was originally developed. Redkmer establishes long reads as chromosomal proxies with excellent correlation to the genome assembly and uses them to rank X-candidate kmers for their level of X-specificity and abundance. Redkmer identified a high-confidence set of 25-mers, many of which belong to previously known X-chromosome specific repeats of An. gambiae, including the ribosomal gene array and the selfish genetics elements harbored within it. WGS data from a control strain in which these repeats are also present on the Y chromosome confirmed the elimination of these kmers in the filtering steps. Finally, we show that redkmer output can be linked directly to gRNA selection and can also inform gRNA off-target prediction. The redkmer pipeline is designed to enable the generation of synthetic sex ratio distorters for the control of harmful insect species of medical or agricultural importance. It proceeds from WGS input data to deliver candidate X-specific CRISPR gRNA candidate target sequences. In addition the output of redkmer, including the prediction of chromosomal origin of single-molecule long reads and chromosome specific kmers, could also be used for the characterization of other biologically relevant sex chromosome sequences, a task that is frequently hampered by the repetitiveness of sex chromosome sequence content.

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

BibTeXRIS

Papathanos, P. A., Windbichler, N.. 2017-09-15. Redkmer: An assembly-free pipeline for the identification of abundant and specific X-chromosome target sequences for X-shredding by CRISPR endonucleases. https://doi.org/10.1101/189431

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗