Biology subjects

Mitrofanov, A.

Publications and source records attributed to Mitrofanov, A..

8 recordsLinked to original sources

bioRxiv2026

CRISCross: Integration of Epigenetic Information for Deep-Learning-Based Prediction of CRISPR-Cas9 Off-Targets Across Cell Types

CRISPR-Cas9 enables precise genome editing but remains limited by off-target effects, raising safety concerns and necessitating accurate prediction. Although epigenetic features influence Cas9 binding and cleavage, their predictive signals are often weak, highlyvariable, and context-dependent, limiting the effectiveness of existing machine learning approaches. Cross-cell-type settings are especially challenging: epigenetic landscapes differ substantially, and models trained on one cell type frequently fail to generalize. Here, we systematically investigate the role of epigenetic context in off-target prediction and demonstrate that its utility depends on modeling interactions with sequence features rather than treating it as an independent signal. Tabular and deep learning experiments demonstrate that epigenetic features provide a consistent but modestly complementary signal, and that late-stage fusion strategies are insufficient to effectively exploit this information. To address this, we introduce CRISCross, a cross-attention-based architecture that captures sequence-epigenetic interactions at the nucleotide level, alongside CRISPRAT, a synthetic pretraining strategy that learns transferable representations from genome-derived sgRNA-off-target pairs without relying on dataset-specific pretraining. CRISCross, incorporating AlphaGenome-derived chromatin accessibility signals, shows better predictive performance than sequence-only baselines across three independent test datasets spanning two cell types, demonstrating improved generalization across guide RNAs, datasets, and cellular contexts. This is particularly relevant for therapeutic applications involving cell types or patient-derived contexts that lack dedicated experimental datasets. In such settings, predicted chromatin accessibility may provide scalable contextual information when direct epigenomic profiling is unavailable or impractical. Together, these results show that accurate CRISPR off-target prediction benefits from interaction-aware integration of epigenetic context alongside generalizable pretraining strategies.

bioinformatics↗

bioRxiv2026

Biological Foundation Models Enable CRISPR Array Detection Without Metagenomic Assembly

Accurate identification of CRISPR arrays is essential for studying prokaryotic adaptive immunity, yet existing tools struggle with short-read sequencing data and arrays containing degenerate repeats. These limitations restrict CRISPR analysis in metagenomic and fragmented genomic datasets. We present a foundation model-based approach for CRISPR array detection that addresses both these challenges. We fine-tune a large genomic foundation model using the Parameter-Efficient Fine-Tuning (PEFT) method, Low-Rank Adaptation (LoRA) to perform per-nucleotide classification of DNA sequences into repeat, spacer, and non-array regions directly from raw input nucleotide sequences. We develop two model variants for different sequence context lengths. The long-context model supporting sequences of up to 8,192 nucleotides achieves 98.16% test accuracy and identifies degenerate repeat candidates missed by similarity-based CRISPR detection tools. The short-context model supports sequences of up to 150 nucleotides, optimized for Illumina reads, reaches 90.03% accuracy and enables direct analysis of individual reads without assembly. On simulated metagenomic data, it achieves a spacer recall of 49.12% and recovers 12.57% of spacers that are otherwise not detected by dedicated metagenomic CRISPR array detection methods which require metagenomic assembly. Together, these results demonstrate that genomic foundation models provide a robust and complementary paradigm for CRISPR array detection.

molecular biology↗

bioRxiv2025

An evolutionary approach to predict the orientation of CRISPR arrays

CRISPR-Cas is a defense system of bacteria and archaea against phages. Parts of the foreign DNA, called spacers, are incorporated into the CRISPR array which constitutes the immune memory. The orientation of CRISPR arrays is crucial for analyzing and understanding the functionality of CRISPR systems and their targets. Several methods have been developed to identify the orientation of a CRISPR array. To predict the orientation, different methods use different features such as the repeat sequences between the spacers, the location of the leader sequence, the Cas genes, or PAMs. However, those features are often not sufficient to predict the orientation with certainty, or different methods disagree. Remarkably, almost all CRISPR systems have been found to insert spacers in a polarized manner at the leader end of the array. We introduce CRISPR-evOr, a method that leverages the resulting patterns to predict the acquisition orientation for (a group of) CRISPR arrays by reconstructing and comparing the likelihood of their evolutionary history with respect to both possible acquisition orientations. The new method is independent of Cas type, leader existence and location, and transcription orientation. CRISPR-evOr is thus particularly useful for arrays that other CRISPR orientation tools cannot predict confidently and to verify or resolve conflicting predictions from existing tools. CRISPR-evOr currently confidently predicts the orientation of 28.3% of the arrays in the considered subset of CRISPRCasdb, which other tools like CRISPRDirection and CRISPRstrand cannot reliably orient. As our tool leverages evolutionary information we expect this percentage to grow in the future when more closely related arrays will be available. Additionally, CRISPR-evOr provides confident decisions for rare subtypes of CRISPR arrays, where knowledge about repeats and leaders and their orientation is limited. Author SummarySome bacteria and archaea possess a CRISPR-Cas defense system, which protects them against phages and mobile genetic elements. This system adapts to new threats by incorporating small fragments of their DNA, so called spacers, in a CRISPR array. Remarkably, the acquisition of new spacers is polarized at one end of this array. To understand how this immune system functions, it is essential to know the orientation of these arrays. Many existing tools try to determine orientation using genetic markers, but these methods are often unreliable or disagree with one another. In our work, we developed a new method that predicts the end at which new spacers are inserted, by considering the evolutionary history of a group of related CRISPR arrays. Unlike other tools, our approach is less reliant on specific genetic markers and can be applied broadly across many types of CRISPR systems. We show that it can confidently determine the orientation of a large number of arrays that other methods cannot resolve. This provides a new way to predict the array orientation, which is particularly useful for rare CRISPR types. Our evolutionary approach will become even more powerful as more genetic data becomes available.

bioinformatics↗

bioRxiv2025

The complexity of multiple CRISPR arrays in strains with (co-occurring) CRISPR systems

CRISPR and their associated Cas proteins provide adaptive immunity in prokaryotes, protecting against invading genetic elements. These systems are categorized into types and are highly diverse. Genomes often harbor multiple CRISPR arrays varying in length and distance from Cas loci. However, the ecological roles of multiple CRISPR arrays and their interactions with multiple Cas loci remain poorly understood. We present a comprehensive analysis of CRISPR systems that uncovers variation between diverse Cas types regarding the occurrence of multiple arrays, the distribution of their lengths and positions relative to Cas loci, and the diversity of their repeat sequences. Some types tend to occur as the sole Cas locus present in the genome, but typically have two or more associated arrays, especially for types I-E and I-F. Multiple Cas types are also common, with some systems showing a preference for specific co-occurrence. Distinct array distributions and orientations around Cas loci indicate substantial differences in functionality and transcriptional behavior among Cas types. Our analysis suggests that arrays with identical repeats in the same genome acquire new spacers at comparable rates, irrespective of their proximity to the Cas locus. Furthermore, repeat similarities indicate that arrays of systems that often co-occur with other systems tend to have more diverse repeats than those mostly appearing alongside solitary systems. Our results indicate that co-occurring Cas type pairs might not only collaborate in spacer acquisition but also maintain independent and complementary functions and that CRISPR systems distribute their defensive spacer repertoire equally across multiple CRISPR arrays.

microbiology↗

bioRxiv2025

Comprehensive Analysis of CRISPR Array Repeat Mutations Reveals Subtype-Specific Patterns and Links to Spacer Dynamics

CRISPR-Cas systems are adaptive immune mechanisms in bacteria and archaea that protect against invading genetic elements by integrating short fragments of foreign DNA into CRISPR arrays. These arrays consist of repetitive sequences interspersed with unique spacers, guiding Cas proteins to recognize and degrade matching nucleic acids. The integrity of these repeat sequences is crucial for the proper function of CRISPR-Cas systems, yet their mutational dynamics remain poorly understood. In this study, we analyzed 56,343 CRISPR arrays across 25,628 diverse prokaryotic genomes to assess the mutation patterns in CRISPR array repeat sequences within and across different CRISPR subtypes. Our findings reveal, as expected to some extent, that mutation frequency is substantially higher in terminal repeat sequences compared to internal repeats consistently across system types. However, the mutation patterns exhibit an unexpected amount of variation among different CRISPR subtypes, suggesting that selective pressures and functional constraints shape repeat sequence evolution in distinct ways. Understanding these mutation dynamics provides insights into the stability and adaptability of CRISPR arrays across diverse bacterial and archaeal lineages. Additionally, we elucidate a novel relationship between repeat mutations and spacer dynamics, demonstrating that hotspots for terminal repeat mutations coincide with regions exhibiting spacer conservation. This observation corroborates recent findings by Fehrenbach et al. (2024) indicating that spacer deletions occur at a frequency 374 times greater than that of mutations and are significantly influenced by repeat misalignment. Our findings suggest that repeat mutations play a pivotal role in spacer retention or loss, or vice versa, thereby highlighting an evolutionary trade-off between the stability and adaptability of CRISPR arrays.

bioinformatics↗

bioRxiv2025

Analysis of tracrRNAs reveals subgroup V2 of type V-K CAST systems

CRISPR-associated transposons (CAST) consist of an integration between certain class 1 or class 2 CRISPR-Cas systems and Tn7-like transposons. Class 2 type V-K CAST systems are restricted to cyanobacteria. Here, we identified a unique subgroup of type V-K systems through phylogenetic analysis, classified as V-K_V2. Subgroup V-K_V2 CAST systems are characterized by an alternative tracrRNA, the exclusive use of Arc_2-type transcriptional regulators, and distinct differences in TnsB and TnsC proteins. Although the occurrence of V-K_V2 CAST systems is restricted to Nostocales cyanobacteria, it shows signs of horizontal gene transfer, indicating its capability for genetic mobility. The predicted V-K_V2 tracrRNA secondary structure has been integrated into an updated version of the CRISPRtracrRNA program available on GitHub under https://github.com/BackofenLab/CRISPRtracrRNA/releases/tag/2.0.

microbiology↗

bioRxiv2024

SpacerPlacer: Ancestral reconstruction of CRISPR arrays reveals the evolutionary dynamics of spacer deletions

Bacteria employ CRISPR-Cas systems for defense by integrating invader-derived sequences, termed spacers, into the CRISPR array, which constitutes an immunity memory. While spacer deletions occur randomly across the array, newly acquired spacers are predominantly integrated at the leader end. Consequently, spacer arrays can be used to derive the chronology of spacer insertions. Reconstruction of ancestral spacer acquisitions and deletions could help unravel the coevolution of phages and bacteria, the evolutionary dynamics in microbiomes, or track pathogens. However, standard reconstruction methods produce misleading results by overlooking insertion order and joint deletions of spacers. Here, we present SpacerPlacer, a maximum likelihood-based ancestral reconstruction approach for CRISPR array evolution. We used SpacerPlacer to reconstruct and investigate ancestral deletion events of 4565 CRISPR arrays, revealing that spacer deletions occur 374 times more frequently than mutations and are regularly deleted jointly, with an average of 2.7 spacers. Surprisingly, we observed a decrease in the spacer deletion frequency towards both ends of the reconstructed arrays. While the resulting trailer-end conservation is commonly observed, a reduced deletion frequency is now also detectable towards the variable leader end. Finally, our results point to the hypothesis that frequent loss of recently acquired spacers may provide a selective advantage.

bioinformatics↗

bioRxiv2020

CRISPRIdentify: Identification of CRISPR arrays using machine learning approach

CRISPR-Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR- Cas systems heavily rely on RNA components. These CRISPR (cr) RNAs are repeat-spacer units that are produced by processing of pre-crRNA, the transcript of CRISPR arrays, and guide Cas protein(s) to the cognate invading nucleic acids, enabling their destruction. Several bioinformatics tools have been developed to detect CRISPR arrays based solely on DNA sequences, but all these tools employ the same strategy of looking for repetitive patterns, which might correspond to CRISPR array repeats. The identified patterns are evaluated using a fixed, built-in scoring function, and arrays exceeding a cut-off value are reported. Here, we instead introduce a data-driven approach that uses machine learning to detect and differentiate true CRISPR arrays from false ones based on several features. Our CRISPR detection tool, CRISPRidentify, performs three steps: detection, feature extraction and classification based on manually curated sets of positive and negative examples of CRISPR arrays. The identified CRISPR arrays are then reported to the user accompanied by detailed annotation. We demonstrate that our approach identifies not only previously detected CRISPR arrays, but also CRISPR array candidates not detected by other tools. Compared to other methods, our tool has a drastically reduced false positive rate. In contrast to the existing tools, our approach not only provides the user with the basic statistics on the identified CRISPR arrays but also produces a certainty score as a practical measure of the likelihood that a given genomic region is a CRISPR array.

bioinformatics↗