Advances in high-throughput sequencing have lowered the cost and complexity of genome sequencing, making it possible for the first time to assemble large pangenomic data sets for many species. These data sets, comprising thousands of individuals, already span from hundreds of gigabytes to petabytes, far exceeding the memory capacity of most machines, and are expected to continue growing in scale over time. Already, many traditional bioinformatics tools fail on inputs at this scale because they cannot construct their necessary data structures within memory limits. There is a growing need for methods that can construct these structures directly from compressed representations. Prefix-free parsing (PFP) addresses this challenge. PFP serves as a preprocessing step that compresses sufficiently repetitive text, yet still permits building important data structures for the original data set from its compressed output. This survey offers an overview of PFP, covering its core principles, the primary data structures it enables, current applications, and future research directions.

Gene transcription is activated through the interaction between cis-regulatory elements (CREs) and transcription factors (TFs). CREs serve as templates to provide binding sites, whereas TFs provide functions to directly initiate transcription. Current research mainly focuses on deciphering cis-regulatory code, while neglecting TF regulatory code. However, CREs alone are not sufficient to determine the binding of TFs, which makes the interpretation of cis-regulatory code ambiguous. In this study, we systematically analyze 13 TF binding profiles associated with transcription initiation and encode them as TF sequence to explore the TF regulatory code. Furthermore, we propose a deep learning model named DeepTF to predict gene expression from TF sequence. Results show that TF binding exhibits conserved positional preferences and combinatorial patterns in promoters and DeepTF is able to predict gene expression with high accuracy (AUROC = 0.97). Meanwhile, cross-cell-line validation (AUROC > 0.90) further confirms the model's transferability. Model interpretation reveals DeepTF successfully captures the TF regulatory grammar associated with gene expression. Compared with the cis-regulatory code, our proposed TF regulatory code is better suited for investigating the relationship between TF binding positions or combinatorial patterns and gene expression. Collectively, DeepTF simplifies gene expression prediction and provides clear biological insights of transcriptional regulation.

Cytosine methylation, a crucial epigenetic modification, plays a vital role in genomic regulation. Leveraging the advancements in long-read sequencing, we investigate the methylation patterns of polymorphic transposable element (TE) insertions of human lymphoblastoid cell lines (LCLs). We validate the high concordance between long-read methylation calls and conventional whole genome bisulfite sequencing (WGBS) method. We then aim to establish general rules of TE methylation with our data by addressing three key questions: 1) what is the methylation profile of each insertion? 2) do newly inserted TEs adopt the methylation pattern of their genomic context? and 3) do new TE insertions affect the methylation of their flanking regions? While most non-TE insertions exhibit DNA methylation patterns consistent with their genomic context, TE insertions are generally highly methylated, exhibiting distinct, class-specific patterns with some variation within TE bodies. A small percentage of Alu insertions are hypomethylated, particularly those inserted within hypomethylated CpG islands. We also reveal that majority of TEs exhibited minimal impact on nearby regions, although numerous exceptions exist where the methylation status of TEs spread into nearby regions. In conclusion, while TE insertions primarily exhibit methylation patterns restricted within their boundaries, some TEs are able to affect the methylation level of their genomic neighborhoods. At last, our findings are limited to human LCLs, and more comprehensive analysis would be needed to test the rules we found here on a broader spectrum of cell types and developmental stages.

Vitamins are essential metabolic cofactors, yet their roles in epitranscriptomic regulation, particularly N 6-methyladenosine (m6A) modification, remain unclear. Here, we investigate the effects of various vitamins (VB2, VB6, and VB12) on the mRNA m6A epitranscriptome in multiple mouse tissues (brain, liver, and testis). Clustering analyses reveal closer similarity between the brain and testis m6A profiles, whereas the liver exhibits a unique pattern, reflecting tissue-specific regulatory dynamics. More than 90% of m6A sites are independent of mRNA abundance, highlighting the post-transcriptional role of m6A modification. Additionally, alternative splicing (AS) variations reveal complex interactions between vitamins, m6A modification, and AS, with tissue- and vitamin-specific effects on biological pathways. We identify 22 comethylation modules, associated with pathways such as neurodegenerative diseases and immune regulation. Key vitamin-responsive genes are found as central regulators of m6A dynamics, aligning with known roles of vitamins in metabolism, neural plasticity, and gene expression. Together, our findings provide the first comprehensive atlas of vitamin-driven, tissue-specific m6A modifications, offering new insights into the post-transcriptional regulatory mechanisms underlying vitamin-mediated cellular functions and their implications for nutritional or pharmacological modulation of the epitranscriptome in health and disease.

Cell lines are invaluable tools for biomedical and evolutionary studies, but their genomic stability over time is often assumed rather than systematically assessed. In this study, we investigate the dynamics of genomic instability and structural rearrangements across multiple batches of a Nomascus siki cell line using a combination of single-cell template strand sequencing (Strand-seq), whole-genome sequencing (WGS), and fluorescence in situ hybridization (FISH). We identify 22 shared inversions in all the Strand-sequenced batches, confirming a common clonal origin. However, we detect additional large-scale rearrangements in all the batches, including trisomy of Chromosome 14 and the formation of isochromosomes of the same chromosome (iso-q and iso-p), leading to the rise of distinct subclonal populations. These rearrangements show evidence of clonal expansion, suggesting a proliferative advantage under in vitro conditions. From an evolutionary perspective, the gibbon genome is known for its exceptional level of chromosomal reshuffling, and this inherent plasticity may have contributed to the cell line's sensitivity to culture-induced structural changes. Despite extensive structural variation, the cell line remains stable at the nucleotide level, with ∼99% of SNPs shared across all batches. Our results illustrate how cell culture can recapitulate aspects of karyotypic evolution and underscore the need for regular genomic surveillance, particularly in long-term cultures. Furthermore, this study demonstrates the power of combining Strand-seq and cytogenetic approaches to detect both balanced and unbalanced rearrangements, especially those present in subclonal populations that would be missed by standard WGS.

The D4Z4 locus is a macrosatellite array on Chromosome 4q normally comprising 8 to >100 3.3-kb repeat units. Its size and repetitiveness render it refractory to most sequencing technologies; consequently, its genetic and epigenetic architectures remain incompletely understood despite their relevance to facioscapulohumeral muscular dystrophy (FSHD). Current FSHD molecular testing relies on complex, multistep and low-resolution assays, which aim to identify contractions on permissive haplotypes (FSHD type 1) or epigenetic reactivation due to pathogenic variants in the epigenetic machinery, most often in SMCHD1 (FSHD type 2). Recent guideline updates highlight the need for more accurate and comprehensive diagnostic approaches. Here, we leverage ultra-long whole-genome and Cas9-targeted sequencing to develop a fast and accurate workflow, D4Z4End2End, for comprehensive genetic and methylation analysis of D4Z4 alleles. We apply it to samples from two controls, four FSHD1 patients, four FSHD2 patients, and two patients with Bosma arhinia microphthalmia syndrome (BAMS) caused by SMCHD1 variants, as well as publicly available data from 30 B-lymphoblastoid cell lines from the 1000 Genomes Project and Human Pangenome Reference Consortium. We attain high-depth sequencing of full-length D4Z4 arrays of up to 40 repeat units (∼132 kb), accurately capture contracted arrays, genetic mosaicism, and pathogenic SMCHD1 variants, and generate consensus sequences of all D4Z4 alleles. We identify new allelic variants, analyze complex D4Z4 rearrangements including in-cis duplications, and reveal length- and SMCHD1-dependent methylation patterns across the D4Z4 array. Our findings offer insights into D4Z4 genetics and epigenetics, and demonstrate the potential of long-read nanopore sequencing to accelerate FSHD research and diagnostics.

Somatic hypermutation (SHM) drives antibody affinity maturation in B cells. By mimicking this process, guided hypermutation (GHM) tools employing CRISPR systems and activation-induced cytidine deaminase (AID) have advanced antibody development. However, GHM-induced mutations in cultured cells exhibit mutation patterns distinct from those observed in natural antibody diversification following in vivo affinity selection. To address this, we engineer a hyper-antibody editor, HAE1, by integrating cytidine and adenine deaminases with a nicked, PAMless Cas9 variant, SpRY, to closely resemble the mutation spectrum of natural SHM. Moreover, to streamline mutation, selection, and validation within the same cells, we develop a dual-expression system in HEK293F cells that allows simultaneous expression of both transmembrane and secreted full-length antibodies. Using this system, we apply HAE1 to the SARS-CoV-2 neutralizing antibody CV07-209 and restore the antibody's binding affinity and neutralization potency against Omicron variants, specifically BA.1, including at least one mutation beyond the reach of current GHM tools. HAE1 thus provides a versatile, high-throughput strategy for expediting antibody evolution, presenting significant potential for therapeutic antibody development and protein engineering.

Predicting phenotypes from genomic mutations remains a major genetic challenge. Traditional statistical methods (such as GBLUP and BayesR) have limitations, including reliance on artificial prior assumptions, and hard to capture epistatic effects. Machine learning (ML) has emerged as a powerful alternative for genomic prediction; however, it often struggles with interpretability because of its black-box nature. Here, we evaluate 12 ML models alongside GBLUP and BayesR to identify key factors influencing genomic prediction performance across traits with different genetic architectures in multiple agricultural species, including pigs, chickens, horses, and maize, and we use a series of simulated data sets to assess the impacts of various parameters. Trait genetic architecture and feature selection are the primary determinants of predictive performance. Boosting algorithms outperform the other ML methods and can be further improved by refining biological feature engineering and optimizing the hyperparameters. We demonstrate how gene-related biometrics influence target traits and how accounting for interaction effects enhances prediction accuracy. In addition, we apply Shapley additive explanations (SHAP) to quantify the SNP additive and epistatic effects. To bridge the gap between algorithmic advancements and biological interpretability, we have developed artificial intelligence genomic prediction (AIGP), an open-source end-to-end toolkit for genomic prediction research. Our findings highlight the potential of ML for genomic prediction and emphasize the importance of explainable ML approaches, integration of prior information, and parameter optimization. The AIGP toolkit enables automated model optimization and interpretability, making ML-driven genomic selection more accessible and providing new tools to support genomic research.

Gene signature scoring is integral to single-cell RNA sequencing (scRNA-seq) data analysis, particularly for unsupervised cellular state annotation based on maximum signature score values. However, this application requires robust and comparable score distributions across diverse signatures and experimental conditions. Our systematic evaluation of established scoring methodologies-Seurat, SCANPY, UCell, and JASMINE-across nine healthy and cancer scRNA-seq data sets demonstrates their insufficiency in fulfilling this requirement. To address this limitation, we present Adjusted Neighborhood Scoring (ANS), a deterministic algorithm with enhanced control gene selection that significantly improves score stability and cross-signature comparability, achieving cell-state annotation accuracy comparable to supervised methods. We demonstrate the practical utility of ANS by developing and validating a gene signature to differentiate cancer-associated fibroblasts from malignant cells undergoing epithelial-to-mesenchymal transition. Overall, ANS provides a robust and reliable gene signature scoring framework, significantly improving the accuracy of score-based annotation of cell types and states in single-cell studies.