The fatal diffuse midline gliomas (DMG) are characterized by an undruggable H3K27M mutation in H3.1 or H3.3. K27M impairs normal development by stalling differentiation. The identification of targetable pathways remains very poorly explored. Toward this goal, we undertake a multi-omics approach to evaluate replication timing profiles, transcriptomics, and cell cycle features in DMG cells from both

Plasma cell-free DNA (cfDNA) is derived from cellular death in various tissues. Investigating the tissue origin of cfDNA through cell type deconvolution, we can detect changes in tissue homeostasis that occur during disease progression or in response to treatment. Consequently, cfDNA has emerged as a valuable noninvasive biomarker for disease detection and treatment monitoring. Although there are many

Longitudinal studies are crucial for understanding complex microbiome dynamics and their link to health. We introduce TEMPoral TEnsor Decomposition (TEMPTED), a time-informed dimensionality reduction method for high-dimensional longitudinal data that treats time as a continuous variable, effectively characterizing temporal information and handling varying temporal sampling. TEMPTED captures key microbial

Single-cell DNA sequencing (scDNA-seq) enables decoding somatic cancer variation. Existing methods are hampered by low throughput or cannot be combined with transcriptome sequencing in the same cell. We propose HIPSD&R-seq (HIgh-throughPut Single-cell Dna and Rna-seq), a scalable yet simple and accessible assay to profile low-coverage DNA and RNA in thousands of cells in parallel. Our approach builds

We present GenomeDelta, a novel tool for identifying sample-specific sequences, such as recent transposable element (TE) invasions, without requiring a repeat library. GenomeDelta compares high-quality assemblies with short-read data to detect sequences absent from the short reads. It is applicable to both model and non-model organisms and can identify recent TE invasions, spatially heterogeneous sequences

Cloud computing allows storing the ever-growing genotype-phenotype datasets crucial for precision medicine. Due to the sensitive nature of this data and varied laws and regulations, additional security measures are needed to ensure data privacy. We develop SQUiD, a secure queryable database for storing and analyzing genotype-phenotype data. SQUiD allows storage and secure querying of data in a low-security

Transcription factors (TFs) bind regulatory genomic regions to orchestrate spatio-temporal expression of target genes. Global dissection of the cistrome is critical for elucidating transcriptional networks underlying complex agronomic traits in crops. Here, we generate a comprehensive genome-wide binding map for 148 TFs using DNA affinity purification sequencing in soybean. We find TF binding sites

Long-read technologies from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) have transformed genomics research by providing diverse data types like HiFi, Duplex, and ultra-long ONT. Despite recent strides in achieving haplotype-phased gapless genome assemblies using long-read technologies, concerns persist regarding the representation of genetic diversity, prompting the development

Synthetic CRISPR-Cas9 gene drive has been developed to control harmful species. However, resistance to Cas9 gene drive can be acquired easily when DNA repair mechanisms patch up the genetic insults introduced by Cas9 and incorporate mutations to the sgRNA target. Although many strategies to reduce the occurrence of resistance have been developed so far, they are difficult to implement and not always

The inherent similarities between natural language and biological sequences have inspired the use of large language models in genomics, but current models struggle to incorporate chromatin interactions or predict in unseen cellular contexts. To address this, we propose EpiGePT, a transformer-based model designed for predicting context-specific human epigenomic signals. By incorporating transcription

The accuracy of machine learning methods is often limited by the amount of training data that is available. We proposed to improve machine learning training regimes by augmenting datasets with synthetically generated samples. We present a method for synthesizing gene expression samples and test the system’s capabilities for improving the accuracy of categorical prediction of cancer subtypes. We developed

Immunotherapy has improved survival for patients with advanced clear cell renal cell carcinoma (ccRCC), but resistance to therapy develops in most patients. We use cellular-resolution spatial transcriptomics in patients with immunotherapy naïve and exposed primary ccRCC tumors to better understand immunotherapy resistance. Spatial molecular imaging of tumor and adjacent stroma samples from 21 tumors

CRISPR/Cas13 system, recognized for its compact size and specificity in targeting RNA, is currently employed for RNA degradation. However, the potential of various CRISPR/Cas13 subtypes, particularly concerning the knockdown of endogenous transcripts, remains to be comprehensively characterized in plants. Here we present a full spectrum of editing profiles for seven Cas13 orthologs from five distinct

Plants respond to environmental stimuli by altering gene transcription that is highly related with chromatin status, including histone modification, chromatin accessibility, and three-dimensional chromatin interaction. Vernalization is essential for the transition to reproductive growth for winter wheat. How wheat reshapes its chromatin features, especially chromatin interaction during vernalization

Epigenetic heterogeneity is a fundamental property of biological systems and is recognized as a potential driver of tumor plasticity and therapy resistance. Single-cell epigenomics technologies have been widely employed to study epigenetic variation between—but not within—cellular clusters. We introduce epiCHAOS: a quantitative metric of cell-to-cell heterogeneity, applicable to any single-cell epigenomics

While spatial transcriptomics offer valuable insights into gene expression patterns within the spatial context of tissue, many technologies do not have a single-cell resolution. Here, we present SMART, a marker gene-assisted deconvolution method that simultaneously infers the cell type-specific gene expression profile and the cellular composition at each spot. Using multiple datasets, we show that

We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction terms (energetic couplings) for specified biophysical models, including from multimodal phenotypic data. When a user-specified model is unavailable, global nonlinearities (epistasis) can be estimated from the data. MoCHI also leverages ensemble, background-averaged

Topologically associating domains (TADs) are essential units of genome architecture, influencing transcriptional regulation and diseases. Despite numerous methods proposed for TAD identification, it remains challenging due to complex background and nested TAD structures. We introduce HTAD, a human-in-the-loop TAD caller that combines machine learning with human supervision to achieve high accuracy

Atypical teratoid rhabdoid tumors (ATRT) are incurable high-grade pediatric brain tumors. Despite intensive research efforts, the prognosis for ATRT patients under currently established treatment protocols is poor. While novel therapeutic strategies are urgently needed, the generation of molecular-driven treatment concepts is a challenge mainly due to the absence of actionable genetic alterations.

Genome-wide association studies (GWAS) have identified thousands of loci for disease-related human traits in cross-sectional data. However, the impact of age on genetic effects is underacknowledged. Also, identifying genetic effects on longitudinal trait change has been hampered by small sample sizes for longitudinal data. Such effects on deteriorating trait levels over time or disease progression

While context-type-specific regulation of genes is largely determined by cis-regulatory regions, attempts to identify cell type-specific eQTLs are complicated by the nested nature of cell types. We present hierarchical eQTL (H-eQTL), a network-based model for hierarchical annotation of bulk-derived eQTLs to levels of a cell type tree using single-cell chromatin accessibility data and no clustering

Correction: Genome Biol 25, 171 (2024) https://doi.org/10.1186/s13059-024-03315-6 Following publication of the original article [1], the authors identified a typesetting error, whereby the equal contribution statement was mistakenly omitted. The correct statement is as follow: Jianxia Niu, Wenxi Wang and Zihao Wang are co-first authors and contributed equally. The original article [1] has been corrected

Understanding tumor cell heterogeneity and plasticity is crucial for overcoming drug resistance. Single-cell technologies enable analyzing cell states at a given condition, but catenating static cell snapshots to characterize dynamic drug responses remains challenging. Here, we propose scStateDynamics, an algorithm to infer tumor cell state dynamics and identify common drug effects by modeling single-cell

The Italic Iron Age is characterized by the presence of various ethnic groups partially examined from a genomic perspective. To explore the evolution of Iron Age Italic populations and the genetic impact of Romanization, we focus on the Picenes, one of the most fascinating pre-Roman civilizations, who flourished on the Middle Adriatic side of Central Italy between the 9th and the 3rd century BCE, until

Epistasis refers to changes in the effect on phenotype of a unit of genetic information, such as a single nucleotide polymorphism or a gene, dependent on the context of other genetic units. Such interactions are both biologically plausible and good candidates to explain observations which are not fully explained by an additive heritability model. However, the search for epistasis has so far largely

Centromeres depend on chromatin containing the conserved histone H3 variant CENP-A for function and inheritance, while the role of centromeric DNA repeats remains unclear. Retroelements are prevalent at centromeres across taxa and represent a potential mechanism for promoting transcription to aid in CENP-A incorporation or for generating RNA transcripts to maintain centromere integrity. In this study

Unveiling functional relationships between various molecular cell phenotypes from data using machine learning models is a key promise of multiomics. Existing methods either use flexible but hard-to-interpret models or simpler, misspecified models. VI-VS (Variational Inference for Variable Selection) balances flexibility and interpretability to identify relevant feature relationships in multiomic data

Inhomogeneous patterns of chromatin-chromatin contacts within 10–100-kb-sized regions of the genome are a generic feature of chromatin spatial organization. These features, termed topologically associating domains (TADs), have led to the loop extrusion factor (LEF) model. Currently, our ability to model TADs relies on the observation that in vertebrates TAD boundaries are correlated with DNA sequences

The safety of CRISPR-based gene editing methods is of the utmost priority in clinical applications. Previous studies have reported that Cas9 cleavage induced frequent aneuploidy in primary human T cells, but whether cleavage-mediated editing of base editors would generate off-target structure variations remains unknown. Here, we investigate the potential off-target structural variations associated

Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing

Spatial transcriptomics is revolutionizing the exploration of intratissue heterogeneity in cancer, yet capturing cellular niches and their spatial relationships remains challenging. We introduce SpottedPy, a Python package designed to identify tumor hotspots and map spatial interactions within the cancer ecosystem. Using SpottedPy, we examine epithelial-mesenchymal plasticity in breast cancer and highlight

The low resolution of spatial transcriptomics data necessitates additional information for optimal use. We developed scDOT, which combines spatial transcriptomics and single cell RNA sequencing to improve the ability to reconstruct single cell resolved spatial maps and identify senescent cells. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells

The rapid advancement of spatial transcriptomics technologies has revolutionized our understanding of cell heterogeneity and intricate spatial structures within tissues and organs. However, the high dimensionality and noise in spatial transcriptomic data present significant challenges for downstream data analyses. Here, we develop GraphPCA, an interpretable and quasi-linear dimension reduction algorithm

Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor

Oxford Nanopore Technologies (ONT) offers ultrahigh-throughput multi-sample sequencing but only provides barcode kits that enable up to 96-sample multiplexing. We present TDFPS-Designer, a new toolkit for nanopore sequencing barcode design, which creates significantly more barcodes: 137 with a length of 20 base pairs, 410 at 24 bp, and 1779 at 30 bp, far surpassing ONT’s offerings. It includes GPU-based

Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity manifests itself in vivo as differences in TF occupancy between the two alleles at heterozygous loci. Genome-scale assays such as ChIP-seq currently are limited in their power to detect allele-specific binding (ASB) both in terms of read coverage and representation of individual variants in the cell lines

Two correspondences raised concerns or comments about our analyses regarding exaggerated false positives found by differential expression (DE) methods. Here, we discuss the points they raise and explain why we agree or disagree with these points. We add new analysis to confirm that the Wilcoxon rank-sum test remains the most robust method compared to the other five DE methods (DESeq2, edgeR, limma-voom

A recent study found severely inflated type I error rates for DESeq2 and edgeR, two dominant tools used for differential expression analysis of RNA-seq data. Here, we show that by properly addressing the outliers in the RNA-Seq data using winsorization, the type I error rate of DESeq2 and edgeR can be substantially reduced, and the power is comparable to Wilcoxon rank-sum test for large datasets. Therefore

A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples. We reproduce the differential expression analysis simulation results and identify a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results

The accurate reconstruction of genome-scale metabolic models (GEMs) for unculturable species poses challenges due to the incomplete and fragmented genetic information typical of metagenome-assembled genomes (MAGs). While existing tools leverage sequence homology from single genomes, this study introduces pan-Draft, a pan-reactome-based approach exploiting recurrent genetic evidence to determine the

Numerous plant taxa are threatened by habitat destruction or overexploitation. To overcome these threats, new methods are urgently needed for rescuing threatened and endangered plant species. Here, we review the genetic consequences of threats to species populations. We highlight potential advantages of genome editing for mitigating negative effects caused by new pathogens and pests or climate change

Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only

Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular

MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Perturbations in plasma miRNA levels are known to impact disease risk and have potential as disease biomarkers. Exploring the genetic regulation of miRNAs may yield new insights into their important role in governing gene expression and disease mechanisms. We present genome-wide association studies of

The identification of gene regulatory networks (GRNs) is crucial for understanding cellular differentiation. Single-cell RNA sequencing data encode gene-level covariations at high resolution, yet data sparsity and high dimensionality hamper accurate and scalable GRN reconstruction. To overcome these challenges, we introduce NetID leveraging homogenous metacells while avoiding spurious gene–gene correlations

The extremely high levels of genetic polymorphism within the human major histocompatibility complex (MHC) limit the usefulness of reference-based alignment methods for sequence assembly. We incorporate a short-read, de novo assembly algorithm into a workflow for novel application to the MHC. MHConstructor is a containerized pipeline designed for high-throughput, haplotype-informed, reproducible assembly

Methylation quantitative trait loci (meQTLs) quantify the effects of genetic variants on DNA methylation levels. However, most published studies utilize bulk methylation datasets composed of different cell types and limit our understanding of cell-type-specific methylation regulation. We propose a hierarchical Bayesian interaction (HBI) model to infer cell-type-specific meQTLs, which integrates a large-scale

Lactylation, a post-translational modification, is increasingly recognized for its role in cancer progression. This study investigates its prevalence and impact in oral squamous cell carcinoma (OSCC). Immunohistochemical staining of 81 OSCC cases shows lactylation levels correlate with malignancy grading. Proteomic analyses of six OSCC tissue pairs reveal 2765 lactylation sites on 1033 proteins, highlighting

Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and

The exponential increase in sequencing data calls for conceptual and computational advances to extract useful biological insights. One such advance, minimizers, allows for reducing the quantity of data handled while maintaining some of its key properties. We provide a basic introduction to minimizers, cover recent methodological developments, and review the diverse applications of minimizers to analyze

Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation

Pervasive translation is a widespread phenomenon that plays a critical role in the emergence of novel microproteins, but the diversity of translation patterns contributing to their generation remains unclear. Based on 54 ribosome profiling (Ribo-Seq) datasets, we investigated the yeast Ribo-Seq landscape using a representation framework that allows the comprehensive inventory and classification of

Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable

Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line

Haplotype information is crucial for biomedical and population genetics research. However, current strategies to produce de novo haplotype-resolved assemblies often require either difficult-to-acquire parental data or an intermediate haplotype-collapsed assembly. Here, we present Graphasing, a workflow which synthesizes the global phase signal of Strand-seq with assembly graph topology to produce chromosome-scale

Diffuse invasion of glioblastoma cells through normal brain tissue is a key contributor to tumor aggressiveness, resistance to conventional therapies, and dismal prognosis in patients. A deeper understanding of how components of the tumor microenvironment (TME) contribute to overall tumor organization and to programs of invasion may reveal opportunities for improved therapeutic strategies. Towards

Methylation of histone 3 lysine 36 (H3K36me) has emerged as an essential epigenetic component for the faithful regulation of gene expression. Despite its importance in development and disease, how the molecular agents collectively shape the H3K36me landscape is unclear. We use mouse mesenchymal stem cells to perturb the H3K36me methyltransferases (K36MTs) and infer the activities of the five most prominent

In response to drought stress (DS), plants undergo complex processes that entail significant transcriptome reprogramming. However, the intricate relationship between the dynamic alterations in the three-dimensional (3D) genome and the modulation of gene co-expression in drought responses remains a relatively unexplored area. In this study, we reconstruct high-resolution 3D genome maps based on genomic

Reconstructing premortem DNA methylation levels in ancient DNA has led to breakthrough studies such as the prediction of anatomical features of the Denisovan. These studies rely on computationally inferring methylation levels from damage signals in naturally deaminated cytosines, which requires expensive high-coverage genomes. Here, we test two methods for direct methylation measurement developed for

Polygenic risk score (PRS) is a major research topic in human genetics. However, a significant gap exists between PRS methodology and applications in practice due to often unavailable individual-level data for various PRS tasks including model fine-tuning, benchmarking, and ensemble learning. We introduce an innovative statistical framework to optimize and benchmark PRS models using summary statistics