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At least 19 recordsLinked to original sources

PubMed2026

Deep generative models in biological sequence and structure analysis and design.

Deep generative models have transformed biological sequence modeling from predictive analysis toward increasingly controllable design. Early biological applications of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) established latent representation learning and sequence synthesis, while recent advances in transformer-based language models, discrete diffusion, flow-matching, and multimodal generative frameworks have substantially expanded the scope of biological design. This review examines generative models for DNA, RNA, and protein sequence design, emphasizing how different model classes represent biological constraints, operate over discrete and continuous spaces, and integrate sequence, structure, and function. We compare VAEs, GANs, autoregressive and masked language models, diffusion models, and flow-based approaches across genomics, transcriptomics, and proteomics, with particular attention to controllability, long-range dependency modeling, structural grounding, generalization, and experimental utility. We further examine evaluation strategies, out-of-distribution generalization, and closed-loop design-build-test-learn workflows that connect in silico generation with empirical validation. We distinguish fundamental modality-dependent constraints including sequence discreteness, context length, structural coupling, and physical or thermodynamic requirements from architecture-dependent advantages that reflect the current state of the field. Current studies suggest that long-context models are particularly useful for genome-scale representation and sequence modeling, whereas structure-aware diffusion, flow-based, and inverse-folding approaches provide better frameworks for geometry-constrained RNA and protein design. This perspective provides a critical framework for understanding the present capabilities, limitations, and convergence of generative approaches toward reliable and experimentally grounded biological design.

Biological sequence analysis↗

PubMed2025

Panaln: indexing pangenome for read alignment.

MOTIVATION: Pangenome indexing is a critical supporting technology in biological sequence analysis such as read alignment applications. The need to accurately identify billions of small sequencing fragments carrying sequencing errors and genomic variants drives the development of scalable and efficient pangenome indexing approach. RESULTS: We propose a new wavelet tree-based approach, called Panaln, for indexing pangenome and introduce a batch computation approach for fast count query over Panaln. We present a simple and effective seeding strategy and develop a pangenome program that uses the seed-and-extend paradigm for read alignment. Experimental results on simulated and real data demonstrate that Panaln uses significantly less space for the compared pangenome methods with generally higher accuracy. We provide a scalable index construction by representing pangenome with a linear model. Additionally, Panaln brings enhanced accuracy compared to the popular single reference methods. AVAILABILITY AND IMPLEMENTATION: Package: https://anaconda.org/bioconda/panaln and source code: https://github.com/Lilu-guo/Panaln.

Software↗

PubMed1990

Purification, biological properties and partial sequence analysis of 67-kDa calcimedin and its 34-kDa fragment from chicken gizzard.

Calcimedin is a group of proteins, originally isolated from chicken gizzard, which are able to bind to several hydrophobic matrices in the presence of Ca2+. Although the molecular properties have been partially discovered, the physiological functions of calcimedins have not yet been clearly defined. In this study, we describe the isolation and characterization of 67-kDa calcimedin and its 34-kDa fragment from chicken gizzard. Both structural and functional studies establish that 67-kDa calcimedin is a member of the calpactin/lipocortin family: it displays phospholipase A2 inhibitory activity, Ca2(+)-dependent F-actin binding and phospholipid binding activity similar to those of calpactins (lipocortins). By comparing the sequence of 67-kDa calcimedin with the predicted sequence of 67-kDa calelectrin, we concluded that the primary structure of these 67-kDa proteins is highly conserved. In particular, the sequences GLGTDEGAIIXVLTQR and EGAGTDESTLIEIMATR conform with the annexin consensus sequence which is characteristic of the calpactin/lipocortin family. A 34-kDa fragment of 67-kDa calcimedin was also purified and their relatedness has been confirmed by antibody cross-reactivity. The sequence data further support that the 34-kDa fragment is derived from the C-terminal portion of 67-kDa calcimedin by limited proteolysis. The 34-kDa fragment, which contains the annexin consensus sequence, preserves the phospholipase A2 inhibitory activity, and binds F-actin and phospholipids.

Actins↗

PubMed1991

Biologic properties and nucleotide sequence analysis of human papillomavirus type 51.

Human papillomaviruses (HPVs) may be grouped according to the site from which they are isolated and the disease with which they are associated. We recently identified and cloned HPV type 51 (HPV-51) from a low-grade precancerous lesion (G. Nuovo, E. DeVilliers, R. Levine, S. Silverstein, and C. Crum. J. Virol. 62:1452-1455, 1988). Molecular epidemiologic analysis of cervical lesions, including condylomata and low- and high-grade precancers, revealed that HPV-51 was present in about 5% of the samples we examined. We have now determined the complete nucleotide sequence of this virus and compared it with other sequenced HPVs. Our analysis reveals that the 7,808-bp genome is composed of eight open reading frames which are encoded on the same strand and that this virus is most closely related to HPV-31. Sequence comparisons place this virus in the group of high-risk viruses (those with an increased risk of progressing to malignancy) along with HPV-16, -18, -31, and -33. Morphologic transformation experiments demonstrated that HPV-51 had transformation potential and that transformed cells contained RNAs homologous to E6 and E7.

Amino Acid Sequence↗

PubMed1978

Isolation and partial characterization of the allergen in mountain cedar pollen.

A biologically active fraction from a crude extract of mountain cedar pollen has been purified and partially chemically characterized. An ammonium bicarbonate extract of commercial defatted pollen was fractionated by G-100 Sephadex chromatography and the biologically active fraction was found to be homogeneous by polyacrylamide gel electrophoresis and N-terminal amino acid sequence analysis. The biologically active component is a 50,000 dalton protein whose N-terminal amino acid sequence is Asp--Asn--Pro--Ile--Asp. These findings provide a further purified pollen allergen for immunologic studies and the first such purified allergen having clinical significance in a limited geographic region.

Allergens↗

PubMed2025

De novo clustering of large long-read transcriptome datasets with isONclust3.

MOTIVATION: Long-read sequencing techniques can sequence transcripts from end to end, greatly improving our ability to study the transcription process. Although there are several well-established tools for long-read transcriptome analysis, most are reference-based. This limits the analysis of organisms without high-quality reference genomes and samples or genes with high variability (e.g. cancer samples or some gene families). In such settings, analysis using a reference-free method is favorable. The computational problem of clustering long reads by region of common origin is well-established for reference-free transcriptome analysis pipelines. Such clustering enables large datasets to be split roughly by gene family and, therefore, an independent analysis of each cluster. There exist tools for this. However, none of those tools can efficiently process the large amount of reads that are now generated by long-read sequencing technologies. RESULTS: We present isONclust3, an improved algorithm over isONclust and isONclust2, to cluster massive long-read transcriptome datasets into gene families. Like isONclust, isONclust3 represents each cluster with a set of minimizers. However, unlike other approaches, isONclust3 dynamically updates the cluster representation during clustering by adding high-confidence minimizers from new reads assigned to the cluster and employs an iterative cluster-merging step. We show that isONclust3 yields results with higher or comparable quality to state-of-the-art algorithms but is 10-100 times faster on large datasets. Also, using a 256 Gb computing node, isONclust3 was the only tool that could cluster 37 million PacBio reads, which is a typical throughput of the recent PacBio Revio sequencing machine. AVAILABILITY AND IMPLEMENTATION: https://github.com/aljpetri/isONclust3.

Algorithms↗

PubMed2026

REACTOR: REgulon Activity analysis and Comparison Tool for single-cell transcriptOmics Research.

SUMMARY: We introduce REACTOR, a computational tool designed to detect differential activity of transcriptional regulators and their target genes (regulons) in single-cell RNA-sequencing data. It expands the currently available framework for regulon analysis by introducing a robust statistical test to detect differential regulon activity between conditions, such as disease versus control, with multiple replicates. By contrasting different conditions, REACTOR enables identification of key condition- and cell type-specific regulons. To demonstrate the use of REACTOR, we illustrate its performance in a publicly available COVID-19 dataset. AVAILABILITY: REACTOR R-package together with an implementation vignette are available at https://www.github.com/elolab/REACTOR.

Regulon↗

PubMed2025

LINNAEUS: Simultaneous Single-Cell Lineage Tracing and Cell Type Identification.

A key goal of biology is to understand the origin of the many cell types that can be observed during diverse processes such as development, regeneration, and disease. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ. However, organizing the resulting taxonomy of cell types into lineage trees to understand the origins of cell states and relationships between cells remains challenging. Here we present LINNAEUS (Spanjaard et al, Nat Biotechnol 36:469-473. https://doi.org/10.1038/nbt.4124 , 2018; Hu et al, Nat Genet 54:1227-1237. https://doi.org/10.1038/s41588-022-01129-5 , 2022) (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences)-a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, LINNAEUS can be used to reconstruct organism-wide single-cell lineage trees. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

Single-Cell Analysis↗

PubMed2026

Comparative essentialome analysis of six Pectobacteriaceae strains using the TNSEEK pipeline identifies conserved and strain-specific fitness determinants.

Transposon sequencing (Tn-seq) is a powerful technique for defining the essential genes required for bacterial survival. However, gene essentiality can vary significantly across taxonomic levels, and comparing large Tn-seq datasets from multiple strains presents considerable analytical challenges. To address this, we developed TNSEEK, a fully automated bioinformatics pipeline for the systematic and comparative analysis of Tn-seq experiments. We applied TNSEEK to analyse newly generated data for six soft rot Pectobacteriaceae strains, encompassing species from the Dickeya and Pectobacterium genera, grown in a rich medium. This approach identified a core essentialome of 225 genes, primarily involved in fundamental cellular maintenance, conserved across all 6 strains, a set comparable in size to that of the neighbouring Enterobacteriaceae family. Only a few genus-specific essential genes were found, highlighting interesting distinct metabolic capabilities between Dickeya and Pectobacterium genera. In striking contrast, we discovered a large variable essentialome comprising 181 strain-specific genes, many of which are of unknown function. A portion of these strain-specific essential genes are components of defence systems and prophage genomic regions. The unexpected essentiality of selected components of these modules is consistent with cellular dependency on cognate toxic, restriction or immunity functions encoded by defence-associated loci under the tested growth condition. Furthermore, a comparison with the Escherichia coli essentialome demonstrates that discrepancies in gene essentiality can often be attributed to differences in growth conditions, particularly temperature, as well as variations in genetic redundancy. In conclusion, the TNSEEK pipeline provides a reproducible framework for comparative analysis of mariner/Himar1 Tn-seq datasets across multiple strains.

Pectobacterium↗

PubMed2025

NanoASV: a snakemake workflow for reproducible field-based Nanopore full-length 16S metabarcoding amplicon data analysis.

SUMMARY: NanoASV is a conda environment and snakemake-based workflow using state-of-the-art bioinformatics software to process full-length SSU rRNA (16S/18S) amplicons acquired with Oxford Nanopore Sequencing technology. Its strength lies in reproducibility, portability, and the possibility to run offline, allowing in-field analysis. It can be installed on the Nanopore MK1C sequencing device and process data locally. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are freely available at https://github.com/ImagoXV/NanoASV and Zenodo archive at https://doi.org/10.5281/zenodo.14730742.

Software↗

PubMed2025

Global Profiling and Analysis of 5' Monophosphorylated mRNA Decay Intermediates.

During RNA turnover, the action of endo- and exo-ribonucleases can yield RNA decay intermediates with specific 5' ends. These RNA decay intermediates have been demonstrated to be the outcome of decapping, microRNA-directed endo-cleavage, or the protected fragments of ribosomes and exon-junction complexes. Therefore, global analysis of RNA decay intermediates can facilitate studies of many RNA decay pathways. In this chapter, we describe a high-throughput sequencing protocol named parallel analysis of RNA ends (PARE), which allows genome-wide profiling of 5' monophosphorylated mRNA decay intermediates from plants or other eukaryotes. Also, we present the tools and scripts necessary for the proper analysis of RNA degradome data obtained from the PARE method. Details and modifications of library construction procedures and bioinformatic analyses to optimize sequencing quality and cope with emerging sequencing platforms and findings are highlighted.

RNA Stability↗

PubMed2026

Nested co-expression network analysis identifies compact gene clusters in a black box.

MOTIVATION: Digital analysis of biological systems requires methods capable of identifying both broad and nested gene modules reflecting complex biological processes. Existing transcriptomic methods often miss compact gene sets corresponding to subprocesses in specialized cell types, limiting insights into functional heterogeneity. RESULTS: We present Nested-WGCNA, a two-stage unsupervised network analysis algorithm designed to identify coarse-grained and fine-grained gene modules. Applied to bulk RNA-Seq data, Nested-WGCNA reveals stable modules reproducible across datasets. When validated against scRNA-Seq data, these modules correspond to both major and minor immune cell subtypes. Application to immunotherapy response datasets uncovers predictive and prognostic biomarkers, highlighting its utility in treatment stratification and biomarker discovery. AVAILABILITY: The NestedWGCNA source code and analysis pipeline are available on GitHub (https://github.com/ilyada/NestedWGCNA) and archived on Zenodo (https://doi.org/10.5281/zenodo.18959244).

Algorithms↗

PubMed2026

Likelihood-based optimization enables accurate copy number estimation for paralogous genes using exome data.

MOTIVATION: Exome sequencing is widely used for genetic studies; however, accurate detection of copy number variants (CNV) in paralogous genes is challenging due to short-read mapping ambiguity and extensive copy-number variation. The human genome contains several hundred paralogous genes, many of which are known to harbor disease-associated CNVs. Existing exome CNV callers are primarily designed for rare CNV detection in uniquely mappable regions and are not well-suited for paralogous genes. METHODS: We describe a computational method (EdgeCopy) for copy number profiling of paralogous genes using whole-exome sequence data. EdgeCopy aggregates reads mapped to all copies of paralogous genes and relates observed read depth to copy number for multiple exome samples using an approximate composite likelihood function. The likelihood function is optimized using numerical optimization to obtain gene-level fractional copy number estimates that are discretized and refined using a Hidden Markov Model to obtain exon-level copy number estimates. RESULTS: Benchmarking of Edgecopy using experimental copy number data showed high concordance (mean = 0.973) for six disease-associated paralogous genes. We evaluated performance using whole-exome data from approximately 2400 samples across five continental populations from the 1000 Genomes Project. EdgeCopy shows robust concordance with whole-genome sequencing based estimates (0.974-0.982) across populations and 130 paralogous genes spanning a wide range of copy-number variation. In comparison, copy number analysis using a state-of-the-art exome CNV caller failed to estimate copy number for paralogous genes with very high mapping ambiguity and showed much lower concordance (0.565) for CNV events compared to EdgeCopy (0.908). AVAILABILITY: EdgeCopy is freely available at https://github.com/vibansal-lab/edgecopy.

Humans↗

PubMed2025

Expanding and improving analyses of nucleotide recoding RNA-seq experiments with the EZbakR suite.

Nucleotide recoding RNA sequencing methods (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) are powerful approaches for assaying transcript population dynamics. In addition, these methods have been extended to probe a host of regulated steps in the RNA life cycle. Current bioinformatic tools significantly constrain analyses of NR-seq data. To address this limitation, we developed EZbakR (https://github.com/isaacvock/EZbakR), an R package to facilitate a more comprehensive set of NR-seq analyses, and fastq2EZbakR (https://github.com/isaacvock/fastq2EZbakR), a Snakemake pipeline for flexible preprocessing of NR-seq datasets, collectively referred to as the EZbakR suite. Together, these tools generalize many aspects of the NR-seq analysis workflow. The fastq2EZbakR pipeline can assign reads to a diverse set of genomic features (e.g., genes, exons, splice junctions), and EZbakR can perform analyses on any combination of these features. EZbakR extends standard NR-seq mutational modeling to support multi-label analyses (e.g., s4U and s6G dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. EZbakR also generalizes dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, EZbakR implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling. The EZbakR suite will thus allow researchers to make full, effective use of NR-seq data.

Software↗

PubMed2025

vcfgl: a flexible genotype likelihood simulator for VCF/BCF files.

MOTIVATION: Accurate quantification of genotype uncertainty is pivotal in ensuring the reliability of genetic inferences drawn from NGS data. Genotype uncertainty is typically modeled using Genotype Likelihoods (GLs), which can help propagate measures of statistical uncertainty in base calls to downstream analyses. However, the effects of errors and biases in the estimation of GLs, introduced by biases in the original base call quality scores or the discretization of quality scores, as well as the choice of the GL model, remain under-explored. RESULTS: We present vcfgl, a versatile tool for simulating genotype likelihoods associated with simulated read data. It offers a framework for researchers to simulate and investigate the uncertainties and biases associated with the quantification of uncertainty, thereby facilitating a deeper understanding of their impacts on downstream analytical methods. Through simulations, we demonstrate the utility of vcfgl in benchmarking GL-based methods. The program can calculate GLs using various widely used genotype likelihood models and can simulate the errors in quality scores using a Beta distribution. It is compatible with modern simulators such as msprime and SLiM, and can output data in pileup, Variant Call Format (VCF)/BCF, and genomic VCF file formats, supporting a wide range of applications. The vcfgl program is freely available as an efficient and user-friendly software written in C/C++. AVAILABILITY AND IMPLEMENTATION: vcfgl is freely available at https://github.com/isinaltinkaya/vcfgl.

Software↗

PubMed2025

Locality-aware pooling enhances protein language model performance across varied applications.

MOTIVATION: Protein language models (PLMs) are amongst the most exciting recent advances for characterizing protein sequences, and have enabled a diverse set of applications, including structure determination, functional property prediction, and mutation impact assessment, all from single protein sequences alone. State-of-the-art PLMs leverage transformer architectures originally developed for natural language processing, and are pre-trained on large protein databases to generate contextualized representations of individual amino acids. To harness the power of these PLMs to predict protein-level properties, these per-residue embeddings are typically "pooled" to fixed-size vectors that are further utilized in downstream prediction networks. Common pooling strategies include Cls-Pooling and Avg-Pooling, but neither of these approaches can capture the local substructures and long-range interactions observed in proteins. RESULTS: We propose the use of attention pooling, which can naturally capture these important features of proteins. To make the expensive attention operator (quadratic in the length of the input protein) feasible in practice, we introduce bag-of-mer pooling, or BoM-Pooling, a locality-aware hierarchical pooling technique that combines windowed average pooling with attention pooling. We empirically demonstrate that both full attention pooling and BoM-Pooling outperform previous pooling strategies on three important, diverse tasks: (i) predicting the activities of two proteins as they are varied; (ii) detecting remote homologs; and (iii) predicting signaling protein interactions with peptides. Overall, our work highlights the advantages of biologically inspired pooling techniques in protein sequence modeling and is a step toward more effective adaptations of language models in biological settings. AVAILABILITY AND IMPLEMENTATION: https://github.com/Singh-Lab/bom-pooling.

Natural Language Processing↗

PubMed2025

Fast and flexible minimizer digestion with digest.

SUMMARY: Minimizer digestion is an increasingly common component of bioinformatics tools, including tools for de Bruijn graph assembly and sequence classification. We describe a new open source tool and library to facilitate efficient digestion of genomic sequences. It can produce digests based on the related ideas of minimizers, modimizers or syncmers. Digest uses efficient data structures, scales well to many threads, and produces digests with expected spacings between digested elements. AVAILABILITY AND IMPLEMENTATION: Digest is implemented in C++17 with a Python API, and is available open-source at https://github.com/VeryAmazed/digest. The python library is available on Bioconda. Rust bindings are available as a public crate at https://crates.io/crates/digest-rs.

Software↗

PubMed2026

seq2ribo: structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

MOTIVATION: Ribosome dynamics are vital in the process of protein expression. Current methods rely on ribosome profiling (Ribo-seq), RNA-seq profiles, and full genomic context. This restricts their use in de novo sequence design, like messenger RNA (mRNA) vaccines. Simulation-only approaches like the Totally Asymmetric Simple Exclusion Process (TASEP) oversimplify translation by focusing solely on codon elongation times. RESULTS: We present seq2ribo, a hybrid simulation and machine learning framework that predicts ribosome A-site locations using only an mRNA sequence as input. Our method first employs a novel structure-aware TASEP (sTASEP), which models translation using a comprehensive set of fitted parameters that include codon wait times and structural features, such as local angles, base-pairing, and discrete positional buckets. The ribosome locations generated by sTASEP are then processed by a polisher model, which learns to refine the simulated ribosome distributions. seq2ribo provides high-fidelity predictions of ribosome locations across diverse cell types (iPSC, HEK293, LCL, and RPE-1), significantly outperforming baselines. seq2ribo is the first method to achieve meaningful positional correlation with observed ribosome profiles from sequence alone, reaching transcript-level Pearson correlations up to 0.920 and within-transcript shape correlations up to 0.186, where all baselines yield near-zero values on these metrics. seq2ribo also reduces elementwise error by up to 37.7% relative to the sequence-only Translatomer baseline. By adding a task-specific head, seq2ribo achieves Pearson correlations up to 0.732 with experimental translation efficiency (TE) across several cell lines, and up to 0.903 with measured protein expression. By operating from sequence alone, seq2ribo provides a new tool for synthetic biology, enabling the rational design and optimization of mRNA sequences without the need for expression-level data or genomic context. AVAILABILITY: seq2ribo is available at https://github.com/Kingsford-Group/seq2ribo.

Machine Learning↗