Three-dimensional (3D) genomic architecture is crucial for the regulation of different biological processes, and the study of multi-way chromatin structures represents a cutting-edge area in this field. Pore-C is an advanced experimental technique integrating chromosome conformation capture (3C) with Nanopore long-read sequencing, which can effectively capture complex multi-way chromatin contacts across the genome. Due to the absence of comprehensive and dedicated tools, the analysis of Pore-C data remains challenging, restricting its broader application. To address this limitation, we developed Pore-C Pipeline Toolbox (PPL-Toolbox), a specialized software for a comprehensive analysis of multi-way chromatin interactions from Pore-C data. PPL-Toolbox incorporates a suite of optimized modules, including quality control, multi-way interaction extraction, noise reduction for multi-way contacts, advanced visualization tools for multi-way contacts, and haplotype map generation. Evaluation on both simulated and real datasets demonstrates that the PPL-Toolbox outperforms existing methods by providing a more comprehensive set of results. PPL-Toolbox is expected to become a powerful and versatile tool, driving advance in multi-way 3D genomics research, and enabling new discoveries in the field. PPL-Toolbox is publicly available on GitHub (https://github.com/versarchey/PPL-Toolbox).

Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which is crucial for understanding molecular mechanisms in biological processes. High-throughput technologies like ChIA-PET, Hi-C, and their derivatives methods have greatly enhanced our knowledge of 3D chromatin architecture. However, the chromatin interaction mechanisms remain largely unexplored. Deep learning, with its powerful feature extraction and pattern recognition capabilities, offers a promising approach for integrating multi-omics data, to build accurate predictive models of chromatin interaction matrices. This review systematically summarizes recent advances in chromatin interaction matrix prediction models. By integrating DNA sequences and epigenetic signals, we investigate the latest developments in these methods. This article details various models, focusing on how one-dimensional (1D) information transforms into the 3D structure chromatin interactions, and how the integration of different deep learning modules specifically affects model accuracy. Additionally, we discuss the critical role of DNA sequence information and epigenetic markers in shaping 3D genome interaction patterns. Finally, this review addresses the challenges in predicting chromatin interaction matrices, in order to improve the precise mapping of chromatin interaction matrices and DNA sequence, and supporting the transformation and theoretical development of 3D genomics across biological systems.

Topologically associated domains (TADs) are one of the important higher order chromatin structures with various sizes in the eukaryotic genomes. TAD boundaries, as the flanking regions between adjacent domains, can restrict the interactions of regulatory elements, including enhancers and promoters, and are generally dynamic and variable in different cells. However, the influence of sequence and epigenetic profile-based features in the identification of TAD boundaries is largely unknown. In this work, we proposed a method called pTADS (prediction of TAD boundary and strength), to predict TAD boundaries and boundary strength across multiple cell lines with DNA sequence and epigenetic profile information. The performance was assessed in seven cell lines and three TAD calling methods. The results demonstrate that the TAD boundary can be well predicted by the selected shared features across multiple cell lines. Especially, the model can be transferable to predict the TAD boundary from one cell line to other cell lines. The boundary strength can be characterized by boundary score with good performance. The predicted TAD boundary and TAD boundary strength are further confirmed by three Hi-C contact matrix-based methods across multiple cell lines. The codes and datasets are available at https://github.com/chrom3DEpi/pTADS.

MOTIVATION: Small non-coding RNAs (ncRNAs), especially microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs), play key roles in many biological processes. However, only a few tools can be used to develop the optimal primer or probe design for the expression profile of small ncRNAs. Here, we developed sRNAPrimerDB, the first automated primer designing and query web service for small ncRNAs. RESULTS: The primer online designing module of sRNAPrimerDB is composed of primer design algorithms and quality evaluation of the polymerase chain reaction (PCR) primer. Five types of primers, namely, generic or specific reverse transcription primers, specific PCR primers pairs, TaqMan probe, double-hairpin probe and hybridization probe for different small ncRNA detection methods, can be designed and searched using this service. The quality of PCR primers is further evaluated using melting temperature, primer dimer, hairpin structure and specificity. Moreover, the sequence and size of each amplicon are also provided for the subsequent experiment verification. At present, 531 306 and 2 941 669 primer pairs exist across 223 species for miRNAs and piRNAs, respectively, according to sRNAPrimerDB. Several primers designed by sRNAPrimerDB are further successfully validated by subsequent experiments. AVAILABILITY AND IMPLEMENTATION: sRNAPrimerDB is a valuable platform that can be used to detect small ncRNAs. This module can be publicly accessible at http://www.srnaprimerdb.com or http://123.57.239.141. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Nuclear receptors (NRs) are a class of transcription factors playing important roles in various biological processes. An NR often impacts numerous genes and different NRs share overlapped target networks. To fulfil the need for a database incorporating binding sites of different NRs at various conditions for easy comparison and visualization to improve our understanding of NR binding mechanisms, we have developed NURBS, a database for experimental and predicted nuclear receptor binding sites of mouse (NURBS). NURBS currently contains binding sites across the whole-mouse genome of 8 NRs identified in 40 chromatin immunoprecipitation with massively parallel DNA sequencing experiments. All datasets are processed using a widely used procedure and same statistical criteria to ensure the binding sites derived from different datasets are comparable. NURBS also provides predicted binding sites using NR-HMM, a Hidden Markov Model (HMM) model. AVAILABILITY: The GBrowse-based user interface of NURBS is freely accessible at http://shark.abl.ku.edu/nurbs/. NR-HMM and all results can be downloaded for free at the website. CONTACT: [email protected]

BACKGROUND: Protein aggregation is a significant problem in the biopharmaceutical industry (protein drug stability) and is associated medically with over 40 human diseases. Although a number of computational models have been developed for predicting aggregation propensity and identifying aggregation-prone regions in proteins, little systematic research has been done to determine physicochemical properties relevant to aggregation and their relative importance to this important process. Such studies may result in not only accurately predicting peptide aggregation propensities and identifying aggregation prone regions in proteins, but also aid in discovering additional underlying mechanisms governing this process. RESULTS: We use two feature selection algorithms to identify 16 features, out of a total of 560 physicochemical properties, presumably important to protein aggregation. Two predictors (ProA-SVM and ProA-RF) using selected features are built for predicting peptide aggregation propensity and identifying aggregation prone regions in proteins. Both methods are compared favourably to other state-of-the-art algorithms in cross validation. The identified important properties are fairly consistent with previous studies and bring some new insights into protein and peptide aggregation. One interesting new finding is that aggregation prone peptide sequences have similar properties to signal peptide and signal anchor sequences. CONCLUSIONS: Both predictors are implemented in a freely available web application (http://www.abl.ku.edu/ProA/). We suggest that the quaternary structure of protein aggregates, especially soluble oligomers, may allow the formation of new molecular recognition signals that guide aggregate targeting to specific cellular sites.

MOTIVATION: Complex diseases induce perturbations to interaction and regulation networks in living systems, resulting in dynamic equilibrium states that differ for different diseases and also normal states. Thus identifying gene expression patterns corresponding to different equilibrium states is of great benefit to the diagnosis and treatment of complex diseases. However, it remains a major challenge to deal with the high dimensionality and small size of available complex disease gene expression datasets currently used for discovering gene expression patterns. RESULTS: Here we present a phase-only correlation (POC) based classification method for recognizing the type of complex diseases. First, a virtual sample template is constructed for each subclass by averaging all samples of each subclass in a training dataset. Then the label of a test sample is determined by measuring the similarity between the test sample and each template. This novel method can detect the similarity of overall patterns emerged from the differentially expressed genes or proteins while ignoring small mismatches. CONCLUSIONS: The experimental results obtained on seven publicly available complex disease datasets including microarray and protein array data demonstrate that the proposed POC-based disease classification method is effective and robust for diagnosing complex diseases with regard to the number of initially selected features, and its recognition accuracy is better than or comparable to other state-of-the-art machine learning methods. In addition, the proposed method does not require parameter tuning and data scaling, which can effectively reduce the occurrence of over-fitting and bias.

MOTIVATION: Protein residue-residue contact prediction can be useful in predicting protein 3D structures. Current algorithms for such a purpose leave room for improvement. RESULTS: We develop ProC_S3, a set of Random Forest algorithm-based models, for predicting residue-residue contact maps. The models are constructed based on a collection of 1490 non-redundant, high-resolution protein structures using >1280 sequence-based features. A new amino acid residue contact propensity matrix and a new set of seven amino acid groups based on contact preference are developed and used in ProC_S3. ProC_S3 delivers a 3-fold cross-validated accuracy of 26.9% with coverage of 4.7% for top L/5 predictions (L is the number of residues in a protein) of long-range contacts (sequence separation ≥24). Further benchmark tests deliver an accuracy of 29.7% and coverage of 5.6% for an independent set of 329 proteins. In the recently completed Ninth Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP9), ProC_S3 is ranked as No. 1, No. 3, and No. 2 accuracies in the top L/5, L/10 and best 5 predictions of long-range contacts, respectively, among 18 automatic prediction servers. AVAILABILITY: http://www.abl.ku.edu/proc/proc_s3.html. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.