NanoCon: contrastive learning-based deep hybrid network for nanopore methylation detection.

MOTIVATION: 5-Methylcytosine (5mC), a fundamental element of DNA methylation in eukaryotes, plays a vital role in gene expression regulation, embryonic development, and other biological processes. Although several computational methods have been proposed for detecting the base modifications in DNA like 5mC sites from Nanopore sequencing data, they face challenges including sensitivity to noise, and ignoring the imbalanced distribution of methylation sites in real-world scenarios. RESULTS: Here, we develop NanoCon, a deep hybrid network coupled with contrastive learning strategy to detect 5mC methylation sites from Nanopore reads. In particular, we adopted a contrastive learning module to alleviate the issues caused by imbalanced data distribution in nanopore sequencing, offering a more accurate and robust detection of 5mC sites. Evaluation results demonstrate that NanoCon outperforms existing methods, highlighting its potential as a valuable tool in genomic sequencing and methylation prediction. In addition, we also verified the effectiveness of our representation learning ability on two datasets by visualizing the dimension reduction of the features of methylation and nonmethylation sites from our NanoCon. Furthermore, cross-species and cross-5mC methylation motifs experiments indicated the robustness and the ability to perform transfer learning of our model. We hope this work can contribute to the community by providing a powerful and reliable solution for 5mC site detection in genomic studies. AVAILABILITY AND IMPLEMENTATION: The project code is available at https://github.com/Challis-yin/NanoCon.

One-Pot Three-Component Synthesis of Indolyl-4H-chromene-3-sulfonyl Fluoride: A Class of Important Pharmacophore.

A one-pot, sequential three-component reaction between salicylaldehyde, indole, and 2-bromoprop-2-ene-1-sulfonyl fluoride (BPESF) has been demonstrated for the synthesis of sulfonyl fluoride substituted 4H-chromene derivatives in moderate to excellent yields (45%-94%). This one-pot sequential method features easily available starting materials, wide substrate scope, mild conditions, and great efficiency.

The Impact of the Soil Survival of the Pathogen of Fusarium Wilt on Soil Nutrient Cycling Mediated by Microorganisms.

Fusarium wilt of Momordica charantia in the greenhouse is one of the most severe crop diseases in Shandong Province, P.R. China. This study aimed to investigate the mechanisms of accumulation and long-term survival of the pathogen in naturally pathogenic soils. Soil physicochemical properties were tested after applying a highly virulent strain of Fusarium wilt to M. charantia in an artificial disease nursery. The functional structure of soil microorganisms was analyzed through amplicon sequencing. The highly virulent strain SG-15 of F. oxysporum f. sp. momordicae was found to cause Fusarium wilt in M. charantia in Shandong Province. The strain SG-15 could not infect 14 non-host crops, including Solanum melongena and Lycopersicon esculentum, but it had varying degrees of pathogenicity towards 11 M. charantia varieties. In the artificial disease nursery for Fusarium wilt of M. charantia, the F. oxysporum was distributed in the soil to a depth of 0-40 cm and was mainly distributed in crop residues at 0-10 cm depth. During crop growth, F. oxysporum primarily grows and reproduces in susceptible host plants, rather than disease-resistant hosts and non-host crops. The colonization of the pathogen of Fusarium wilt significantly changed the soil physicochemical properties, the functional structure of soil microorganisms and the circulation of soil elements such as carbon, nitrogen, phosphorus and sulfur. Soil pH value, organic matter content, available iron content, available manganese content, FDA hydrolase activity and polyphenol oxidase activity were significantly correlated with the relative abundance of Fusarium wilt pathogens in the soil. In general, this study suggests that susceptible host plants facilitate the accumulation of Fusarium wilt pathogens in the soil. These pathogens can mediate the decomposition process of plant residues, particularly those of diseased plants, and indirectly or directly affect soil's chemical properties.

MolFPG: Multi-level fingerprint-based Graph Transformer for accurate and robust drug toxicity prediction.

Drug toxicity prediction is essential to drug development, which can help screen compounds with potential toxicity and reduce the cost and risk of animal experiments and clinical trials. However, traditional handcrafted feature-based and molecular-graph-based approaches are insufficient for molecular representation learning. To address the problem, we developed an innovative molecular fingerprint Graph Transformer framework (MolFPG) with a global-aware module for interpretable toxicity prediction. Our approach encodes compounds using multiple molecular fingerprinting techniques and integrates Graph Transformer-based molecular representation for feature learning and toxic prediction. Experimental results show that our proposed approach has high accuracy and reliability in predicting drug toxicity. In addition, we explored the relationship between drug features and toxicity through an interpretive analysis approach, which improved the interpretability of the approach. Our results highlight the potential of Graph Transformers and multi-level fingerprints for accelerating the drug discovery process by reliably, effectively alarming drug safety. We believe that our study will provide vital support and reference for further development in the field of drug development and toxicity assessment.

DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis.

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learning architectures to answer any biological question. Specifically, given any biological sequence data, DeepBIO supports a total of 42 state-of-the-art deep-learning algorithms for model training, comparison, optimization and evaluation in a fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for predictive models covering several aspects, such as model interpretability, feature analysis and functional sequential region discovery. Additionally, DeepBIO supports nine base-level functional annotation tasks using deep-learning architectures, with comprehensive interpretations and graphical visualizations to validate the reliability of annotated sites. Empowered by high-performance computers, DeepBIO allows ultra-fast prediction with up to million-scale sequence data in a few hours, demonstrating its usability in real application scenarios. Case study results show that DeepBIO provides an accurate, robust and interpretable prediction, demonstrating the power of deep learning in biological sequence functional analysis. Overall, we expect DeepBIO to ensure the reproducibility of deep-learning biological sequence analysis, lessen the programming and hardware burden for biologists and provide meaningful functional insights at both the sequence level and base level from biological sequences alone. DeepBIO is publicly available at https://inner.wei-group.net/DeepBIO.

The development of next-generation sequencing techniques has led to an exponential increase in the amount of biological sequence data accessible. It naturally poses a fundamental challenge­how to build the relationships from such large-scale sequences to their functions. In this work, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. It enables researchers to develop new deep-learning architectures to answer any biological question in a fully automated pipeline. We expect DeepBIO to ensure the reproducibility of deep-learning-based biological sequence analysis, lessen the programming and hardware burden for biologists and provide meaningful functional insights at both the sequence level and base level from biological sequences alone.

scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods.

With the advent of single-cell RNA sequencing (scRNA-seq), one major challenging is the so-called 'dropout' events that distort gene expression and remarkably influence downstream analysis in single-cell transcriptome. To address this issue, much effort has been done and several scRNA-seq imputation methods were developed with two categories: model-based and deep learning-based. However, comprehensively and systematically comparing existing methods are still lacking. In this work, we use six simulated and two real scRNA-seq datasets to comprehensively evaluate and compare a total of 12 available imputation methods from the following four aspects: (i) gene expression recovering, (ii) cell clustering, (iii) gene differential expression, and (iv) cellular trajectory reconstruction. We demonstrate that deep learning-based approaches generally exhibit better overall performance than model-based approaches under major benchmarking comparison, indicating the power of deep learning for imputation. Importantly, we built scIMC (single-cell Imputation Methods Comparison platform), the first online platform that integrates all available state-of-the-art imputation methods for benchmarking comparison and visualization analysis, which is expected to be a convenient and useful tool for researchers of interest. It is now freely accessible via https://server.wei-group.net/scIMC/.