In the manufacturing sector, establishing a system for fault diagnosis and analysis on production lines is of paramount importance. This research presents a novel hierarchical anomaly management method, addressing issues such as data category imbalance, challenges in detecting abnormal signs. The study utilizes the LSTM-Rola (rolling accumulation) approach, specifically designed for time series forecasting, to effectively identify anomalies. This method employs a stacked LSTM structure in an encoder-decoder framework, combining single-step and multi-step predictions to enhance both short-term and long-term forecasting capabilities. The anomaly detection aspect of the method incorporates an accumulation of abnormal occurrences and categorizes anomalies into different levels. This strategy not only improves detection accuracy but also resonates with traditional fault mechanism theories, facilitating easier interpretation of the model. Additionally, the paper includes comparative studies on various normalization methods and early warning accumulation tactics, demonstrating the model's effectiveness. The model shows remarkable performance in time series anomaly detection, achieving an F_0.5 score of 0.8259, a high precision of 91.8%, and a recall rate of approximately 60%.

BACKGROUND: Horizontal gene transfer (HGT) is an important driver in genome evolution, gain-of-function, and metabolic adaptation to environmental niches. Genome-wide identification of putative HGT events has become increasingly practical, given the rapid growth of genomic data. However, existing HGT analysis toolboxes are not widely used, limited by their inability to perform phylogenetic reconstruction to explore potential donors, and the detection of HGT from both evolutionarily distant and closely related species. RESULTS: In this study, we have developed HGTphyloDetect, which is a versatile computational toolbox that combines high-throughput analysis with phylogenetic inference, to facilitate comprehensive investigation of HGT events. Two case studies with Saccharomyces cerevisiae and Candida versatilis demonstrate the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, HGTphyloDetect enables phylogenetic analysis to illustrate a likely path of gene transmission among the evolutionarily distant or closely related species. CONCLUSIONS: The HGTphyloDetect computational toolbox is designed for ease of use and can accurately find HGT events with a very low false discovery rate in a high-throughput manner. The HGTphyloDetect toolbox and its related user tutorial are freely available at https://github.com/SysBioChalmers/HGTphyloDetect.

SUMMARY: The field of synthetic biology lacks a comprehensive knowledgebase for selecting synthetic target molecules according to their functions, economic applications and known biosynthetic pathways. We implemented ChemHub, a knowledgebase containing >90 000 chemicals and their functions, along with related biosynthesis information for these chemicals that was manually extracted from >600 000 published studies by more than 100 people over the past 10 years. AVAILABILITY AND IMPLEMENTATION: Multiple algorithms were implemented to enable biosynthetic pathway design and precursor discovery, which can support investigation of the biosynthetic potential of these functional chemicals. ChemHub is freely available at: http://www.rxnfinder.org/chemhub/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A proliferation of chemical, reaction and enzyme databases, new computational methods and software tools for data-driven rational biosynthesis design have emerged in recent years. With the coming of the era of big data, particularly in the bio-medical field, data-driven rational biosynthesis design could potentially be useful to construct target-oriented chassis organisms. Engineering the complicated metabolic systems of chassis organisms to biosynthesize target molecules from inexpensive biomass is the main goal of cell factory design. The process of data-driven cell factory design could be divided into several parts: (1) target molecule selection; (2) metabolic reaction and pathway design; (3) prediction of novel enzymes based on protein domain and structure transformation of biosynthetic reactions; (4) construction of large-scale DNA for metabolic pathways; and (5) DNA assembly methods and visualization tools. The construction of a one-stop cell factory system could achieve automated design from the molecule level to the chassis level. In this article, we outline data-driven rational biosynthesis design steps and provide an overview of related tools in individual steps.

SUMMARY: The biosynthetic ability of living organisms has important applications in producing bulk chemicals, biofuels and natural products. Based on the most comprehensive biosynthesis knowledgebase, a computational system, BCSExplorer, is proposed to discover the unexplored chemical space using nature's biosynthetic potential. BCSExplorer first integrates the most comprehensive biosynthetic reaction database with 280 000 biochemical reactions and 60 000 chemicals biosynthesized globally over the past 130 years. Second, in this study, a biosynthesis tree is computed for a starting chemical molecule based on a comprehensive biotransformation rule library covering almost all biosynthetic possibilities, in which redundant rules are removed using a new algorithm. Moreover, biosynthesis feasibility, drug-likeness and toxicity analysis of a new generation of compounds will be pursued in further studies to meet various needs. BCSExplorer represents a novel method to explore biosynthetically available chemical space. AVAILABILITY AND IMPLEMENTATION: BCSExplorer is available at: http://www.rxnfinder.org/bcsexplorer/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The development of sequencing technologies has generated large amounts of protein sequence data. The automated prediction of the enzymatic reactions of uncharacterized proteins is a major challenge in the field of bioinformatics. Here, we present Bio2Rxn as a web-based tool to provide putative enzymatic reaction predictions for uncharacterized protein sequences. Bio2Rxn adopts a consensus strategy by incorporating six types of enzyme prediction tools. It allows for the efficient integration of these computational resources to maximize the accuracy and comprehensiveness of enzymatic reaction predictions, which facilitates the characterization of the functional roles of target proteins in metabolism. Bio2Rxn further links the enzyme function prediction with more than 300 000 enzymatic reactions, which were manually curated by more than 100 people over the past 9 years from more than 580 000 publications. AVAILABILITY AND IMPLEMENTATION: Bio2Rxn is available at: http://design.rxnfinder.org/bio2rxn/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Synthetic biology has a great potential to produce high value pharmaceuticals, commodities or bulk chemicals. However, many biosynthetic target molecules have no defined or predicted biosynthetic pathways. Biosynthetic precursors are crucial to create biosynthetic pathways. Thus computer-assisted tools for precursor identification are urgently needed to develop novel metabolic pathways. To this end, we present PrecursorFinder, a computational tool that explores biosynthetic precursors for the query target molecules using chemical structure, similarity as well as MCS (maximum common substructure). This platform comprises more than 60 000 compounds biosynthesized for being promising precursors, which are extracted from >500 000 scientific literatures and manually curated by more than 100 people over the past 8 years. The PrecursorFinder could speed up the process of biosynthesis research and make synthetic biology or metabolic engineering more efficient. AVAILABILITY AND IMPLEMENTATION: PrecursorFinder is available at: http://www.rxnfinder.org/precursorfinder/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.