A deep learning approach for orphan gene identification in moso bamboo (Phyllostachys edulis) based on the CNN + Transformer model

ORFanID: A web-based search engine for the discovery and identification of orphan and taxonomically restricted genes

With the numerous genomes sequenced today, it has been revealed that a noteworthy percentage of genes in a given taxon of organisms in the phylogenetic tree of life do not have orthologous sequences in other taxa. These sequences are commonly referred to as "orphans" or "ORFans" if found as single occurrences in a single species or as "taxonomically restricted genes" (TRGs) when found at higher taxonomic levels. Quantitative and collective studies of these genes are necessary for understanding their biological origins. However, the current software for identifying orphan genes is limited in its functionality, database search range, and very complex algorithmically. Thus, researchers studying orphan genes must harvest their data from many disparate sources. ORFanID is a graphical web-based search engine that facilitates the efficient identification of both orphan genes and TRGs at all taxonomic levels, from DNA or amino acid sequences in the NCBI database cluster and other large bioinformatics repositories. The software allows users to identify genes that are unique to any taxonomic rank, from species to domain, using NCBI systematic classifiers. It provides control over NCBI database search parameters, and the results are presented in a spreadsheet as well as a graphical display. The tables in the software are sortable, and results can be filtered using the fuzzy search functionality. The visual presentation can be expanded and collapsed by the taxonomic tree to its various branches. Example results from searches on five species and gene expression data from specific orphan genes are provided in the Supplementary Information.

Bamboo leaf: A review of traditional medicinal property, phytochemistry, pharmacology, and purification technology

ETHNOPHARMACOLOGICAL RELEVANCE

Bamboos are perennial evergreen plants that belong to the subfamily Bambusoideae of the true grass family Poaceae, with more than thousands of species distributed around the world. They are used as a traditional medicine with demonstrated effects of anti-oxidation, free radical scavenging, anti-inflammatory, liver protection and ameliorating cognitive deficits. Bamboo leaf is mainly used for the treatment of atherosclerotic, diabetic and nervous system diseases.

AIM OF THE STUDY

This review aims to provide up-to-date information on the traditional medicinal properties, phytochemistry, pharmacology, and purification technologies of bamboo leaf.

MATERIALS AND METHODS

Relevant information on bamboo leaf was obtained by an online search of worldwide accepted scientific databases (Web of Science, ScienceDirect, Elsevier, SpringerLink, ACS Publications, Wiley Online Library and CNKI).

RESULTS

More than 100 chemical compounds, including flavonoids and flavonoid glycosides, volatile components, phenolic acids, polysaccharide, coenzyme Q10, phenylpropanoid and amino acids have been reported to be present. These compounds were usually extracted by column chromatography and membrane separation technologies. Preparative high performance liquid chromatography (PHPLC), high-speed counter-current chromatography (HSCCC), simulated moving bed chromatography (SMB) and dynamic axial compression chromatography (DAC) were the advanced separation technologies have been used to isolate C-glycosides from bamboo leaf flavonoid, the main bioactive ingredient of bamboo leaf. Currently, bamboo leaf is mainly used for the treatment of atherosclerotic, diabetic, hepatic diseases and nervous system related symptoms, which are attributed to the presence of bioactive components of bamboo leaf.

CONCLUSIONS

Phytochemical and pharmacological analyses of bamboo leaf have been revealed in recent studies. However, most of the pharmacological studies on bamboo leaf have focused on bamboo leaf flavonoids. Further studies need to pay more attention to other phytochemical components of bamboo leaf. In addition, there is lack of sufficient clinical data and toxicity studies on bamboo leaf. Therefore, more clinical and toxicity researches on this plant and constituents are recommended.

CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model

Circular RNAs (circRNAs), which are produced post-splicing of pre-mRNAs, are strongly linked to the emergence of several tumor types. The initial stage in conducting follow-up studies involves identifying circRNAs. Currently, animals are the primary target of most established circRNA recognition technologies. However, the sequence features of plant circRNAs differ from those of animal circRNAs, making it impossible to detect plant circRNAs. For example, there are non-GT/AG splicing signals at circRNA junction sites and few reverse complementary sequences and repetitive elements in the flanking intron sequences of plant circRNAs. In addition, there have been few studies on circRNAs in plants, and thus it is urgent to create a plant-specific method for identifying circRNAs. In this study, we propose CircPCBL, a deep-learning approach that only uses raw sequences to distinguish between circRNAs found in plants and other lncRNAs. CircPCBL comprises two separate detectors: a CNN-BiGRU detector and a GLT detector. The CNN-BiGRU detector takes in the one-hot encoding of the RNA sequence as the input, while the GLT detector uses k-mer (k = 1 - 4) features. The output matrices of the two submodels are then concatenated and ultimately pass through a fully connected layer to produce the final output. To verify the generalization performance of the model, we evaluated CircPCBL using several datasets, and the results revealed that it had an F1 of 85.40% on the validation dataset composed of six different plants species and 85.88%, 75.87%, and 86.83% on the three cross-species independent test sets composed of Cucumis sativus, Populus trichocarpa, and Gossypium raimondii, respectively. With an accuracy of 90.9% and 90%, respectively, CircPCBL successfully predicted ten of the eleven circRNAs of experimentally reported Poncirus trifoliata and nine of the ten lncRNAs of rice on the real set. CircPCBL could potentially contribute to the identification of circRNAs in plants. In addition, it is remarkable that CircPCBL also achieved an average accuracy of 94.08% on the human datasets, which is also an excellent result, implying its potential application in animal datasets. Ultimately, CircPCBL is available as a web server, from which the data and source code can also be downloaded free of charge.

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent's attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the "feature store & return" module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.

Taxonomically Restricted Genes Are Associated With Responses to Biotic and Abiotic Stresses in Sugarcane (Saccharum spp.)

Orphan genes (OGs) are protein-coding genes that are restricted to particular clades or species and lack homology with genes from other organisms, making their biological functions difficult to predict. OGs can rapidly originate and become functional; consequently, they may support rapid adaptation to environmental changes. Extensive spread of mobile elements and whole-genome duplication occurred in the Saccharum group, which may have contributed to the origin and diversification of OGs in the sugarcane genome. Here, we identified and characterized OGs in sugarcane, examined their expression profiles across tissues and genotypes, and investigated their regulation under varying conditions. We identified 319 OGs in the Saccharum spontaneum genome without detected homology to protein-coding genes in green plants, except those belonging to Saccharinae. Transcriptomic analysis revealed 288 sugarcane OGs with detectable expression levels in at least one tissue or genotype. We observed similar expression patterns of OGs in sugarcane genotypes originating from the closest geographical locations. We also observed tissue-specific expression of some OGs, possibly indicating a complex regulatory process for maintaining diverse functional activity of these genes across sugarcane tissues and genotypes. Sixty-six OGs were differentially expressed under stress conditions, especially cold and osmotic stresses. Gene co-expression network and functional enrichment analyses suggested that sugarcane OGs are involved in several biological mechanisms, including stimulus response and defence mechanisms. These findings provide a valuable genomic resource for sugarcane researchers, especially those interested in selecting stress-responsive genes.