SemanticGO: a tool for gene functional similarity analysis in Arabidopsis thaliana and rice

Potentilla sericea stress-responsive spermine synthase PsSPMS enhances cadmium tolerance in Arabidopsis thaliana

Potentilla sericea is resistant and tolerates rough management. It is an excellent garden groundcover for ecological restoration and soil consolidation for slope protection. Polyamines have functions such as promoting tissue growth and physiological resistance, while spermine synthase catalyzes the production of spermine. The PsSPMS gene from Potentilla sericea was cloned and transformed into Arabidopsis thaliana to study the response of transgenic Arabidopsis thaliana to cadmium stress. The results showed that the contents of spermidine, spermine as well as glutathione were higher in PsSPMS overexpressing Arabidopsis thaliana than the control, while the contents of putrescine were less than the control. Net photosynthetic rate, stomatal conductance, chlorophyll content, water use efficiency, electron transfer rate, PSII-related parameters, proline content, superoxide dismutase, and glutathione reductase activities were higher in PsSPMS overexpressing Arabidopsis thaliana than the control, while malondialdehyde, superoxide anion, and hydrogen peroxide contents were lower than the control. Correlation analysis showed significant differences between the indicators (P < 0.05 and P < 0.01). Expression of AtSPMS, AtSPD3, AtGSH2 and AtGR in transgenic Arabidopsis thaliana was higher than that of the control. Therefore, this study provides a genetic reference for the cultivation of cadmium-tolerant plants through genetic engineering and lays the foundation for further research on cadmium-tolerant Potentilla sericea.

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.

Representation of polysaccharide molecules by SNFG and 3D-SNFG methods--Take Potentilla anserina L polysaccharide molecule as an example

With the continuous deepening of international research in the field of biology, more and more studies have found that polysaccharides have multiple biological functions, so that polysaccharides have gradually become the research objects of more and more scientists in the world, and a large number of relevant researchers have carried out Glycobiology research, most of the current research is on the separation, extraction, structural characterization and activity experiments of polysaccharides. However, at this stage, research on the structure-activity relationship of various polysaccharides extracted from plants is relatively rare, and the representation method of polysaccharide structures is not perfect, not unified, complicated in drawing, and not beautiful and convenient to read. The SNFG (Symbol Nomenclature For Glycans) method, which is the symbolic nomenclature of polysaccharides and the 3D-SNFG method, can solve the above problems well, and can use unified rules to describe and describe the molecular structure of polysaccharides, and the painting process is more convenient and more convenient. It is beautiful and makes it easier for readers to read. In this paper, the fern hemp polysaccharide molecule is taken as an example. After drawing it with chemoffice, SNFG and 3D-SNFG are used to describe it, and then compared. It is clear at a glance that the use of SNFG and 3D-SNFG methods has been widely recognized and accepted internationally, which can provide great convenience for sugar-related research and information exchange.

TissueSpace: a web tool for rank-based transcriptome representation and its applications in molecular medicine

BACKGROUND

Cross-platform or cross-experiment transcriptome data is hard to compare as the original gene expression values from different platforms cannot be compared directly. The inherent gene expression ranking information is rarely utilized.

OBJECTIVE

Use of reduced vector to represent transcriptome data independent of platforms.

METHODS

Thus, we turned the expression profile into a rank vector, where a higher expression has a higher rank value, then applied Latent semantic analysis (LSA) to get compact and continuous 100-dimensional vector representations for samples.

RESULTS

Results showed that the reconstructed vector has a precision of 96.7% in recovering tissue labels from an independent dataset. A user-friendly tool TissueSpace was developed, which provides users the following functionalities: (1) convert different gene ID types to Ensembl gene IDs; (2) project any human transcriptome profile to get vector representation for downstream analysis; (3) functional enrichment for each of the 100-dimensional vector features. Case studies for its applications in human common diseases indicate its usefulness.

CONCLUSIONS

TissueSpace could be used to generate testable hypotheses for translational medicine. The TissueSpace tool is available at http://bioinformatics.fafu.edu.cn/tissuespace/ .