Background: Presently, toxicity decreased oral live rotavirus is a candidate for vaccine for the prevention of rotavirus induced diarrhea. According to the World Health Organization, the seed lot system is robustly checked, in which determining the stable of gene sequence. Objective: To determine the sequence of genes 4: 6: 9: 10 with base pair correlative 855: 824: 1314: 734 of seed lot system G1P4 (2001019210) during production of rotavirus vaccine. Subject and methods: Gene 4 (VP4), gene 6 (VP6), gene 9 (VP7) and gene 10 (NSP4) of seed lot system G1P4 were determined for gene sequencing and then comparing the nucleotide sequence as well as deduced amino acids from original strain with the produced strain and vaccine virus. Results and Conclusion: There was no different for nucleotide and deduced amino acid sequence from the original strain during the production of rotavirus vaccine of G1P4 MS (2001019210) to producing strains of G1P4 WS and vaccine strains of G1P4 VX.
rotavirus vaccine ; gene sequence

Background: Rotavirus strain (KH0118) is used as the primary material to produce original rotavirus vaccine strains with the symbol of G1P8 MS. According to the World Health Organization\u2019s standard, the strain is needed to evaluate the stability of gene throughout analysis of gene and amino acid sequence during vaccine production. Objective: To determine the sequence of genes 4 (VP4), 6 (VP6), 9 (VP7) and 10 (NSP4) with base pair correlative 855:866:1345:745 of seed lot system and vaccine of G1P8 strain and to evaluate the stability of seed lot system during vaccine production. Subject and methods: ARN was divided from the original strain of rotavirus vaccine G1P8 MS, rotavirus vaccine productive strain (G1P8 WS) and rotavirus vaccine (G1P8 VX). Then using primer pairs to determine gene sequence VP4, VP6, VP7, NSP4 and comparing gene and amino acid sequence of the seed lot system. Results and Conclusion: The study demonstrated that, there was no difference for the nucleotide and amino acid sequence from the original strain during production of rotavirus vaccine G1P8 KH0118.
rotavirus vaccine ; G1P8 ; gene sequence

Few tools of gene editing have been developed in Bacillus licheniformis at present. In order to enrich the tools, an FLP/FRT gene editing system that can repeatedly use a single selectable marker was constructed in Bacillus licheniformis, and the system was verified by knocking out an alpha amylase gene (amyL), an protease gene (aprE) and knocking in an exogenous Vitreoscilla hemoglobin gene (vgb). First, knock-out plasmids pNZTT-AFKF of amyL and pNZTT-EFKF of aprE were constructed using thermosensitive plasmid pNZT1 as a carrier. The two knock-out plasmids contained respective homology arms, resistance genes and FRT sites. Then the knock-out plasmids were transformed into Bacillus licheniformis and the target genes were replaced by respective deletion cassette via twice homologous exchange. Finally, an expression plasmid containing FLP recombinase reading frane was introduced and mediated the excision of resistance marker. In order to expand the practicability of the system, knock-in plasmid pNZTK-PFTF-vgb was constructed, with which knock-in of vgb at pflB site was carried out successfully. The results showed that amyL and aprE were successfully knocked out and the marker kanamycin cassette exactly excised. The activities of amylase and protease of deletion mutants were reduced by 95.3% and 80.4% respectively. vgb was successfully knocked in at pflB site and the marker tetracycline cassette excised. The expression of integrated vgb was verified via real-time PCR. It is the first time to construct an FLP/FRT system for gene editing in Bacillus licheniformis, which could provide an effective technical means for genetic modification.
Bacillus licheniformis ; Gene Editing ; Plasmids ; Sequence Deletion

To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.
Gene Expression ; Methods ; Phenotype ; Sequence Analysis, RNA

The emergence of single-cell sequencing technology enables people to observe cells with unprecedented precision. However, it is difficult to capture the information on all cells and genes in one single-cell RNA sequencing (scRNA-seq) experiment. Single-cell data of a single modality cannot explain cell state and system changes in detail. The integrative analysis of single-cell data aims to address these two types of problems. Integrating multiple scRNA-seq data can collect complete cell types and provide a powerful boost for the construction of cell atlases. Integrating single-cell multimodal data can be used to study the causal relationship and gene regulation mechanism across modalities. The development and application of data integration methods helps fully explore the richness and relevance of single-cell data and discover meaningful biological changes. Based on this, this article reviews the basic principles, methods and applications of multiple scRNA-seq data integration and single-cell multimodal data integration. Moreover, the advantages and disadvantages of existing methods are discussed. Finally, the future development is prospected.
Base Sequence ; Gene Expression Profiling ; Gene Expression Regulation ; Humans ; Sequence Analysis, RNA ; Single-Cell Analysis

OBJECTIVEScreening for special genes of matrix proteins of dentin and enamel of mouse dental germ.

METHODSA cDNA library of dental germ of mouse was screened by differential display. The interesting clones were sequenced.

RESULTSSix positive clones were isolated from the cDNA library. The sequence of one of the six positive clones was homologous with the ameloblastin sequence of rat. There are 497 homologous base pairs between the 526 base pairs sequenced by pTriplEX 3' primer of this clone and the 32-580 sequence of the rat ameloblastin gene; and there are 533 homologous base pairs between the 567 base pairs sequenced by pTriplEX 5' primer of this clone and the 1285-1854 sequence of the rat ameloblastin gene.

CONCLUSIONSThe full length cDNA sequence of the mouse ameloblastin was cloned.

High-throughput transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a standard technology for measuring gene expression with unprecedented accuracy. Numerous bioconductor packages have been developed for the statistical analysis of RNA-Seq data. However, these tools focus on specific aspects of the data analysis pipeline, and are difficult to appropriately integrate with one another due to their disparate data structures and processing methods. They also lack visualization methods to confirm the integrity of the data and the process. In this paper, we propose an R-based RNA-Seq analysis pipeline called TRAPR, an integrated tool that facilitates the statistical analysis and visualization of RNA-Seq expression data. TRAPR provides various functions for data management, the filtering of low-quality data, normalization, transformation, statistical analysis, data visualization, and result visualization that allow researchers to build customized analysis pipelines.
Base Sequence ; Gene Expression ; Gene Expression Profiling ; Molecular Sequence Data ; Programming Languages ; Sequence Analysis, RNA ; Statistics as Topic ; Transcriptome

DNA microarray is currently the most prominent tool for investigating large-scale gene expression data. Different algorithms for measuring gene expression levels from scanned images of microarray experiments may significantly impact the following steps of functional genomic analyses. Affymetrix(R) recently introduced high-density microarrays and new statistical algorithms in Microarray Suit (MAS) version 5.0(R). Very high correlations (0.92 - 0.97) between the new algorithms and the old algorithms (MAS 4.0) across several species and conditions were reported. We found that the column-wise array correlations had a tendency to be much higher than the row-wise gene correlations, which may be much more meaningful in the following higher-order data analyses including clustering and pattern analyses. In this paper, not only the detailed comparison of the two sets of algorithms is illustrated, but the impact of the introducing new algorithms on the further clustering analysis of microarray data and of possible pitfalls in mixing the old and the new algorithms were also described.
DNA* ; Gene Expression ; Oligonucleotide Array Sequence Analysis* ; Statistics as Topic

The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demonstrated good performance with the ability to find the 'globally optimal' binary partitions. In addition, the objects that have not been clustered at small non-zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.
Cluster Analysis ; DNA* ; Gene Expression* ; Genomics ; Oligonucleotide Array Sequence Analysis*

Gene Expression Omnibus (GEO) has kept the largest amount of gene-expression microarray data that have grown exponentially. Microarray data in GEO have been generated in many different formats and often lack standardized annotation and documentation. It is hard to know if preprocessing has been applied to a dataset or not and in what way. Standard-based integration of heterogeneous data formats and metadata is necessary for comprehensive data query, analysis and mining. We attempted to integrate the heterogeneous microarray data in GEO based on Minimum Information About a Microarray Experiment (MIAME) standard. We unified the data fields of GEO Data table and mapped the attributes of GEO metadata into MIAME elements. We also discriminated non-preprocessed raw datasets from others and processed ones by using a two-step classification method. Most of the procedures were developed as semi-automated algorithms with some degree of text mining techniques. We localized 2,967 Platforms, 4,867 Series and 103,590 Samples with covering 279 organisms, integrated them into a standard-based relational schema and developed a comprehensive query interface to extract. Our tool, GEOQuest is available at http://www.snubi.org/software/GEOQuest/
Data Mining ; DNA ; Gene Expression ; Mining ; Oligonucleotide Array Sequence Analysis