2026-10-14T09:05:32Z https://www.scidb.cn/oai

10.57760/sciencedb.0136n 2026-09-28T10:37:56Z

2026-09-28 Co-cultured detection data set doi:10.57760/sciencedb.0136n en Experimental Procedurea. Sample Information Analysis ProcessFirst, use FastQC to conduct quality assessment on the raw data, filter the raw data to obtain optimized data for quality control, complete alignment on the filtered and qualified data, and conduct statistics on the alignment rate. Next, complete the prediction of transcripts, expression quantification, analysis of alternative splicing, and variation analysis.b. Quality Control of Downstream Data1) RNA sequencing is based on high-throughput sequencing technology. The original downstream data is called raw data.2) Data FilteringFilter out reads containing adapter sequences.3) Quality Assessment of Sequencing DataUse the fastp software to remove low-quality reads and those containing adapter sequences from the sequencing results. Use FastQC and self-developed software to count the number of sequencing reads in the clean data, etc.4) Evaluation of rRNA contamination rateUse the bowtie2 software to compare the clean data with the eukaryotic rRNA database to identify the rRNA contamination rate of each sample.c. Data Alignment and Quality Control1) Quality Control Results of Alignment with Reference GenesThe first step of the analysis requires aligning the clean reads with the reference genome (Alignment). Sequence alignment aims to determine the distribution of reads on the reference genome, which is the basis for subsequent analysis of gene expression levels and other contents.This study uses the hisat2 software to align the clean data with the reference genome, and counts the number of unique alignments to the reference sequence (Uniq-mapping), multiple alignments to the reference sequence (Multiple-mapping), and no alignments to the reference sequence.2) Quality Control Results of Alignment with Reference TranscriptsUse bowtie2 to align the clean data to the reference transcript sequences, and count the number of unique alignments to the reference sequence (Uniq-mapping), multiple alignments to the reference sequence (Multiple-mapping), and no alignments to the reference sequence.3) Results of Sample Homogeneity AnalysisBased on the alignment results of clean data with reference transcripts, all transcripts are scaled to 200 nt, and the number of reads covering each nucleotide is calculated. Finally, a coverage spectrum along the gene body is calculated.d. Analysis of Differential Gene Expression Levels1) Expression Quantification Results of GenesUse the stringtie software to predict all transcripts of the samples, and then use RSEM software to calculate statistics based on the alignment results from bowtie2.2) PCA Analysis Results of Differential GenesPrincipal component analysis (PCA) recombines the original indicators that are somewhat correlated, making the combined indicators uncorrelated and replacing the original indicators. PCA uses linear algebraic calculation methods to reduce the dimensionality and extract principal components of thousands of gene variables, identify the main components, and then make a PCA graph. The ideal result is that the samples in the PCA graph are relatively dispersed between groups and clustered together within the group.e. Overall Quality Assessment of Expression Quantification1) Sample Saturation Analysis Results2) Venn Diagram of Genes and Isoforms between Samples in the Same GroupSamples in the same group can be regarded as biological replicates. Theoretically, the more overlapping genes and isoforms between them, the better the sample repeatability. For the expression status of genes and isoforms between samples, statistics are made separately and a Venn diagram is drawn.3) Sample Correlation Analysis ResultsThe correlation coefficient is closer to 1, indicating a higher similarity of expression patterns between samples.f. Differential Expression Analysis1) Isoform Clustering Results of Differential Expression GenesAfter a comprehensive analysis of gene expression levels, conducting differential gene analysis between sample groups can help us gain a deeper understanding of biological issues. The differential expression analysis is performed using the expected_count from the quantitative results of genes and isoforms as the input data.2) Differential expression GO enrichment resultsGene Ontology (GO) is the international standard classification system for gene functions. After screening the differential genes based on the analysis purpose, studying the distribution of the differential genes in Gene Ontology will clarify the manifestation of sample differences in gene functions in the experiment.3) Differential expression KEGG enrichment resultsPathway significance enrichment analysis is conducted on a unit basis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway, applying the hypergeometric test to identify Pathways that are significantly enriched in the differentially expressed genes compared to the entire genomic background.4) Differential transcription factor analysis resultsUsing the diamond software to compare the predicted transcripts with the transcription factor database (http://bioinfo.life.hust.edu.cn/AnimalTFDB#!/), and filtering based on the differential expression results, a list of differential transcription factors is obtained.g. New transcriptome mining and annotationFor all samples, assembly is performed and compared with the reference gene transcripts to mine new transcripts. The new transcripts are annotated with NR, KEGG, GO, and Swiss-Port.h. IncRNA identificationlncRNA plays an important role in numerous biological activities such as dose compensation effect, epigenetic regulation, cell cycle regulation, and cell differentiation regulation, and has become a research hotspot in genetics.i. Protein interaction analysisThe STRING protein interaction database (http://string-db.org/) can be used to construct the protein-protein interaction network.The transcriptome sequencing analysis of the experimental group and control group was completed by Wuhan Fosai Genomics Information Co., Ltd. Mesonephros; OSCs; single-cell sequencing Jie Chen RESTRICTED dataset Science Data Bank