Multiomics Analysis FAQ

• • How Can Transcriptome Analysis Rapidly Identify Differentially Expressed Genes for qPCR Validation in My Research?

  To efficiently identify differentially expressed genes (DEGs) relevant to a specific research focus for qPCR validation, a systematic workflow involving multiple analytical steps is required. The following strategy outlines a possible approach:   Differential Expression Analysis In the RNA-seq data processing pipeline, computational tools such as DESeq2 and edgeR are commonly used to identify DEGs. This step generates a list of genes exhibiting statistically significant expression differences between ......

• • How to Analyze Data and Generate Figures for Eukaryotic Reference-Based Transcriptome Sequencing?

  The analysis of eukaryotic reference-based transcriptome sequencing (RNA-Seq) data is a multi-step process that includes quality control, sequence alignment, expression quantification, differential expression analysis, functional annotation, and pathway analysis. Below is an overview of the analytical workflow:   Data Analysis Workflow 1. Quality Control Use FastQC to assess raw sequencing data quality, including sequence quality scores, nucleotide composition, and sequence duplication rates.   2. Ada......

• • How Do the V3-4 and V4 Regions of 16S rRNA Differ in Microbial Community Analysis?

  The V3-4 and V4 regions of the 16S rRNA gene exhibit distinct characteristics in microbial community analysis, influencing sequencing depth, taxonomic resolution, and cost-effectiveness.   Sequence Coverage 1. V3-4 Region Spanning the third and fourth variable regions of the 16S rRNA gene, this region is longer than the V4 region and captures a broader range of genetic information, enabling more comprehensive microbial profiling.   2. V4 Region Covering only the fourth variable region, this shorter se......

• • What Do "Reads" Represent in Transcriptome Sequencing?

  In transcriptome sequencing, “reads” are fundamental data units that refer to short sequence fragments derived from DNA or RNA samples using sequencing platforms.   When sequencing a sample (such as a transcriptome, which comprises all RNA molecules in a cell or tissue), modern sequencing technologies do not process an entire long DNA or RNA molecule in a single read. Instead, they utilize a series of complex procedures to break the original biomolecules into smaller fragments and then determine the n......

• • How Can DADA2 Be Integrated with QIIME2 for Full-Length 16S rRNA Analysis Using Third-Generation Sequencing?

  To analyze full-length 16S rRNA sequencing data from third-generation platforms (e.g., PacBio, Oxford Nanopore) using both DADA2 and QIIME2, the following workflow can be applied:   1. Installation and Setup (1) Install the latest version of QIIME2 following the official installation guidelines. (2) DADA2 is integrated as a QIIME2 plugin and does not require separate installation.   2. Data Import Before importing data into QIIME2 using the 'qiime tools import' command, ensure that the raw sequencing ......

• • How Does Third-Generation Long-Read Transcriptome Sequencing Differ from RNA-Seq?

  Third-generation long-read transcriptome sequencing and RNA-Seq are two widely used transcriptomic sequencing technologies that differ in sequencing methodology, applications, and data analysis approaches.   Sequencing Methodology 1. Third-Generation Long-Read Transcriptome Sequencing This approach, primarily represented by PacBio SMRT (Single Molecule Real-Time) and Oxford Nanopore technologies, directly sequences full-length RNA molecules without the need for reverse transcription or amplification. ......

• • Introduction to Transcriptomics: What is RNA-Seq?

  RNA sequencing (RNA-Seq), also known as whole-transcriptome sequencing, is a high-throughput sequencing technique used to analyze the RNA composition of a sample. This method provides insights into RNA abundance and composition within a cell and enables the identification and quantification of all RNA molecules present at a specific time point or under particular conditions, including mRNA, non-coding RNA, and small RNA.   RNA-Seq begins with RNA extraction and purification. Subsequently, reverse tran......

• • How Do Transcriptome Sequencing and Whole Genome Sequencing Differ?

  The primary differences between transcriptome sequencing and whole genome sequencing are as follows:   Research Focus 1. Transcriptome Sequencing Analyzes RNA to investigate gene expression patterns.   2. Whole Genome Sequencing Analyzes DNA to determine gene sequences and structural variations.   Type of Information Provided 1. Transcriptome Sequencing Offers insights into gene expression levels and transcriptional variations.   2. Whole Genome Sequencing Provides a complete genomic sequence along wi......

• • What Are the Probability Distributions of Counts, TPM, and Log-TPM in Single-Cell Transcriptome Data?

  Counts 1. Counts refer to the number of RNA molecules detected for each gene, derived from gene quantification in sequencing data.   2. In single-cell transcriptome data, the probability distribution of counts is typically discrete, as the values are integer-based. The counts for each gene can range from 0 to very high numbers.   3. For an individual cell, the counts for each gene represent the expression level of that gene in that specific cell. In the entire single-cell dataset, the distribution of ......

• • How to Plot a Heatmap for Pairwise Comparison of Three Datasets in Transcriptome Sequencing?

  In transcriptome sequencing, when working with three datasets and aiming for pairwise comparisons, you can follow the steps below:   Data Preprocessing 1. First, perform quality control and filtering on the raw transcriptome sequencing data to remove low-quality reads and potential contaminants.   2. Next, use appropriate alignment and assembly tools to map the sequencing data to a reference genome, which will provide the expression levels of each gene.   3. Finally, normalize the expression matrix us......