Multiomics Analysis FAQ

• • 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......

• • What Are the Differences Between ITS and 18S Sequencing in Fungi?

  The Internal Transcribed Spacer (ITS) is a highly variable region in the fungal genome, located between the 18S, 5.8S, and 28S rRNA genes within the eukaryotic ribosomal genome. The ITS sequence generally consists of two sub-regions, ITS1 and ITS2, which are separated by the 5.8S rRNA sequence.   In contrast, the 18S rRNA gene is a conserved region within the fungal ribosomal genome, widely present across eukaryotes, and is commonly used for constructing phylogenetic trees and species identification. ......

• • How to Convert Gene IDs to Gene Symbols in Transcriptome Sequencing Data?

  Converting gene IDs (e.g., ENSEMBL ID, RefSeq ID) to gene symbols in transcriptome sequencing data is a common task in bioinformatics workflows. The following methods and tools are frequently employed for this conversion:   1. Using Bioinformatics Software Packages Bioinformatics software packages, such as R packages from Bioconductor, can be used for ID conversion. In R, annotation packages like org.Hs.eg.db (for humans) and org.Mm.eg.db (for mice) can be used to facilitate conversion.   2. Using Onl......

• • What Are the Differences and Application Ranges of 16S, 18S, and ITS in Amplicon Sequencing

  16S and 18S refer to the small subunit ribosomal RNA genes in bacteria and eukaryotes, respectively. These genes are involved in the process of protein synthesis within the cell. ITS refers to the Internal Transcribed Spacer, a non-coding region located between the coding regions in the ribosomal RNA genes of fungi and other eukaryotes.

• • Should OTU or ASV Clustering Be Used in 16S Sequencing?

  In 16S rRNA sequencing, two main clustering approaches are commonly employed: OTU (Operational Taxonomic Units) clustering and ASV (Amplicon Sequence Variants) clustering. Each method offers distinct advantages and limitations.   OTU Clustering 1. Advantages OTU clustering is a conventional approach that groups sequences into OTUs based on a predefined similarity threshold, typically set at 97%. This method effectively mitigates the impact of random sequencing errors and has been extensively applied i......