Bioinformatics Analysis FAQ

• • How to Analyze KEGG Pathways of Differential Metabolites?

  To analyze the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways of differential metabolites, the following steps can be followed:   Data Collection First, it is essential to collect and prepare data on differentially expressed genes or metabolites. These data are typically obtained from transcriptomic or metabolomic experiments.   Data Preprocessing The collected data must undergo necessary preprocessing, such as normalization and the selection of differentially significant metabolites or genes......

• • How to Plot PCA and PLS in Chemometrics Without Programming Using Methods Such as Chemometric Analysis?

  To plot Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression in chemometrics without programming, you can use specialized data analysis software such as Origin or Minitab, which provide intuitive user interfaces and comprehensive graphical options. Below are the basic steps for using these types of software:   Plotting PCA and PLS with Origin 1. Data Import (1) Open Origin and create a new project. (2) Import your dataset into the worksheet.   2. Perform PCA Analysis (1) Click......

• • Is There a Difference Between Using UniProt for Protein Annotation and Converting Protein IDs to Gene IDs for GO/KEGG Analysis?

  The use of protein IDs for GO/KEGG enrichment analysis and the conversion of protein IDs to gene IDs for analysis may produce similar results; however, some differences still exist.   UniProt primarily focuses on providing detailed functional, structural, and interaction annotations for individual proteins. In contrast, GO/KEGG enrichment analysis aims to identify biological functions or pathway enrichment patterns within a set of genes or proteins.When investigating the characteristics of a specific ......

• • How to Determine the Quality of Transcriptomic and Genomic Data Obtained from Sequencing?

  Evaluating the quality of sequencing data, such as transcriptomic and genomic data, is a critical step in the sequencing workflow. The following strategies and tools are helpful for assessing the quality of sequencing data:   Quality Check of Raw Data 1. FastQC This widely used tool provides an overview of the data quality for each sequencing sample. It offers multiple quality metrics, including sequencing quality, sequence length distribution, and the proportion of duplicated sequences.   2. MultiQC ......

• • How to Select Differential Pathways and Proteins in Proteomics? Pathways or Proteins First? How to Analyze GO?

  When selecting differential pathways and proteins, both strategies are viable: proteins can be selected based on known pathways, or pathways can be inferred from differentially expressed proteins. Additionally, Gene Ontology (GO) analysis can be employed for functional interpretation.   Should Proteins Be Chosen Based on Pathways, or Pathways Based on Proteins 1. Selecting Proteins Based on Pathways If a specific pathway is known to be altered under particular conditions, proteomic analysis can be uti......

• • How Can Uncharacterized or Novel Genes Be Identified from Known Transcriptomic Data? Is Literature Search the Only Option?

  Identifying uncharacterized or novel genes from existing transcriptomic data involves multiple strategies beyond literature review. One effective approach is to compare the transcriptomic dataset against publicly available gene annotation databases to detect transcripts that lack known annotations. Bioinformatics tools such as BLAST, Ensembl, NCBI, or the UCSC Genome Browser can be employed to align transcriptomic sequences with reference genomes, allowing the identification of sequences without known......

• • How Should Principal Component Analysis (PCA) Score Plots Be Interpreted and What Is Their Significance?

  In a principal component analysis (PCA) score plot, the horizontal and vertical axes represent different principal components (PCs). The position of each sample within the principal component space is determined by its scores on the respective components. These scores are obtained by projecting the original data onto the principal components. In general, higher scores indicate greater contributions of a sample to the corresponding component.   PCA score plots serve as powerful tools for interpreting t......

• • What Considerations Should Be Taken into Account When Preparing Samples for Principal Component Analysis?

  Principal Component Analysis (PCA) is a powerful dimensionality reduction technique, but careful attention must be paid to sample preparation to ensure meaningful and reliable results. The following considerations are essential prior to performing PCA:   Standardization/Normalization PCA is sensitive to the scale of variables. It is generally necessary to standardize each feature—typically by centering to a mean of zero and scaling to unit variance—so that all variables contribute equally to the analy......

• • Can Principal Component Analysis Accommodate Both Continuous and Categorical Variables?

  Principal Component Analysis (PCA) was originally developed for continuous variables. When a dataset includes both continuous and categorical variables, directly applying PCA may introduce challenges. This limitation arises because PCA relies on computing a covariance or correlation matrix, which is not well-defined for categorical variables, particularly nominal variables that lack a meaningful numerical scale.   Nevertheless, several approaches can facilitate PCA or similar dimensionality reduction ......

• • What Software Is Used for Metabolomics and Bioinformatics Visualization?

  Visualization in bioinformatics plays a vital role in metabolomics research by enabling researchers to effectively interpret data, elucidate metabolic networks, and analyze the regulation of metabolic pathways. Various software tools are commonly employed for data visualization and analysis:   R Language R is a statistical programming language widely used in bioinformatics and biostatistics. It offers powerful visualization packages such as ggplot2, pheatmap, and heatmap.2, which facilitate the creati......