Bioinformatics Analysis FAQ

• • 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......

• • How to Generate an OPLS-DA Plot Using SIMCA 13?

  SIMCA is a specialized software for statistical analysis, widely used in multivariate data analysis, including orthogonal partial least squares discriminant analysis (OPLS-DA). The following steps outline the general procedure for generating an OPLS-DA plot:   1. Data Preparation Ensure that the dataset is appropriately formatted. This typically involves organizing variables (e.g., chemical components, physical properties) in a structured table, along with one or more categorical variables to classify......

• • How Can the Lack of Significant Pathway Enrichment (p > 0.1) in Proteomic KEGG Analysis Be Resolved?

  In KEGG enrichment analysis of proteomic data, when all p-values exceed 0.1, it suggests that no significantly enriched pathways have been identified within the analyzed protein set. This issue may arise due to several factors:   1. Insufficient Sample Size A small sample size reduces statistical power, making it challenging to detect significant pathway enrichment.   2. Quality of the Protein List The input protein list may be incomplete or biologically irrelevant.   3. Selection of the Background Ge......

• • What Does Each Letter Represent in the COG Database? Is "mot" a Subcategory of "M" in COG Annotation?

  In the Clusters of Orthologous Groups (COG) classification system, each letter corresponds to a specific functional category:   INFORMATION STORAGE AND PROCESSING [J] Translation, ribosomal structure, and biogenesis [A] RNA processing and modification [K] Transcription [L] Replication, recombination, and repair [B] Chromatin structure and dynamics   CELLULAR PROCESSES AND SIGNALING [D] Cell cycle control, cell division, chromosome partitioning [Y] Nuclear structure [V] Defense mechanisms [T] Signal tr......

• • How to Interpret KEGG Analysis Results?

  Following KEGG analysis, a results table and a pathway diagram are generated. These outputs provide critical insights into the functional significance of the identified differentially expressed genes.   Understanding the KEGG Pathway Table 1. Pathway Name Lists the biological pathway name associated with the gene set.   2. Pathway ID Provides a unique identifier for each pathway (e.g., hsa04110, corresponding to the human cell cycle pathway).   3. p-value Represents the statistical test result, reflec......

• • How to Identify Important Variables After Principal Component Analysis?

  After performing Principal Component Analysis (PCA), it is essential to determine which original variables contribute the most to the principal components, thereby identifying the most influential variables. This can be achieved through the following steps:   Examine the Explained Variance Ratio of Principal Components PCA generates a set of principal components, each representing a linear combination of the original variables. The explained variance ratio quantifies the proportion of total variance c......