Q&A Center
• Bioinformatics Analysis FAQ Summary
"Bioinformatics Analysis" is an abbreviation for "Bioinformatics Analysis". This is an interdisciplinary field that mainly focuses on using methods from computer science, mathematics, and statistics to analyze biological data, particularly data related to molecular biology. In recent years, with the rapid development of high-throughput sequencing technologies (such as RNA-seq, ChIP-seq, Whole Genome Sequencing, etc.), bioinformatics has played an increasingly important role in biomedical research, drug discovery, and precision medicine.
• Biological Product Characterization FAQ Summary
Does ADC drug analysis require an auxiliary drug? Antibody-Drug Conjugates (ADCs) are a class of targeted anti-cancer drugs that link antibodies to cytotoxic drugs (i.e., drug carriers) through stable chemical bonds. Antibodies can deliver the drugs precisely to cancer cells, thereby reducing toxicity to normal cells.
• Single-Cell Analysis FAQ Summary
How to determine the functions of new cell populations identified by single-cell sequencing? There are many methods to determine the functions of new cell populations identified by single-cell sequencing, such as: 1. Transcriptome analysis: First, perform gene expression profiling on the newly discovered cell populations. Identify the genes specifically expressed by these cells and compare them with the gene expressions of known cell types. This helps to reveal the potential functional characteristics of the new cell populations.
• Multi-Omics Analysis FAQ Summary
How can transcriptomics quickly select differentially expressed genes related to my research direction for qPCR validation? In the RNA-seq analysis process, tools such as DESeq2 and edgeR have been used for differential expression gene analysis. This step will yield a list of differentially expressed genes that have significant differences in expression levels between the experimental group and the control group.
How should metabolomics data be analyzed? 1. Data preprocessing: Preprocessing is an important step in data analysis, including baseline correction, noise filtering, normalization, missing value imputation, and data alignment. 2. Data processing: The purpose of data processing is to extract useful information from a large amount of metabolite data.
• Protein Analysis FAQ Summary
What are the common factors for unsuccessful protein mass spectrometry identification? There can be various reasons for unsuccessful protein mass spectrometry identification, and here are some common factors: Sample preparation: Improper sample handling, extraction, and purification steps may lead to protein loss or degradation, affecting mass spectrometry identification. It is crucial to select an appropriate sample preparation method to preserve protein integrity and activity. Protein concentration: If the protein concentration in the sample is too low.
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