1. What is TPM Calculation?

TPM (Transcripts Per Million) is a normalization method for RNA-seq data that accounts for both gene length and sequencing depth. It provides a standardized measure of gene expression levels across different samples.

2. How Does the Calculator Work?

The calculator uses the TPM formula:

Where:

• \( Counts \) — Number of reads mapped to a specific gene
• \( Total\ Counts \) — Total number of mapped reads in the sample

Explanation: The formula normalizes gene counts by the total number of mapped reads and scales to one million to facilitate comparison between samples.

3. Importance of TPM Calculation

Details: TPM normalization is crucial for accurate comparison of gene expression levels across different samples and experiments in transcriptomics studies.

4. Using the Calculator

Tips: Enter the gene read counts and total mapped read counts. Both values must be positive numbers, with total counts greater than zero.

5. Frequently Asked Questions (FAQ)

Q1: What is the difference between TPM and FPKM/RPKM?
A: TPM sums to one million across all genes, making it more intuitive for comparing expression proportions across samples than FPKM/RPKM.

Q2: When should I use TPM normalization?
A: TPM is ideal for comparing expression levels of the same gene across different samples or for comparing the relative abundance of different genes within the same sample.

Q3: What are typical TPM values?
A: TPM values range from 0 to over 1,000,000, with highly expressed genes having higher TPM values. The sum of all TPM values in a sample equals 1,000,000.

Q4: Are there limitations to TPM normalization?
A: While TPM accounts for sequencing depth, it doesn't account for gene length bias in the same way as FPKM/RPKM. The choice of normalization method depends on the specific research question.

Q5: Can TPM be used for differential expression analysis?
A: TPM values are useful for visualization and exploratory analysis, but specialized statistical methods like DESeq2 or edgeR are typically recommended for formal differential expression analysis.