1. What is TPM Calculation?

TPM (Transcripts Per Million) is a normalized unit for measuring gene expression levels in RNA-seq experiments. It accounts for both gene length and sequencing depth, allowing for more accurate comparisons between samples.

2. How Does the Calculator Work?

The calculator uses the TPM formula:

Where:

• \( Reads \) — Number of reads mapped to the gene
• \( Gene Length \) — Length of the gene in base pairs (bp)
• \( Total Normalized Reads \) — Total number of normalized reads in the sample

Explanation: The formula first normalizes for gene length by dividing reads by gene length (in kilobases), then normalizes for sequencing depth by dividing by total normalized reads, and finally scales to per million transcripts.

3. Importance of TPM Calculation

Details: TPM provides a standardized measure of gene expression that allows for meaningful comparisons across different genes and different samples, making it essential for RNA-seq data analysis and interpretation.

4. Using the Calculator

Tips: Enter the number of reads mapped to the gene, the gene length in base pairs, and the total normalized reads in the sample. All values must be valid positive numbers.

5. Frequently Asked Questions (FAQ)

Q1: What is the difference between TPM and FPKM/RPKM?
A: TPM is considered superior to FPKM/RPKM because it sums to the same value across samples, making between-sample comparisons more straightforward.

Q2: When should I use TPM?
A: TPM is most useful when comparing expression levels of the same gene across different samples or when comparing expression of different genes within the same sample.

Q3: What are typical TPM values?
A: TPM values can range from 0 to over 1,000,000, with highly expressed genes typically having TPM values in the hundreds or thousands.

Q4: How is TPM different from counts?
A: Raw counts are the actual number of reads mapping to a gene, while TPM is a normalized value that accounts for gene length and sequencing depth.

Q5: Can TPM values be compared between different experiments?
A: While TPM helps normalize within an experiment, comparisons between different experiments should be done cautiously due to potential technical variations.