Hello Bioinformatics lovers,

I can not believe it is already the second month of 2025!

I want to grab every opportunity to remind you that it is never too late to start learning. ( I wrote this tutorial to show you how to analyze TCGA bulk RNAseq data, Enjoy! )

For those who celebrate the Lunar New Year, Happy New Year!

It is the year of the snake. My wife wrote the couplets:

Today we will talk about the complexity of bioinformatics.

Why is bioinformatics so complicated? Because biology is.

Here’s a quick example to show just how nuanced even a "simple" analysis can be.
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Genes aren’t simple entities. Most genes have multiple transcripts.

Different transcripts can have unique TSS(transcription start site), TES,(transcription end site) and exon compositions.
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This complexity cascades.

Take DNA methylation and gene expression correlation as an example. How do we even start?
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If you’re using gene-level mRNA values, should you average CpG site methylation in the 1kb upstream of which TSS?
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Options abound: canonical transcripts, RefSeq transcripts, or MANE (Matched Annotation from NCBI and EMBL-EBI) transcripts. Which is best?
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Or maybe you ditch gene-level mRNA values and use transcript-level data from tools like kallisto or Salmon.
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With transcript-level data, you can correlate specific TSS methylation beta values to corresponding transcript mRNA values.
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​But wait—what about CpG islands? Not all genes have them, and gene expression can be tied to CpG sites far from the TSS.​
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Some genes’ expression is anti-correlated with methylation at distal CpG sites, not within the 1kb upstream of the TSS.
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​Even for this “simple” question—DNA methylation vs. gene expression—there are countless variables, decisions, and nuances to consider.​
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• Biology’s inherent complexity makes bioinformatics challenging.
• Clear analysis requires informed decisions at every step.
• Tools like MANE transcripts or transcript-level quantification help refine results.
• Always define your biological question before diving into analysis.
• Choose annotation strategies that match your data and goals.
• Stay updated with best practices in transcriptomics and epigenomics.
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My other posts that you may find helpful:

1. ​12 years ago, Tommy was naive about bioinformatics.
2. ​You can not trust a boxplot with a small sample size.

3. Genome browsers are essential tools for genomics data analysis. I introduced three of them.​

4. Survival analysis is a critical skill for bioinformaticians. I introduced you to tools and caveats when interpreting survival curves.​

5. How to Reorder Bars in ggplot2 (and What Happens Behind the Scenes)​

6. Jupyter Notebook alternative.​

7. How can I start learning bioinformatics? This is my story.​

8. Tackling Ambient RNA in Single-Cell RNA-seq Data Ambient RNA contamination​

9. A unix one-liner to split a multi-fasta to multiple files ​

10. No, you do not always need Single-Cell RNA-seq.​

Sharing is caring, if you can send this newsletter to someone who may find it helpful, that will be great!

Happy Learning!

Tommy aka Crazyhottommy

PS:

If you want to learn Bioinformatics, there are other ways that I can help:

1. My free YouTube Chatomics channel, make sure you subscribe to it.
2. I have many resources collected on my github here.
3. I have been writing blog posts for over 12 years https://divingintogeneticsandgenomics.com/​

Stay awesome!