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Motivation What’s the most common problem you need to solve when dealing with genomics data?
For me, it is Genomic Intervals!
The genomics data usually represents linearly: chromosome name, start and end.
We use it to define a region in the genome ( A peak from ChIP-seq data); the location of a gene, a DNA methylation site ( a single point), a mutation call (a single point), and a duplication region in cancer etc.
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I have been writing blog posts for over 10 years. I was using blogspot and in 2018, I switched to blogdown and I love it.
My blogdown website divingintogeneticsandgenomics.com was using Hugo v0.42 and blogdown v1.0. It has been many years and now I have a macbook pro with an M3 chip. I could not install the old versions of the R packages to serve the site.
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In my last blog post, I talked about some common bioinformatics mistakes.
Today, we are going to talk about THE MOST common bioinformatics mistake people make. And I think it deserves a separate post about it. Even some experienced programmers get it wrong and the mistake prevails in many bioinformatics software:
The one-off mistake!
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This post is inspired by this popular thread in https://www.biostars.org/.
Common mistakes in general Off-by-One Errors: Mistakes occur when switching between different indexing systems. For example, BED files are 0-based while GFF/GTF files are 1-based, leading to potential misinterpretations of genomic coordinates. This is one of the most common mistakes!
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If you apply for a Bioinformatics position, hundreds of CVs get to sent to the hiring manager. How to stand out among all of them? Below are 6 tips from my hiring experience:
Include a GitHub Link: Ensure your CV has a GitHub link with relevant content like Python or R packages, data analysis projects, or replicated figures from published papers.
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R or Python for Bioinformatics? Watch the video here:
If you need to pick Python or R for bioinformatics, which one should you choose? This is a decades-old question from many beginners.
This is my story.
I started learning Unix Commands 12 years ago (See an example of how powerful Unix commands can be).
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The other day, I saw this tweet:
Machine learning and bioinformatics tutorials these days pic.twitter.com/0FhWWG09TB — Ramon Massoni Badosa (@rmassonix) May 15, 2024 Many of the bioinformatics tutorials are like that. I am not saying the tutorial is not good. For beginners, we need something basic first to understand it.
In R, S3 and S4 objects are related to object-oriented programming (OOP), which allows you to create custom data structures with associated behaviors and methods. Let me explain them using simple language and metaphors, along with practical examples.
S3 Objects Imagine you have a collection of toys, like cars, dolls, and action figures. Each toy has its own set of properties (color, size, material) and behaviors (move, make sounds, etc.
I was asked this question very often: “Tommy, what’s the p-value cutoff should I use to determine the differentially expressed genes; what log2 Fold change cutoff should I use too?”
For single-cell RNAseq quality control, what’s the cutoff for mitochondrial content?
My answer is always: it depends. I was joking: determining a cutoff is 90% of the work a bioinformatician does.
Why is that?
Biology is more than just statistics.
Context and Problem In scRNA-seq, each cell is sequenced individually, allowing for the analysis of gene expression at the single-cell level. This provides a wealth of information about the cellular identities and states. However, the high dimensionality of the data (thousands of genes) and the technical noise in the data can lead to challenges in accurately clustering the cells. Over-clustering is one such challenge, where cells that are biologically similar are clustered into distinct clusters.