About This Blog
Welcome to Daily Bioinformatics from Jojy’s Desk — my living notebook of daily bioinformatics work, microbial ecology, MAGs, MTX/MG, HPC troubleshooting, and coding.
- Metagenome & metatranscriptome analysis
- MAGs, viruses, CAZymes, energy metabolism markers
- Functional redundancy (FRed) modeling
- Hybrid-assembly & whole-genome workflows
- Machine-learning MAG binning (GPU/CPU)
- Daily troubleshooting, R/Python tips, figures
I post short updates daily. Use the folders below to browse ↓
📚 Series
- amplicon (7 posts)
- Applied Statistics for Microbiome Data (8 posts)
- From Laptop to HPC: Scaling Computational Biology Workflows (6 posts)
- Metagenome Analysis Series (11 posts)
- Metatranscriptomics: From Raw RNA to Ecological Interpretation (5 posts)
- mycobiome (1 posts)
- Size-Fractionated Microbiome Series (8 posts)
- viromics (6 posts)
- Visualization Series (5 posts)
- Whole Genome Analysis Series (5 posts)
🗂 Topics
- Mt (2 posts)
- R (1 posts)
- Databases (2 posts)
- Genomics (13 posts)
- Linux (5 posts)
- Machine learning (2 posts)
- Pangenomics (3 posts)
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Daily Bioinformatics #98: Keeping Scratch Files Alive on HPC Clusters
Many HPC clusters automatically delete files in scratch storage after a fixed number of days. Here's a simple touch-based workflow I learned to extend the lifetime of active projects without repeatedly copying large datasets.
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New Lab, New Machine: Switching from Linux to macOS as a Bioinformatician
After years of Linux as my daily operating system — running pipelines on HPC clusters, writing Bash scripts, processing terabytes of sequencing data — I just started my new position at the University of Maryland School of Medicine and, for the first time, my daily machine is a MacBook. The transition is mostly smooth (macOS is Unix-based, the terminal feels familiar), but there were enough small differences in the first few days to slow me down. This post covers the commands, shortcuts, and setup steps every Linux-trained bioinformatician needs when switching to macOS — from the keyboard muscle memory problem to Homebrew, iTerm2, Conda, and VS Code.
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How Bioinformaticians Think: Asking the Right Questions Before Running Any Tool
Anyone can run a pipeline. Type the command, wait for the job to finish, get a table of results. The hard part of bioinformatics has never been execution — it is deciding what question the pipeline should actually answer. This post is not a tutorial. It is about the thinking that happens before the first command is typed: how to turn a vague curiosity into a testable question, and two underrated sources of analytical maturity that nobody talks about — reviewing manuscripts and reading GitHub issues.
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Your Code Is a Publication: How to Make Bioinformatics Workflows Citable with Zenodo
A peer-reviewed paper is not the only thing that counts as a scholarly contribution. The workflow you spent six months developing, the tutorial series that helped 200 students understand QIIME2, the reproducible pipeline that three other labs are now using — these are real contributions to science. They should be citable, findable, and part of your academic record. This post covers how to archive bioinformatics workflows on Zenodo, get a permanent DOI, add resources to your ORCID, CV, and website, and why this matters for faculty applications, grants, and building long-term scholarly impact.
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Excel Skills Every Bioinformatician Should Know (And Isn't Embarrassed to Use)
Let's be honest. Every bioinformatician uses Excel. Metadata lives in Excel. Collaborators send Excel files. You do a quick check in Excel before you write the R code. This post covers the Excel skills I reach for every single day — filters, freeze panes, conditional formatting, pivot tables, VLOOKUP, XLOOKUP, COUNTIF, SUMIF, text functions — with real examples from MAG tables, taxonomy summaries, differential abundance results, and sample metadata. Plus: an honest answer to Excel vs R vs Python, because pretending you never use spreadsheets is just not true.
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Why Every Bioinformatician Should Learn Git (And How to Set It Up Today)
You spend weeks writing a pipeline. It works. You make a small change. It breaks. You cannot remember what you changed. This is the scenario Git was built to prevent. This post explains what Git and GitHub are, why they matter for bioinformatics specifically, and walks through a complete setup guide — installing Git, connecting to GitHub with HTTPS authentication, cloning lab repositories, and the daily push-pull-commit workflow. Includes a lab organization setup, common authentication problems, and an optional SSH configuration for password-free access.
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Re-working Figure 5: Visualizing Functional Redundancy (FRed) Across Bays, Seasons, and Salinity
Final version of my R-based figure for the FRed manuscript revision, including data cleaning, outlier filtering, and multi-layered ggplot styling.
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My Journey into Microbiology, the Deep Sea, and Bioinformatics
From traditional medicine to deep-sea expeditions to becoming a computational microbial ecologist — this is my story.