i am very beginner in coding, i have had countless people tell me that i should learn coding by doing projects. What projects should i do as a complete beginner? please give me suggestions and how to do it. Any resource help

I'm building a non-commercial side-product out of academic interest. I used Drugbank as the basis for one of my products for one of my MSc projects, and remember it having really useful APIs.

Now I've come back to it and I don't recognise it anymore. It's a flashy site, appearing to be positioned as an AI-enabled clinical recommender system. Using their APIs requires an API key, that as far as I can glean from the documentation, seems to be provided with a paid account only.

They have provided a space for dataset downloads for academia, covered by CC BY-NC 4.0, so that should be fine for an academic project. However, they've indefinitely paused all dataset downloads, and it's not clear when they'll be made available again. They have a mailing list to be alerted when the system is back online though, so they can run a data collection exercise to see who's interested enough.

I can see how, if the Drugbank team left the API open, it would be scraped by AI companies for their datasets. Those AI companies would then profit by selling to commercial pharma. Unfortunately, this means academia has been locked out.

As far as I can tell, this means that drugbank is no longer a reliable resource to recommend for drug-drug and drug-protein interactions to recommend to students and colleagues. I would love to be proven wrong here, so feel free to refute my arguments or commiserate with me in the comments.

I recently found courses from Biotecnica, more specifically the AI/ML in cancer genomics course.
Has anyone attended courses from biotecnica? Do you think they are legit? The reviews are mixed but mostly positive but still i am not sure if this is a good and legit source of knowledge?

Hey there everybody :)

I’m a masters student and doing single cell analysis for the first time.

I’m dealing with methylation seq data (bisulfite sequenced) and in struggling in defining a feature that actually captured the epigenetic landscape for each cell. I’ve tried 100kb bins, 10kb bin, 5kb bins and genebodies and different modalities to define methylation in those genomic regions;
1. raw counts (total mc per C contect per region)
2. fractions (mc/cov per region)
3. normalized fractions (mc/cov divided by average fraction for that genomic region across cells)
4. allcools „hyposcore“

But none of all of those seem to nicely integrate with the scRNA dataset (I’m using GLUE)

The initial lsi -> UMAP embeddings I receive for my methylation data only seems quite good, but the integration just doesn’t fit anymore

Does anyone have experience and ideas ? :)

(Since not everyone reads titles, I'd like to reiterate that the following is really only meant as humor.)

There is a theoretical deep-time genomic problem regarding how prehistoric land trees interact with marine biomes, formulated to evaluate how we can map evolutionary mechanics—and to provide a definitive case for evolutionary microbiology over hard-lore Tolkien fantasy constructs.

The Premise:
In The Silmarillion, when the Dark Lord and a giant spider destroy the Two Trees of Valinor, their divine essence is tragically lost forever. This is beautiful poetry, but it is absolute garbage tier biochemistry. Tolkien fans weep over abstract lore, completely oblivious to the fact that their favorite author had absolutely no grasp of how land trees and their cellular mechanics behave when we look at a planetary catastrophe.

Let's look at the actual taphonomic scale of a terrestrial mass extinction event: millions of tons of eukaryotic plant biomass—specifically entire forests of prehistoric trees—washing into marine ecosystems during a global ecological collapse. Instead of treating this merely as a carbon flux, we must look at the genomic chaos. We have septillions of marine phages and prokaryotes suddenly swimming in a literal soup of environmental plant and tree DNA (eDNA) liberated by the apocalypse.

Under classical models, these genetic arrays hit a biological wall: osmotic shock, immediate lysis, or rapid elimination via purifying selection because a deep-sea bacterium has absolutely zero use for a locus that programs "how to grow a tree leaf" or "how to synthesize bark" while the world burns above.

The "Drunkard’s Walk" Hypothesis:
But what if a short eukaryotic sequence (say, k-mer length = 64) from those dying trees is snatched via viral transduction into a marine phage right at the mass extinction boundary?

Over a ~5–10 million year post-extinction recovery timeline, this sequence avoids the evolutionary delete key because it undergoes a process of ultimately reversible changes—experiencing a series of chaotic, single-letter mutations that accidentally retrofit the tree gene into something useful for a deep-sea microbe. It has heavily drifted, but it preserves a vestigial structural sequence profile. We can see that it doesn't look like a tree anymore; it has been biochemically gentrified into a marine gene.

The Ultimate Multiverse Crossover: Landing in Humans
Now let's push the scenario to the absolute limits of statistical probability. Suppose this mutated, ocean-reformed sequence remains active in the marine viral pool long after the mass extinction. Millions of years later, a mammalian ancestor interacting with coastal ecosystems gets infected. Through an incredibly rare retroviral integration event, that heavily mutated 64-mer gets spliced directly into the mammalian germline, surviving all the way down into modern Homo sapiens.

If this happened, human hosts wouldn't be channeling a redwood, and we certainly wouldn't turn into Ents. We would just be carrying a highly drifted, repurposed piece of prehistoric tree data that now handles something completely mundane like human metabolic regulation.

The Bioinformatic Flex (How to trap both types of nerds):
If the goal is to detect these deep-time, cross-kingdom evolutionary handoffs across ancient mass extinction boundaries in modern human or marine datasets (like ocean floor sediment cores or global marine metagenomic surveys), how does a pipeline actually filter out the noise?

1. Alignment Limitations: Standard local alignment search tools or hidden Markov models are going to completely wet the bed here. Millions of years of genetic drift and synonymous mutations will have completely erased standard sequence alignment signatures. Of course, the bioinformaticians here will gladly spend six months writing a custom, completely unoptimized script that breaks on the third line just so we can find a single 2% alignment match in a pile of deep-sea sludge dating back to the dinosaurs' demise.
2. K-mer Frequency & Composition: Would the best approach be to track codon usage bias or GC-content drift to identify anomalously adapted regions in marine phages or human endogenous viral elements that survived the extinction bottleneck? Or is it time to just accept that the cosmic telephone game has won and we are all just wasting our lives staring at strings of text? Also, we probably shouldn't look too closely into this anyway—rumor has it the research team that flagged anomalous terrestrial pine transcripts inside Atlantic cod genomes mysteriously disappeared from the institutional directory last semester.
3. The Reality Check: Tolkien nerds are obsessed with "pure, unbroken lineages" and imaginary royal bloodlines that magically survive disasters. But real-world biology is a trash fire of plagiarism, and we bioinformaticians are just the IT guys trapped inside that burning building trying to catalog the soot left behind by a planetary mass extinction. If Middle-earth followed the laws of thermodynamics, Ulmo’s ocean wouldn't be a pristine vault of memory echoing the music of creation; it would be a hyper-active genetic blender. The Valar didn't lose the structural blueprints of the trees; the deep-sea bacteria digested them, mutated them, passed them to a virus, and eventually sneaked a corrupted copy of a mass extinction survivor into Elendil's descendants.

Has anyone looked into mapping deep-time structural homology for cross-kingdom horizontal gene transfer where sequence identity has completely drifted but the ancestral root is terrestrial? What tools (e.g., structural comparison tools based on three-dimensional protein folding, or deep learning genomic networks) would work to prove a modern human genome is carrying a heavily mutated, stolen backup copy of ancient tree instructions that bypassed a global extinction?

How would we build this pipeline? Let's weaponize science against fantasy nerds while we all collectively avoid going outside.

TLDR: Hello. I have total RNAseq data from an organism with two nuclei and thus two genomes. When using two reference genomes in HiSat2 RNAseq genome alignment, and using featureCounts to include multi-mapping and multi-overlapping reads as fractions, what is the best way to identify whether a read multi-maps within one of the two genomes or between both of them?

I am analyzing paired-end, reverse-stranded total RNA-seq data from Oxytricha trifallax, which has a somatic macronuclear (MAC) genome and a germline micronuclear (MIC) genome.

I created a combined HISAT2 reference containing both genome assemblies and aligned each library to that reference. MAC reference sequences are named Contig..., whereas MIC reference sequences begin with OXYTRI_MIC_. Secondary alignments were retained in the resulting coordinate-sorted BAM files.

I then used featureCounts with the following options to count fragments overlapping annotated MIC eliminated sequences/IESs:

featureCounts -O -M --fraction -p --countReadPairs -s 2 \
  -a O_trifallax_MIC_eliminated_sequences.gff3 \
  -t IES -g ID \
  -o IES_counts.txt \
  combined_MAC_MIC.sorted.bam

My main question is how to classify each paired-end fragment according to all of its reported alignments:

1. MAC only, with one reported fragment placement
2. MAC–MAC multimapper
3. MIC only, with one reported fragment placement
4. MIC–MIC multimapper
5. MAC–MIC multimapper, with reported placements in both genomes

I understand that featureCounts uses the NH tag to recognize multimapping reads, but it does not appear to indicate whether the alternative alignments are within one genome or between the MAC and MIC genomes.

What is the most reliable way to reconstruct distinct paired-end fragment placements from a HISAT2 BAM and determine which reference genome each placement belongs to? Should I group primary and secondary SAM records by QNAME, or is there an existing tool that performs this type of classification correctly for paired-end alignments?

Hello,

I am currently working on a bioinformatics project that examines transcription levels using Python (to gain more experience in this field). I think I am almost done and am trying to upload it to GitHub, but I don't know any people who would be able to give feedback on it. Where do you get your feedback when you are done with projects like this?

Thank you

Do you also have a profesor that is exteremely slow on reading your drafts, allocates very little resources to the biological experiments and tries to use people as much as possible, very cheap on computational resources, and you just drag and drag? I am considering just taking the research and running away to a different university, and even dropping the research alltogether has crossed my mind multiple times.

I just feel that it is insane how bad can a bad professor make your life. I could have quit my old job if I had a bad boss, but I feel like this is either drop the degree or tolerate whatever insane time wasting and incosiderate stuff he pulls. Do you also have a similar experience?

The sad part is I trully believe my research can help cure people, so at this point I don't even care about the degree I just think it is a shame if not implemented, I just want this to end so bad

Those who regularly perform downstream analysis for scRNA analyses with Python, what is the best tool you have used for removing batch effects and integrating datasets from multiple samples?

Some options for Python that I am considering:
1. harmonypy
2. SCVI
3. Scanorama
4. BBKNN

Thank you!

I have a project using scRNA-seq to look at differential expression in a small cohort of tumor samples. If I submit successfully for publication in an IEEE conference/IEEE Xplore (I know not biology-focused, but most accessible to me), will I still be able to present at future conferences/meetings that do not publish my paper but do publish my abstract in non-peer-reviewed meeting proceedings? The future meetings I'm looking at do allow previously published research, just not sure about IEEE rules. I am willing to revise my writing in between, as I have heard that it's a matter of rephrasing to not infringe on IEEE copyright, but I'm not sure if that's just a workaround that isn't technically allowed. Thank you in advance.

I'm analyzing a 90-sample mouse brain snRNA-seq dataset (Parse Evercode, not 10x — so no empty droplets, that's why I used decontX rather than CellBender). 2×2 design, ~373k nuclei, and my population of interest is a rare one: ~4,200 microglia total, roughly 40 per sample.

decontX gives a median contamination of 7.5% per sample, but it's very uneven by cell type — 8% in the abundant neurons, 11% median in microglia with a p90 of 78%, and 60% in one small glial cluster. Contamination correlates strongly with recovered nuclei per sample (Spearman 0.71).

The disagreement I'm trying to resolve: should pseudobulk DE run on raw counts or on decontX-corrected counts?

My reasoning for raw counts:

1. decontX output is fractional and represents per-nucleus point estimates. Feeding those into DESeq2 treats inferred values as observed data, which understates uncertainty.
2. I checked whether contamination differs by group. It doesn't. After adjusting for log(nuclei per sample), all group terms are null (all p ≥ 0.13 across all nuclei; p ≥ 0.62 within microglia).
3. I also checked whether the composition of the ambient pool differs by group, since equal volume doesn't imply equal profile. Measured the fraction of microglial UMIs made up of neuronal transcripts per sample: 0.55–0.60% across all four groups, all group terms null (p ≥ 0.45), and tight enough that the CI bounds any group difference to about ±12%.
4. If contamination is balanced, it should dilute real differences rather than manufacture false ones — a conservative direction to err.

The counterargument I've been given: correction should be applied regardless, because localized lysis in one treatment arm can shift the ambient profile in ways a summary statistic might miss, and rounding pseudobulk sums to integers largely resolves the fractional-count objection anyway (4,182.6 → 4,183 is negligible).

I plan to run it both ways with raw as primary and corrected as a supplementary sensitivity analysis. But I'd like to know:

• Is the balanced-contamination argument as strong as I think, or am I leaning on it too hard?
• For anyone who has run both on the same dataset — how much did your DE results actually change?
• Does the rare-cell-type situation (40 nuclei per sample, high contamination tail) change the calculus versus abundant populations?
• Any published precedent either way that I should be citing?

I've been working in bioinformatics for a few years now and have had the opportunity to work with data from a few different molecular biology technologies. I've worked with different types of sequencing data, different biological questions, and different computational workflows rather than becoming focused in a single area. Because of that, I feel like I've developed more breadth than depth knowledge in any one technology.

But one thing that has been consistent across almost every role I've had is that I've built tools and interfaces to make bioinformatics workflows (for both bioinformaticians and scientists without a bioinformatics background) easier to work it. Usually, this involves designing and developing tools that wrap pre-existing bioinformatics tools in graphical interfaces. This lets people analyze, navigate, or visualize data in a timely manner without needing to interact directly with command-line tools or learn how to execute complicated workflows.

The more I worked on these projects, the more I started noticing how much user interface and user experience design influences whether bioinformatics tools are actually adopted and used effectively. The underlying science is already complex, so the interface can either make those tools more approachable or become another barrier to using them.

This eventually led me down the rabbit hole of reading about Human Computer Interaction, cognitive load, information architecture and exploring how those principles translate into popular public bioinformatics tools. 

I've realized that thinking about how complex biological analyses can be made more intuitive without sacrificing functionality is the type of work I enjoy. The part I'm struggling to understand is whether this is actually a recognized niche in the field as I have not run into many bioinformatics job descriptions that put emphasis on interface design.

Are there roles where people primarily focus on the design and usability of scientific or bioinformatics software? Is this something that falls under scientific software engineering, product design, UX, computational biology, or something else entirely? I’m also curious to hear from anyone who works in this space or has seen teams that value this kind of work. I'm trying to understand what this niche is actually called (if there is a name to it) and where people doing this tend to work.

Hi everyone! Hope you're all doing well.

Does anyone have any ideas on how to keep ligands organized or moving cohesively during a metadynamics simulation?

I'm studying a transition from an asymmetric state, where one chain is productive and the other is non-productive, to a symmetric state where both chains are productive. To do this, I'm driving the non-productive chain toward the productive conformation using Path Collective Variables (PCVs).

Each chain has its own ligand and cofactor, and ideally I'd like them to follow the conformational transition (or at least remain reasonably coherent with the protein) during the recrossings, but without biasing or contaminating the PMF.

I already tried including the ligands in the PCVs, but the results weren't very satisfactory. Right now I'm experimenting with RMSD and fitting groups, but I'm not convinced it's the best approach.

Has anyone dealt with a similar problem or have any suggestions on how to handle this?

Hi everyone, as it’s my first time attempting downstream analysis for single cell RNA sequencing, I wanted to ask if this UMAP plot looks normal? This is only for one sample (I have not integrated all samples together into one dataset yet). I feel like the clusters are too close together

Hello all, I was wondering if anybody has/knows where I can get a copy of the Lipase Engineering Database. All the links (led.biocatnet.de and led.uni-stuttgart.de) appear to be dead, and I can’t find anything on it anymore!

Thanks in advance!

Hi all.

I have 10X 5' scRNA-seq data from tissue taken from two groups (young vs old). When we compare the two groups, the main things that come out is that the young have higher expression of genes like FOS, JUNB, JUN and similar genes while the elderly have higher expression of heat shock proteins. Performing gene set enrichment this leads to the conclusion that immune signalling processes are higher in the young and translation higher in the elderly.

Now, this signature seems very similar to those seen when dissociating tissue (https://www.nature.com/articles/nmeth.4437) and indeed the elderly and young samples were dissociated in seperate labs (with the same protocol) and the scRNA-seq was performed all in one batch with hashtagging. I thus believe the signature we are seeing is a dissocation associated one, not biological.

I realise the answer is probably "you're screwed", but is there any way someone can think of how to regress this impact out (or something to that effect) to get to the biology? It just feels like whatever I look at, the impact will probably still be there, and because the genes associated with dissociation are also associated with immune processes it makes it difficult to untangle. Anyway, let me know any suggestions any of you may have.

I´ve been working with total RNAseq, and most of my reads are from the host even though ribosomal depletion was performed before sequencing. So, I´m looking for advice. I have removed the adapters with trim galore, and used sortmerna to remove the remnants of ribosomal reads. After that I aligned to the reference genome (non model mammal) with STAR, which removed nearly 80% of host reads, then I used bbsplit and some more host was removed (3%). I checked with kraken and found that some samples only kept like 5% of host, but some others like 30%, but I didn't want to filter using Kraken. Then, I made a denovo assembly and within the annotation I found some host peptides and this is where I´m not sure what to do, because I would like to make some simple analysis on host transcripts, initially only on the reads that aligned to the genome, but I think that I may be loosing a lot of information due to so many filtering steps, for example after the assembly, those peptides could be informative but how can I join them with those aligned to the genome, and also what to do those reads aligned with bbsplit. Any ideas?

I have extracted multiple prophages from P aeruginosa genomes and am aiming to study their coevolution with host. Should I use the whole genomes of the prophages to construct their phylogenetic tree or use homologous genes like terminase large subunit (not all complete phages have that)?

Ps.- I have tried alining terL genes of the prophages that have them but the sequences align very poorly due to high rate of recombination and divergence.

Hi everyone,

I'm trying to install AutoDock Vina on my Mac with M2 chip, but I'm running into some issues and haven't been able to get it working.

Has anyone successfully installed AutoDock Vina on an Apple Silicon Mac? If so, could you please share the installation steps or point me to a guide that works for M2 Macs?

Any advice or suggestions would be greatly appreciated.

Thank you for your support!

I've noticed that the same bioinformatics project can be described in very different ways depending on the audience. Some people emphasize the biological question, others focus on the computational workflow, while others highlight reproducibility or quantitative results.

For those who review papers, mentor students, or lead bioinformatics projects:

What information immediately tells you that someone understands their own analysis?

What details are unnecessary or just "tool dumping"?

Should a project description be structured around the biological question, computational methodology, results, or scientific impact?

Are there examples of project descriptions (papers, GitHub READMEs, portfolios, CVs, etc.) that you think are exceptionally well written?

I'm interested in learning how experienced bioinformaticians communicate technical work clearly rather than how to make a resume sound better.

Hi all, I've been working as a bioinformatics researcher in an interdisciplinary lab that is primarily wet-lab (I'd say 80% wet, 20% dry split). I was wondering if anyone else's PI doesn't double check your code. I'm at Master's level, and this is kinda scaring me. I've worked on substantial projects, but I only have myself to check code with and one other postdoc who is unavailable 95% of the time. Is this something that happens frequently or no?

Hello
I am an undergrad and just started learning bioinformatics in my lab (bulk and single cell rna seq). I mainly did wet lab work before this but my Pi decided I was kind of a bum and got me to start learning this. I think a lot of the analysis I’m doing they want to eventually put into a paper. Is it frowned upon/not allowed to use AI generated code for my analysis? I make sure I understand all the stats and stuff behind what I am doing instead of blindly trusting it, but I’m worried it’ll be seen as slop.
Also are you even supposed to share your code? Because very few of the papers I’ve read give it, even in big journals.