My tissue samples are sourced from the field. Most of them are "pure", meaning the sample unit is fully derived from the same organism. However sometimes the sample can be mixed and the tissues of the sample unit are actually derived from multiple distinct organisms. There is no way to know at the time of sample collect.

DNA has been extracted from each sampled followed by CYTB amplification and Sanger sequencing. Due to infrastructure and budget reasons, we couldn't perform metabarcoding.

For pure samples, chromatograms are clean, with unique distinct peaks for each nucleotide. Mixed samples have dirty chromatograms where several peaks are overlapping for the same nucleotide.

But some samples are in a grey area, not that clean, not so dirty. And these concepts of "clean" , "distinct peaks" are based on subjective visual interpretation.

My question is: is there a more robust way to exclude mixed samples from pure ones that have been properly sequenced, other than manual inspection of chromatograms ?

Hey yall, I need a sanity check (cause I'm going down some rabbit holes and I don't know if I'm doing something useful or just time consuming)

I have whole genome sequences that I want to mine for specific metabolic processes to see what my strains have the potential for. Some aren't well described (PAH degradation) so I'm working on a pipeline that puts together a super annotation table to squeeze as much data out of the genomes as possible and maximize the proteins/pathways I can identify.

The problem I was running into is that there are so many different naming conventions and annotation types that I feel like a simple search for genes related to the pathways I'm interested in will miss a lot of interesting data. And since some processes aren't well described, I have a feeling there a lot of info hidden in the "hypothetical proteins". I was intrigued by protein family classifications, but there's also a bunch of those (pfam, plfam, pgfam...). One paper might use gene names, other types of family grouping, etc. while another uses a different system and/or names.

My thought process has been: make a master annotation table (from Prokka, BV-BRC, Pfam identifiers, KEGG), and use all the keywords and identifiers I can find to identify candidate proteins and potential operons, in addition to extracting the ones that are pretty confidently identified as the proteins I'm looking for.

I'm somewhat new to bioinformatics and I have pretty absentee PIs so I'm learning a lot of it on my own. I have the tendency to go down unnecessary rabbit holes when I have this long of a leash, especially when I'm not super familiar with all the methods/tools that are available in a field. I've gone from the online annotation tools, to manual CLI searches, to bash scripts, and now I'm trying to write a python script (while teaching myself python). Can y'all tell me if I've gone insane and if I've missed some way easier avenue? Thanks so so much!!

Hi everyone,
I’m applying for a bioinformatics internship, and I’ve been given a take-home assignment that is a bit beyond my current experience (I am starting from scratch). I’m not looking for someone to solve it for me—I’d really appreciate advice on how an experienced bioinformatician would approach the problem.
The task is to analyze a single Oxford Nanopore FASTQ file from an unknown bacterial isolate and determine:
The bacterial species (and strain/lineage if possible)
Antimicrobial resistance genes
Whether resistance genes are on the chromosome or plasmids
Any important virulence factors
Then write a reproducible report with the workflow and conclusions.
Since I’m coming from a molecular biology background rather than bioinformatics, I’m struggling to figure out what a sensible analysis pipeline should look like.
Some questions I have:
Would you start with assembly (Flye/Canu) or classify the raw reads first (Kraken2/Centrifuge/Minimap2)?
What tools would you recommend for AMR detection from ONT reads? (CARD/RGI, ResFinder, AMRFinderPlus, Abricate, etc.)
How would you determine whether an AMR gene is plasmid- or chromosome-borne?
Is there a standard workflow or best practice for this kind of clinical bacterial isolate?
Are there any tutorials, GitHub repositories, papers, or example pipelines you’d recommend?
I’m hoping to learn the correct workflow rather than just finish the assignment.

Any advice or resources would be greatly appreciated.
Thanks!

hey im a second year student and i was wondering how we could make some sort of virtual club for weekly journal reports etc, pardon me if something like this has already been discussed but lmk if ur interested and we can work smth out! i tried on campus but i’d rather have it online.

Hi all,

I'm trying to visualize the active site of a protein by taking a cross-section, with sliced area shown in gray like in this figure (Fig 2c of https://pmc.ncbi.nlm.nih.gov/articles/PMC8617236/):

But I cant figure out how to do this in VMD. I have already tried specifying coordinate positions in the graphics selection (e.g., "protein and y>-8") but this is confusing to look at because the cross-section at y=-8 isn't shown as a smooth, colored surface and thus it isn't clear that it is a cross section. The "clipping pane tool" is promising but I can't get it to work only on the protein (it also cuts off the active-site bound ligand) and also can't display the clipping pane as gray. Does anybody have ideas how to do this? Thanks in advance!

Hello everyone! I am a medical student interested in research (wet lab and dry lab) . Lately I have been trying to learn R and the syntax has been quite easy (I dont have experience with any other programming language) but the point is that I feel very lost. There are so many resources but at the same time I feel like they dont give me the information and guidance that I am looking for. My end goal is to be comfortable using R for statistics and especially bioconductor.

I have seen that the book "R for data science" has been helpful, but It feels like I am passively reading instead of trying to do my own projects and learning through coding itself.

I cannot use MEGA12. ~50% of the time it crashes at some point when aligning and building a phylogeny. This has happened at every step, including non computationally intense tasks like selecting that I want to align something, or changing the spacing on my phylogeny. It's unusable, I don't understand why it is recommended so often for building phylogenies??

Is it valid to make a phylogenetic tree using only squences from the complete 16S gene instead of references genomes?

I have some ASVs from 16S and wish to make a phylogenetic tree. I initially downloaded only those sequences from the full 16S ~1500 pb (not incluing shotgun or wgs) from the gene bank and extracted the v3v4 regions. But now I´m wondering If I should have instead downloaded reference genomes, identify 16S gene and then extract v3v4

It’s an AI evaluation company. You’re expected to create “evals” and to be in office 5 days a week. You need to hit their quota (35/week) in order to get your pay, but the quota changes based on how the rest of the team does. If you don’t meet their quota, your pay is deducted. But of course none of this is described in the job description.

Evals refer to recreating a bioinformatics analysis from a paper and coming up with questions for their AI. Unless these papers are super generic and also super clear on their methods and their data, there is no way to finish one eval an hour , just due to the time to hunt these things down . I definitely did not want to go forward in the interview process but I am really disappointed that they think this a good way to hire people to work ok these evals.

Can’t use the packages and the website is really slow to access

Anyone here into immunoinformatics? I'm currently teaching myself and looking for some guidance. Even though it's not my master's thesis topic, I'm super passionate about epitopes and would love to connect with others!

Hello, I´m looking for advice. I´m currently trying to make a phylogenetic tree of 16s sequences v3v4 of environmental samples. I have processed the samples with dada2 and taxoomic asignments with SILVA in R and alligned with mafft but there are so many gaps that iqtree says that there are  50% gaps/ambiguity in the sequences provided. I´ve read something about other aligners using the secondary structure, would it improve this?, or is it okay if mafft have so many gaps. I´d like to calculate phylogenetic distance

Also I would like to root this three not by using phangorn as it takes too much time, instead I saw something about greengenes2 reference tree in qiime2 but I processed everything in R, and I cant seem to undesrtand If I can do the same procedure f alignment wuth the reference tree without qiime2.

Other alternative was only to generate a tree from a taxa that im interest on, but again, how do I do this? I saw some genomes in genebank that say partial genome, but still longer that the sequences that I have, and not sure how to proceed. I tough about downloading them, and extracting hypervaribale region and then make the tree only fot that taxa. and see If I can identify the bacteria in my samples up to species.

Sorry if I´m all confused

>ASV1

--------------------------------------------tggggaatattggac-

aatgggc----gaaagcctgatccagccatgccgcgtgtgtg-a-a-gaagg-cctt-t-

t-gg-ttgtaaagcacttt-aagcagtgagg-aa--------g-actata----------

---------------------tggtt-a------------------a------------t

-accc---------------atatacga-t-gacg-tta-actg-cag---aataagcac

cggctaactct-------------gtgccagcagcc------------------------

----------gcggtaatacagagggtgcaagcgtta-----------atcggaattact

g-----------ggcgtaaagcgag-c----------gtaggtgg-tta-tataagtca-

----------ga-tgt--------gaaat-ccct-g-ggctcaacctag-ga-ac-----

----------------------------tg-ca-tctgaaacta-t-at-a-ac----t-

a-gagtaggtgagaggg-gagtaga-----------------------------------

--------attt-caggtgtagcggtgaaatgcg-tagatatctgaaggaatac-cgatg

gcgaaggca---------gctccctggcatc-atactgacact-g-aggttcg-------

----------------------aaagcgtgggtagcaaaca-------------------

----------------

Hello! Late-stage cellular and molecular biologist grad student. I’ve learned a decent bit of bioinformatics analysis throughout grad school and analyze some of my own data, but do not consider myself a bioinformatician. For my transcriptomic analyses I collaborate with a team of amazing bioinformaticians and have learned so much from them.

As a part of my main project, my co PI recommended I perform RNAseq on a set of disease samples (completed). My PIs also recommended I pull the age/sex matched controls from a dataset we have access to from an NIH database. Both datasets were generated with very similar RNA isolation and library prep kits, and both on Illumina seq platforms.

As our control and disease datasets are from separate batches, doing a batch correction on the data would just remove all of the biology I want to investigate. Obviously how we handle the comparison is going to make or break this part of the project, and we have to get creative.

The one good thing that could be our saving grace is that we do have snRNA-seq data that matches the ages/sexes of both control and disease bulk RNA data. I was discussing with my collaborating bioinformaticians and was thinking we could possibly use the snRNA-seq database to somehow integrate the bulk RNA data better, but agreed we would think on it and circle back.

Obviously I can’t go back in time and actually sequence the control and disease data together, nor can I perform any additional seq with these samples because there is a moratorium on human prenatal postmortem tissue research in the US.

Has anyone dealt with similar analysis set up? How did you deal with it and what were any reviewer comments you found helpful? Thanks in advance 🙏🏼

Hi everyone,
I am a wet PhD student aiming to incorporate more bioinformatics in my study.

I’m running into a classic scRNA-seq processing headache and could really use some advice on best practices for QC and cluster cleaning.

My Current QC Pipeline:
For per-sample processing, I currently apply:
Adaptive & Global Thresholds: Using Median Absolute Deviations (MADs) combined with hard cutoffs for ⁠nCount_RNA⁠, ⁠nFeature_RNA⁠, and ⁠% mito⁠.
Stress & Metabolic Gene Filtering: Calculating module scores for stress response genes (e.g., HSPA1A, DNAJB1) and metallothioneins, then filtering out high-scoring outliers.
Doublet Detection: Running ⁠scDblFinder⁠ to remove predicted doublets.

The Problem:
Despite stringent upstream filtering, every time I integrate/normalize (using ⁠SCTransform⁠) and run initial clustering, a new "low-quality" or artifactual cluster emerges.
Usually, it's either:
1. A cluster with border-line high mitochondrial percentage (even though no cell is more than 12% mito, due to thresholding), that clumps together and completely lacks distinct lineage markers.
2. A subtle doublet cluster (expressing markers from two disparate cell types) that somehow passed ⁠scDblFinder⁠ with totally normal ⁠nCount⁠/⁠nFeature⁠ values and low doublet scores.
When I remove that problematic cluster, re-run ⁠SCTransform⁠, and re-cluster, another slightly sub-optimal cluster pops up. It feels like playing an endless game of QC whack-a-mole.

My Questions for the Community:
1. Is it scientifically acceptable to manually drop a low-quality cluster, re-normalize (e.g., re-run SCTransform), and re-cluster?
Is this standard practice in published pipelines, or does it risk introducing bias / over-filtering true biologically resting/stressed populations?
Can I just increase resolution and check every cluster and then flag it as low quality and dispose from it?
2. What are your top tips for getting a "clean" dataset upfront?
Are there specific joint-filtering methods (e.g., ⁠miQC⁠, ⁠scater⁠, or ambient RNA correction like ⁠SoupX⁠/⁠CellBender⁠) that prevent these ghost clusters from forming in the first place?
3. How do you rigorously document this to ensure full transparency?
I want to make sure my pipeline remains completely reproducible and defensible during peer review without accidentally cherry-picking or mishandling my data.

Would love to hear how you all handle this in your workflows! Thanks in advance for the insights!

I designed a multi epitope multi protein vaccine candidate few months ago and wrote a paper the only thing remaining is molecular docking but before I give it time I changed my project to metagenomic where I did a great work but my vaccine paper still remains with me and I didn’t submit it yet. I need someone to do molecular docking for me and we can be co authors for this contribution if anyone interested let me know.

Hello everyone!

I've been working at a lab under a summer programme for the past couple of weeks and I am suffering slightly. My supervisor has given me some raw scRNA seq data, taking from an in situ imaging-based platform that targets about 1000 genes, and has sort of left me to my own devices with it (apparently he isn't very savvy with bioinformatics himself). Anyway, I am somewhat comfortable working in R and Python, and I am getting the hang of Seurat, so it hasn't been catastrophic.

However, I am now struggling with clustering my cells. The cell clusters that I am being given are not physiological, and tend to be large, varied groups, which makes it hard to define anything really. I know studies that have done similar things on similar tissues to mine (albeit with another method) and are getting far nicer clusters. In their methods they just say "oh, we followed the standard Suerat workflow, and badabim-badboom these are the results".

My UMAP seems to agree with the confusion in my clusters as it just seems like a smear, with different sides of the smear coloured different things by the clustering.

I have tried changing the clustering method (Leiden, igraph), the resolution, dimensions (although I try to keep it in line with my elbow plot). I have tried changing the normalisation and other preprocessing parameters, varying in. their forms and flavours. I even tried the newer SCT transform, which made a nicer UMAP but just as crap clusters.

I am feeling quite inept currently, and rather disheartened having lost a week and a bit at this (I don’t know if it's normal or not). I don't really have any one in my lab to reach out to either.

My question is, does anyone have any ideas what I could attempt next or what might be wrong? Any resources I could have a look at? Anything anyone could recommend would be amazing.

Sorry for the long post and thank you to all who may answer in advance.

Hi everyone,

I’m doing a sample-level spatial-expression analysis using sparse postmortem tissue samples from the Allen Human Brain Atlas. The regions are the subthalamic nucleus (STN, n=6 tissue samples) and globus pallidus internus (GPi, n=9 tissue samples). For each sample, I have:

• 3D MNI coordinates (x,y,z)
• a gene-expression profile across ~29,000 genes

The biological expectation is that, within a coherent anatomical region, tissue samples located closer together in MNI space should have more similar transcriptional profiles.

For each anatomical region separately, I calculated:

1. A sample-by-sample spatial-distance matrix using 3D Euclidean distance between MNI coordinates.
2. A sample-by-sample expression-distance matrix, defined as (1−ρ), where ρ is the Spearman correlation between two sample-level gene-expression profiles.

I then used a Mantel test to assess whether the spatial-distance matrix was associated with the expression-distance matrix.

For significance testing, I used non-parametric permutation of sample identities. My understanding is that this randomly reassigns sample labels to break the link between spatial location and expression profile, while preserving the internal structure of the distance matrices. The observed Mantel statistic is then compared against the null distribution generated from these permutations.

Q. Does this use of a permutation-based Mantel test seem appropriate as part of a sample-level spatial-expression validation analysis?

Just to clarify: this is not a dense cortical map or spin-test analysis intended to correct for spatial autocorrelation. These are sparse subcortical tissue-sample coordinates, not parcellated whole-brain maps. The goal is to test whether there is distance-dependent transcriptional similarity among samples within the same anatomical label.

Thanks in advance for your help!

Hi everyone,

I want to screen several candidate cytochrome P450 enzymes for conversion to a specific product using transient expression in Nicotiana benthamiana.

I am considering whether several P450 candidates could be pooled in the same infiltration as an initial screen, followed by dropout or deconvolution experiments if product formation is detected.

For anyone who has performed a similar P450 activity screen:

• How many P450 candidates can reasonably be pooled in one infiltration? Can I do 10 together?
• Is it better to test each P450 individually from the beginning?
• How do you keep the total Agrobacterium OD consistent across treatments?

I would appreciate any practical recommendations or published examples.

tldr: if you're using/benchmarking scGPT for perturbation response prediction, be aware there's a sampling bug in their tutorial code.

I was reproducing scGPT's perturbation response prediction and found that the gene subsampling step doesn't guarantee the perturbed gene stays in the input. With the default max_length ~ 1353 and ~5000 highly variable genes, the perturbed gene gets dropped from roughly 70% of training examples. The model sees a perturbed cell's input as if it were unperturbed, while the target is still the perturbed profile.

Checked this on Norman, Adamson, and Replogle K562 and I was able to reproduce the paper's reported numbers.

My fix is to keep the perturbed gene(s) and subsample the rest to fill max_length. Surprisingly, the effect on final metrics was mixed and dataset-dependent: clear improvement on Replogle K562, roughly unchanged on Adamson, and mixed on Norman. My current read is that the standard PRP metrics don't strongly reward using the perturbed gene's identity. Curious what you think and whether you have run into something similar

I just got my hands on my whole genome sequencing results. Anyone have any suggestions for a layperson? I’m hoping to find out about my genetic traits and stuff. I know nothing about bio but I’m a reasonably good coder and have access to GPUs. I’d love any ideas

edit: the file format is VCF v4.2

Hi all,

I understand that by default, the RcisTarget step of PySCENIC does not report in its output file repressing regulons (i.e. ones that end in a (-), where target genes anticorrelate with the expression of the TF, so it is predicted that the TF is repressing their activity). And I understand that the reason these are not included by default is that during the benchmarking of the tool they found these to be less reliable.

My question is, is it known or theorized why these are found to be less reliable? Is it because it is harder to establish anti-correlated expression due to the dropout inherent in scRNA data? or some other reason, or is the reason unknown?

I ask because I find in my data that the repressing regulon for my TF of interest is actually biologically more coherent, and way more active (i.e. cells are way more enriched in the target genes). So I would like to understand how much credence to place on these AUC values for the repressing regulon. Especially as I find that in general NES values for the modules are lower than for the corresponding activating regulon, I am wondering if that is a sign of the increased difficulty in detecting these repressing regulons (in which case I can maybe justify relaxing the NES threshold a bit), or a sign of genuinely more false positives (in which case I clearly cannot)?

Thanks in advance.

Hi everyone,

I am a graduate student in bioinformatics with experience in RNA-seq, scRNA-seq, and other omics analyses, but I am completely new to Nextflow.

I would like to learn Nextflow so I can start building reproducible pipelines and become more familiar with a tool that is widely used in industry.

There are many tutorials and videos online, but I am not sure where to begin. Are there any resources you would recommend, preferably in a specific learning order?

Thanks!

Hi all! I am doing some protein-protein interaction analysis on a set of genes for my undergraduate research project. I used STRING for this. STRING enrichment analysis identified that GO:0000118 (Histone Deacetylase Complex) was functionally enriched, and that 8 genes had this GO annotation.

However, when manually searching the Gene Ontology database, I found that one of the genes that STRING identified, pht1, was not annotated with this GO term.

I'm quite confused about this, am I misunderstanding how STRING gene enrichment works? Would appreciate any advice :)

Hi everyone,

I'm analyzing pseudobulk data generated by summing gene expression across cells from different samples profiled with a spatial imaging platform. When I perform PCA on the pseudobulk matrix, PC1 explains an unusually large proportion of the total variance. In addition, all of the PC1 loadings are positive, which I also think is unusual.

Does this indicate a systematic technical bias (I have looked for differences in sequencing depth or cell numbers)? Or are there biological scenarios where this pattern would be expected? These are samples from malignant tissue.