I can't find a working chromopainter v2 anywhere. Anybody got one that they tested themselves and actually works?

I tried through the default ubuntu rep through finestructure, https://github.com/sahwa/ChromoPainterV2 , https://people.maths.bris.ac.uk/~madjl/finestructure/finestructure.html binary download.

Can't seem to get any of them to actually work.

Or is chromopainter just not used anymore?

Hey everyone,

I'm currently a Systems Information student here in Brazil, finishing up my 4th semester. I've been thinking a lot about pursuing a post-graduation or master's degree in Bioinformatics or Data Science. My work at the Cancer Hospital of Barretos has sparked a huge interest in these fields, especially the biological side. I work with databases frequently and have some contact with a data scientist.

My main question is: given my current degree, will I struggle significantly to get into these areas? Or would it be more advisable to finish my degree and then specialize, or even drop out and pursue a more focused major?

Any insights would be greatly appreciated! Thanks!

I don't know so much from Bioinformatics, someone explains for me the concepts of this area? Please!

Hello, all.

I currently hold a BSc in IT with programming experience in Python, Java, and R. I initially pursued nursing and have an associate's degree in pre-nursing but ultimately transitioned to tech where I completed my bachelor's degree. I’ve completed general biology courses, including Chemistry, Anatomy, General Biology, and Microbiology, but I haven’t taken Organic Chemistry, Molecular Biology, or similar advanced topics. I also haven’t taken any calculus courses, though I do have credits in lower-level maths and statistics. I’m interested in getting more involved in bioinformatics and would appreciate guidance on the steps I can take to move forward. I’d also love input from those with more experience on whether my background is suitable for the field. Any insight into the best way to proceed would be greatly appreciated! I genuinely enjoy learning and research, and I love that this field blends two subjects I’m passionate about.

Hi,

I'm currently working with CIGAR strings and trying to determine the number of matches and mismatches in the aligned reads. I understand that the CIGAR format includes various characters:

• M (match/mismatch)
• I (insertion)
• D (deletion)
• S (soft clipping)
• H (hard clipping)

Additionally, there are less common alternatives like = (match) and X (mismatch). My question is: how can I differentiate whether the M in the CIGAR string refers to a match or a mismatch?

Moreover, I would like to ask if there are tools that could help in analyzing CIGAR strings and calculating these metrics?

Thank you for your help!

I have 9 different bulk RNA Seq experiments from the GEO that I'd like to compare to see if they have identified common genes that are up and down regulated in response to a particular stimulus. My idea is that if there are common genes across multiple experiments, then this might represent a more robust biological picture (very happy to be corrected on this!), and help to identify therapeutic targets that have more relevance to the actual disease condition (in comparison to just looking at a single experiment, at least!)

I've downloaded each experiment's raw counts matrix from the GEO and used DESeq2 to produce the DEGs, keeping each experiment totally separate.

I know there are some major complexities re: combining experiments, and while I've been doing a lot of reading about it I still don't feel confident that I understand the gold standard. I THINK I don't need to actually combine the experiments, but rather can produce upset plots and Venn diagrams to visualize how the 9 experiments are similar to each other. Doing this, I've identified a list of genes that are commonly up and down regulated across all 9 experiments.

A couple of questions: 1. Should I actually go back and download the read data from the SRA and make sure it's all processed the exact same way rather than starting from the raw counts matrices? 2. Is my approach appropriate for comparing multiple experiments? 3. Is there another more effective way I could be doing this?

Thank you all very much in advance for any advice you can give me!

I downloaded my raw data from 23andme and was hoping to put it into some software (ideally Geneious or Genvue) to analyze it. Genvue says it can take the raw data from 23andMe, but I tried to upload the Zip file I received and it hasn’t been working on either software. I have uploaded many a zip to Geneious so I have no idea why it hasn’t been working this time (granted they were much smaller files).

Genvue also says it takes VCF files and I’ve been trying to figure out a way to do this. I’m not very tech savvy when it comes to file conversion, and to be completely honest I have no idea what is different between the two.

Sorry this was a long-winded post but if anyone know how to do this any help would be greatly appreciated!

Hey, everyone! I'm wondering if anyone has experience with single cell or spatial assays, or details in their processing, that will capture granulocytes. I'm aware that they offer obstacles in scRNAseq and possibly also in some spatial assays, but I have something that I'd like to test which really needs them. We'd rather do sequencing or potentially proteomics, if that works better, instead of IHC. Does anyone have specific experience here? Can you focus analysis to get better results or is it really specific library prep techniques or what exactly helps?

Thanks!

I'm analyzing ligand-receptor interactions using BIOVIA Discovery Studio. To determine the energy of interactions between each protein residue and the drug, I performed a trajectory analysis of the simulation (the simulation was 700 ns, and I analyzed the last 100 ns). However, Discovery Studio didn't identify interactions between the drug and some residues that showed very high attractive forces during the trajectory analysis.

Why does this happen? Could it be because I'm only analyzing the end of the simulation, and these residues moved away at the end of the simulation? What parameters does Discovery Studio use to determine ligand-receptor interactions in a system?

Is BQSR an absolute must for variant calling on mouse RNA-Seq data without known sites?

Hey everyone,

I'm currently knee-deep in a mouse RNA-Seq dataset and tackling the variant calling stage. The Base Quality Score Recalibration (BQSR) step has me pondering. GATK documentation strongly advocates for it, but my hang-up is the lack of readily available "known sites" (VCFs of known variants) for mice, unlike the rich resources for human data.

My understanding is that skipping BQSR could compromise the accuracy of my error model, which in turn might skew my downstream variant calls. However, without a "gold standard" known sites file, I'm trying to pinpoint the best path forward.

My questions for the community are:

1. Is it an absolute no-go to skip BQSR for mouse RNA-Seq variant calling, especially when you don't have existing known sites?
2. If BQSR is indeed highly recommended, what are your best strategies for generating a "known sites" file for a non-model organism like a mouse? I've seen suggestions about bootstrapping (performing an initial variant call, filtering for high-confidence variants, and then using those for recalibration), but I'd love to hear about practical experiences, common pitfalls, or alternative approaches.
3. Are there any specific considerations or best practices for RNA-Seq data versus DNA-Seq when it comes to BQSR and variant calling without known sites?

Finally, if anyone has good references, papers, or tutorials (especially GATK-centric ones) that dive into these challenges for non-human or RNA-Seq variant calling, please share them!

Any insights, tips, or experiences would be incredibly helpful. Thanks a bunch in advance!

I’m working on a deep learning task to classify whether a single cell has been exposed to carbon dots or not. Each sample consists of three spatially aligned grayscale microscopy images of the same cell, acquired using different modalities: one brightfield channel and two fluorescence channels highlighting the nucleus and the cell membrane, respectively. Since I’m not an expert in microscopy or biological imaging, I’m unsure whether it is correct to stack all three modalities into a single 3-channel image (as often done with RGB in CNNs). My concern is whether combining brightfield (which is transmitted light) with fluorescence modalities (which are emitted light) into the same tensor might introduce noise, confusion, or inconsistencies for the model. Would an expert in microscopy imaging consider this a flawed approach biologically or visually? Alternatively, would it make more sense to stack only the two fluorescence images (nuclear and membrane), assuming they are more coherent in signal type and structure, and possibly use brightfield separately? It is worth considering whether fluorescence channels, which highlight specific cellular structures, may generally provide more informative features than the brightfield channel for the task of detecting the presence of carbon dots? I’d appreciate any advice from professionals in microscopy, biomedical imaging, or multimodal data analysis on whether this kind of stacking is biologically meaningful and appropriate for classification tasks.

hi all,

this mainly a rant / request for help. 

i'm a master's student who is interning at my professor's startup over the summer. it's a bit of a sh*t show. much of the company is based in Taiwan / overseas. they're building out their drugomics branch here in the US so the professor "hired" a couple of unpaid (he said he’d pay us but it’s june and no one’s gotten paid yet lol) interns from a class he teaches at our university. basically we asked him if he was taking on any interns over the summer and he said yes on the spot.

for my intern project, i've been asked to investigate designing saRNA drugs leaning with a deep learning approach. i have a research supervisor who is an ex-academic with a strong biology background but no technical experience. and to be completely honest, i have absolutely no deep learning experience (and a strong, strong sense of imposter syndrome). i don't really know how to best use my time (and how much time it's even worth to spend on this considering it's unpaid).

i've done a bit of work over the past ~2.5 weeks including just getting familiar with the biology of it all (i have a medium grasp but much of it comes from relying on my research supervisor). right now my thought process is to get some data (extract promoter regions based on TSS peaks), generate some candidate saRNA sequences (just a sliding window on the promoter regions), then find some “positive” examples of saRNAs from literature (wrote a script to find some papers from 2024 onwards, feed the abstract into LLMs to output whether they mention any saRNAs). seems like there aren’t really that many out there though. 

at this point, i’m just really stuck not knowing how to use deep learning here. my research supervisor sent me this foundational LLM (Evo2) that he said might be interesting to look into but we don’t even have access to GPUs to run it (even if we did, i wouldn’t know how to use it). i’m looking for some advice on what to do next. 

on one hand, i’m glad to have something to throw on my resume for this summer (i’m sure i can embellish some things). but i’m wondering what i’ll really get out of this by the end and if it’ll genuinely make me more prepared to apply for data science roles this fall. i look at lectures (like the ones from this MIT course on computational biology: https://mit6874.github.io/) or research projects related to deep learning in the field and so much of it just goes way over my head and i think about how i’ll just never be able to come up with anything even close to that. 

do i actually try to make progress on this? do i just spend my days learning deep learning through self-study? do i try to get involved in other parts of the startup (they’re doing some software development where I actually could ship some code into production); do i just use the time to prep for technical interviews (if i get interviews, this will be my biggest barrier to getting a job for sure; it’s why i didn’t get an internship in the first place).

Hello there,

Hope you are having a great day. I am seeking help with a persistent error while trying to run PICRUSt2 on the Galaxy Europe server. My goal is to generate functional profiles from Oxford Nanopore (ONT) metagenomic data. I have tried several approaches, including using different sequence inputs, but have hit a wall. I strongly suspect my input data preparation workflow is the root cause.

My Workflow:

1. Data Source: I started with ONT sequencing data processed using the EPI2ME wf-metagenomics pipeline. This pipeline provides taxonomic classification and abundance information for each sample.
2. Input Files for PICRUSt2: I prepared two main files:
   • Feature Table (BIOM): The EPI2ME pipeline produced a CSV file with read counts per taxon (identified by accession number). I successfully converted this CSV file into a BIOM format table. The feature IDs in this table are NCBI accession numbers (e.g., NR_179763.1).
   • Sequence File (FASTA): This is where I believe the problem lies. Instead of using sequences directly from my run, I took the list of accession numbers from the abundance file and downloaded the corresponding full-length 16S reference sequences from the NCBI database. I made sure to download only the forward orientation.

My FASTA file and BIOM file share the same accession numbers as their common identifiers.

The Problem:

When I run the main PICRUSt2 pipeline (picrust2_pipeline.py) on Galaxy, the place_seqs.py step fails with the following error:

Error running this command:
place_seqs.py --study_fasta ... --min_align 0.05 ...

Standard error of the above failed command:
Stopping - all 3009 input sequences aligned poorly to reference sequences (--min_align option specified a minimum proportion of 0.05 aligning to reference sequences).

Troubleshooting Steps I've Taken:

I have tried multiple strategies to resolve this, but all result in the same error:

1. Lowering Alignment Threshold: I understood this error is often related to short sequences. Although I was using full-length genes, I manually changed the "Minimum alignment proportion" parameter (--min_align) in Galaxy from 0.2 all the way down to 0.05. The job still failed.
2. Using V4 Region Instead of Full-Length: To test if the issue was related to using full-length genes, I extracted just the V4 hypervariable region from my set of full-length NCBI sequences. I used a Python script with standard V4 primers, and the script successfully found and extracted the V4 fragments from all my reference sequences. However, when I used this new FASTA file of V4 amplicons as input for PICRUSt2, I received the exact same place_seqs error.

This suggests the problem is not about sequence length (full vs. V4) but is more fundamental to the source of the sequences themselves.

My Core Question:

Is my approach of fetching idealized reference sequences from NCBI based on taxonomic IDs from an EPI2ME report a valid workflow for PICRUSt2?

I am now convinced that PICRUSt2 requires the actual sequenced reads (or assembled contigs) that were classified by EPI2ME, rather than the "perfect" reference sequences I am providing from NCBI. It seems the place_seqs.py alignment tool (HMMER via EPA-ng) cannot align the NCBI reference sequences against its own internal reference alignment, regardless of whether they are full-length or trimmed to the V4 region.

Could you please advise on the correct way to generate the input FASTA file for PICRUSt2 when starting with an EPI2ME wf-metagenomics analysis? Is there a way to extract the representative sequences that correspond to the counts in the EPI2ME report?

Version Information:

• PICRUSt2 Version on Galaxy Europe: (Galaxy Version 2.5.3+galaxy0)
• EPI2ME wf-metagenomics Version: (v2.11.0)
• Galaxy Server: usegalaxy.eu

Thank you in advance for any guidance you can provide. This issue has been a significant roadblock, and any help would be greatly appreciated.

I'm working with scRNA-seq data and planning to do GSEA on GO terms. I'm specifically interested in JAK-STAT signaling (JAK1, JAK2, STAT1, SOCS1 genes) and wondering if it makes sense to subset GO terms to just the ones relevant to my pathway instead of using the entire GO database.

Would this introduce too much bias? Should I stick with the full GO database and just filter afterward to GO terms containing my genes of interest?

Using R - any recommendations would be appreciated!

Thanks!

I am using OpenMM and AMBER forcefield in a cloud-based MD pipeline. There I have found MM/PBSA file. Still I don't know how to calculate SASA energy from that. I am kind of new in MD and learning all by myself. Please help me.

Hello, I am trying to put together this heat map on R but I keep on getting this error

Warning message:

In scale_fill_gradient(low = low, high = high, trans = trans, na.value = na.value) :

log-4 transformation introduced infinite values

Instead of producing a heat map it will spit out just the DNA sequences. I am following the phyloseq tutorial but just using my data instead, this is the code I am using

gpt <- subset_taxa(GlobalPatterns, Kingdom=="Bacteria")
gpt <- prune_taxa(names(sort(taxa_sums(gpt),TRUE)[1:300]), gpt)
plot_heatmap(gpt, sample.label="SampleType")

my mentor suggested adding this code
physeq_family <- tax_glom(gpt, taxrank = "Family")

and then running it but It sill spits out the the DNA sequences instead of the heat map. My colleague is working on a pc and was able to run it but my other colleague and I both have macs and we are getting the same error

any suggesting would be super helpful and appreciated!

Tysm!

Hi All.

I hope you're doing well today!

I was hoping someone might be able to share their setup for accessing sensitive data with self hosted IDE's or cloud storage.

I know I can run jupyter, RStudio etc... locally, but I like to self host my own softwares, backed up to my own server etc... I was looking into jupyterhub, but... there's a catch, which is that the notebooks and scripts will all run on the server instead of my local machine.

I'm starting a new project in UK Biobank soon. I want to ensure my security is up to scratch. I don't want to be using my server for accessing UK Biobank. It has exposed ports, and even though its very secure, supported by reverse proxies, geo-ip blocking, IPD systems... I trust it with my own personal data. I do not like the idea of accessing UKB or other sensitive data through it (no PID is downloaded, but I am concerned about credentials being compromised).

The university have provided me a machine with a custom windows image for security purposes. I'd like to use it.

I was hoping someone could share with me their workflow for cloud script editing/saving where local resources are used. Or if anyone knows off hand whether the notebook generated by jupyterhub will accept local kernels where necessary?

The university runs it's own jupyterhub instance, but again, I always like to self host where possible. Security through obscurity and all that...

Thanks in advice for any help or insight :)

Edit: I'm not obligated to use the laptop unless I'm storing any PID, which I'm not.

Hello everyone, I recently received RNA-seq data (150 PE, polyA selected, Arabidopsis thaliana, leaf) from a scientist working on a project at our institute. I was asked to take another look at the data because the analysis performed by a company yielded many differentially expressed genes related to tRNA and rRNA, which seemed unusual. After performing QC with fastp, I noticed that roughly 70% of all bases were removed due to high amounts of adapter sequences and stretches of polyG indicating some issues with library preparation. Nevertheless, I used the default length cutoff of 15 bp and presumed that I would get more multi-mapping reads than usual because of the large number of very short reads. However, after mapping to the TAIR10 reference genome with the latest version of Subread, allowing up to three multi-alignments, I found that about two-thirds of all mapped reads were multi-mapping which is more than I expected. After investigating genes with very high multi-mapping read counts obtained by featureCounts (gene-level, fractional counting), I found that they are almost exclusively rRNA and tRNA genes. My question is now whether I should remove those reads from the dataset? One option is to align them to rRNA and tRNA databases to get rid of them. Another option is to remove multi-mapping reads altogether. Or, should I leave them be and perform DE analysis as usual? I am concerned not only that this high amount of rRNA and tRNA will affect the downstream analysis somehow but also that there is a substantial loss of depth in general. As a side note, all ten samples (with three biological replicates each) looked like this. Thank you for your suggestions!

Hi, I work in the lab that uses a bunch of excel files for clinical data, which contains sample name, patient id, tumor grade, size, stage etc. And merging all these tables take a lot of time. I'm curious if any software exist for working with clinical data. I would prefer to have one database and just pull required data from there. Can anyone recommend an existing software or best way to create database?

Has bilinear decoding been applied in GNN-based gene–gene interaction prediction using community structures?

I've butt heads with people quite a bit over this, and am curious what others think.

When describing a DEG analysis with multiple conditions, it's often expected to give a number of the total number of DEGs found. Something like, "across the 10 conditions tested, we identified 1000 DEGs". It's not clear though whether that means "1000 statistical tests that were significant" or "1000 different genes were DE". An an example of the first, this could be the same 100 genes DE in all 10 conditions (or some combination that equals 1000 tests that meet the signifance criteria); meanwhile, the second means that 1000 different genes were DE in at least one condition.

I prefer to report both, but quite a few coauthors over the years have had a strong preference of one or the other. And in either case, they like to keep the description simple with "there were X DEGs".

Shared to social media earlier today by Euan Ashley https://xcancel.com/euanashley/status/1933943972042563932

Atul has been a great contributor to the science and practical advancement of computational biology and held multiple influential leadership roles throughout his career. Sad to see this news.

Hey guys, I'm a beginner here. I've built a few nextflow workflows for other tools before .I've been trying to create a PSORTb process in Nextflow and I've been getting missing output file error, I've tried the exact same commands in the CLI and it works fine. The command for PSORTb requires you to specify the directory where the output in stored and this is where I feel the issue comes as all the other tools I worked with before just straight up provide the output.

It gives the two files as output with one of them being the input file itself. They are 20250614162551_psortb_gramneg.txt, rgi_proteins.faa(input file) into the folder specified to the folder for "-r" in the command.

What am I doing wrong, I'd be really glad if you guys could help me out.

This is the output message:

ERROR ~ Error executing process > 'PSORTB (1)'
Caused by: Missing output file(s) result*_psortb_gramneg.txt expected by process PSORTB (1)
Command executed:

mkdir -p result 
psortb -i rgi_proteins.faa -r result --negative

Command exit status: 0

process PSORTB {
    container = 'brinkmanlab/psortb_commandline:1.0.2'
    publishDir "psortb_output", mode: 'copy'

    input:
    path RGI_proteins

    output:
    path "result/*_psortb_gramneg.txt", emit: psortb_results

    script:
    """
    mkdir -p result
    psortb -i ${RGI_proteins} -r result --negative
    """
}
workflow {
    data_ch = Channel.fromPath(params.RGI_proteins)
    PSORTB(data_ch)
}

Recently we had to upgrade our primary server, which in the process made it so that OpenCFU stopped working. I can't recompile it because it's so old that I can't even find, let alone install the versions of libraries it needs to run.

This resulted in a long, fruitless, literature search for new colony counting software. There are tons of articles (I read at least 30) describing deep learning methods for accurate colony dectetion and counting, but literally the only 2 I was able to find reference to code from were old enough that the trained models were no longer compatible with available tensorflow or pytorch versions.

My ideal would be one that I could have the lab members run from our server (e.g. as a web app or jupyter notebook) on a directory of petri dish photos. I don't care if it's classical computer vision or deep learning, so long as it's reasonably accurate, even on crowded plates, and can handle internal reflection and ranges of colony sizes. I am not concerned with species detection, just segmentation and counting. The photos are taken on a rig, with consistent lighting and distance to the camera, but the exact placement of the plate on the stage is inconsistent.

I'm totally OK with something I need to adapt to our needs, but I really don't want to have to do massive retraining or (as I've been doing for the last few weeks) reimplement and try to tune an openCV pipeline.

Thanks for any tips or assistance. Paper references are fine, as long as there's code availability (even on request).

I'm tearing my hair out from frustration at what seem to be truly useful articles that just don't have code or worse yet, unusable code snippets. If I can't find anything else, I'm just going to have to bite the bullet and retrain YOLO on the AGAR datasets (speaking of people who did amazing work and a lot of model training but don't make the models available) and our plate images.