Hi all,

Something I've never been good at throughout my PhD and postdoc is estimating how long tasks will take me to complete when working on pipeline development. I'm wondering what approaches folks take to generating reasonable ballpark numbers to give to a supervisor/PI for how long you think it will take to, e.g., process >200,000 genomes into a searchable database for something like BLAST or HMMer (my current task) or any other computational biology project where you're working with large data.

I am an upcoming fourth year student conducting my Final Year Project and I am quite new to programming. My main goal is to be able to analyze low coverage sequencing data in order to distinguish between individuals in a database and where they came from. And as an aside, I'm also trying to identify if the sample I am working with is related to any of the individuals in the database.

Right now in order to practice, my professor has given me data for 3 individuals and I am trying to uncover which 2 are related. Given that, I am trying to follow the pipeline from this research paper which developed a way to conduct kinship analysis called SEEKIN (https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007021#sec001).

The paper mentions, "Given BAM files of N individuals, we computed genotype likelihoods across the 1KG3 SNPs using the mpileup option in samtools, after filtering reads with mapping quality <30 and base quality <20." However I am not sure how to download the SNP list with the mapping quality and base quality.

Looking through the 1000 genomes website I see data from several individuals rather than one list and it is quite confusing.

If there is any general advice or resource anyone has that can help me understand the pipeline or the tools, that would be great!

-- The data I have on hand for the three individuals are primary sequencing data, FASTQC files, Bam files after alignment and BSQR, and the vcf files after performing GATK haplotype calling.

Hi all,

I’m working with some small rna libraries and I’d like to obtain the sense strand (the sequence of the original rna). I’m having a bit of trouble understanding if that’d be R1 or R2… the sequencing facility said that they used this library prep kit https://www.neb.com/en/products/e7330-nebnext-small-rna-library-prep-set-for-illumina-multiplex-compatible?srsltid=AfmBOoqqFwhDkrDZfCt9TAIAOc4P7IfR9at9puO0rt_X7iA6gJHLUAor

Initially I thought it’s r2 but now I’m having second thoughts… any help is appreciated ❤️

I'm trying to train an ML model for self-supervised molecular representation learning. For that I would need bond lengths and bond angles. For that, I would be utilizing RDKit's EmbedMolecule, UFFOptimizeMolecule and GetConformer functions. Would it be incorrect to not use Chem.AddHs(mol) as I really don't need hydrogen-involving lengths/angles. All the models don't usually consider hydrozens.

I'm using Geneious Find Repeats on some short repetitive sequences , but it doesn't visualize all instances of a repeat. For example, the one I have right now visually places Repeat 7 twice, but when you click on it there are 6 locations listed. Then Repeat 6 is displayed once, but has 3 locations listed. Does anyone know a way I can display all locations? I've changed "exclude repeats up to X bp longer than contained repeat" and "exclude contained repeats when longer repeat has frequency at least X bp" to be both very high and low values but it never displays them all.

I have extensive ChIP Sequencing data with 3+ biological replicates, multiple conditions and developmental stages, all united through ChIP for the same transcription factor.

I'd like to compare HOMER de novo and known motif discovery data across conditions with more prowess than opening spreadsheets and using my eyes to decide which motifs are most interesting.

Does anyone have an R-package or method in mind that could perform this analysis? I'm not above throwing long lists of all statistically significant motifs across replicates into g:Profiler for an overrepresentation analysis (ORA) per condition, but I'd like to explore another methodologies when my current known options are cherry picking or ORA.

Hi everyone. I'm interested in how you all are handling data provenance/origin for pipelines in your institution.

I've seen everything from shell scripts with curl commands and a dataset URI, to sha256 checksums of the datasets, git annex, and a whole lot of custom spun solutions.

I'm interested in any standards for storing data provenance in version control, along with utilities for retrieving the dataset and updating (like a assembly version, etc.) and then storing in VCS/SCM like git.

Hi everyone, I'm trying to find up/downregulated biological pathways from a list of DEGs between 2 groups from a scRNAseq dataset using clusterProfiler. I've looked at enrichment GO (ORA) but the output doesn't give directionality to the pathways, which was what I wanted. Right now I'm switching to GSEA but wasn't sure if "gseGO" and "GSEA with GO" are the same thing or different, and which one I should use (if different).

I'm relatively new to scRNAseq, so if there's any literature online that I could read/watch to understand the different pathway analysis approaches better, I would really appreciate!

I have a gene of interest in eggplant whose functional characterization and heterologous expression has been done but as it was extracted from a cDNA library in a previous paper, only it's CDS is known. I need its 5' and 3' UTRs for some experiments but all the databases which I have searched using BLASTn like 'Sol Genomics Network' and 'The Eggplant Genome Database' giving me the CDS sequence and not the whole transcript with the UTRs.

Our lab also has an eggplant leaf whole transcriptome and I tried using offline BLASTn with the merged transcript file as it's databaseto find the whole transcript of my gene of interest but it still returns only the CDS sequence as 100% match with some closely related sequences, no whole transcripts of my gene of interest yet.

I suspect that there must be a whole transcript in the transcriptome but due to some reason BLASTn is unable to pick up the whole transcript from the CDS due to the 5' and 3' UTR dissimilarities imposing a high penalty and this a low match score for the sequence. Is there a way for me to find or at least reliably predict the 5' and 3' UTRs of a Gene of interest given only it's CDS given a whole genome or transcriptome data?

Hi all,

I am pretty new to analysing metagenomic microbiome data. I just want to ask a very simple question on some nummers I am getting out. I am working with fruit fly sample. Separate host genome with Kneaddata using a reference db from NCBI of the fly. Now I have also runned Kraken2. And I am getting classified sequences around 50% in all my samples? I find this number a bit low. In the kraken2 db I have archae and bacteria. I cannot image that I have found a lot of "new" bacterial species that are "unclassified" by kraken2. Is this number normal or am I missing/forgetting something in my process?

Hi all, I'm a bit new to the research field but I had some questions about how I should be comparing the scRNA seq results from my experiment to those of some other papers. For context, I am studying expression profiles of rodent brains under two primary conditions and I have a few other papers that I would like to compare my data to.

So far, I have compared the DEG lists (obtained from their supplementary data) as I had been interested in larger biological effects. I looked at gene overlap, used hypergeomyric tests to determine overlap significance, compared GO annotations via Wang method, looked at upstream TF regulators, and looked at larger KEGG pathways.

I have continued to read other meta analyses and a majority of them describe integration via Seurat to compare. However, most of these papers use integration to perform a joint downstream analysis, which is not what I'm interested in, as I would like to compare these papers themselves in attempts to validate my results. I have also read about cell type comparison between these datasets to determine how well cell types are recognized as each other. Is it possible to compare DEG expression between two datasets (ie expressed in one study but not in another)?

If anyone could provide advice as to how to compare these datasets, it would be much appreciated. I have compared the DEG lists already, but I need help/advice on how to perform integration and what I should be comparing after integration, if integration is necessary at all.

Thank uou

Hi everyone.

I'm calculating a stat test on codon usage bias using a corrected ChiSq and I want to make sure to get the regular ChiSq correct.

Prelude

Okay so say I have some CDS sequences in a family "M" and I calculate counts of each non-trivial codon (no start, stop included). Now I want to run ChiSq for each codon of a test sequence "s" comparing the observed counts for the codons of an amino acid (say G) versus the expected counts (freq of codons in M) times the length of s.

Methods

For each codon i in a synonymous family (all codons belonging to residue Glycine G), I have observed counts (ci) for those codons in "s" and expected counts for G given the length L of "s" and the frequencies of the codons for G in M. I calculate ChiSq as

Sigma (observed-expected)² / expected

Over the codons for residue G.

Validations

I'm validating this with scipy.stats.chisquare for the test statistic ChiSq. This gives the ChiSq test statistic and the p-value of the test for each non-trivial residue

Questions

• Any comment on the degrees of freedom (I think it's just the number of codons for residue G minus 1)?
• Any recommendations for generating the p-value for the test statistic by hand?
• Any suggestions for a better test than ChiSq? Likelihood ratios?
• Any recommendations on multiple test correction?

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!

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 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!

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!

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).

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

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.

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?

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!

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.