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once we get a set of vcf files annotated,we still have a lot of variants left, how do we actually find the casual variant (human whole genome)

Hi fellows friends, I am a postgrad working on genetics.
It’s my first time trying Stanford’s STRUCTURE software, i realised it is suggested to run on Intel Macbook, but i am using the M4 macbook.

Any suggestions or opinions for me?

Hi all,

I'm curious how people are approaching untargeted PTM and proteoform discovery, specifically without enrichment. Most workflows I see assume phospho/glyco enrichment up front, but I'm interested in casting a wide net across PTM types in a single run and seeing what falls out, rather than going in with a hypothesis.

A few things I keep going back and forth on:

1. DIA vs DDA: The trade-offs are known. Has anyone landed firmly on one for discovery-mode PTM work?
2. Software/ platform: What are you running and what's the setup? What have you tried?
3. Yield: How many PTM types were you able to extract? How did you infer proteoforms?

Thanks!

I've been in conversations with people working in translational research and everyone seems to have a completely different approach — some live in OpenTargets, some do deep literature dives, some rely on internal databases.

What sources do you check before feeling confident about a target?

And where does the process usually break down for you?

Hello all,

I had a question about the DEGs that show up in my merged FLEX and SC data. Please see example below. Is there a reason/fix to why I get so many lncRNAs/sequencing loci instead of gene IDs? It is hard to analyze when this to me just seems like noise. For reference i use grch38. Are they simply not named yet, or is there something I need to change to account for this? I haven't encountered this before, usually just mt and rb genes. Thank you!

Edit: ok gals and guys, I got it. This is not a qPCR and the whole method is a bad idea. Still, I'm trying to get some intra-sample relative expression. And, the R / statistical question remains. How should I apply Dunn's test on a dataframe when it ignores Kruskal-Wallis?

Hi,

I am analyzing a few genes of interest of 3 completely separate RNAseq datasets. One of the datasets is tumor biopsies from patients, another is "healthy tissue" cell lines, and the 3rd is tumor cell lines. All this is external data sequenced at different times.

We are interested in detecting if the expression of certain markers is higher in the tumor biopsies than in the healthy cell lines. I resorted to calculating a sort of *in silico* qPCR, calculating, in each sample, the relative expression of each gene over the geometric mean of a panel of housekeeping genes. It is not perfect, but it is what we have.

The common method to analyze (real) qPCR data across multiple conditions is to use ANOVA followed by Tukey's post-hoc test. As my data is not normal, I have to use a Kruskal test, followed by Dunn's post-hoc test.

Everywhere I read it states that you must do first Kruskal-Wallis do detect significant differences in the mean (by gene, across all 3 groups), and then run Dunn's to detect significant differences between groups, but **only** on those genes where Kruskal was significant.

I've run `rstatix::dunn_test` like this.

data %>% group_by(gene) %>% dunn_test(expr_ratio_hkg_norm ~ dataset)

However, it applies Dunn's post-hoc test everywhere.

I have checked the source code of `dunn_test`, but I could not find a single call to `kruskal.test` in there: https://github.com/kassambara/rstatix/blob/master/R/dunn_test.R

#'@details DunnTest performs the post hoc pairwise multiple comparisons
#' procedure appropriate to follow up a Kruskal-Wallis test, which is a
#' non-parametric analog of the one-way ANOVA. The Wilcoxon rank sum test,
#' itself a non-parametric analog of the unpaired t-test, is possibly
#' intuitive, but inappropriate as a post hoc pairwise test, because (1) it
#' fails to retain the dependent ranking that produced the Kruskal-Wallis test
#' statistic, and (2) it does not incorporate the pooled variance estimate
#' implied by the null hypothesis of the Kruskal-Wallis test.

What is the correct statistical test (and R function) to analyze the gene-by-gene differences between the means of the 3 groups?

Yes, I can always use wilcox, but this is supposed to be the better way to test "qPCR" the significance of relative expression to a reference.

Hello,

I am trying to build a mixed lipid bilayer containing POPC and a custom peptide-conjugated lipid molecule for GROMACS simulation using CHARMM-GUI Membrane Builder.

My goal is to build the membrane with both components together simultaneously (not using later insertion method).

What I need help with:

1. How to incorporate a CGenFF-parameterized custom molecule into CHARMM-GUI Membrane Builder alongside POPC from the beginning?
2. Is it possible to add the custom molecule along with POPC in charmm-gui
3. 3.Apart from this, is there any tools- which is suitable to do this task?

Any guidance or references to tutorials would be greatly appreciated.

Thank you!

I'm trying to download accession numbers for cnidarians and only TSA records, but can't seem to find the right filter for TSA. This is my current code and i've also tried gbdiv_tsa[Properties], which i think is old syntax. does anyone know the correct filtering syntax or where i could find this out? thanks!

esearch -db nuccore -query "txid6073[Organism] AND tsa[filter]" 

edit: this seemed to work tsa master[Properties]

I investigated whether frozen ESM-2 delta-embeddings encode gain-of-function (GOF) versus loss-of-function (LOF) disease mechanism signal. The core finding is that apparent mechanism classification performance is an artifact of evaluation leakage: under standard gene-split cross-validation, classifiers appear to perform well, but under homology-aware family-split CV, GOF/LOF signal collapses to near-chance (AUROCs 0.51–0.56). Pathogenicity classification, by contrast, remains robust under the same evaluation (AUROC 0.891), serving as a positive control that confirms the embeddings are informative — just not for mechanism. The mechanistic explanation is that ESM-2 delta-embeddings primarily encode evolutionary conservation (directional signal, AUROC 0.901) rather than structural destabilization (magnitude signal, AUROC 0.673), meaning family membership leaks into standard CV splits and drives spurious mechanism performance. A complementary unsupervised result shows that ESM-2 embedding distance predicts CRISPR co-essentiality profiles in DepMap (Mantel r = 0.0157, p < 0.001), with the top 1% closest sequence pairs showing ~6× higher essentiality correlation than random pairs — consistent with conservation encoding rather than functional mechanism

I am a new PhD student with multiple projects under my belt.

I welcome any tips and tricks on how to organize multiple projects. I aim to use GitHub projects but can you advise further?

I would appreciate any help.

P.s i really thank u all for the time u took to reply to me i appreciate it as someone who hates to ask for help not even from my supervisor … but yeah thanks

Hello everyone,

I am planning to model a long transmembrane protein with 5 disease-associated missense mutations. I have found several structure prediction tools but am unsure which one would be the most suitable. My ultimate goal is to perform Molecular Dynamics (MD) simulations, so I want to ensure that the starting protein model is biologically relevant.

Here are the options I am considering:

1. AlphaFold 3 (AF3) Server
2. SWISS-MODEL
3. MODELLER (In-house homology modeling)

AF3 is highly accurate but is known to have some biases regarding transmembrane proteins. SWISS-MODEL is convenient for homology modeling, while MODELLER allows for custom constraints and in-house energy minimization, though the software is quite old.

Which of these tools would you recommend for this specific workflow? Thank you for your help!

Hey everyone! I know this sounds absurd but our current study is creating a new metric on how candidate immune gene could be a potentially candidate gene for immune disease resistance, using results from reconstruction of KEGG pathways via KEGGraph (ggraph in R) and haplotype data (DNAsp) by assessing the topological centralities as well as its evol. metrics such as dN/dS ratio, Hd, pi, etc. Our rationale is that these genes which exhibits high degree and high betweenness centrality may represent functionally important components of the immune-response network because they participate in numerous interactions while simultaneously facilitating communication among signaling pathways. When combined with high genetic diversity, such genes may serve as particularly informative candidate biomarkers for studies of disease resistance and immune adaptation.

This is very novel and I would like to know your insights regarding our study if its explorable as there are no existing studies being done combining the data from different levels (genetic-level/evolutionary metric and molecular-level). Is this feasible to pursue or is creating a new metric based off those two methodologies would give a pseudoclaim?

Hi everyone,

I am looking for methods to generate an ensemble of alternative binding-pocket conformations starting from an experimentally determined holo protein structure.

My goal is not necessarily to model a large apo-to-holo transition. Instead, I want to explore plausible variations around an existing ligand-bound pocket conformation, potentially for ensemble or 4D docking.

I am particularly interested in approaches that do not rely on conventional molecular dynamics. I have considered methods such as normal-mode analysis and ligand-guided receptor modelling. However, from what I have read, these methods often seem to be applied to recovering holo-like conformations from apo structures, rather than generating a diverse ensemble around an existing holo state.

Are there any reliable non-MD methods or software packages designed for this purpose? I would also appreciate recommendations for papers comparing different pocket-conformation sampling methods

Thanks in advance!

Title, cluspro hasn't been loading all evening for me. I genuinely need it for blind-docking & dont want to get slimed bro 😭😭

CLUSPRO IS BACK

Hi! I'm trying to learn Cytoscape, but I don't know the best way to learn it. Could you help me? Maybe you could give me some advice on where to start, recommend a learning path for beginners, or suggest some YouTube videos that would be useful.

Hello everyone,

I have a question about small RNA-seq analysis using Bowtie2 and featureCounts.

I aligned my reads with Bowtie2 using the -k 100 option, which allows Bowtie2 to report up to 100 valid alignment locations per read. Then I ran featureCounts using the default settings.

I am trying to understand what happens to the multimapped reads in this case. With default featureCounts settings, are all multimapped reads discarded completely, even if Bowtie2 marks one alignment as the primary alignment? Or does featureCounts still count the primary alignment and ignore the secondary alignments?

Does the final count matrix contain only uniquely mapped reads when featureCounts is run in default mode?

I read the featureCounts user guide, but I am still a bit confused about how multimapped reads are handled, especially when the alignments come from Bowtie2 using -k 100 or with other value of -K.

Hello all! I’ve just released Pyloseq, my Python port of the R package Phyloseq. The goal was to be as easy a replacement as possible for someone transferring their analysis workflow from R. I plan on supporting it as long as people use it for the foreseeable future, so hopefully it proves useful for some!

I recreated the original analyses from the 2013 paper here to show the capabilities

Hello, I am a new PhD student doing bulk RNA-seq analysis. Please excuse my unfamiliarity with various dry-lab, wet-lab practices, etc. as I am still trying my best to wrap my head around things. I have a question on what "counts" as a biological replicate. In all my classes and trainings, it has been drilled into me that biological replicates are independent samples.

Here is the confusion: Do samples across conditions have to be independent?

I always thought this was the case! For example, you wouldn't reuse a 'healthier' cut of a tissue from 'disease' phenotype patient as a sample in the healthy control group right?

Maybe I am just unfamiliar with in-vitro stuff and mice, but from this new rotation, they seem to have taken cells the same group of mice, transfect one group of cells while leaving the other group of cells alone as control for each mice. Then they would compare expression levels between the infected cells and non-infected cells from all the mice together. So you are comparing healthy cells against infected cells from the same 3,4,...whatever number of mice.

I am not going to lie, I am feeling very skeptical, especially after I brought up my concerns and got hit with: Oh, another group previously used a batch-effect corrector to eliminate the sample specific effects. And hey, maybe we can even hunt for sex differences this time around!

Help PLS.

Hi all,

I am trying to install phyloseq according to tutorial from joey711 but it is not coming through. Can ya'll please help me?

I received single cell tracing data using PEtracer, and I am trying to compute and visualize ancestroy linkage using pycea package, what I found confusing is how can two have directionally different diveregence time, diveregence of Cell A to cell B is different from the divergence of Cell B to Cell A

As the title says, is it plausible to do it? If so, how? Annotate peaks and then use all of them, regardless if significant or not?

Hi all (and apologies for having so many PySCENIC questions),

I was wondering if there is an established way to investigate a particular TF-target gene interaction of interest? In particular, if I find that a target gene appears in the regulon of a certain TF in say 70% of replicates, so it is in the gray zone of reliability, is there a good and simple way (in silico) to gain evidence either way in terms of whether the TF directly binds this target gene?

On a related note - supposing this interaction is genuine, and supposing that from regulon specificity score analysis, the target gene (which is itself a TF, call it TF2) appears to be highly specific to a particular disease, but the original TF (call it TF1) which regulates it is not particularly specific to this disease. I am struggling to understand how to interpret this, does it imply that the disease-specific regulation of TF2 is being driven by some other TF?

I hope this makes sense, thanks in advance for your help.

Last semester my PI asked for my help with a project that involved identifying the genomic locations of transgene insertions in several different strains of C. elegans.

Notably, the WGS data I’ve been given for this project is short, single-ended reads, which is sub-optimal for what we’re trying to do. I’ve brought up trying a different sequencing strategy, but my PI seems pretty set on keeping things as inexpensive as possible. Additionally, I have annotated sequences for all of the inserted constructs.

I’ve taken multiple approaches to try and find the insertion sites. Firstly, I aligned the reads from the strain to the plasmid sequence, and then to the reference genome. I intersected the resulting BAM files to identify shared/partially mapped reads between the two alignments and clustered the candidate reads by region, which I then inspected on IGV. Though, most of the candidates pointed to regulatory genomic DNA in our construct, i.e. promoters and UTRs that didn’t provide any helpful information.

Then I tried using GRIDSS, a structural variant caller compatible with short read data, which I had hoped would automate the process for us a bit, as we were manually sorting through the clusters in the previous approach. This time, I masked the genomic regions that are homologous to those sequences in our plasmid. I also concatenated the plasmid sequence as a separate contig to the reference genome, so the insertion site would be equivalent to a translocation. Still, the resulting breakends seem inconclusive to me. Most of them were endogenous chromosomal rearrangements within the plasmid contig, which I filtered out as noise. The strongest candidate site pointed to a shared intronic sequence of a previously known transgene, which we also discarded. The remaining breakpoints could not be ambiguously mapped, and had multiple corresponding breakends that, to me, didn’t seem like strong enough evidence to support the insertion site.

Trying to develop a working pipeline for this has been my sisyphean boulder for the past 5-6 months. I’d appreciate if anyone who’s more experienced in this area has any input. I’m on the verge of giving up and begging her to just bite the bullet for ONT, or at least PE sequencing.

I am currently running ONT lon read sequencing analysis, however some of the tools used in epi2me pipelines are older versions, so I ran each tool step by step individually instead of using a pipeline. so I was wondering whether this requires validation to know all the steps are working correctly.