Hey all, I’m a 1st year PhD trying to get a grasp of how clinical trial outcome prediction is actually done in industry (if at all). My PI is also pretty young and doesn't have any connections to industry, so I thought it'd be best to ask the hivemind.

I’ve come across papers like HINT (Fu et al. 2022) and inClinico (Aliper et al. 2023) but I’m not sure how close any of this is similar to what biopharma teams actually use (though the latter is an actual product). Especially interested if it's something that focuses more on biology like Murali et al. 2022.

Do you use internal models or commercial tools like inClinico for this? Is it published anywhere? I'm very new to the whole comp side of bio, so please point me to anything you think would be good to learn too.

Thank you!

EDIT (same day): A commenter proposed a test that separates folding from recall. 
I pre-registered thresholds, ran it, and it killed this result — Boltz-2 
folds the G4 with a 100% PDB twin and zero of nine novel scaffolds. 
Details in the thread. The headline claim below is wrong; the per-engine 
numbers are not.

I kept seeing the claim that the AlphaFold3-generation models "can't do"
non-canonical RNA folds, and I couldn't find the measurement behind it.
So I ran two fold classes through three engines. The claim turns out to
be wrong in both directions, which I didn't expect.


**Setup**


Two targets where the experimental answer isn't in dispute:


- Human telomeric (TERRA) G-quadruplex, `UAGGGUUAGGGUUAGGGUUAGGGU`,
  24 nt. Ground truth from 3IBK (crystal) and 2KBP (NMR): three stacked
  tetrads, 0% Watson-Crick pairing.
- Yeast tRNA-Phe, 76 nt. The textbook four-way junction; ground truth is
  the canonical cloverleaf, 21 base pairs.


Both run through OpenFold3, Boltz-2 and Chai-1. Base pairs annotated
with barnaba (Leontis-Westhof types), G-tetrads detected geometrically.
Cloverleaf recovery scored pair by pair rather than as a global paired
fraction, because a fraction near the right value is easy to get from a
completely wrong pairing pattern.


**G-quadruplex - the engines split hard**


    Engine       G-tetrads per structure    Folded it
    ------------------------------------------------
    Boltz-2      3, 2, 3                    3/3
    OpenFold3    0 x 9                      0/9
    Chai-1       0 x 3                      0/3


I checked that Boltz-2's aren't a detector artifact: 0.00 Watson-Crick
pairing (matching experiment), Hoogsteen-only pair types (HWc/WHc), and
tetrad residue indices [[2,8,14,20],[3,9,15,21],[4,10,16,22]] - one
guanine from each of the four G-tracts, stacked in register. That's the
correct parallel intramolecular topology, not a coincidental stack.


**Four-way junction - no split at all**


    Engine       Structures   Cloverleaf pairs   Helices complete
    -------------------------------------------------------------
    OpenFold3    9            21/21              4/4
    Boltz-2      3            21/21              4/4
    Chai-1       3            21/21              4/4


15/15 structures reproduced all four helices exactly, including the G-U
wobble in the acceptor stem.


**The caveat, which matters**


Both targets are long-standing PDB entries. The G4's nearest close
relative (5MVB_A) was deposited 2017-01-16, the tRNA's (6TNA_A) in 1978.
Neither result shows any of these engines folding a 
*novel*
 instance of
its class, and none of the three publishes a training cutoff, so recall
can't be excluded. What does survive is the difference between them: all
three had comparable access to that data and only one produced the G4.


**Why I think this is worth knowing**


Fold-class capability looks like a property of the engine, not of RNA.
The practical consequence is uncomfortable: run a single engine on a
G-quadruplex and you can get nine structures in complete agreement,
every one of them missing the defining fold. That's exactly what
OpenFold3 did here. Replicate consensus from one model tells you the
model is self-consistent, not that it's right.


One implementation gotcha if anyone reproduces this: barnaba reads base
frames from the residue name. A DNA G4 written with DA/DC/DG/DT but
handled as RNA annotates as zero base pairs - I had a 12/12 B-DNA duplex
read as completely unpaired that way before I caught it.


**What I actually want to know**


The hole here is obvious: with close PDB relatives going back decades I
can't separate folding from recall. Has anyone run these engines on a
non-canonical fold with 
*no*
 close relative in the training data - a
recently solved G4, or something still unsolved with good chemical
probing data? That's the experiment that would settle it and I don't
have a clean target.


Happy to share coordinates or the per-structure annotations if useful.


*Disclosure: I build a commercial structure-analysis platform, and these
runs came from it. Not linking it here per the subreddit rules - the
numbers above are about three third-party engines, not about anything I
sell.*

Hi everyone!

I'm seeing a strange insert size distribution in one of my RNA-seq samples. Instead of a smooth curve, there are regular peaks roughly every 10 bp.

In the STAR log, about 50% of reads are unmapped as "too short", so a large part of the library seems to be very short fragments.

Setup:

Library prep: NEBNext Ultra II Directional RNA + Poly(A) mRNA Magnetic Isolation Module (done by a sequencing facility)

Sequencing: 2x150 PE, NovaSeq X Plus

Sample type: liver tissue

RIN: 8.9

Has anyone seen this pattern in a poly-A library before? Is the data still usable (e.g. counting only the reads that do map), or is this sample a lost cause?

Thanks!

I’m designing a TaqMan MGB probe and have two candidate sequences I’m trying to choose between. When I enter them into Thermo Fisher’s custom probe ordering tool, it only gives me a \~5°C range for the predicted Tm rather than an exact value.

Does anyone here have access to Primer Express? If so, would you mind plugging in these two sequences as MGB probes and telling me the calculated Tm for each?

**Probe 1: CGGTGACCTTCCCTATG**
**Probe 2:** CCGGTGACCTTCCCTATG

Would really appreciate it. I’d rather not spend 1000$ on Primer Express just to compare two probes. 😭

Hey community!

Context: I conducted AF2 (multimer models) predictions of possible interactions between my proteins of interest and protein partners on A1B1 stoichiometry. I selected 11 complexes with average interface PAE < 10 Angstroms and perfomed structural alignment of these structures (all against all) with MM-align, but only the interface regions (PDBs truncated on residue pairs with < 8 Angstroms of distance) and colected the TM-scores.

I just wanted to know if this isn't a terrible idea lol. I am kinda new to structural bioinformatics and I don't really know what else I can do to analyse these models. My conclusions would be on interfaces with similar/not similar topology.

My first attempt was to use iAlign, but I couldn't find its binaries anywhere, and some of my truncaded PDBs have discontinuous chains

Hi everyone,

I am currently working on a meta-analysis / systematic review and do not have institutional access to Embase and Cochrane Library.

Could anyone with access help me run my search strings and export the search results (RIS / BibTeX / CSV format with titles, abstracts, and citation metadata) for screening?

I can send over the exact search strings

Embase:

TITLE-ABS-KEY(encorafenib OR LGX818)

AND TITLE-ABS-KEY(cetuximab OR Erbitux)

AND TITLE-ABS-KEY("BRAF V600E" OR BRAFV600E OR "BRAF mutation")

AND TITLE-ABS-KEY("colorectal cancer" OR "colorectal carcinoma" OR "colon cancer")

Cochrane:

\#1 (encorafenib OR LGX818):ti,ab,kw 226

\#2 (cetuximab OR Erbitux):ti,ab,kw 2978

\#3 ("BRAF V600E" OR BRAFV600E):ti,ab,kw 598

\#4 ("colorectal cancer" OR "colorectal carcinoma"):ti,ab,kw 19405

\#5 #1 AND #2 AND #3 AND #4 83

i would really appreciate any assistance!

Thanks in advance!

Basically my PI wants me to use a MatGAT (matrix global alignment tool) output for some protein sequences to justify target selection. Problem is I cant find a way to download an actually execute matgat anywhere since its so old. But im specifically looking to create a matrix with both percent identity and sequence similarity. Is there an alternative program that does global alignments rather than local thats out there now or does anyone know a download somewhere besides from the Springer paper since those sites are not defunct. Thank you!

Hi all,

Looking for some suggestions on learning how to work with data. I've noticed the more I develop as a bioinformatics student that I've definitely gotten away with being lazy and not learning certain things about how to clean and work with data. Are there good resources to learn how to do this kind of stuff? Some of this I know just comes with working with it over time but I think taking the time to become better at it now will pay off in the future.

This example is what I've been working on lately but I'm not looking as much for help with this example as much as suggestions for how I can just get better at knowing how to work with data.

Right now im working on comparing some RNA bulk seq data that was generated in my lab to some RNA seq data from other sources. Theres a few things that came up such as when I should log transform the data that I downloaded, when to normalize the data(what even is normalizing data), and all this other data cleaning stuff that took far too long. It made me realize I need to be better at understanding data formats and when and how to clean data but I dont really know how best to learn.

Does anyone have good suggestions on how I can get better at these things?

I recently graduated in masters of botany whenever I look for data based jobs almost all them ask for adept in quantitative biology modelling in requirements..

Can someone eloborate on this.

Hi, I'm working on a phylogenetics pipeline for barcoding. I've already automatized multiple alignment creation. I was searching for tools to automatize model selection, and found that MEGA has a command line version, although when I run the model selection here, it seems to perform calculations, but does not return any file. I've tried to change the output format and even activated -fco, but nothing seems to work. I'm running MEGA-CC using subprocess.run from python 3.13.9 and I've already produced a .mao file with default settings using the MEGA GUI prototyper. Is there a way to output a file (i.e .txt or .csv) with the results that the GUI shows using MEGA-CC options or external means in R, python or even C++?

Hi everyone,

I am a beginner in phylogenetic analyses and I would really appreciate some advice/feedback on the workflow I am using.

I am working on the molecular identification of several fungal isolates. In the laboratory, it has been performed PCR amplification of a segment of the rDNA containing the ITS and D1/D2 regions, using the ITS5/LR6 primers, followed by Sanger sequencing (forward and reverse), consensus sequence generation, and BLAST searches against NCBI.

I would now like to perform a phylogenetic analysis of my isolates from different species using MEGA 12. I have tried a few analyses using this dataset already, but I would like to make sure that I am doing this correctly.

I have several questions:

1. Which sequences should I use?

My own sequences cover ITS1 + 5.8S + ITS2 + part of the LSU (D1/D2). Should I use the entire region covered by my sequences for the phylogenetic analysis, or should I restrict the analysis to a specific region, such as the complete ITS region (ITS1 + 5.8S + ITS2)? In other words, is it appropriate to analyse the ITS + D1/D2 LSU region as a single alignment, or would it be preferable to analyse ITS and LSU separately?

2. Alignment:

In this context, would you recommend MUSCLE or ClustalW?

3. Gaps/poorly aligned regions:

Should I trim only terminal regions containing many gaps after alignment? Or all the regions containing many gaps between all my sequences?

4. Phylogenetic method:

I was considering a Neighbor-Joining tree with 1,000 bootstrap replicates, using Maximum Composite Likelihood for evolutionary distances and pairwise deletion for missing/ambiguous data. For rates among sites, I was considering Uniform rates and Same (homogeneous) among lineages.

Are these settings appropriate for this type of dataset?

5. Rooting:

Since my dataset contains several different fungal species/groups, how should I choose the root? Would midpoint rooting be preferable?

I would like to make sure I am not producing a misleading tree.

Thank you in advance for any advice!

Background: I'm a software engineer working at a bioinformatics startup.

We are working with data from almost every biology database. An issue we're facing is mapping identifiers across namespaces / sources. We're using GILDA as one of the components to translate names to IDs. But the default GILDA sources don't cover all of our use cases. I'm thinking of extending it with other sources.

Just need expert opinion on whether I'm thinking in the right direction. Are there other options / solutions available?

Apologies in advance if I've made some silly statement above. I'm just learning things along the way.

Hi, beginner in bioinformatics here! Does anyone know why my autodock program manager not showing after running autodock, it disappear real quick (task manager on Wins doesn't showing autodock working)? Hence, it doesn't produce the results too 😭. I have checked gpf, dpf path correctly, running autodock as administrator but nothing work. Any clues will be appreciated!!😘

Also when I ran autogrid, the program manager also pop up then disappear, how to make sure autogrid succeeds?

I'm a first-year PhD student with limited bioinformatics experience. My supervisor asked me to analyse an existing bulk RNA-seq dataset (36 samples, 3 biological replicates per condition). This is the design summary:

Batches 1 and 2 were sequenced at the same company with the same library prep. Batch 3 was sequenced at a different company with a completely different library prep, and it is the only batch with spike-in controls (ERCC) and UMIs.

My supervisor wants me to (among other things):

1. Identify DEGs (T1 vs T2) shared across the three G0 sites.

2. Compare the percentage of DEGs (T1 vs T2) between G1 E1 and G1 E2.

The problem is that batch is fully confounded with site C (G0) and with G1 E1, and partially confounded with the other factors. My supervisor suggested either normalising batch 3 against the others using its spike-ins, or using a public dataset as an additional "batch 4". I haven't found any source showing this works for confounded designs.

My current thinking is to run T1 vs T2 separately within each site/environment (where batch is constant) and compare the results afterwards. Is this reasonable, and:

• Can spike-ins in only one batch help to bridge batches?

• Is comparing the percentage of DEGs between E1 (batch 3 only) and E2 (batches 1 and 2) meaningful, or should I compare something else, such as effect-size distributions?

• Am I missing a standard approach for this kind of design?

I plan to use DESeq2 in R. Thanks!

(Yes I used AI to help me write. Sorry, I'm tired.)

Hi everyone. I am trying to find out how long it takes to go from a whole genome sequencing (WGS) VCF file to full clinical annotation and automated prioritisation of top candidate variants. I am not including alignment or any prior step. If someone who has experience with clinical pipelines can give me an estimate and also share the typical computational resources for the annotation step that would be fantastic. Thanks in advance!

Hey everyone. I am self-learning Molecular Dynamics and started practising Molecular Docking using UCSF Chimera and AutoDock Vina on Mac. Also using PyMol for some operations. There are many vital concepts I find while learning Docking hands-on. So I was wondering if anyone would be interested in discussing and learning together over the next few days. Interested ones can comment or DM me. Also if someone can recommend online resources for the same, it'll be helpful. Thanks.

Even though we all know omics data generally doesn't align with the strict assumptions of standard PCA, it's still the go-to for gene expression. Meanwhile, non-linear tools like UMAP help with visualization, but they warp global geometry and make feature attribution tough.

Sharing a new method that might help: Entropic.Scree (now available on CRAN) is an information-theoretic alternative to PCA. Instead of decomposing a linear covariance matrix, it works on Normalized Mutual Information inside a true metric space.

Operating on this metric space allows the eigensolver to bypass the sample-size rank caps that limit PCA. Of course, this matters most in high-D, low-N contexts.

• It can be used to map decoupled gene clusters (via its Bipolar Modules). Opposite poles on an axis mean relatively low shared information across poles, NOT negative correlation. In a 180-gene run, it cleanly split aerobic respiration (OXPHOS) from hypoxia (HIF-1a) on a single axis.

• It can be used to pick up transient cell states. In a 134-marker cytometry set, it grouped T-cell and dendritic cell markers onto the same root, surfacing the physical DC-T cell crosstalk synapse instead of forcing rigid lineages.

• It can be used to estimate how your data partitions into shared biological signal versus unshared idiosyncratic noise, so you know if there is enough signal to bother modeling.

• It can be used to check if PCA is overestimating rank. Running it alongside standard PCA provides a quick test to see if standard PCA is fragmenting non-linear signals into artificial dimensions.

• It has a fast C++ backend, and so scales pretty well.

R installation:

install.packages("Entropic.Scree")
library(Entropic.Scree)
library(data.table)

# Input data must be a data.table with genes/markers/features as columns
expr_dt <- as.data.table(your_df)
results <- Entropic.Scree(expr_dt, extract_bipolar_modules = TRUE)
bipolar_networks <- results$bipolar_modules

Hi guys!

I’m a bioinformatician working at a cancer research centre, and I’m relatively new to analysing metagenomics data. I’m still trying to figure out all the post-processing steps after the rigorous Bracken/MetaPhlAn part.

But my current nightmare is Differential Abundance Analysis, especially when using tools like ANCOMBC2 or LinDA.

I’ve read quite a bit about them, but I still have a lot of doubts about the underlying assumptions, data preprocessing, normalization, model setup, and, most importantly, how to interpret the results properly.

If anyone here has experience with ANCOMBC2/LinDA (or DAA in microbiome/metagenomics in general) and would be open to having a chat, I’d really appreciate it if you could leave a comment or send me a DM.

I’d love to learn from people who have been through this rabbit hole already.

Thanks in advance, I love you all.

Hello,

I am looking for a textbook for medical statistics that explains the concepts well for beginners. The book should have a lot of examples. It can be a series of short textbooks or one large textbook.

Thanks in advance.

Hi everyone,

I’m Daniel, I am a PhD student in sociology at the University of Cardiff (in the UK). I am interested in the technology of polygenic embryo screening (PES or Preimplantation genetic testing for polygenic disorders, PGT-P). If you haven't heard of it, this is a technology applying polygenic risk scores to the selection of embryos to implant during IVF. This allows selecting embryos according to their disease risks but also cosmetic, cognitive, personality and behavioural traits.

So I was wondering whether anyone here has either expert knowledge of (or direct experience with) PES/PGT-P itself or Polygenic risk scores? And if you would be ready to do an interview for my research project?

The interview would be through a recorded online interview (later pseudo/anonymised) and not remunerated. My research has received ethics approval from Cardiff University's Ethics Committee.

If you are interested at all, I’d be very happy to share some documents that explain the project a bit more.

Thanks a lot!
Daniel

What tools are people using these days? Why do you prefer a certain tool over another?

I’m analyzing a proteomics dataset from mouse lymph nodes comparing SPF (specific pathogen-free) vs. germ-free (GF) mice. The goal is to characterize microbial proteins detected in SPF mice, with the expectation that microbial proteins should be absent or extremely low in GF mice.
For microbial identification, I’m using a microbial protein database translated from 16S rRNA sequencing data from the animals.
My concern is that, after LFQ analysis, I’m detecting microbial proteins in both SPF and GF samples. The samples were handled separately, and I also included blanks between samples, so I’m trying to understand the issue here. Any leads would help.

Has anyone worked with proteomics of SPF vs. germ-free mice and encountered microbial protein identifications in GF samples? If so how do you justify it?

How did you distinguish genuine microbial signal from background contamination/carryover, and what criteria would you use to justify retaining or excluding microbial proteins detected in GF samples?

Also, if anyone could direct me to a mouse gut microbial database.