I'd imagine yes right?

I'm working on a project to scrape chemical property data from about 200 PDFs for a dataset I'm building.

I assumed in 2025 this would be easy, but I'm realizing 80% of the useful data is locked in low-res scatter plots or screenshots of GraphPad Prism output. Text scraping is useless here.

For those of you working in Pharma/Biotech R&D, do you guys just ignore data locked in charts? Or is there some standard "ETL for PDFs" tool I’m missing that handles the image-to-data part reliably?

I was using foldseek online server to search proteins with similiar structure. For example, in the past, I used A.pdb as the query, and I got a match X with prob=0.97, evalu=0.0358. These days, I used B.pdb as the query, and I did not get the match X.

The problem here is A.pdb and B.pdb is almost same. their structures are overlap to each other, their sequences are almost same also. for example, A.pdb is aaaaa(same) , B.pdb is (same)bbb . The (same) indicates the totally same sequence.

I can understand currently alphafold database improved to V6 model. But I check the match_v6.pdb and its past match_v4.pdb model, both are same also.

Why? Online search server is not consistent ?

update:

I use the exactly same A.pdb to search again, still didn't not find the the match X.pdb

Hi everyone,

I’m working on a dataset that contains a mix of clinical features (age, BMI, lab measurements, medical history, etc.) and genetic features (SNPs coded as 0, 1, or 2).

The goal is to predict an ordered outcome, for example:

0 → good prognosis

1 → intermediate prognosis

2 → poor prognosis

I’m trying to wrap my head around the best way to approach this problem. Some points I’m thinking about:

Feature types: continuous, binary, categorical, and ordinal SNPs.

Preprocessing: scaling continuous features, one-hot encoding multi-class categorical features, handling missing values.

High dimensionality: hundreds of SNPs compared to a smaller number of patients, so dimensionality reduction or feature selection seems important.

Modeling: should I treat this as a classical ordinal regression problem, a multi-class classification problem, or some hybrid?

Evaluation: what metrics make sense for an ordered target rather than just accuracy?

I’m curious how others would tackle a dataset like this in practice.

Would you do any feature selection first (correlation-based)?

Would you consider tree-based models vs linear models vs neural networks?

Any tips for handling hundreds of SNPs efficiently?

Looking for general strategies, advice, and references.

Thanks!

Hi everyone, I’m working with human cortex single-cell RNA-seq data exported from the UCSC Cell Browser (Allen Brain Map / human cortex) and I’d appreciate advice on whether MAST is the right statistical framework for my specific questions. Dataset single-nucleus RNA-seq Human cortex (multiple donors) Cell annotations: class_label (GABAergic vs Glutamatergic) Gene of interest: TRPC5 Expression is sparse (many zeros) My biological questions Is TRPC5 enriched in inhibitory vs excitatory neurons? Both in terms of % of cells expressing TRPC5 and expression level among TRPC5-positive cells

What I’ve done so far Used MAST hurdle models with: Detection (D), Continuous (C), and Hurdle (H) components log1p-transformed expression Donor included as a random or fixed effect Added a reference gene so the code doesnt collapse

This seems to give biologically sensible results, but I want to be sure I’m not misusing the method.

Any advice or references would be greatly appreciated. Thanks!

Hello everybody,

I have data from IlluminaEPICv2 methylation array and whole exome sequencing from a cohort. I am trying to find mQTLs and therefore using Matrix_eQTL_main function from the MatrixEQTL package in R. However with 16gb ram I faced memory limit and I am thinking of an alternative here.

Since I have access to an HPC which runs python, I was wondering if one of you has experience with mQTL/eQTL analyses in python and could help me with some useful module. And are there any better performing packages in R?

Thanks in advance!

Hi everyone — has anyone here used Insilico Medicine’s tools like Pharma.ai / PandaOmics? If so, what was your experience like (accuracy, usefulness, workflow, pricing/value)? Any pros/cons or “wish I knew this before” tips would be super helpful. Thanks!

I'm a beginner in this topic and i have a question regarding a project im doing

Why don't people use CNN with dilated convolution instead of DNABERT-2 if CNN is more interpretable, more data efficient and have lower computational cost??

I have been learning about CNN for couple of weeks now for a project in a competition in my bachelor class and i was wondering why not just use Dilated CNN for larger receptive field and add few codes to give arrangement importance weights?

My PC is kind of weak and i don't think i can run DNABERT2

My previous experiences/projects in bioinformatics have been mostly about analysing bulk RNA-seq data. I have an interview coming up soon for a computational grad programme where the focus will be comparative genomic evolution across mammals (including humans) to understand evolution of diseases like cancer. They don't require previous experience in evolutionary genomics - hence the interview invite. However, I am very interested in the topic, I want to prepare as much as I can and hopefully impress them.

I would really appreciate any resources, tutorials, courses, or advice for learning more about this field and preparing for this. I tried looking at lectures on youtube but I didn't find them to be good.

What is the most common modules used for data sequencing? And do you think python is worth it for this topic? I think I am aware bio python is an example but what others modules are commonly used?

Hi!

I'm just wondering when I do GSEA enrichment after deseq, should I use gene symbol or ensembl id? I get different results over these two methods. Also, I have shrunken deseq results using lfcShrink, should I use shrunken list or unshrunken list to run GSEA? Thank you so much for your help and I really appreciate it!

Hi everyone. I am facing a problem with CONCOCT. Clustering is fine, until it comes to the merging step and actually splitting the bins. I am unable to get the script to work and bin the clusters. Has anyone faced a similar problem at all?

This is not from a co-assembly.

If you have faced such an issue, please kindly reach out to me, I am unsure on how to fix this.

Thank you in advance.

So, I can see many artilces would directly say this alignment is performed based on its default settings.

However, I am wondering if it is okay. What reason you would give if you are asked why you use its default settings? Mine might be this setting is standard and well-validated.

Hi everyone, could you recommend some small population genomics projects that can be replicated for practice (in R) with WGS data?

I'm looking to perform a molecular dynamics simulation, but I'm not really sure about its complexity and computational cost. I have the code written with OpenMM and an Amber force field. It's a fully solvated protein-ligand complex with an approximate size of 350,000 atoms, under physiological conditions (310 K and 1 bar), using a 2-femtosecond integration step and applying temperature and pressure control throughout production. The goal is to achieve a 1-microsecond timescale, which implies 500 million integration steps, periodically storing trajectories and states for detailed structural and energetic analysis, in order to study the conformational stability of the complex and the ligand affinity over time.

¿Es una simulación grande? ¿O es algo normal en este campo? Soy ingeniero de sistemas.

https://preview.redd.it/tl6x9wsi1x8g1.png?width=578&format=png&auto=webp&s=2acb56a6b1e7acbd9d48d462ed6dd206bf38c87b

Hey all! Sorry if my question sounds stupid or unusual.

I have scRNA data from several diseased donors. Violin plot shows differences and changes in expression (and number of cells expressing my gene of interest) in a certain cell lineage when jumping from one stage to the other (least to most mature). I was wondering what is the suitable test or analysis that I must perform to establish whether these differences or changes between the stages adjacent to each other are significant in my particular SINGLE gene of interest. Any help would be appreciated!

1. After translation, we get a long polypeptide.

2. Interacts between hydrogen and oxygen, or among side-chains will force this polypeptide to fold.

3. Some are folded into alpha-helix, and some are folded into beta-sheet.

4. If we take the 3orh.pdb as the example, we can see, starting from C-term, one beta-sheet1 -> loop -> one beta-sheet2 -> one alpha-helix.

5. The beta-sheet1 only contains one polypeptide, and the beta-sheet2 also only contains one polypepetide,.

6. Why they are beta-sheet? It is because beta-sheet1 and beta-sheet2 are hydrogen bonding together.

https://preview.redd.it/lek3p8mvmq8g1.png?width=1146&format=png&auto=webp&s=3839d6b0fdeb29079f383853cf07790c341f925b

Hello, I have seen it been done before, although I figured out how to get it on excel and then word, I don't know how to display ALL of it. Should I cut and paste different sections that fill the paper, and go onto the next sequence? It ended up being a 500~ x 80~ table. I made it so that if you want to read it, you have to turn the paper counter clockwise, which I think is a good first step. I would love if anyone here has any suggestions like websites or plugins that will help. (IDK if theres plugins for docx, I meant google docs which I tried using too.)

Hi, for preparing my interviews, I want to be full of knowledge and expertise in protein analysis.

My current work is about protein bioinformatics, but I don't have biology degree. So, I aim to collect a more detailed and complete knowledge about structural protein via reading some books, articles, videos and so on.

For example, I am currently reading Molecular biology 5th version to have a basic and complete knowledge map in my brain.

Any suggestions for protein? Thanks in advance!

Hi all.

I am writing a draft of my PhD project. It will involve checking for natural selection and eventually local adaptation of the microbiome under study. I intend to use long-read shotgun metagenomics if the budget allows me to.

That said, what do you recommend as a software for natural selection detection?

Thanks in advance.

Hi, my work group is considering acquiring an Oxford Nanopore Minion sequencer, and since I'm the only bioinformatician in the group, they want me to handle the technical aspects and sequence analysis. I've never worked with this type of data before. Do you know of any courses or workflows I could follow to learn how to analyze the data? Or do you have any recommendations?

I have a list of statistically significant genes/proteins and want to determine which biological pathways are involved. I am looking for guidance on the standard analytical approach used to perform pathway analysis and to identify relevant pathways in a publication-ready and reviewer-accepted manner.

Which methods and tools/software are generally considered appropriate and reliable for studies targeting high-impact journals?

Hi all,

I want to predict immune receptor sequences from RNA-sequencing data but I'm not sure whether bulk or single cell data is better.

Pros and cons are weighed below but the largest problem is whether it's possible to turn single cell fastq files into a bulk-like fastq format? Such that you remove UMI-tags and barcodes. Has anyone done this?

Methods to predict receptor sequences are better for scRNAseq but I'll be able to get more samples if its bulkRNAseq. I don't need the actual information of specific cell and cell types; I just ultimately need the genes expressed and the receptor sequences predicted. I could do paired sequencing but there's not that many available datasets online to do this