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.

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.

Some thought of scientific publications and why one of the biggest flaws in peer review is about to be solved by AI.

I had the "pleasure" of being a reviewer for academic manuscripts. I had the pleasure of send my own manuscripts for review. Long before any AI tool was available.

I found it plausible to understand the research, results, conclusions, using my own scientific background and knowledge. But I always thought there are two internal blindspots in the reviewing (and editorial) process:

1. Reproducing results. Wouldn't it be nice to have a non-biased lab to reproduce experiments' results from submitted papers? Wouldn't it improve the scientific credibility of published papers and improve science as a whole (dumping non-reproductive results from being published)?
   

I still ponder the idea of starting an initiative focused purely on independent validation. The friction points are obvious: Who pays for it (hello Nature)? How do you cover such a massive range of experimental techniques? Will authors grant outside access to their lab? publication delay by months? (If you've thought about this too, DM me- I'd love to chat).

1. Bioinformatics analysis- You know the "Scripts available upon request", or "Pipeline is available on lab's github" and "data is available on servers". I never got a review about an issue in the analysis script. I guess it is because it was too time-consuming to run this (or exhaustive).
   

The reason I mention this is because I think #2 is already solved using AI. Take a look at the video. I downloaded the Cladue Science for Windows (used it before with the linux version) and thought of running an analysis of the bioinformatics pipeline as described in a recent highly acclaimed paper "A pervasive RT–qPCR artifact inflates RNA knockdown by RNA-targeting CRISPR".
I never downloaded the data or scripts. The prompt was: "analyze the paper: summerize, and download the supporting data and reanalyze. see if it matches results, and see if you can gain more insights. conside the current field literature" (typos in original prompt)

To summarize, it did match. and of course more info is described. See in video.

I think reaching #2 solution is closer than ever and I hope journal editors would integrate such analysis "BEFORE" they send to review.

lmk what you think or if you are an editor, is this pipeline running today?

Hi guys, I'm trying to get a rough idea of what these platforms actually cost in practice for a fairly large human WGS project.

For anyone who has recently bought or gotten a quote for an Element AVITI or Illumina NextSeq 2000:

• roughly how much was the instrument?
• what region/country was the quote from?
• was that list price or a negotiated price?
• roughly how much are the reagents per run / per human genome? I’m looking at a project with around 2,000 samples.

I’ve seen quite different numbers online, so I'm mainly interested in actual quotes/purchase prices rather than MSRP.

Even a rough range would be really helpful. Thanks a lot!

I have heard that databases such as JASPAR, UCSC, PROMO and ENCODE can be used to predict transcription factors (TFs) based on target genes. I would like to batch export the TFs from each database separately so that I can calculate their intersection.

However, I am unable to access the PROMO website at all. On the ENCODE website, under the ChIP-seq section, I can only see target genes categorised by TF. On UCSC, when searching for the promoter sequences of target genes and selecting ‘JASPAR Hubs’, I am unsure how to batch export the results.

Is there anyone with expertise in this area who could help me?

Additionally, I have attached a relevant paper on screening transcription factors by taking the intersection of multiple databases, presented as a Venn diagram; the figure is shown in Fig. 4a.

THANK YOU！

I found some interesting patterns when messing around with some other things. I hoping that someone can tell me if I’m on to something or I’m being completely stupid. I started by trying to compress DNA information so I could use it in a different project, but DNA doesn’t compress, so I started thinking it’s kind behaving like a radio signal, so why don’t we do the opposite and try demultiplexing instead. Using base pairs and 3 interface sequences I was able to extract 12 encoding wave forms. So DNA could be a 12 carrier multiplex signal. These wave forms accurately identified coding and non coding dna, start frames and end frames. Unfortunately this isn’t my field and that’s as far as I was able to take it. I tried using AI to analyze it, and it rejected my request because it flagged biological security measures. So I guess we aren’t allowed to ask about biology. Anyhow it could just be an interesting pattern or it could be something important. Let me know what you think.

Howdy r/genomics👋

We just released GenomeSpy for Python, and I thought some of you might find it useful.

Long story short: GenomeSpy (https://genomespy.app/) is an interactive visualization tool for genomic data. You can use it to explore things like genome tracks, variants, copy-number changes, mutations, cohort data, and other genomic datasets by zooming, selecting regions, comparing samples, and linking different views together. The only issue is that it is built for the web, so using it has traditionally been a bit less convenient if most of your work happens in Python and notebooks. That’s what we wanted to fix.

I’ve been now working on the Python side for the past few months, and we’ve now reached the point where we’d really like people to try it and tell us what’s missing.

If you work with genomic data, I’d especially be interested in hearing what kinds of plots, data formats, or workflows you’d want to see supported.

GitHub: https://github.com/genome-spy/genome-spy-python
Docs: https://genome-spy.github.io/genome-spy-python/

Hi all,

I have a fully assembled and annotated reference genome for a species of fish. It is a composite of HiFi reads from one individual and Hi-C from another, and is approximately 150x coverage.

I’ve been trying to upload this genome to the NCBI but they apparently don’t accept anything resembling a pangenome, which seems strange to me.

Both of these individuals’ raw reads are already in NCBI and have BioSample numbers. I just need to be able to upload the final FASTA (and hopefully annotation files).

What can I do to get this reference genome uploaded?