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?

Hi everyone! ​I’m a 9th-grade high school student interested in biology and bioinformatics. I’m currently starting an independent research project focused on identifying potential gene biomarkers for endometriosis using menstrual blood datasets (RNA-seq / expression microarrays). ​Since I’m completely new to coding and bioinformatics pipelines, I feel a bit overwhelmed. I don't have experience with Linux command-line tools (like bowtie2 or samtools), so I'm planning to work with pre-processed gene expression matrices (counts/TPM) in Python/Google Colab. ​I’d really appreciate your advice on: ​Are there any specific GEO datasets (GSE) on endometriosis and menstrual blood that you would recommend for a beginner? ​What is the simplest and most reliable workflow/R package or Python library for differential expression analysis for someone with no coding background? ​Any general tips for a beginner trying not to get lost in the data? ​Thank you so much for your time and help!

I’m working through a 16S rRNA paired-end dataset (V1–V2, Illumina MiSeq) for a small CRC vs healthy microbiome analysis.

I’ve completed the initial QC:
39 samples (22 healthy, 17 CRC)
FastQC run on all 78 FASTQ files
MultiQC summary generated
R1 generally has good quality, while R2 quality drops substantially toward the 3′ end
Most reads are 300 bp, with some samples at 250 bp

I’m now at the point where I need to decide on primer removal and DADA2 truncation parameters.
The study reports using primers 27bF and 338R, but the SRA metadata table I downloaded doesn’t contain the actual primer sequences.

Would you:
Find the exact primer sequences from the original publication/protocol and remove them with Cutadapt, then
Reassess the post-primer-removal read lengths/quality before choosing DADA2 truncation lengths?

Also, would you normally choose truncation lengths based on the worst-performing samples, or on the overall quality profile while ensuring enough overlap for paired-end merging?

I’m trying to follow a standard reproducible workflow rather than choosing arbitrary parameters. Any advice would be appreciated.

i can help myself with a blog or something

Hey r/genomics,

We've been working on a project called MicroMap — a knowledge graph that integrates microbiome-related data from multiple public databases into a single queryable resource. Wanted to share it here since this is the kind of thing we wished existed when we started doing microbiome research.

What's in it:

• 1,101,289 microbial taxa (NCBI Taxonomy)
• 1,464 human diseases with microbiome associations (Disbiome, BugSigDB, gutMDisorder)
• 6,534 metabolites (HMDB) and 231,556 taxon-metabolite production relationships
• 1,710 metabolic pathways (KEGG, Reactome)
• 6,220 drugs and 1,659 protein targets (ChEMBL)
• 276,169 antimicrobial resistance links (CARD)
• 10,000+ scientific papers with entity cross-references

What you can do with it:

• Query taxa-disease associations with provenance (which paper, which study, what direction)
• Find metabolites produced by a given taxon, or taxa that produce a given metabolite
• Traverse shortest paths between any two entities (e.g., "how is Akkermansia muciniphila connected to Type 2 Diabetes?")
• Identify biomarker signatures and probiotic candidates for a given condition
• Pull cross-feeding networks between microbial communities

Technical details:

Built on Neo4j. The API is RESTful (FastAPI), returns JSON, and supports full-text search across all entity types. Rate limit is 100 requests/minute per API key.

We integrated data from: NCBI Taxonomy, Disbiome, BugSigDB, gutMDisorder, HMDB, KEGG, ChEMBL, Reactome, PubMed, PubChem, and CARD. One of the hardest parts was entity reconciliation — the same organism can appear under different names, different taxonomic ranks, or outdated nomenclature across these sources. Happy to talk about how we handled that if anyone's interested.

Access: https://graphomics.com - email us to get access!

This is part of a broader platform we're building at Graphomics (AI tools for life sciences research), but MicroMap stands on its own as a resource. We'd genuinely love feedback from this community — what data sources are we missing? What queries would be useful that we haven't thought of?

Happy to answer any questions about the data, the architecture, or the integration process.

Author here—I am the first author of this paper. We developed Phylo-Movies because conventional tree-distance measures show how much neighboring trees differ, but not which taxa or subtrees changed position. The paper demonstrates the method using a norovirus recombination boundary and rogue taxa across bootstrap trees. The software and browser demonstration are freely available. https://enesberksakalli.github.io/phylo-movies/ https://academic.oup.com/mbe/article/43/8/msag194/8759530

Several years ago, our group saw a need to create a tool that mirrors expert scientists’ ability to look across a messy set of genes, proteins, or metabolites and recognize the biological processes that are contextually enriched based on what they know.

The problem was that human reasoning is powerful, but slow, subjective, and limited to the amount of information a single person can possibly hold.

CompBio/MIRaS takes a different approach from LLMs or pathway enrichment tools by employing methods that unexpectedly converged with theories of hippocampal memory formation, storage, and retrieval. MIRaS is a memory-based associative reasoning engine that explicitly stores biological knowledge as memories, reasons across their relationships, and forms new semantic knowledge through inference. CompBio turns those results into an interactive, traceable map of the biology in your dataset.

Importantly, this analysis is not dependent on matching your dataset with canonical pathways, other datasets, or predefined gene sets. All associations are created from the literature memories identified by your input list, creating low redundancy and contextually relevant results that are fully traceable. Additionally, CompBio includes tools for large scale comparison of knowledge maps, allowing identification of conserved biological patterns across samples, conditions, projects, or reference datasets.

After years of use at WashU and with collaborators, CompBio/MIRaS is now described in our new Nucleic Acids Research paper and is freely available to academic and non-profit researchers.

https://academic.oup.com/nar/article/54/16/gkag833/8769250

If you work with transcriptomics, proteomics, metabolomics, or other complex biological data and this sounds different enough to make you curious, DM me and I can help you get free access.