Hi everyone, I’m an incoming masters student.

I’m working with environmental DNA (eDNA) samples and I’m trying to detect certain algal species. I’ve been using universal 18S primers, and they’re good at helping me know how diverse the water I sampled from is, but I was wondering if I could use a more targeted primer, and if I can design a targeted primer from 18s dataset?

My current idea is to align multiple 18S sequences from my algae of interest and closely related species, and then identify regions that are conserved within my algae of interest but differ from other related species and design primers from those regions.

My questions are:

1. Is this a reasonable approach in designing a genus-specific primer?
   
2. How many reference sequences would you recommend including in the multiple sequence alignment?
   
3. Are there any tools or pipelines you would recommend for identifying candidate primer-binding regions from an MSA?
   
4. Would you recommend using one
   
5. algae of interest-specific primer paired with a universal reverse primer, or designing both genus-specific (?) primers?

Any advice or references would be greatly appreciated. Thanks!

Looking for recommendations of sites that allow for raw rs data input from whole genomic sequencing reports to identify trends, associations and details as a whole, not analyzing one at a time.

The premise: most people who’ve done DNA testing end up with a raw file they can’t use, lab results in a portal they can’t interpret, and a health history nobody’s connecting to either. We’re building a platform that takes whole-genome sequencing, your labs, and your history and pulls them into one picture using a systems biology approach — pathways, not isolated SNPs.

We’re running a short market validation survey to pressure-test whether this is actually a problem worth solving and what people would want out of it. Takes a few minutes:

👉 https://survey.ravelgenetics.com

More about what we’re building: ravelgenetics.com

Happy to answer questions in the comments — including skeptical ones.

Disclosure: I’m the founder. Posting per subreddit rules.

[https://www.nature.com/articles/s42256-026-01266-0\](https://www.nature.com/articles/s42256-026-01266-0)

Hi! My dad is 100% Ashkenazi Jewish (I’m 50%) and my mom has some autoimmune disorders. I purchased 23andme back in 2017 so I know I have some pretty rare and serious variants based off the limited raw data and reports 23andme can provide.

I’ve read through tons of negative reviews on direct to consumer WGS and I understand going to a specialist/doctor/etc is best to collect and interpret results and I will.

For now I’m looking for a direct to consumer whole genome sequencing recommendation. I do not need a company with great reports just one that provides the most accurate data that I can upload into different databases. I do not care about costs. I understand there could be mistakes and false negatives.

I have a brother with ID which is of unknown origin. I recently bought a direct to customer with no use. No clinical geneticist in my country was trained to understand it. They suggested the USA. How does one go about getting their gene sequenced? Im a foreigner so if anyone has advice to that, it will be best.

Quick context: I've spent the last while building CRISPRR with a research collaborator, aimed at cutting down the time between "I have a target gene" and "I have a guide I'm confident enough to order."

What it does: you paste in a sequence, and you get back gRNAs ranked by CRISPRon efficiency scoring, off-target risk across the genome, and a 3D model of the Cas9-gRNA complex so you can actually look at the structure before committing to a guide. All in about 5 seconds.

The motivation was pretty simple — designing guides well usually means juggling 3-4 tools and a lot of manual cross-referencing, and that's before you've touched a pipette.

What I'd love feedback on from people actually running CRISPR experiments:

- Does seeing the 3D structure actually change how you'd pick between two similarly-scored guides, or is it more of a nice-to-have?

- What's missing that would make this part of your actual workflow rather than just a curiosity?

- Any horror stories about a guide that scored well computationally but failed at the bench? Curious what scoring tends to miss.

It's free, no account needed: crisprr.bio. Would rather hear what's wrong with it than just feedback that's nice to hear. Link in the comments

New preprint:

We performed ultra-high frequency metatranscriptomic sampling (every 2 hours over 72h) in Daya Bay using autonomous drone arrays. 

To analyze the data, we:
- Resolved the transcriptional landscape into six distinct temporal modules
- Applied community-wide and genome-resolved approaches
- Used deep learning structural modeling to analyze cyanophage psbA variants
- Quantified taxonomic-functional coupling across temporal scales

Key observations include >90% turnover of transcribed gene clusters every ~2 hours and strong evidence of viral auxiliary metabolic gene activity synchronized with host photosynthesis.

Preprint: 

https://doi.org/10.64898/2026.06.22.733315

Methods and analytical pipeline details are described in the paper. Feedback from the bioinformatics community is very welcome, especially regarding temporal module detection and viral AMG analysis.

#Metatranscriptomics #Bioinformatics #Microbiome #Genomics

Despite extensive genomic surveillance, the evolutionary origin of the SARS-CoV-2 Omicron variant remains the subject of intense debate. [Preprint in JXIV, Japan preprint server]

I'm in the US and looking to get WGS that offers full data download. I know this question gets asked a lot, but the recent posts I found were for Europeans + commenters said the best option changes month-to-month.

I have heard bad things about every DNA sequencing option, so I'm torn haha. Please share your experiences + recommendations!

I'm in the SF area, so I'm also interested in volunteer options where you donate your data to science in exchange for sequencing. Seems like All of Us doesn't allow data download anymore though?

I'm looking for advice on choosing an appropriate workflow for a low-coverage Oxford Nanopore whole-genome sequencing dataset.

I'm evaluating a research dataset with substantially lower coverage than is typically used for standard ONT variant-calling workflows. The initial pipeline proposed was:

FASTQ → alignment → Clair3 → phasing/imputation → PRS calculation.

Before proceeding, I wanted to ask the community:

1. At what approximate ONT whole-genome coverage would you consider standard Clair3 variant calling to be reliable?
2. Below that range, would you recommend a dedicated low-pass sequencing workflow (genotype likelihoods + reference-panel imputation) instead?
3. Are there published benchmarks or best-practice papers comparing these approaches for downstream polygenic risk score analyses?

I'm interested in understanding the methodological decision rather than troubleshooting software. My goal is to choose the most scientifically appropriate workflow based on the characteristics of the sequencing data.

Any references or recommendations would be greatly appreciated.
Thanks in advance for any recommendations or relevant publications.