This study hasn’t ruled out contamination as the cause for the results. Given that, it’s very likely to be BS.
We are currently in the WTF is going on here phase. $5 of anti-fungal medication cuts some cancer death rates in half? Is a tiny amount of fungus in cancer tissue driving growth and metastasis? Does Terbinafine have some other anti-cancer mechanism?
I read an interesting hypothesis that many cancers can be treated by changing (perhaps unpredictable) variables in the patient's body.
We already know cancers thrive in nutrient-rich environments and that many tumors have a narrower set of survival conditions than the normal tissue around them. That's why chemo works: you start to kill the whole person and the tumor (hopefully) dies first.
So what if you just randomly messed around with the conditions of the body? Give someone a diabetes drug, change their diet, add an antifungal... we've seen random successes like this, and the unifying theory might be that the cancer was thriving because it had exactly the right conditions, and now we can change them to cause the cancer to stop thriving.
Can you provide a source for this? There are a lot of cranks right now claiming that there's no such thing as a virus because we've never been able to get a non-contaminated sequence (something I've assumed was due to culturing). If this is true of everything there's no limit to what can and cannot exist per their insane arguments.
In this case, I don't really know much about this either way, but 30 seconds of effort by just copy/pasting the exact sentence you quoted into Google found me something that seems to fit the bill, showing that contaminants in sequencing is "pervasive" and must be accounted for--such as by filtering things that look out of scope--to get reasonable results.
https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-02...
> Contaminant DNA in bacterial sequencing experiments is a major source of false genetic variability
> We found that contamination is pervasive and can introduce large biases in variant analysis. We showed that these biases can result in hundreds of false positive and negative SNPs, even for samples with slight contamination. Studies investigating complex biological traits from sequencing data can be completely biased if contamination is neglected during the bioinformatic analysis, and we demonstrate that removing contaminant reads with a taxonomic classifier permits more accurate variant calling.
As much as I would love to be able to do this, I’m unsure where to start, and while I’m certain there would be a stackoverflow community for it, I wouldn’t even know what to ask.
I know this sounds like a joke comment, but I feel like we live in a time where someone could post some Jupyter notebooks on the matter, and for someone to create a step by step video on YouTube to guide people through the process.
Yet.
Find some data of interest: https://www.ncbi.nlm.nih.gov/sra?term=(%22Homo%20sapiens%22[... (This searches SRA for human genome sequences on illumina with fastq files available)
Run fasterq-dump on the SRR (listed as "Runs" in the SRA page of your choice): fasterq-dump SRR21812682
Download a microbial genome of interest, here is the link for common yeast: https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/146/045/GCF...
Install an alignment tool like bwa: https://github.com/lh3/bwa
Unzip the the genome file and create a bwa index: gunzip GCF_000146045.2_R64_genomic.fna.gz && bwa index GCF_000146045.2_R64_genomic.fna
Align: bwa GCF_000146045.2_R64_genomic.fna SRR21812682.fastq (or whatever the fastq files are named)
If you get any alignment results, you've "found" fungal DNA in a human sample. This is a highly simplified workflow, but covers the basic ideas. One of the papers is free and the method sections covers their workflow (it is much more complicated):
https://www.cell.com/cell/fulltext/S0092-8674(22)01127-8
Useful resources: https://www.biostars.org/ https://rosalind.info/problems/list-view/?location=bioinform... https://www.cancer.gov/about-nci/organization/ccg/research/s... (source data for this paper, cancer specific sequencing data)
Yes, this is an approximate workflow. It doesn’t take that much specialized knowledge to get it running.
However step 2/3 (find/download a dataset of interest) is harder. Finding whole genome sequencing data for a cancer that you can download without being part of a research institution is difficult. There are a lot of controls over who can access raw DNA sequences from patients. RNA data are much more readily available as they are less identifiable.
Specifically, here is the type of access you need:
https://gdc.cancer.gov/access-data/obtaining-access-controll...
The reasons for this are good and I’m not trying to say otherwise. Just that from a practical perspective, being able to technically perform the analysis is doable for many non-biomedical people here. However, accessing the raw data is much more difficult.
I feel there is a "basic bootcamp for the 21C" that I missed
If you had thousands of sequence reads from multiple unknown organisms, then you’d have to implement a meta genomics pipeline.
All the software for this is already made. You take your sequence reads, trim them a bit, assemble them into “contigs”, and then map those contigs to known genomes in the magical tree of life.