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.
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.