The 21-sample 315GB plant-associated dataset includes 894 genomes. Of
these.
224 are from the proGenomes terrestrial representative genomes.
216 are newly sequenced genomes from an A. thaliana root rhizosphere.
55 are fungal genomes associated with the rhizosphere.
398 are plasmids or circular elements.
1 A. thaliana genome.
Of these genomes, 15.3% (137) have at least one closely related genome present.
Note that 90% of metagenome sequence data originate from bacterial genomes, 9% are fungal genome sequences and 1% is from A. thaliana.
BSc Thesis
The CAMI2 challenge data is based on rhizosphere samples, so it would be inaccurate to assume that the same microbial abundance ratios will hold true for our sterilized root samples. Most of the endophytes can be said to be a subset of the rhizosphere microbiome (Gaiero et al., 2013). Therefore, rather than creating a completely new set of genomes, I will utilize the genomes that CAMI used to create their rhizosphere data in CAMI2. Nonetheless, I plan to adjust the ratios and exclude certain bacterial genomes
GAME PLAN
Get a sense of the fungi to bacteria ratios by reading papers so i have a reference. My wild estimate is that fungi is 70% or more and bacteria are the remaining 30%.
Remove some of the genomes as we don't required 894 different genomes. (or?) These files hold info about the genomes.
fungal_metadata.tsv holds information of all the fungal genomes (genome_id, taxid, path).
plasmids.tsv is not super helpful, the majority has taxid 45202 unidentified plasmid or 32644 which is straight out unidentified. So it is just a circular piece of DNA. There are a few viruses.
references.tsv holds not all but many of the genomes. The three columns are "taxid, NCBI bioproject id, path".
genome_to_id.tsv lists the genom_id and which genome it identifies with by stating the path to the genome.
With the new set of genomes, calculate the relative abundances to create abundance*.tsv files
Idea A: Have five different abundance.tsv files where the abundance for P. zijinensis is 0, 50, 80, 90, and 98%.
Even with a mutual seed set, the reads for the other genomes will probably differ and not cover the same positions of each genome. Might introduce some bias...?
The file does not get so exaggeratedly large as if I would have diluted the 4.5GB mock data set myself. Here i can set the file to be x Gigabasepairs in the contig.ini by setting size=x and still get the correct abundance.
Generate gold standard assembly (GSA) for each sample so I can benchmark correctly.
From the paper:
BSc Thesis The CAMI2 challenge data is based on rhizosphere samples, so it would be inaccurate to assume that the same microbial abundance ratios will hold true for our sterilized root samples. Most of the endophytes can be said to be a subset of the rhizosphere microbiome (Gaiero et al., 2013). Therefore, rather than creating a completely new set of genomes, I will utilize the genomes that CAMI used to create their rhizosphere data in CAMI2. Nonetheless, I plan to adjust the ratios and exclude certain bacterial genomes
GAME PLAN
fungal_metadata.tsvholds information of all the fungal genomes (genome_id, taxid, path).plasmids.tsvis not super helpful, the majority has taxid 45202 unidentified plasmid or 32644 which is straight out unidentified. So it is just a circular piece of DNA. There are a few viruses.references.tsvholds not all but many of the genomes. The three columns are "taxid, NCBI bioproject id, path".metadata.tsv: holds, genome_ID, OTU, NCBI_ID,novelty_categorygenome_to_id.tsvlists the genom_id and which genome it identifies with by stating the path to the genome.abundance*.tsvfilesIdea A: Have five different
abundance.tsvfiles where the abundance for P. zijinensis is 0, 50, 80, 90, and 98%.seedset, the reads for the other genomes will probably differ and not cover the same positions of each genome. Might introduce some bias...?contig.iniby settingsize=xand still get the correct abundance.