Get data from HoloFood database
Arguments
- type
NULLorcharacter scalarspecifying the type of data to query. Must be one of the following options:"analysis-summaries","animals","genome-catalogues","samples","sample_metadata_markers"or"viral-catalogues". When genome or viral catalogues is fetched by their accession ID, the type can also be"genomes"or"fragments". (Default:NULL)- accession.type
NULLorcharacter scalarspecifying the type of accession IDs. Must be one of the following options:"animals","genome-catalogues","samples"or"viral-catalogues". (Default:NULL)- accession
NULLorcharacter vectorspecifying the accession IDs of typeaccession.type. (Default:NULL)- flatten
Logical scalarspecifying whether to flatten the resultingdata.frame. This means that columns with multiple values are separated to multiple columns. (Default:FALSE)- ...
optional arguments:
max.hits
NULLorinteger scalarspecifying the maximum number of results to fetch. When NULL, all results are fetched. (Default:NULL)use.cache
Logical scalarspecifying whether to use cache (Default:FALSE)cache.dir
Character scalarspecifying cache directory. (Default:tempdir())clear.cache
Logical scalarspecifying whether to remove and clear cache (Default:FALSE)
Details
With getData, you can fetch data from the database. Compared to
getResult, this function is more flexible since it can fetch any kind
of data from the database. However, this function returns the data
without further wrangling as list or data.frame which are not
optimized format for fetching data on samples.
Search results can be filtered; for example, animals can be filtered based on available samples. See [Api browser](https://www.holofooddata.org/api/docs) for information on filters. You can find help on customizing queries from [here](https://emg-docs.readthedocs.io/en/latest/api.html#customising-queries).
Examples
# Find genome catalogues
catalogues <- getData(type = "genome-catalogues")
head(catalogues)
#> id title
#> 1 salmon-gut-v2-0 HoloFood Salmon Gut v2
#> 2 chicken-gut-v2-0 HoloFood Chicken Gut v2
#> biome related_mag_catalogue_id system
#> 1 root:Host-associated:Fish:Digestive system non-model-fish-gut-v2-0 salmon
#> 2 root:Host-associated:Birds:Digestive system chicken-gut-v1-0-1 chicken
#> analysis_summaries
#> 1 c("HoloF....
#> 2 c("HoloF....
# Find genomes based on certain genome catalogue iD
res <- getData(
type = "genomes", accession.type = "genome-catalogues",
accession = catalogues[1, "id"], max.hits = 100)
# See the data.
head(res)
#> accession cluster_representative
#> 1 MGYG000307500 MGYG000307500
#> 2 MGYG000307501 MGYG000307501
#> 3 MGYG000307502 MGYG000307501
#> 4 MGYG000307503 MGYG000299622
#> 5 MGYG000307504 MGYG000299579
#> 6 MGYG000307505 MGYG000299579
#> taxonomy
#> 1 Bacteria > Firmicutes_A > Clostridia > Oscillospirales > Ruminococcaceae
#> 2 Bacteria > Proteobacteria > Gammaproteobacteria > Pseudomonadales > Pseudomonadaceae > Pseudomonas > Pseudomonas aeruginosa
#> 3 Bacteria > Proteobacteria > Gammaproteobacteria > Pseudomonadales > Pseudomonadaceae > Pseudomonas > Pseudomonas aeruginosa
#> 4 Bacteria > Proteobacteria > Gammaproteobacteria > Enterobacterales > Aeromonadaceae > Aeromonas > Aeromonas sobria
#> 5 Bacteria > Firmicutes > Bacilli > Mycoplasmatales > Mycoplasmoidaceae
#> 6 Bacteria > Firmicutes > Bacilli > Mycoplasmatales > Mycoplasmoidaceae
#> representative_url metadata1
#> 1 https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000307500 196
#> 2 https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000307501 197
#> 3 https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000307501 198
#> 4 https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000299622 199
#> 5 https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000299579 200
#> 6 https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000299579 201
#> metadata.Genome_type metadata.Length metadata.N_contigs metadata.N50
#> 1 MAG 1319783 19 153761
#> 2 MAG 6676386 136 83355
#> 3 MAG 4766025 4741 1104
#> 4 MAG 2381842 367 8519
#> 5 MAG 681326 38 37281
#> 6 MAG 627706 26 43119
#> metadata.GC_content metadata.Completeness metadata.Contamination
#> 1 27.3 85.79 0.0
#> 2 66.27 98.37 0.192
#> 3 64.82 68.36 2.053
#> 4 59.04 58.95 0.09
#> 5 25.14 96.02 0.384
#> 6 25.04 95.25 0.384
#> metadata.rRNA_5S metadata.rRNA_16S metadata.rRNA_23S metadata.tRNAs
#> 1 0.0 0.0 0.0 18
#> 2 0.0 0.0 0.0 17
#> 3 93.28 36.2 99.04 15
#> 4 91.6 0.0 0.0 14
#> 5 0.0 99.74 99.59 19
#> 6 0.0 99.74 99.59 19
#> metadata.Genome_accession metadata.Sample_accession metadata.Study_accession
#> 1 ERZ15182294 SAMEA112246717 ERP136460
#> 2 ERZ15182298 SAMEA112246686 ERP136460
#> 3 ERZ15182309 SAMEA112246683 ERP136460
#> 4 ERZ15182314 SAMEA112246713 ERP136460
#> 5 ERZ15233654 SAMEA112264417 ERP125469
#> 6 ERZ15233655 SAMEA112264472 ERP125469
#> metadata.Country metadata.Continent
#> 1 Norway Europe
#> 2 Norway Europe
#> 3 Norway Europe
#> 4 Norway Europe
#> 5 Norway Europe
#> 6 Norway Europe
#> metadata.FTP_download
#> 1 ftp://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/non-model-fish-gut/v2.0/all_genomes/MGYG0003075/MGYG000307500/genomes1/MGYG000307500.gff.gz
#> 2 ftp://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/non-model-fish-gut/v2.0/all_genomes/MGYG0003075/MGYG000307501/genomes1/MGYG000307501.gff.gz
#> 3 ftp://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/non-model-fish-gut/v2.0/all_genomes/MGYG0003075/MGYG000307501/genomes1/MGYG000307502.gff.gz
#> 4 ftp://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/non-model-fish-gut/v2.0/all_genomes/MGYG0002996/MGYG000299622/genomes1/MGYG000307503.gff.gz
#> 5 ftp://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/non-model-fish-gut/v2.0/all_genomes/MGYG0002995/MGYG000299579/genomes1/MGYG000307504.gff.gz
#> 6 ftp://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/non-model-fish-gut/v2.0/all_genomes/MGYG0002995/MGYG000299579/genomes1/MGYG000307505.gff.gz
#> metadata.Host_species annotations.cazy.GH annotations.cazy.PL
#> 1 Salmo salar 7 0
#> 2 Salmo salar 24 0
#> 3 Salmo salar 24 0
#> 4 Salmo salar 27 0
#> 5 Salmo salar 0 0
#> 6 Salmo salar 0 0
#> annotations.cazy.CE annotations.cazy.AA annotations.cazy.CB
#> 1 0 0 1
#> 2 3 1 4
#> 3 3 1 4
#> 4 1 1 6
#> 5 0 0 0
#> 6 0 0 0
#> annotations.cazy.GT annotations.cazy.CL
#> 1 7 0
#> 2 34 0
#> 3 34 0
#> 4 20 0
#> 5 2 0
#> 6 2 0
# It includes for instance summary of the CAZy
# (Carbohydrate-Active enZymes) annotations as a counts per category
cazy <- res[ , grepl("annotations.cazy", colnames(res)), drop = FALSE]
head(cazy)
#> annotations.cazy.GH annotations.cazy.PL annotations.cazy.CE
#> 1 7 0 0
#> 2 24 0 3
#> 3 24 0 3
#> 4 27 0 1
#> 5 0 0 0
#> 6 0 0 0
#> annotations.cazy.AA annotations.cazy.CB annotations.cazy.GT
#> 1 0 1 7
#> 2 1 4 34
#> 3 1 4 34
#> 4 1 6 20
#> 5 0 0 2
#> 6 0 0 2
#> annotations.cazy.CL
#> 1 0
#> 2 0
#> 3 0
#> 4 0
#> 5 0
#> 6 0
# Moreover, it includes a sample list. This sample list represents a
# collection of samples where the MAG was identified. Thr data has also the
# completeness of MAG in a sample.
head(res[ c("metadata.Sample_accession", "metadata.Completeness")])
#> metadata.Sample_accession metadata.Completeness
#> 1 SAMEA112246717 85.79
#> 2 SAMEA112246686 98.37
#> 3 SAMEA112246683 68.36
#> 4 SAMEA112246713 58.95
#> 5 SAMEA112264417 96.02
#> 6 SAMEA112264472 95.25