hmdbQuery: working with Human Metabolome Database (hmdb.ca)

1 Initial remarks

The human metabolomics database (HMDB, http://www.hmdb.ca) includes XML documents describing 114000 metabolites. We will show how to manipulate the metadata on metabolites fairly flexibly.

2 Key utilities of the package

The hmdbQuery package includes a function for querying HMDB directly over HTTP:

library(hmdbQuery)
lk1 = HmdbEntry(prefix = "http://www.hmdb.ca/metabolites/", 
       id = "HMDB0000001")

The result is parsed and encapsulated in an S4 object

lk1

## HMDB metabolite metadata for 1-Methylhistidine:
## There are 10 diseases annotated.
## Direct association reported for 5 biospecimens and 2 tissues.
## Use diseases(), biospecimens(), tissues() for more information.

The size of the complete import of information about a single metabolite suggests that it would not be too convenient to have comprehensive information about all HMDB constituents in memory. The most effective approach to managing the metadata will depend upon use cases to be developed over the long run.

Note however that this package does provide snapshots of certain direct associations derived from all available information as of Sept. 23 2017. Information about direct associations reported in the database is present in tables hmdb_disease, hmdb_gene, hmdb_protein, hmdb_omim. For example

data(hmdb_disease)
hmdb_disease

## DataFrame with 75360 rows and 3 columns
##         accession                        name
##       <character>                 <character>
## 1     HMDB0000001           1-Methylhistidine
## 2     HMDB0000001           1-Methylhistidine
## 3     HMDB0000001           1-Methylhistidine
## 4     HMDB0000001           1-Methylhistidine
## 5     HMDB0000002          1,3-Diaminopropane
## ...           ...                         ...
## 75356 HMDB0094706                 Serylvaline
## 75357 HMDB0094708        Tetraethylene glycol
## 75358 HMDB0094712                Serylleucine
## 75359 HMDB0100002      TG(i-14:0/17:0/i-13:0)
## 75360 HMDB0101657 TG(15:0/i-14:0/a-21:0)[rac]
##                                                    disease
##                                                <character>
## 1                                      Alzheimer's disease
## 2                                 Diabetes mellitus type 2
## 3                                           Kidney disease
## 4                                                  Obesity
## 5     Perillyl alcohol administration for cancer treatment
## ...                                                    ...
## 75356                                                   NA
## 75357                                                   NA
## 75358                                                   NA
## 75359                                                   NA
## 75360                                                   NA

3 Working with the metadata

3.1 Disease associations

Some HMDB metabolites have been mapped to diseases.

d1 = diseases(lk1) # matrix
pmids = unlist(d1[3,1][[1]]["pubmed_id",])
library(annotate)

## Loading required package: AnnotationDbi

## Loading required package: stats4

## Loading required package: BiocGenerics

## Loading required package: parallel

## 
## Attaching package: 'BiocGenerics'

## The following objects are masked from 'package:parallel':
## 
##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
##     clusterExport, clusterMap, parApply, parCapply, parLapply,
##     parLapplyLB, parRapply, parSapply, parSapplyLB

## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs

## The following objects are masked from 'package:base':
## 
##     Filter, Find, Map, Position, Reduce, anyDuplicated, append,
##     as.data.frame, basename, cbind, colnames, dirname, do.call,
##     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
##     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
##     pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
##     tapply, union, unique, unsplit, which, which.max, which.min

## Loading required package: Biobase

## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.

## Loading required package: IRanges

## Loading required package: S4Vectors

## 
## Attaching package: 'S4Vectors'

## The following object is masked from 'package:base':
## 
##     expand.grid

pm = pubmed(pmids[1])
ab = buildPubMedAbst(xmlRoot(pm)[[1]])
ab

## An object of class 'pubMedAbst':
## Title: Dimethylglycine accumulates in uremia and predicts elevated
##      plasma homocysteine concentrations.
## PMID: 11380830
## Authors: DO McGregor, WJ Dellow, M Lever, PM George, RA Robson, ST
##      Chambers
## Journal: Kidney Int
## Date: Jun 2001

3.2 Biospecimen and tissue location metadata

Note that pre HMDB v 4.0, biospecimens were called biofluids.

There are arbitrarily many biospecimen and tissue associations provided for each HMDB entry. We have direct accessors, and by default we capture all metadata, available through the store method.

biospecimens(lk1)

## [1] "Blood"                     "Cerebrospinal Fluid (CSF)"
## [3] "Feces"                     "Saliva"                   
## [5] "Urine"

tissues(lk1)

## [1] "Placenta"        "Skeletal Muscle"

st = store(lk1)
head(names(st))

## [1] "version"              "creation_date"        "update_date"         
## [4] "accession"            "status"               "secondary_accessions"

length(names(st))

## [1] 46

st$protein_assoc["name",]

## $protein
## [1] "Beta-Ala-His dipeptidase"
## 
## $protein
## [1] "Protein arginine N-methyltransferase 3"

st$protein_assoc["gene_name",]

## $protein
## [1] "CNDP1"
## 
## $protein
## [1] "PRMT3"