MAAB classification of hydroxyproline rich glycoproteins

Perform Motif and amino acid bias classification of hydroxyproline rich glycoproteins according to Johnson et al. (2017)

maab(data, ...)

# S3 method for character
maab(data, ...)

# S3 method for data.frame
maab(data, sequence, id, ...)

# S3 method for list
maab(data, ...)

# S3 method for default
maab(
  data = NULL,
  sequence,
  id,
  order = c("ext", "tyr", "prp", "agp"),
  gpi = NULL,
  get_gpi = c("bigpi", "predgpi", "netgpi", "none"),
  spec = 0.99,
  progress = FALSE,
  ...
)

# S3 method for AAStringSet
maab(data, ...)

Arguments

data	A data frame with protein amino acid sequences as strings in one column and corresponding id's in another. Alternatively a path to a .fasta file with protein sequences. Alternatively a list with elements of class `SeqFastaAA` resulting from `read.fasta` call. Alternatively an `AAStringSet` object. Should be left blank if vectors are provided to sequence and id arguments.
...	currently no additional arguments are accepted apart the ones documented bellow.
sequence	A vector of strings representing protein amino acid sequences, or the appropriate column name if a data.frame is supplied to data argument. If .fasta file path, or list with elements of class "SeqFastaAA" provided to data, this should be left blank.
id	A vector of strings representing protein identifiers, or the appropriate column name if a data.frame is supplied to data argument. If .fasta file path, or list with elements of class "SeqFastaAA" provided to data, this should be left blank.
order	Order of motif counting, the default is as in Johnson et al. (2017).
gpi	A Boolean vector indicating if the corresponding id contains a GPI or not. Can be the 'is.bigpi' column from the output of get_big_pi.
get_gpi	A string indicating if `get_big_pi`, `get_pred_gpi` or `get_netGPI` should be called on sequences that belong to one of the HRGP classes thus resolving class ambiguities that depend on GPI knowledge. At default set to 'none'.
spec	Numeric in the 0-1 range, indicating the threshold specificity of `get_pred_gpi`. Only valid if argument get_gpi = "predgpi".
progress	Boolean, whether to show the progress bar, at default set to FALSE.

Value

A data frame with columns:

id protein identifiers as from input
ext_sp number of extensin SPn motifs, counted using SP3,5
ext_tyr number of extensin TYR motifs, sum of matches for: [FY].Y, KHY, VY[HKDE], V.Y, YY
prp number of proline rich protein motifs, sum of matches for: PPV.[KT], PPV[QK], KKPCPP
agp number of arabinogalactan motifs, sum of matches for: [AVTG]P1,3, [ASVTG]P1,2
past_percent summed percent of "P", "A", "S" and "T" amino acids
pvyk_percent summed percent of "P", "V", "Y" and "K" amino acids
psky_percent summed percent of "P", "S", "K" and "Y" amino acids
p_percent percent of "P"
coverage the coverage of sequence by the identified motifs
maab_class determined maab class

Details

The function provides motif and amino acid bias descriptors used for classification of HRGP's by the MAAB pipeline (Johnson et al. 2017) as well as the determined HRGP classes. The motifs are counted in a specific order ext > tyr > prp > agp, and overlapping motifs are not counted. Hence the classification depends on the order of counting, this is most noticeable for tyr and prp, we recommend using both the default order and 'order = c("ext", "prp","tyr", "agp")'.

References

Johnson KL, Cassin AM, Lonsdale A, Bacic A, Doblin MS, Schultz CJ. (2017) Pipeline to Identify Hydroxyproline-Rich Glycoproteins. Plant Physiol 174(2): 886-903.

Examples