genoPlotR (version 0.8.11)

read_functions: Reading functions

Description

Functions to parse dna_seg objects from tab, embl, genbank, fasta, ptt files or from mauve backbone files, and comparison objects from tab or blast files.

Usage

read_dna_seg_from_tab(file, header = TRUE, ...)
read_dna_seg_from_file(file, tagsToParse=c("CDS"), fileType = "detect",
                       meta_lines = 2, gene_type = "auto", header = TRUE,
                       extra_fields = NULL, ...)
read_dna_seg_from_embl(file, tagsToParse=c("CDS"), ...)
read_dna_seg_from_genbank(file, tagsToParse=c("CDS"), ...)
read_dna_seg_from_fasta(file, ...)
read_dna_seg_from_ptt(file, meta_lines = 2, header = TRUE, ...)
read_comparison_from_tab(file, header = TRUE, ...)
read_comparison_from_blast(file, sort_by = "per_id",
                           filt_high_evalue = NULL,
                           filt_low_per_id = NULL,
                           filt_length = NULL,
                           color_scheme = NULL, ...)
read_mauve_backbone(file, ref = 1, gene_type = "side_blocks",
                    header = TRUE, filter_low = 0,
                    common_blocks_only = TRUE, ...)

Arguments

file

Path to file to load. URL are accepted.

header

Logical. Does the tab file has headers (column names)?

tagsToParse

Character vector. Tags to parse in embl or genbank files. Common tags are 'CDS', 'gene', 'misc_feature'.

fileType

Character string. Select file type, could be 'detect' for automatic detection, 'embl' for embl files, 'genbank' for genbank files or 'ptt' for ptt files.

meta_lines

The number of lines in the ptt file that represent "meta" data, not counting the header lines. Standard for NCBI files is 2 (name and length, number of proteins. Default is also 2.

gene_type

Determines how genes are visualized. If 'auto' genes will appear as arrows in there are no introns and as blocks if there are introns. Can also be set to for example 'blocks' or 'arrows'. Do note, currently introns are not supported in the ptt file format. Default for mauve backbone is side_blocks. See gene_types page for more details, or use function gene_types.

extra_fields

NULL by default. If a character vector, parses extra fields in the genbank or embl file that have corresponding keys and put them in the resulting dna_seg.

sort_by

In BLAST-like tabs, gives the name of the column that will be used to sort the comparisons. Accepted values are per_id (percent identity, default), mism (mismatches), gaps (gaps), e_value (E-value), bit_score (bit score).

filt_high_evalue

A numerical, or NULL (default). Filters out all comparisons that have a e-value higher than this one.

filt_low_per_id

A numerical, or NULL (default). Filters out all comparisons that have a percent identity lower than this one.

filt_length

A numerical, or NULL (default). Filters out all comparisons that have alignments shorter than this value.

color_scheme

A color scheme to apply. See apply_color_scheme for more details. Possible values include grey and red_blue. NULL by default. Color schemes can be applied while running plot_gene_map.

ref

In mauve backbone, which of the dna segments will be the reference, i.e. which one will have its blocks in order.

…

Further arguments passed to generic reading functions and class conversion functions. See as.dna_seg and as.comparison.

For read_comparison* functions, see details.

filter_low

A numeric. If larger than 0, all blocks smaller that this number will be filtered out. Defaults to 0.

common_blocks_only

A logical. If TRUE (by default), reads only common blocks (core blocks).

Value

read_dna_seg_from_tab, read_dna_seg_from_file, read_dna_seg_from_embl, read_dna_seg_from_genbank and read_dna_seg_from_ptt return dna_seg objects. read_comparison_from_tab and read_comparison_from_blast return comparison objects. read_mauve_backbone returns a list containing a list of dna_segs and comparisons. objects.

Details

Tab files representing DNA segements should have at least the following columns: name, start, end, strand (in that order. Additionally, if the tab file has headers, more columns will be used to define, for example, the color, line width and type, pch and/or cex. See dna_seg for more information. An example:

name	start	end	strand	col
feat1A	2	1345	1	blue
feat1B	1399	2034	1	red
feat1C	2101	2932	-1	grey
feat1D	2800	3120	1	green

Embl and Genbank files are two commonly used file types. These file types often contain a great variety of information. To properly extract data from these files, the user has to choose which features to extract. Commonly 'CDS' features are of interest, but other feature tags such as 'gene' or 'misc_feature' may be of interest. Should a feature contain an inner "pseudo" tag indicating this CDS or gene is a pseudo gene, this will be presented as a 'CDS_pseudo' or a 'gene_pseudo' feature type respectively in the resulting table. Certain constraints apply to these file types, of which some are: embl files must contain one and only one ID tag; genbank files may only contain one and only one locus tag. In these two files, the following tags are parsed (in addition to the regular name, start, end and strand): protein_id, product, color (or colour). In addition, extra tags can be parsed with the argument extra_fields. If there are more than one field with such a tag, only the first one is parsed.

Fasta files are read as one gene, as long as there are nucleotides in the fasta file.

Ptt (or protein table) files are a tabular format giving a bunch of information on each protein of a genome (or plasmid, or virus, etc). They are available for each published genome on the NCBI ftp site (ftp://ftp.ncbi.nlm.nih.gov/genomes/). As an example, look at ftp://ftp.ncbi.nlm.nih.gov/genomes/Bacteria/Bartonella_henselae_Houston-1/NC_005956.ptt.

Tabular comparison files should have at least the following columns: start1, end1, start2, end2. If no header is specified, the fifth column is parsed as the color.

start1	end1	start2	end2	col
2	1345	10	1210	red
1399	2034	2700	1100	blue
500	800	3000	2500	blue

BLAST tabular result files are produced either with blastall using -m8 or -m9 parameter, or with any of the newer blastn/blastp/blastx/tblastx using -outfmt 6 or -outfmt 7.

In the subsequent plot_gene_map, the comparisons are drawn in the order of the comparison object, i.e. the last rows of the comparison object are on the top in the plot. For comparisons read from BLAST output, the order can be modified by using the argument sort_by. In any case, the order of plotting can be modified by modifying the order of rows in the comparison object prior to plotting.

Mauve backbone is another tabular data file that summarizes the blocks that are similar between all compared genomes. Each genome gets two columns, one start and one end of the block. There is one row per block and eventually a header row. If named, columns have sequence numbers, not actual names, so be careful to input the same order in both Mauve and genoPlotR. See http://asap.ahabs.wisc.edu/mauve-aligner/mauve-user-guide/mauve-output-file-formats.html for more info on the file format. Normally, the function should be able to read both progressiveMauve and mauveAligner outputs. The function returns both the blocks as dna_segs and the links between the blocks as comparisons.

References

For BLAST: http://www.ncbi.nlm.nih.gov/blast/ For Mauve: http://asap.ahabs.wisc.edu/mauve/

See Also

comparison, dna_seg, apply_color_scheme.

Examples

# NOT RUN {
##
## From tabs
##
## Read DNA segment from tab
dna_seg3_file <- system.file('extdata/dna_seg3.tab', package = 'genoPlotR')
dna_seg3 <- read_dna_seg_from_tab(dna_seg3_file)

## Read comparison from tab
comparison2_file <- system.file('extdata/comparison2.tab',
                                package = 'genoPlotR')
comparison2 <- read_comparison_from_tab(comparison2_file)

##
## Mauve backbone
##
## File: this is only to retrieve the file from the genoPlotR
## installation folder. 
bbone_file <- system.file('extdata/barto.backbone', package = 'genoPlotR')
## Read backbone
## To read your own backbone, run something like
## bbone_file <- "/path/to/my/file.bbone"
bbone <- read_mauve_backbone(bbone_file)
names <- c("B_bacilliformis", "B_grahamii", "B_henselae", "B_quintana")
names(bbone$dna_segs) <- names
## Plot
plot_gene_map(dna_segs=bbone$dna_segs, comparisons=bbone$comparisons)

## Using filter_low & changing reference sequence
bbone <- read_mauve_backbone(bbone_file, ref=2, filter_low=2000) 
names(bbone$dna_segs) <- names
plot_gene_map(dna_segs=bbone$dna_segs, comparisons=bbone$comparisons)

## Read guide tree
tree_file <- system.file('extdata/barto.guide_tree', package = 'genoPlotR')
tree_str <- readLines(tree_file)
for (i in 1:length(names)){
  tree_str <- gsub(paste("seq", i, sep=""), names[i], tree_str)
}
tree <- newick2phylog(tree_str)
## Plot
plot_gene_map(dna_segs=bbone$dna_segs, comparisons=bbone$comparisons,
              tree=tree)

##
## From embl file
##
bq_embl_file <- system.file('extdata/BG_plasmid.embl', package = 'genoPlotR')
bq <- read_dna_seg_from_embl(bq_embl_file)

##
## From genbank file
##
bq_genbank_file <- system.file('extdata/BG_plasmid.gbk', package = 'genoPlotR')
bq <- read_dna_seg_from_file(bq_genbank_file, fileType="detect")

## Parsing extra fields in the genbank file
bq <- read_dna_seg_from_file(bq_genbank_file,
                             extra_fields=c("db_xref", "transl_table"))
names(bq)


##
## From ptt files
##
## From a file
bq_ptt_file <- system.file('extdata/BQ.ptt', package = 'genoPlotR')
bq <- read_dna_seg_from_ptt(bq_ptt_file)
## Read directly from NCBI ftp site:
url <- "ftp://ftp.ncbi.nih.gov/genomes/Bacteria/Bartonella_henselae_Houston-1/NC_005956.ptt"
attempt <- 0
# }
# NOT RUN {
while (attempt < 5){
  attempt <- attempt + 1
  bh <- try(read_dna_seg_from_ptt(url))
  if (!inherits(bh, "try-error")) {
    attempt <- 99
  } else {
    print(paste("Tried", attempt, "times, retrying in 5s"))
    Sys.sleep(5)
  }
}
# }
# NOT RUN {
## If attempt to connect to internet fails
if (!exists("bh")){
  data(barto)
  bh <- barto$dna_segs[[3]]
}

##
## Read from blast
##
bh_vs_bq_file <- system.file('extdata/BH_vs_BQ.blastn.tab',
                             package = 'genoPlotR')
bh_vs_bq <- read_comparison_from_blast(bh_vs_bq_file, color_scheme="grey")

## Plot
plot_gene_map(dna_segs=list(BH=bh, BQ=bq), comparisons=list(bh_vs_bq),
              xlims=list(c(1,50000), c(1, 50000)))


# }