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BioVizSeq

1. Introduction

The goal of BioVizSeq is to visualize the types and distribution of elements within bio-sequences. At the same time, We have developed a geom layer, geom_rrect(), that can generate rounded rectangles. No external references are used in the development of this package.

2. Installation

Install from CRAN:

# Install from CRAN
install.packages("BioVizSeq")

Install from Github: the development version of BioVizSeq:

install.packages("devtools")
devtools::install_github("zhaosq2022/BioVizSeq")

3. Libary packages

library(BioVizSeq)
#> Registered S3 methods overwritten by 'treeio':
#>   method              from    
#>   MRCA.phylo          tidytree
#>   MRCA.treedata       tidytree
#>   Nnode.treedata      tidytree
#>   Ntip.treedata       tidytree
#>   ancestor.phylo      tidytree
#>   ancestor.treedata   tidytree
#>   child.phylo         tidytree
#>   child.treedata      tidytree
#>   full_join.phylo     tidytree
#>   full_join.treedata  tidytree
#>   groupClade.phylo    tidytree
#>   groupClade.treedata tidytree
#>   groupOTU.phylo      tidytree
#>   groupOTU.treedata   tidytree
#>   inner_join.phylo    tidytree
#>   inner_join.treedata tidytree
#>   is.rooted.treedata  tidytree
#>   nodeid.phylo        tidytree
#>   nodeid.treedata     tidytree
#>   nodelab.phylo       tidytree
#>   nodelab.treedata    tidytree
#>   offspring.phylo     tidytree
#>   offspring.treedata  tidytree
#>   parent.phylo        tidytree
#>   parent.treedata     tidytree
#>   root.treedata       tidytree
#>   rootnode.phylo      tidytree
#>   sibling.phylo       tidytree
#> Package BioVizSeq loaded successfully!

# Extra package
library(ggplot2)
#> Warning: 程辑包'ggplot2'是用R版本4.3.3 来建造的

4. Usage cases

4.1 GFF/GTF

gff or gtf file

4.1.1 Step by step

gff_path <- system.file("extdata", "idpro.gff3", package = "BioVizSeq")
gff_data <- read.table(gff_path, header = FALSE, sep = '\t')
gff_loc <- gff_to_loc(gff_data)

motif_plot(gff_loc$table_loc, gff_loc$gene_length) +
    labs(x="DNA length (5'-3')", y="Gene name")

4.1.2 One step

gff_path <- system.file("extdata", "idpro.gff3", package = "BioVizSeq")
gff_plot(gff_path)

4.2 MEME

meme.xml or mast.xml

4.2.1 Step by step

meme_path <- system.file("extdata", "mast.xml", package = "BioVizSeq") 
meme_file <- readLines(meme_path)
motif_loc <- meme_to_loc(meme_file)

motif_plot(motif_loc$table_loc, motif_loc$gene_length)

4.2.2 One step

meme_path <- system.file("extdata", "mast.xml", package = "BioVizSeq")
meme_plot(meme_path)

4.3 PFAM

PFAM

Download: .tsv

4.3.1 Step by step

pfam_path <- system.file("extdata", "iprscan.tsv", package = "BioVizSeq")
pfam_file <- read.table(pfam_path, sep='\t', header = FALSE)
domain_loc <- pfam_to_loc(pfam_file)

motif_plot(domain_loc$table_loc, domain_loc$gene_length)

4.3.2 One step

pfam_path <- system.file("extdata", "iprscan.tsv", package = "BioVizSeq")
pfam_plot(pfam_path)

4.4 CDD

Batch CD-Search

Download “Superfamily Only”

Type: .txt

4.4.1 Step by step

hitdata_path <- system.file("extdata", "hitdata.txt", package = "BioVizSeq")
cdd_file <- readLines(hitdata_path)
domain_loc <- cdd_to_loc(cdd_file)

fa_path <- system.file("extdata", "idpep.fa", package = "BioVizSeq")
gene_length <- fastaleng(fa_path)
motif_plot(domain_loc, gene_length)

4.4.2 One step

hitdata_path <- system.file("extdata", "hitdata.txt", package = "BioVizSeq")
fa_path <- system.file("extdata", "idpep.fa", package = "BioVizSeq")

cdd_plot(hitdata_path, fa_path)

4.5 SMART

protein file (.fa or .fasta)

4.5.1 Step by step

fa_path <- system.file("extdata", "target.fa", package = "BioVizSeq")
domain_loc <- smart_to_loc(fa_path)
#> Submitting sequence AtAP2_002...
#> Submitting sequence AtAP2_003...
#> Job entered the queue with ID12315310532459281744966748fjuQJesKfo. Waiting for results.
#> Submitting sequence AtAP2_004...
#> Submitting sequence AtAP2_005...

motif_plot(domain_loc$table_loc, domain_loc$gene_length)

4.5.2 One step

fa_path <- system.file("extdata", "target.fa", package = "BioVizSeq")

smart_plot(fa_path)
#> Submitting sequence AtAP2_002...
#> Submitting sequence AtAP2_003...
#> Job entered the queue with ID12315310532468761744966784YObRQLBBcV. Waiting for results.
#> Submitting sequence AtAP2_004...
#> Submitting sequence AtAP2_005...

4.6 Plantcare

promoter sequence(.fa or .fasta)

4.6.1 Step by step

# 1. upload fasta file to plantcare, get the result file(.tab)
# upload_fa_to_plantcare(fasta_file, email)

# 2. Classify the functions of cis element

plantcare_path <- system.file("extdata", "plantCARE_output.tab", package = "BioVizSeq") 
plantcare_file <- read.table(plantcare_path, header = FALSE, sep = '\t', quote="")
plantcare_data <- plantcare_classify(plantcare_file)
plantcare_loc <- plantcare_to_loc(plantcare_data)

promoter_length <- data.frame(ID = unique(plantcare_loc$ID), length=2000)

motif_plot(plantcare_loc, promoter_length) +
    labs(x="Promoter Length", y="Gene")

4.6.2 One step

plantcare_path <- system.file("extdata", "plantCARE_output.tab", package = "BioVizSeq") 
plantcare_plot(plantcare_path, promoter_length = 2000)

4.7 Advance Plot

p_tree, p_gff, p_pfam, p_meme, p_smart, p_cdd, p_plantcare

library(patchwork)
tree_path <- system.file("extdata", "idpep.nwk", package = "BioVizSeq")
gff_path <- system.file("extdata", "idpro.gff3", package = "BioVizSeq")
meme_path <- system.file("extdata", "mast.xml", package = "BioVizSeq")
pfam_path <- system.file("extdata", "iprscan.tsv", package = "BioVizSeq")
plot_file <- combi_p(tree_path = tree_path, gff_path = gff_path,
                     meme_path = meme_path, pfam_path = pfam_path)

plot_file$p_tree + plot_file$p_gff + plot_file$p_pfam +
  plot_file$p_meme +plot_layout(ncol = 4, guides = 'collect') +
  plot_annotation(
    tag_levels = 'A'
  )

library(patchwork)
tree_path <- system.file("extdata", "idpep.nwk", package = "BioVizSeq")
plantcare_path <- system.file("extdata", "plantCARE_output.tab", package = "BioVizSeq") 
plot_file <- combi_p(tree_path = tree_path, plantcare_path = plantcare_path, promoter_length = 2000)

plot_file$p_tree + plot_file$p_plantcare1 + plot_file$p_plantcare2 + plot_layout(ncol = 3, guides = 'collect', widths = c(1, 3, 1)) + plot_annotation( tag_levels = 'A' )

4.8 Gene and Protein calc

gff_path <- system.file("extdata", "idpro.gff3", package = "BioVizSeq")
gff_data <- read.table(gff_path, header = FALSE, sep = '\t')
gene_statistics_data <- gff_statistics(gff_data)
head(gene_statistics_data)
#>       ID                Location Chain gene_length CDS_length protein_length
#> 1 gene01 Chr15:31085288-31086321     -        1034        531            176
#> 2 gene02   Contig862:15967-16631     -         665        555            184
#> 3 gene03 Chr15:31004816-31005518     +         703        564            187
#> 4 gene04 Chr15:30780257-30780955     +         699        564            187
#> 5 gene05 Chr15:30976079-30976776     +         698        564            187
#> 6 gene06  Chr2:12719447-12720989     +        1543       1224            407
#>   exon_number intron_number CDS_number UTR_number
#> 1           2             1          2          2
#> 2           2             1          2          0
#> 3           2             1          2          0
#> 4           2             1          2          0
#> 5           2             1          2          0
#> 6           1             0          1          2

pep_path <- system.file("extdata", "idpep2.fa", package = "BioVizSeq")
pep_calc_result <- ProtParam_calc(pep_path)
#> Submitting sequence gene01...
#> Submitting sequence gene02...
#> Submitting sequence gene03...
head(pep_calc_result)
#>       ID Number of amino acids Molecular weight Theoretical pI
#> 1 gene01                   176         19433.92           6.22
#> 2 gene02                   184         20288.83           9.07
#> 3 gene03                   187         21042.90           7.68
#>   The instability index Aliphatic index Grand average of hydropathicity
#> 1                 80.30           67.16                          -0.611
#> 2                 68.69           73.15                          -0.580
#> 3                 72.86           69.41                          -0.637

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.