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README.md
In BioVizSeq: Visualizing the Elements Within Bio-Sequences
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
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
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
Try the BioVizSeq package in your browser
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BioVizSeq documentation built on Aug. 22, 2025, 9:13 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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
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
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
Try the BioVizSeq package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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