README.knit

# 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 ID12315310532488431744966904bYquQszoda. 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 ID12315310532128801744967280LnqxLapgKZ. 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