R/conf.R
In tangshao2016/neoantigenR: neoantigen prediction from NGS sequencing data
##--------------------------------
## required input file names
##--------------------------------
#' the organism used to search protein database
#' @examples
#' org="hg19"
#' @return the organism used to search protein database
org="hg19"
#' the path for reference protein database file (Swiss-uniprot)
#' @examples
#' protein.database.file.name="swissuniprots.fasta"
#' @return the path for reference protein database file (Swiss-uniprot)
protein.database.file.name = system.file("extdata", "uniprot-taxonomy%3A9606.fasta", package="neoantigenR")
#' the path for reference annotated gene database file
#' @examples
#' reference.gff.file="gencode.v19.annotation.gff3"
#' @return the path for reference annotated gene database file
reference.gff.file = system.file("extdata", "Homo_sapiens.GRCh38.90.ensembl.gff3", package="neoantigenR")
#' the predicted gff file
#' @examples
#' pacbio.gff="cufflinks.gff"
#' @return the predicted gff file
pacbio.gff = system.file("extdata", "model.gff", package="neoantigenR")
#' the predicted gff file intersecting with reference gene annotation file by BEDTools
#' @examples
#' pacbio.gencode.overlapping.file = "bedtool.intersect.overlaps.txt"
#' @return the predicted gff file intersecting with reference gene annotation file by BEDTools
pacbio.gencode.overlapping.file = system.file("extdata", "bedtool.intersect.overlaps.txt", package="neoantigenR")
#' the folder of the output files
#' @examples
#' output.folder = "analysis"
#' @return the folder of the output files
output.folder = system.file("extdata", package="neoantigenR")
##-----------------------------------
## setup parameters
##-----------------------------------
#' indicate whether we will use reference gene annotation or now
#' @examples
#' use.reference.annotation.gff=TRUE
#' @return indicate whether we will use reference gene annotation or now
use.reference.annotation.gff = TRUE
#' indicate whether we will write dna sequence for protein translation
#' @examples
#' write.dna.seq.by.reference.genome = TRUE
#' @return indicate whether we will write dna sequence for protein translation
write.dna.seq.by.reference.genome = TRUE # if true, name Hsapiens instance
#' the alternative isoform and reference protein sequence must be 80 percentage or more overlap
#' @examples
#' percOverlap = 0.8
#' @return the alternative isoform and reference protein sequence must be 80 percentage or more overlap
percOverlap = 0.8 # used in determing the overlap in protein MSA analysis, disabled !!
#' mimimum anchor size in the indel region
#' @examples
#' minimum.flanking.region.size = 10
#' @return mimimum anchor size in the indel region
minimum.flanking.region.size = 10
#' at least 3 amino acids must be different from the annotation
#' @examples
#' min.mismatch.size=3
#' @return at least 3 amino acids must be different from the annotation
min.mismatch.size = 3 # need at least a region >= 3 AAs
#' at most 1000 amino is different from the annotation
#' @examples
#' max.mismatch.size=1000
#' @return at most 1000 amino is different from the annotation
max.mismatch.size = 1000
#' the alternative isoform and reference protein sequence must be 50 percentage or more overlap
#' @examples
#' minimum.sequence.similarity = 0.5
#' @return the alternative isoform and reference protein sequence must be 50 percentage or more overlap
minimum.sequence.similarity = 0.5 # at least 50 percentage sequence overlap
#' look for the genomic location of the indel sequence in the predicted gff gene models
#' @examples
#' compute.query.exon.details=TRUE
#' @return look for the genomic location of the indel sequence in the predicted gff gene models
compute.query.exon.details = TRUE
#' look for the genomic location of the indel sequence in the annotated gff gene models
#' @examples
#' compute.database.exon.details=TRUE
#' @return look for the genomic location of the indel sequence in the annotated gff gene models
compute.database.exon.details = TRUE
#' whether to validate indels in the annotated protein database
#' @examples
#' validate.in.database.boolean=TRUE
#' @return whether to validate indels in the annotated protein database
validate.in.database.boolean = TRUE
#' the annotated gene models for chosen genes
#' @examples
#' reference.gff.info.chosen=data.frame()
#' @return the annotated gene models for chosen genes
reference.gff.info.chosen = data.frame()
#' coverage file to calculate sequence coverage for indel regions with BAM files
#' @examples
#' coverage.file= "alignment.bam"
#' @return coverage file to calculate sequence coverage for indel regions with BAM files
coverage.file = "" # might need to be removed
#' the predicted gene model's gene ids (prediction names)
#' @examples
#' refseq.pacbio.name.ids=c()
#' @return the predicted gene model's gene ids (prediction names)
refseq.pacbio.name.ids = c() # might need to be removed
##-----------------------------------
## define global variables
##-----------------------------------
#' the gff file in the dat frame mode
#' @examples
#' gff.file=data.frame()
#' @return the gff file in the dat frame mode
gff.file=data.frame()
#' the gff file's gene names
#' @examples
#' gff.names="prediction.gff"
#' @return the gff file's gene names
gff.names=""
#' the directly where code and example data are stored
#' @examples
#' mainDir="data"
#' @return the directly where code and example data are stored
mainDir=""
#' the directly where data is stored
#' @examples
#' dataDir=""
#' @return the directly where data is stored
dataDir=""
#' the human protein names from uniprot
#' @examples
#' uniprot.protein.names.new=""
#' @return the human protein names from uniprot
uniprot.protein.names.new=""
#' the human protein names from uniprot swissport (only the rows or indexes out of the entire uniprot file)
#' @examples
#' uniprot.protein.swissprot.index=""
#' @return the human protein names from uniprot swissport (only the rows or indexes out of the entire uniprot file)
uniprot.protein.swissprot.index=""
#' the annotated gene in data frame mode
#' @examples
#' reference.gff=data.frame()
#' @return the annotated gene in data frame mode
reference.gff=data.frame()
#' the annotated gene in data frame mode with attributes splitted
#' @examples
#' reference.gff.info=data.frame()
#' @return the annotated gene in data frame mode with attributes splitted
reference.gff.info=data.frame()
#' the chosen column of gff file (to remove unused columns from analysis)
#' @examples
#' chosen.column.indicies=c()
#' @return the chosen column of gff file (to remove unused columns from analysis)
chosen.column.indicies=c()
#' the uniprot human dataset in data frame
#' @examples
#' uniprot.human.database=data.frame()
#' @return the uniprot human dataset in data frame
uniprot.human.database=data.frame()
#' the gene list finally chosen for neoantigen analysis
#' @examples
#' target.gene.list=c("MET")
#' @return the gene list finally chosen for neoantigen analysis
target.gene.list=c()
#' the mapping between predicted gene names and reference annotated gene names
#' @examples
#' sample.pacbio.gencode.id.mapping=data.frame()
#' @return the mapping between predicted gene names and reference annotated gene names
sample.pacbio.gencode.id.mapping=data.frame()
#' the data frame of predicted gene models
#' @examples
#' gene.df.pacbio=data.frame()
#' @return the data frame of predicted gene models
gene.df.pacbio=data.frame()
#' the isoform or gene names from predicted gff model
#' @examples
#' pacbio.isof.ids=c()
#' @return the isoform or gene names from predicted gff model
pacbio.isof.ids=c()
#' the attribute information of the gff file
#' @examples
#' att.info=c()
#' @return the attribute information of the gff file
att.info=c()
tangshao2016/neoantigenR documentation built on May 21, 2019, 3:04 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.
##--------------------------------
## required input file names
##--------------------------------
#' the organism used to search protein database
#' @examples
#' org="hg19"
#' @return the organism used to search protein database
org="hg19"
#' the path for reference protein database file (Swiss-uniprot)
#' @examples
#' protein.database.file.name="swissuniprots.fasta"
#' @return the path for reference protein database file (Swiss-uniprot)
protein.database.file.name = system.file("extdata", "uniprot-taxonomy%3A9606.fasta", package="neoantigenR")
#' the path for reference annotated gene database file
#' @examples
#' reference.gff.file="gencode.v19.annotation.gff3"
#' @return the path for reference annotated gene database file
reference.gff.file = system.file("extdata", "Homo_sapiens.GRCh38.90.ensembl.gff3", package="neoantigenR")
#' the predicted gff file
#' @examples
#' pacbio.gff="cufflinks.gff"
#' @return the predicted gff file
pacbio.gff = system.file("extdata", "model.gff", package="neoantigenR")
#' the predicted gff file intersecting with reference gene annotation file by BEDTools
#' @examples
#' pacbio.gencode.overlapping.file = "bedtool.intersect.overlaps.txt"
#' @return the predicted gff file intersecting with reference gene annotation file by BEDTools
pacbio.gencode.overlapping.file = system.file("extdata", "bedtool.intersect.overlaps.txt", package="neoantigenR")
#' the folder of the output files
#' @examples
#' output.folder = "analysis"
#' @return the folder of the output files
output.folder = system.file("extdata", package="neoantigenR")
##-----------------------------------
## setup parameters
##-----------------------------------
#' indicate whether we will use reference gene annotation or now
#' @examples
#' use.reference.annotation.gff=TRUE
#' @return indicate whether we will use reference gene annotation or now
use.reference.annotation.gff = TRUE
#' indicate whether we will write dna sequence for protein translation
#' @examples
#' write.dna.seq.by.reference.genome = TRUE
#' @return indicate whether we will write dna sequence for protein translation
write.dna.seq.by.reference.genome = TRUE # if true, name Hsapiens instance
#' the alternative isoform and reference protein sequence must be 80 percentage or more overlap
#' @examples
#' percOverlap = 0.8
#' @return the alternative isoform and reference protein sequence must be 80 percentage or more overlap
percOverlap = 0.8 # used in determing the overlap in protein MSA analysis, disabled !!
#' mimimum anchor size in the indel region
#' @examples
#' minimum.flanking.region.size = 10
#' @return mimimum anchor size in the indel region
minimum.flanking.region.size = 10
#' at least 3 amino acids must be different from the annotation
#' @examples
#' min.mismatch.size=3
#' @return at least 3 amino acids must be different from the annotation
min.mismatch.size = 3 # need at least a region >= 3 AAs
#' at most 1000 amino is different from the annotation
#' @examples
#' max.mismatch.size=1000
#' @return at most 1000 amino is different from the annotation
max.mismatch.size = 1000
#' the alternative isoform and reference protein sequence must be 50 percentage or more overlap
#' @examples
#' minimum.sequence.similarity = 0.5
#' @return the alternative isoform and reference protein sequence must be 50 percentage or more overlap
minimum.sequence.similarity = 0.5 # at least 50 percentage sequence overlap
#' look for the genomic location of the indel sequence in the predicted gff gene models
#' @examples
#' compute.query.exon.details=TRUE
#' @return look for the genomic location of the indel sequence in the predicted gff gene models
compute.query.exon.details = TRUE
#' look for the genomic location of the indel sequence in the annotated gff gene models
#' @examples
#' compute.database.exon.details=TRUE
#' @return look for the genomic location of the indel sequence in the annotated gff gene models
compute.database.exon.details = TRUE
#' whether to validate indels in the annotated protein database
#' @examples
#' validate.in.database.boolean=TRUE
#' @return whether to validate indels in the annotated protein database
validate.in.database.boolean = TRUE
#' the annotated gene models for chosen genes
#' @examples
#' reference.gff.info.chosen=data.frame()
#' @return the annotated gene models for chosen genes
reference.gff.info.chosen = data.frame()
#' coverage file to calculate sequence coverage for indel regions with BAM files
#' @examples
#' coverage.file= "alignment.bam"
#' @return coverage file to calculate sequence coverage for indel regions with BAM files
coverage.file = "" # might need to be removed
#' the predicted gene model's gene ids (prediction names)
#' @examples
#' refseq.pacbio.name.ids=c()
#' @return the predicted gene model's gene ids (prediction names)
refseq.pacbio.name.ids = c() # might need to be removed
##-----------------------------------
## define global variables
##-----------------------------------
#' the gff file in the dat frame mode
#' @examples
#' gff.file=data.frame()
#' @return the gff file in the dat frame mode
gff.file=data.frame()
#' the gff file's gene names
#' @examples
#' gff.names="prediction.gff"
#' @return the gff file's gene names
gff.names=""
#' the directly where code and example data are stored
#' @examples
#' mainDir="data"
#' @return the directly where code and example data are stored
mainDir=""
#' the directly where data is stored
#' @examples
#' dataDir=""
#' @return the directly where data is stored
dataDir=""
#' the human protein names from uniprot
#' @examples
#' uniprot.protein.names.new=""
#' @return the human protein names from uniprot
uniprot.protein.names.new=""
#' the human protein names from uniprot swissport (only the rows or indexes out of the entire uniprot file)
#' @examples
#' uniprot.protein.swissprot.index=""
#' @return the human protein names from uniprot swissport (only the rows or indexes out of the entire uniprot file)
uniprot.protein.swissprot.index=""
#' the annotated gene in data frame mode
#' @examples
#' reference.gff=data.frame()
#' @return the annotated gene in data frame mode
reference.gff=data.frame()
#' the annotated gene in data frame mode with attributes splitted
#' @examples
#' reference.gff.info=data.frame()
#' @return the annotated gene in data frame mode with attributes splitted
reference.gff.info=data.frame()
#' the chosen column of gff file (to remove unused columns from analysis)
#' @examples
#' chosen.column.indicies=c()
#' @return the chosen column of gff file (to remove unused columns from analysis)
chosen.column.indicies=c()
#' the uniprot human dataset in data frame
#' @examples
#' uniprot.human.database=data.frame()
#' @return the uniprot human dataset in data frame
uniprot.human.database=data.frame()
#' the gene list finally chosen for neoantigen analysis
#' @examples
#' target.gene.list=c("MET")
#' @return the gene list finally chosen for neoantigen analysis
target.gene.list=c()
#' the mapping between predicted gene names and reference annotated gene names
#' @examples
#' sample.pacbio.gencode.id.mapping=data.frame()
#' @return the mapping between predicted gene names and reference annotated gene names
sample.pacbio.gencode.id.mapping=data.frame()
#' the data frame of predicted gene models
#' @examples
#' gene.df.pacbio=data.frame()
#' @return the data frame of predicted gene models
gene.df.pacbio=data.frame()
#' the isoform or gene names from predicted gff model
#' @examples
#' pacbio.isof.ids=c()
#' @return the isoform or gene names from predicted gff model
pacbio.isof.ids=c()
#' the attribute information of the gff file
#' @examples
#' att.info=c()
#' @return the attribute information of the gff file
att.info=c()
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.