Package 'customProDB'

Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search
Description: Database search is the most widely used approach for peptide and protein identification in mass spectrometry-based proteomics studies. Our previous study showed that sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in the samples and thus improve protein identification. More importantly, single nucleotide variations, short insertion and deletions and novel junctions identified from RNA-Seq data make protein database more complete and sample-specific. Here, we report an R package customProDB that enables the easy generation of customized databases from RNA-Seq data for proteomics search. This work bridges genomics and proteomics studies and facilitates cross-omics data integration.
Authors: Xiaojing Wang
Maintainer: Xiaojing Wang <[email protected]> Bo Wen <[email protected]>
License: Artistic-2.0
Version: 1.47.0
Built: 2024-10-30 05:38:51 UTC
Source: https://github.com/bioc/customProDB

Help Index

get the functional consequencece of SNVs located in coding region

Description

Variations can be divided into SNVs and INDELs. By taking the output of positionincoding() as input, aaVariation() function predicts the consequences of SNVs in the harbored transcript, such as synonymous or non-synonymous.

Usage

aaVariation(position_tab, coding, ...)

Arguments

position_tab

a data frame from Positionincoding()
coding

a data frame cotaining coding sequence for each protein.
...

Additional arguments

Details

this function predicts the consequence for SNVs. for INDELs, use Outputabberrant().

Value

a data frame containing consequence for each variations.

Author(s)

Xiaojing Wang

Examples

vcffile <- system.file("extdata/vcfs", "test1.vcf", package="customProDB")
vcf <- InputVcf(vcffile)
table(values(vcf[[1]])[['INDEL']])

index <- which(values(vcf[[1]])[['INDEL']]==FALSE)
SNVvcf <- vcf[[1]][index]
load(system.file("extdata/refseq", "exon_anno.RData", package="customProDB"))
load(system.file("extdata/refseq", "dbsnpinCoding.RData", package="customProDB"))
load(system.file("extdata/refseq", "procodingseq.RData", package="customProDB"))
postable_snv <- Positionincoding(SNVvcf,exon,dbsnpinCoding)
txlist <- unique(postable_snv[,'txid'])
codingseq <- procodingseq[procodingseq[,'tx_id'] %in% txlist,]
mtab <- aaVariation (postable_snv,codingseq)
mtab[1:3,]

Generate a GRanges objects from BED file.

Description

Read BED file into a GRanges object. This function requires complete BED file. Go to https://genome.ucsc.edu/FAQ/FAQformat.html#format1 for more information about BED format.

Usage

Bed2Range(bedfile, skip = 1, covfilter = 5, ...)

Arguments

bedfile

a character contains the path and name of a BED file.
skip

the number of lines of the BED file to skip before beginning to read data, default 1.
covfilter

the number of minimum coverage for the candidate junction, default 5.
...

additional arguments

Details

Read BED file contain junctions into a GRanges object.

Value

a GRanges object containing all candidate junctions from the BED file.

Author(s)

Xiaojing Wang

Examples

bedfile <- system.file("extdata/beds", "junctions1.bed", package="customProDB")
jun <-  Bed2Range(bedfile, skip=1,covfilter=5)
length(jun)

Caculate RPKM for each transcripts based on exon read counts.

Description

Normalized expression level based on exon read counts. The default output is a vector containing RPKMs for each transcript. vector name is the transcript name. calculate the RPKMs by chromosome. If proteincodingonly=TRUE, vetor name will be set to protein name, and only output RPKMs for the protein coding transcripts.

Usage

calculateRPKM(bamFile, exon, proteincodingonly = TRUE,
    ids = NULL, ...)

Arguments

bamFile

a the input BAM file name.
exon

a dataframe of exon annotations.
proteincodingonly

if TRUE only output RPKMs for protein coding transcripts, the name of output vector will be protein id. if FALSE, output the RPKM for all transcripts.
ids

a dataframe containing gene/transcript/protein id mapping information.
...

additional arguments

Details

caculate RPKM from a BAM file based on exon read counts

Value

RPKM value for all transcripts or protein coding transcripts.

Author(s)

Xiaojing Wang

Examples

##test1.bam file is part of the whole bam file.
load(system.file("extdata/refseq", "exon_anno.RData", package="customProDB"))
bamFile <- system.file("extdata/bams", "test1_sort.bam", package="customProDB")
load(system.file("extdata/refseq", "ids.RData", package="customProDB"))
RPKM <- calculateRPKM(bamFile,exon,proteincodingonly=TRUE,ids)

An integrated function to generate customized protein database for a single sample

Description

Generate a customized protein database for a single sample.

Usage

easyRun(bamFile, RPKM = NULL, vcfFile, annotation_path,
    outfile_path, outfile_name, rpkm_cutoff = 1,
    INDEL = FALSE, lablersid = FALSE, COSMIC = FALSE,
    nov_junction = FALSE, bedFile = NULL, genome = NULL,
    ...)

Arguments

bamFile

Input BAM file name
RPKM

Alternative to bamFile,default NULL, a vector containing expression level for proteins. (e.g. FPKMs from cufflinks)
vcfFile

Input VCF file name.
outfile_path

Folder path for the output FASTA files.
outfile_name

Output FASTA file name.
annotation_path

The path of saved annotation.
rpkm_cutoff

The cutoff of RPKM value. see 'cutoff' in function Outputproseq for more detail.
INDEL

If the vcfFile contains the short insertion/deletion. Default is FALSE.
lablersid

If includes the dbSNP rsid in the header of each sequence, default is FALSE. Users should provide dbSNP information when running function Positionincoding() if put TRUE here.
COSMIC

If output the cosmic ids in the variation table.Default is FALSE. If choose TRUE, there must have cosmic.RData in the annotation folder.
nov_junction

If output the peptides that cover novel junction into the database. if TRUE, there should be splicemax.RData in the annotation folder.
bedFile

The path of bed file which contains the splice junctions identified in RNA-Seq.
genome

A BSgenome object(e.g. Hsapiens). Default is NULL.
...

Additional arguments

Details

The function gives a more convenient way for proteomics researchers to generate customized database for a single sample.

Value

A table file contains detailed variation information and several FASTA files.

Author(s)

Xiaojing Wang

Examples

bamFile <- system.file("extdata/bams", "test1_sort.bam",
            package="customProDB")
vcffile <- system.file("extdata/vcfs", "test1.vcf", package="customProDB")
annotation_path <- system.file("extdata/refseq", package="customProDB")
outfile_path <- tempdir()
outfile_name <- 'test'

easyRun(bamFile, RPKM=NULL, vcffile, annotation_path, outfile_path,
        outfile_name, rpkm_cutoff=1, INDEL=TRUE, lablersid=TRUE,
        COSMIC=TRUE, nov_junction=FALSE)

An integrated function to generate consensus protein database from multiple samples

Description

Generate consensus protein database for multiple samples in a single function.

Usage

easyRun_mul(bamFile_path, RPKM_mtx = NULL, vcfFile_path,
    annotation_path, rpkm_cutoff, share_num = 2,
    var_shar_num = 2, outfile_path, outfile_name,
    INDEL = FALSE, lablersid = FALSE, COSMIC = FALSE,
    nov_junction = FALSE, bedFile_path = NULL,
    genome = NULL, junc_shar_num = 2, ...)

Arguments

bamFile_path

The path of BAM files
RPKM_mtx

Alternative to bamFile_path,default NULL, a matrix containing expression level for proteins in each sample. (e.g. FPKMs from cufflinks)
vcfFile_path

The path of VCF files
annotation_path

The path of already saved annotation, which will be used in the function
rpkm_cutoff

Cutoffs of RPKM values. see 'cutoff' in function OutputsharedPro for more information
share_num

The minimum share sample numbers for proteins which pass the cutoff.
var_shar_num

Minimum sample number of recurrent variations.
outfile_path

The path of output FASTA file
outfile_name

The name prefix of output FASTA file
INDEL

If the vcfFile contains the short insertion/deletion. Default is FALSE.
lablersid

If includes the dbSNP rsid in the header of each sequence, default is FALSE. Users should provide dbSNP information when running function Positionincoding() if put TRUE here.
COSMIC

If output the cosmic ids in the variation table.Default is FALSE. If choose TRUE, there must have cosmic.RData in the annotation folder.
nov_junction

If output the peptides that cover novel junction into the database. if TRUE, there should be splicemax.RData in the annotation folder.
bedFile_path

The path of BED files which contains the splice junctions identified in RNA-Seq.
genome

A BSgenome object(e.g. Hsapiens). Default is NULL. Required if nov_junction==TRUE.
junc_shar_num

Minimum sample number of recurrent splicing junctions.
...

Additional arguments

Details

The function give a more convenient way for proteinomics researchers to generate customized database of multiple samples.

Value

A table file contains detailed variation information and several FASTA files.

Author(s)

Xiaojing Wang

Examples

bampath <- system.file("extdata/bams", package="customProDB")
vcfFile_path <- system.file("extdata/vcfs", package="customProDB")
annotation_path <- system.file("extdata/refseq", package="customProDB")
outfile_path <- tempdir()
outfile_name <- 'mult'

easyRun_mul(bampath, RPKM_mtx=NULL, vcfFile_path, annotation_path, rpkm_cutoff=1,
            share_num=2, var_shar_num=2, outfile_path, outfile_name, INDEL=TRUE,
            lablersid=TRUE, COSMIC=TRUE, nov_junction=FALSE)

Generate a list of GRanges objects from a VCF file.

Description

The InputVcf() function generates a list of GRanges object from a single VCF file.

Usage

InputVcf(vcfFile, ...)

Arguments

vcfFile

a character contains the path and name of a VCF file
...

additional arguments

Details

Read all fields in a VCF file into GRanges object.

Value

a list of GRanges object containing a representation of data from the VCF file

Author(s)

Xiaojing Wang

Examples

## multiple samples in one VCF file

vcffile <- system.file("extdata", "test_mul.vcf", package="customProDB")
vcfs <- InputVcf(vcffile)
length(vcfs)

## single sample

vcffile <- system.file("extdata/vcfs", "test1.vcf", package="customProDB")
vcf <- InputVcf(vcffile)
length(vcf)

Annotates the junctions in a bed file.

Description

For identified splice junctions from RNA-Seq, this function finds the junction types for each entry according to the given annotation. Six types of junctions are classified. find more details in the tutorial.

Usage

JunctionType(jun, splicemax, txdb, ids, ...)

Arguments

jun

a GRange object for junctions, the output of function Bed2Range.
splicemax

a known exon splice matrix from the annotation.
txdb

a TxDb object.
ids

a dataframe containing gene/transcript/protein id mapping information.
...

additional arguments

Details

Go to https://genome.ucsc.edu/FAQ/FAQformat.html#format1 for more information about BED format.

Value

a data frame of type and source for each junction.

Author(s)

Xiaojing Wang

Examples

bedfile <- system.file("extdata/beds", "junctions1.bed", package="customProDB")
jun <-  Bed2Range(bedfile,skip=1,covfilter=5)
load(system.file("extdata/refseq", "splicemax.RData", package="customProDB"))
load(system.file("extdata/refseq", "ids.RData", package="customProDB"))
txdb <- loadDb(system.file("extdata/refseq", "txdb.sqlite",
            package="customProDB"))
junction_type <- JunctionType(jun, splicemax, txdb, ids)
table(junction_type[, 'jun_type'])

Generate shared variation dataset from multiple VCF files

Description

Load multiple vcf files and output a GRange object with SNVs present in multiple samples.

Usage

Multiple_VCF(vcfs, share_num, ...)

Arguments

vcfs

a list of GRanges object which input from multiple VCF files using function InputVcf.
share_num

Two options, percentage format or sample number.
...

additional arguments

Details

This function allows to limit SNVs that are present in at least m out of n VCF files.

Value

a GRange object that contains the shared variations

Author(s)

Xiaojing Wang

Examples

path <- system.file("extdata/vcfs", package="customProDB")
vcfFiles<- paste(path, '/', list.files(path, pattern="*vcf$"), sep='')
vcfs <- lapply(vcfFiles, function(x) InputVcf(x))
shared <- Multiple_VCF(vcfs, share_num=2)

generate FASTA file containing short INDEL

Description

Short insertion/deletion may lead to aberrant proteins in cells. We provide a function to generate FASTA file containing this kind of proteins.

Usage

Outputaberrant(positiontab, outfile, coding, proteinseq,
    ids, RPKM = NULL, ...)

Arguments

positiontab

a data frame which is the output of function Positionincoding() for INDELs.
outfile

output file name
coding

a data frame cotaining coding sequence for each protein.
proteinseq

a data frame cotaining amino acid sequence for each protein.
ids

a dataframe containing gene/transcript/protein id mapping information.
RPKM

if includes the RPKM value in the header of each sequence, default is NULL.
...

Additional arguments.

Details

the function applys the INDEL into the coding sequence, then translates them into protein sequence, terminated by stop codon. Remove the sequences the same as normal ones or as part of normal ones.

Value

FASTA file containing aberrant proteins.

Author(s)

Xiaojing Wang

Examples

vcffile <- system.file("extdata/vcfs", "test1.vcf", package="customProDB")
vcf <- InputVcf(vcffile)
table(values(vcf[[1]])[['INDEL']])
index <- which(values(vcf[[1]])[['INDEL']] == TRUE)
indelvcf <- vcf[[1]][index]

load(system.file("extdata/refseq", "exon_anno.RData", package="customProDB"))
load(system.file("extdata/refseq", "dbsnpinCoding.RData",
        package="customProDB"))
load(system.file("extdata/refseq", "procodingseq.RData",
        package="customProDB"))
load(system.file("extdata/refseq", "proseq.RData", package="customProDB"))
load(system.file("extdata/refseq", "ids.RData", package="customProDB"))
postable_indel <- Positionincoding(indelvcf, exon)
txlist_indel <- unique(postable_indel[, 'txid'])
codingseq_indel <- procodingseq[procodingseq[, 'tx_id'] %in% txlist_indel, ]
outfile <-  paste(tempdir(), '/test_indel.fasta', sep='')
Outputaberrant(postable_indel, coding=codingseq_indel,
proteinseq=proteinseq, outfile=outfile, ids=ids)

generate peptide FASTA file that contains novel junctions.

Description

Three-frame translation of novel junctions. And remove those could be found in normal protein sequences. This function requires a genome built by BSgenome package.

Usage

OutputNovelJun(junction_type, genome, outfile,
    proteinseq, ...)

Arguments

junction_type

a data frame which is the output of function JunctionType()
genome

a BSgenome object. (e.g. Hsapiens)
outfile

output file name
proteinseq

a data frame cotaining amino acid sequence for each protein.
...

Additional arguments.

Value

FASTA file that contains novel junction peptides.

Author(s)

Xiaojing Wang

Examples

bedfile <- system.file("extdata/beds", "junctions1.bed", package="customProDB")
jun <-  Bed2Range(bedfile,skip=1,covfilter=5)
load(system.file("extdata/refseq", "splicemax.RData", package="customProDB"))
load(system.file("extdata/refseq", "ids.RData", package="customProDB"))
txdb <- loadDb(system.file("extdata/refseq", "txdb.sqlite",
            package="customProDB"))
junction_type <- JunctionType(jun, splicemax, txdb, ids)
table(junction_type[, 'jun_type'])
chrom <- paste('chr',c(1:22,'X','Y','M'),sep='')
junction_type <- subset(junction_type, seqnames %in% chrom)
outf_junc <- paste(tempdir(), '/test_junc.fasta', sep='')
load(system.file("extdata/refseq", "proseq.RData", package="customProDB"))
library('BSgenome.Hsapiens.UCSC.hg19')
OutputNovelJun <- OutputNovelJun(junction_type, Hsapiens, outf_junc,
            proteinseq)

output FASTA format file contains proteins that have expression level above the cutoff

Description

Get the FASTA file of proteins that pass RPKM cutoff. the FASTA ID line contains protein ID, gene ID, HGNC symbol and description

Usage

Outputproseq(rpkm, cutoff = "30%", proteinseq, outfile,
    ids, ...)

Arguments

rpkm

a numeric vector containing RPKM for each protein
cutoff

cutoff of RPKM value. Two options are available, percentage format or RPKM. By default we use "30 proteins according to their RPKMs.
proteinseq

a dataframe containing protein ids and protein sequences.
outfile

output file name.
ids

a dataframe containing gene/transcript/protein id mapping information.
...

additional arguments

Details

by taking the RPKM value as input, the function outputs sequences of the proteins that pass the cutoff.

Value

FASTA file contains proteins with RPKM above the cutoff.

Author(s)

Xiaojing Wang

Examples

load(system.file("extdata/refseq", "exon_anno.RData", package="customProDB"))
load(system.file("extdata/refseq", "proseq.RData", package="customProDB"))
bamFile <- system.file("extdata/bams", "test1_sort.bam",
    package="customProDB")
load(system.file("extdata/refseq", "ids.RData", package="customProDB"))
RPKM <- calculateRPKM(bamFile, exon, proteincodingonly=TRUE, ids)
outf1 <- paste(tempdir(), '/test_rpkm.fasta', sep='')
Outputproseq(RPKM, 1, proteinseq, outf1, ids)

Output the sequences of proteins with high expressions in multiple samples.

Description

Output a FASTA file containing shared proteins with expression above cutoff in multiple samples

Usage

OutputsharedPro(RPKMs, cutoff = "30%",
    share_sample = "50%", proteinseq, outfile, ids, ...)

Arguments

RPKMs

RPKM matrix; row name (protein name) is required.
cutoff

a percentage format cutoff (e.g. '30 a vector with each element as a vlaue cutoff referring to one sample
share_sample

the minimum share sample numbers for proteins which pass the cutoff.
proteinseq

a dataframe containing protein ids and protein sequences
outfile

output file name
ids

a dataframe containing gene/transcript/protein id mapping information.
...

additional arguments

Details

this function takes RPKM matrix as input, users can set two paramteters,cutoff and shared, to generated a consensus expressed database

Value

a FASTA file containing proteins with RPKM above the cutoff in at least certain number of samples

Author(s)

Xiaojing Wang

Examples

path <- system.file("extdata/bams", package="customProDB")
load(system.file("extdata/refseq", "exon_anno.RData", package="customProDB"))
load(system.file("extdata/refseq", "proseq.RData", package="customProDB"))
load(system.file("extdata/refseq", "ids.RData", package="customProDB"))
bamFile<- paste(path, '/', list.files(path, pattern="*bam$"), sep='')
rpkms <- sapply(bamFile,function(x)
            calculateRPKM(x, exon, proteincodingonly=TRUE, ids))
outfile <- paste(tempdir(), '/test_rpkm_share.fasta', sep='')
OutputsharedPro(rpkms, cutoff=1, share_sample=2, proteinseq,
            outfile, ids)

Output the variant(SNVs) protein coding sequences

Description

Output 'snvprocoding'

Usage

OutputVarprocodingseq(vartable, procodingseq, ids, lablersid = FALSE, ...)

Arguments

vartable

A data frame which is the output of aaVariation().
procodingseq

A dataframe containing protein ids and coding sequence for the protein.
ids

A dataframe containing gene/transcript/protein id mapping information.
lablersid

If includes the dbSNP rsid in the header of each sequence, default is FALSE. Must provide dbSNP information in function Positionincoding() if put TRUE here.
...

Additional arguments

Details

This function uses the output of aaVariation() as input, introduces the nonsynonymous variation into the protein database.

Value

a data frame containing protein coding sequence proteins with single nucleotide variation.

Author(s)

Xiaojing Wang

Examples

vcffile <- system.file("extdata/vcfs", "test1.vcf", package="customProDB")
vcf <- InputVcf(vcffile)
table(values(vcf[[1]])[['INDEL']])
index <- which(values(vcf[[1]])[['INDEL']] == FALSE)
SNVvcf <- vcf[[1]][index]
load(system.file("extdata/refseq", "exon_anno.RData", 
package="customProDB"))
load(system.file("extdata/refseq", "dbsnpinCoding.RData", 
    package="customProDB"))
load(system.file("extdata/refseq", "procodingseq.RData", 
    package="customProDB"))
load(system.file("extdata/refseq", "ids.RData", package="customProDB"))
load(system.file("extdata/refseq", "proseq.RData", package="customProDB"))
postable_snv <- Positionincoding(SNVvcf, exon, dbsnpinCoding)
txlist <- unique(postable_snv[, 'txid'])
codingseq <- procodingseq[procodingseq[, 'tx_id'] %in% txlist, ]
mtab <- aaVariation (postable_snv, codingseq)
OutputVarprocodingseq(mtab, codingseq, ids, lablersid=TRUE)

Output the variant(SNVs) protein sequences into FASTA format

Description

Output the non-synonymous SNVs into FASTA file.

Usage

OutputVarproseq(vartable, proteinseq, outfile, ids, lablersid = FALSE,
  RPKM = NULL, ...)

Arguments

vartable

A data frame which is the output of aaVariation().
proteinseq

A dataframe containing protein ids and the protein sequence.
outfile

Output file name.
ids

A dataframe containing gene/transcript/protein id mapping information.
lablersid

If includes the dbSNP rsid in the header of each sequence, default is FALSE. Must provide dbSNP information in function Positionincoding() if put TRUE here.
RPKM

If includes the RPKM value in the header of each sequence, default is NULL.
...

Additional arguments

Details

This function uses the output of aaVariation() as input, introduces the nonsynonymous variation into the protein database.

Value

a FASTA file and a data frame containing proteins with single nucleotide variation.

Author(s)

Xiaojing Wang

Examples

vcffile <- system.file("extdata/vcfs", "test1.vcf", package="customProDB")
vcf <- InputVcf(vcffile)
table(values(vcf[[1]])[['INDEL']])
index <- which(values(vcf[[1]])[['INDEL']] == FALSE)
SNVvcf <- vcf[[1]][index]
load(system.file("extdata/refseq", "exon_anno.RData", 
package="customProDB"))
load(system.file("extdata/refseq", "dbsnpinCoding.RData", 
    package="customProDB"))
load(system.file("extdata/refseq", "procodingseq.RData", 
    package="customProDB"))
load(system.file("extdata/refseq", "ids.RData", package="customProDB"))
load(system.file("extdata/refseq", "proseq.RData", package="customProDB"))
postable_snv <- Positionincoding(SNVvcf, exon, dbsnpinCoding)
txlist <- unique(postable_snv[, 'txid'])
codingseq <- procodingseq[procodingseq[, 'tx_id'] %in% txlist, ]
mtab <- aaVariation (postable_snv, codingseq)
outfile <- paste(tempdir(), '/test_snv.fasta',sep='')
snvproseq <- OutputVarproseq(mtab, proteinseq, outfile, ids, lablersid=TRUE, RPKM=NULL)

Output the variant(SNVs) protein sequences into FASTA format

Description

Output the non-synonymous SNVs into FASTA file, one SNV per sequence.

Usage

OutputVarproseq_single(vartable, proteinseq, outfile,
    ids, lablersid = FALSE, RPKM = NULL, ...)

Arguments

vartable

A data frame which is the output of aaVariation().
proteinseq

A dataframe containing protein ids and the protein sequence.
outfile

Output file name.
ids

A dataframe containing gene/transcript/protein id mapping information.
lablersid

If includes the dbSNP rsid in the header of each sequence, default is FALSE. Must provide dbSNP information in function Positionincoding() if put TRUE here.
RPKM

If includes the RPKM value in the header of each sequence. default is NULL.
...

Additional arguments

Details

This function uses the output of aaVariation() as input, introduces the nonsynonymous variation into the protein database. If a protein have more than one SNVs, introduce one SNV each time, end up with equal number of sequences.

Value

FASTA file containing proteins with single nucleotide variation.

Author(s)

Xiaojing Wang

Examples

vcffile <- system.file("extdata/vcfs", "test1.vcf", package="customProDB")
vcf <- InputVcf(vcffile)
table(values(vcf[[1]])[['INDEL']])
index <- which(values(vcf[[1]])[['INDEL']] == FALSE)
SNVvcf <- vcf[[1]][index]
load(system.file("extdata/refseq", "exon_anno.RData",
package="customProDB"))
load(system.file("extdata/refseq", "dbsnpinCoding.RData",
    package="customProDB"))
load(system.file("extdata/refseq", "procodingseq.RData",
    package="customProDB"))
load(system.file("extdata/refseq", "ids.RData", package="customProDB"))
load(system.file("extdata/refseq", "proseq.RData", package="customProDB"))
postable_snv <- Positionincoding(SNVvcf, exon, dbsnpinCoding)
txlist <- unique(postable_snv[, 'txid'])
codingseq <- procodingseq[procodingseq[, 'tx_id'] %in% txlist, ]
mtab <- aaVariation (postable_snv, codingseq)
outfile <- paste(tempdir(), '/test_snv_single.fasta',sep='')
OutputVarproseq_single(mtab, proteinseq, outfile, ids, lablersid=TRUE)

Find the position in coding sequence for each variation.

Description

For those variations labeled with "Coding", positionincoding() function computes the position of variation in the coding sequence of each transcript.

Usage

Positionincoding(Vars, exon, dbsnp = NULL, COSMIC = NULL,
    ...)

Arguments

Vars

a GRanges object of variations
exon

a dataframe of exon annotations for protein coding transcripts.
dbsnp

provide a GRanges object of known dbsnp information to include dbsnp evidence into the output table, default is NULL.
COSMIC

provide a GRanges object of known COSMIC information to include COSMIC evidence into the output table, default is NULL.
...

additional arguments

Details

this function prepares input data frame for aaVariation().

Value

a data frame containing the position in coding sequence for each variation

Author(s)

Xiaojing Wang

Examples

vcffile <- system.file("extdata/vcfs", "test1.vcf", package="customProDB")
vcf <- InputVcf(vcffile)
table(values(vcf[[1]])[['INDEL']])
index <- which(values(vcf[[1]])[['INDEL']] == TRUE)
indelvcf <- vcf[[1]][index]

index <- which(values(vcf[[1]])[['INDEL']] == FALSE)
SNVvcf <- vcf[[1]][index]
load(system.file("extdata/refseq", "exon_anno.RData",
    package="customProDB"))
load(system.file("extdata/refseq", "dbsnpinCoding.RData",
    package="customProDB"))
load(system.file("extdata/refseq", "procodingseq.RData",
    package="customProDB"))
load(system.file("extdata/refseq", "cosmic.RData",
    package="customProDB"))
postable_snv <- Positionincoding(SNVvcf, exon, dbsnpinCoding, COSMIC=cosmic)

prepare annotation from ENSEMBL

Description

prepare the annotation from ENSEMBL through biomaRt.

Usage

PrepareAnnotationEnsembl(mart, annotation_path, splice_matrix = FALSE,
  dbsnp = NULL, transcript_ids = NULL, COSMIC = FALSE, ...)

Arguments

mart

which version of ENSEMBL dataset to use. see useMart from package biomaRt for more detail.
annotation_path

specify a folder to store all the annotations
splice_matrix

whether generate a known exon splice matrix from the annotation. this is not necessary if you don't want to analyse junction results, default is FALSE.
dbsnp

specify a snp dataset you want to use for the SNP annotation, default is NULL.
transcript_ids

optionally, only retrieve transcript annotation data for the specified set of transcript ids
COSMIC

whether to download COSMIC data, default is FALSE.
...

additional arguments

Details

this function automaticlly prepares all annotation infromation needed in the following analysis.

Value

several .RData file containing annotations needed for following analysis.

Author(s)

Xiaojing Wang

Examples

ensembl <- useEnsembl(biomart = 'genes', 
 dataset = 'hsapiens_gene_ensembl',
 version = 111)

annotation_path <- tempdir()
transcript_ids <- c("ENST00000234420", "ENST00000269305", "ENST00000445888", 
    "ENST00000257430", "ENST00000508376", "ENST00000288602", 
    "ENST00000269571", "ENST00000256078", "ENST00000384871")

PrepareAnnotationEnsembl(mart=ensembl, annotation_path=annotation_path, 
    splice_matrix=FALSE, dbsnp=NULL, transcript_ids=transcript_ids, 
    COSMIC=FALSE)

prepare annotation for Refseq

Description

prepare the annotation for Refseq through UCSC table browser.

Usage

PrepareAnnotationRefseq(genome = "hg19", CDSfasta, pepfasta, annotation_path,
  dbsnp = NULL, transcript_ids = NULL, splice_matrix = FALSE,
  ClinVar = FALSE, ...)

Arguments

genome

specify the UCSC DB identifier (e.g. "hg19")
CDSfasta

path to the fasta file of coding sequence.
pepfasta

path to the fasta file of protein sequence, check 'introduction' for more detail.
annotation_path

specify a folder to store all the annotations.
dbsnp

specify a snp dataset to be used for the SNP annotation, default is NULL. (e.g. "snp148")
transcript_ids

optionally, only retrieve transcript annotation data for the specified set of transcript ids. Default is NULL.
splice_matrix

whether generate a known exon splice matrix from the annotation. this is not necessary if you don't want to analyse junction results, default is FALSE.
ClinVar

whether to download ClinVar data, default is FALSE.
...

additional arguments

Value

several .RData file containing annotations needed for further analysis.

Author(s)

Xiaojing Wang

Examples

## Not run: 

transcript_ids <- c("NM_001126112", "NM_033360", "NR_073499", "NM_004448",
        "NM_000179", "NR_029605", "NM_004333", "NM_001127511")
pepfasta <- system.file("extdata", "refseq_pro_seq.fasta", 
            package="customProDB")
CDSfasta <- system.file("extdata", "refseq_coding_seq.fasta", 
            package="customProDB")
annotation_path <- tempdir()
PrepareAnnotationRefseq(genome='hg38', CDSfasta, pepfasta, annotation_path, 
            dbsnp=NULL, transcript_ids=transcript_ids, 
            splice_matrix=FALSE, ClinVar=FALSE)


## End(Not run)

Generate shared junctions dataset from multiple BED files

Description

Load multiple BED files and output a GRange object with junctions present in multiple samples.

Usage

SharedJunc(juns, share_num = 2, ...)

Arguments

juns

a list of GRanges object which input from multiple VCF files using function InputVcf.
share_num

Junctions must occurs in this number of samples to be consider. Two options, percentage format or sample number.
...

additional arguments

Details

This function allows to limit junctions that are present in at least m out of n BED files.

Value

a GRange object that contains the shared junctions

Author(s)

Xiaojing Wang

Examples

path <- system.file("extdata/beds", package="customProDB")
bedFiles<- paste(path, '/', list.files(path, pattern="*bed$"), sep='')
juncs <- lapply(bedFiles, function(x) Bed2Range(x, skip=1, covfilter=5))
shared <- SharedJunc(juncs, share_num=2)
shared

Annotates the variations with genomic location.

Description

For a given GRange object of variations, the Varlocation() function finds the genomic locations for each entry according to the given annotation. Seven labels are used to describe the location (intergenic, intro_nonProcoding, exon_nonProcoding, intron, 5utr, 3utr and coding). details of the definition can be found in the tutorial.

Usage

Varlocation(Vars, txdb, ids, ...)

Arguments

Vars

a GRange object of variations
txdb

a TxDb object.
ids

a dataframe containing gene/transcript/protein id mapping information
...

additional arguments

Details

see 'introduction' for more details

Value

a data frame of locations for each variation

Author(s)

Xiaojing Wang

Examples

## Not run: 
vcffile <- system.file("extdata/vcfs", "test1.vcf", package="customProDB")
vcf <- InputVcf(vcffile)

table(values(vcf[[1]])[['INDEL']])
index <- which(values(vcf[[1]])[['INDEL']] == TRUE)
indelvcf <- vcf[[1]][index]

index <- which(values(vcf[[1]])[['INDEL']] == FALSE)
SNVvcf <- vcf[[1]][index]

txdb <- loadDb(system.file("extdata/refseq", "txdb.sqlite", package="customProDB"))
load(system.file("extdata/refseq", "ids.RData", package="customProDB"))
SNVloc <- Varlocation(SNVvcf,txdb,ids)
indelloc <- Varlocation(indelvcf,txdb,ids)
table(SNVloc[,'location'])

## End(Not run)