Package 'hiReadsProcessor'

Description

Given a sampleInfo object, the function adds a new feature for the given samples & sectors.

Usage

addFeature(
  sampleInfo,
  sector = NULL,
  samplename = NULL,
  feature = NULL,
  value = NULL
)

Arguments

Value

modified sampleInfo object with new feature(s) added.

See Also

findPrimers, extractSeqs, trimSeqs, extractFeature, getSectorsForSamples

Examples

load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
extractFeature(seqProps, sector="2", 
samplename="Roth-MLV3p-CD4TMLVWell6-MseI", feature="metadata")
seqProps <- addFeature(seqProps, sector="2", 
samplename="Roth-MLV3p-CD4TMLVWell6-MseI", feature="foo", 
value=c("Roth-MLV3p-CD4TMLVWell6-MseI"="woo"))
extractFeature(seqProps, sector="2", 
samplename="Roth-MLV3p-CD4TMLVWell6-MseI", feature="metadata")

Description

Given a named listed DNAStringSet object returned from extractSeqs, the function prepends the sample name to read names.

Usage

addListNameToReads(dnaSet, flatten = FALSE)

Arguments

Value

listed DNAStringSet with the names attribute prepended with the name of the list. If flatten is TRUE, then a DNAStringSet object

See Also

extractFeature, extractSeqs, getSectorsForSamples, write.listedDNAStringSet

Examples

load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
samples <- c('Roth-MLV3p-CD4TMLVWell6-Tsp509I', 
'Roth-MLV3p-CD4TMLVWell6-MseI', 'Roth-MLV3p-CD4TMLVwell5-MuA')
seqs <- extractSeqs(seqProps, sector = '2', samplename = samples,
feature="genomic")
addListNameToReads(seqs, TRUE)

Description

Given a sampleInfo object, the function finds 5' primers for each sample per sector and adds the results back to the object. This is a specialized function which depends on many other functions shown in 'see also section' to perform specialized trimming of 5' primer/adaptor found in the sampleInfo object. The sequence itself is never trimmed but rather coordinates of primer portion is recorded back to the object and used subsequently by extractSeqs function to perform the trimming.

Usage

annotateSites(
  sampleInfo,
  annots = NULL,
  samplenames = NULL,
  parallel = TRUE,
  ...
)

Arguments

Value

a SimpleList object similar to sampleInfo paramter supplied with new data added under each sector and sample. New data attributes include: primed

See Also

clusterSites, isuSites, crossOverCheck, findIntegrations, getIntegrationSites, pslToRangedObject

Examples

## Not run: 
load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
data(genes)
genes <- makeGRanges(genes)
cpgs <- getUCSCtable("cpgIslandExt","CpG Islands")
cpgs <- makeGRanges(cbind(cpgs,strand="*"), chromCol = "chrom")
annots <- list("RefGenes"=genes,"CpG"=cpgs)
annotateSites(seqProps, annots, annotType="nearest", side="5p")

## End(Not run)

Description

Align sequences from a listed DNAStringSet object returned from extractSeqs to an indexed reference genome using gfServer/gfClient protocol or using standalone BLAT and return the psl file as a GRanges object. This function heavily relies on defaults of blatSeqs.

Usage

blatListedSet(dnaSetList = NULL, ...)

Arguments

Value

a list of GRanges object reflecting psl file type per set of sequences.

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, startgfServer, stopgfServer, blatSeqs, read.psl, pslToRangedObject, read.blast8

Description

Align batch of sequences using standalone BLAT or gfServer/gfClient protocol against an indexed reference genome. Depending on parameters provided, the function either aligns batch of files to a reference genome using gfClient or takes sequences from query & subject parameters and aligns them using standalone BLAT. If standaloneBlat=FALSE and gfServer is not launched apriori, this function will start one using startgfServer and kill it using stopgfServer upon successful execution.

Usage

blatSeqs(
  query = NULL,
  subject = NULL,
  standaloneBlat = TRUE,
  port = 5560,
  host = "localhost",
  parallel = TRUE,
  numServers = 1L,
  gzipResults = TRUE,
  blatParameters = c(minIdentity = 90, minScore = 10, stepSize = 5, tileSize = 10,
    repMatch = 112312, dots = 50, maxDnaHits = 10, q = "dna", t = "dna", out = "psl")
)

Arguments

Value

a character vector of psl filenames. Each file provided is split by number of parallel workers and with read number denoting the cut. Files are cut in smaller pieces to for the ease of read & write into a single R session.

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, startgfServer, stopgfServer, read.psl, splitSeqsToFiles, read.blast8

Examples

## Not run: 
blatSeqs(dnaSeqs, subjectSeqs, blatParameters=c(minIdentity=90, minScore=10, 
tileSize=10, dots=10, q="dna", t="dna", out="blast8"))
blatSeqs(dnaSeqs, "/usr/local/genomeIndex/hg18.2bit", standaloneBlat=FALSE)
blatSeqs("mySeqs.fa", "/usr/local/genomeIndex/hg18.2bit", standaloneBlat=FALSE)
blatSeqs("my.*.fa", "/usr/local/genomeIndex/hg18.2bit", standaloneBlat=FALSE)

## End(Not run)

Description

Given a linear/vector like object, the function breaks it into equal sized chunks either by chunkSize. This is a helper function used by functions in 'See Also' section where each chunk is sent to a parallel node for processing.

Usage

chunkize(x, chunkSize = NULL)

Arguments

Value

a list of object split into chunks.

See Also

primerIDAlignSeqs, vpairwiseAlignSeqs, pairwiseAlignSeqs

Examples

x <- c("GAGGCTGTCACCGACAAGGTTCTGA", "AATAGCGTGGTGACAGCCCACATGC", 
       "GGTCTTCTAGGGAACCTACGCCACA", "TTTCCGGCGGCAGTCAGAGCCAAAG", 
       "TCCTGTCAACTCGTAGATCCAATCA", "ATGGTCACCTACACACAACGGCAAT", 
       "GTCAGGACACCTAATCACAAACGGA", "AGACGCAGGTTCAGGCTGGACTAGA", 
       "ATCGTTTCCGGAATTCGTGCACTGG", "CAATGCGGGCACACGCTCTTACAGT")
chunkize(DNAStringSet(x), 5)

Description

Given a group of discrete factors (i.e. position ids) and integer values, the function tries to correct/cluster the integer values based on their frequency in a defined windowsize.

Usage

clusterSites(
  posID = NULL,
  value = NULL,
  grouping = NULL,
  psl.rd = NULL,
  weight = NULL,
  windowSize = 5L,
  byQuartile = FALSE,
  quartile = 0.7,
  parallel = TRUE,
  sonicAbund = FALSE
)

Arguments

Value

a data frame with clusteredValues and frequency shown alongside with the original input. If psl.rd parameter is defined then a GRanges object is returned with three new columns appended at the end: clusteredPosition, clonecount, and clusterTopHit (a representative for a given cluster chosen by best scoring hit!).

Note

The algorithm for clustering when byQuartile=TRUE is as follows: for all values in each grouping, get a distribution and test if their frequency is >= quartile threshold. For values below the quartile threshold, test if any values overlap with the ones that passed the threshold and is within the defined windowSize. If there is a match, then merge with higher value, else leave it as is. This is only useful if the distribution is wide and polynodal. When byQuartile=FALSE, for each group the values within the defined window are merged with the next highest frequently occuring value, if freuquencies are tied then lowest value is used to represent the cluster. When psl.rd is passed, then multihits are ignored and only unique sites are clustered. All multihits will be tagged as a good 'clusterTopHit'.

See Also

findIntegrations, getIntegrationSites, otuSites, isuSites, crossOverCheck, pslToRangedObject, getSonicAbund

Examples

clusterSites(posID = c('chr1-', 'chr1-', 'chr1-', 'chr2+', 'chr15-', 
'chr16-','chr11-'), value = c(rep(1000, 2), 5832, 1000, 12324, 65738, 928042), 
grouping = c('a', 'a', 'a', 'b', 'b', 'b', 'c'), parallel = FALSE)
## Not run: 
data(psl)
psl <- psl[sample(nrow(psl), 100), ]
psl.rd <- getIntegrationSites(pslToRangedObject(psl))
psl.rd$grouping <- sub("(.+)-.+", "\\1", psl.rd$qName)
clusterSites(grouping = psl.rd$grouping, psl.rd = psl.rd)

## End(Not run)

Description

Given a group of discrete factors (i.e. position ids) and integer values, the function tests if they overlap between groups. If overlap is found, then the group having highest frequency of a given position wins, else the position is filtered out from all the groups. The main use of this function is to remove crossover sites from different samples in the data.

Usage

crossOverCheck(
  posID = NULL,
  value = NULL,
  grouping = NULL,
  weight = NULL,
  windowSize = 1,
  psl.rd = NULL
)

Arguments

Value

a data frame of the original input with columns denoting whether a given row was a Candidate and isCrossover. If psl.rd parameter is defined, then a GRanges object with 'isCrossover', 'Candidate', and 'FoundIn' columns appended at the end.

See Also

clusterSites, otuSites, findIntegrations, getIntegrationSites, pslToRangedObject

Examples

crossOverCheck(posID = c('chr1-', 'chr1-', 'chr1-', 'chr1-', 'chr2+', 'chr15-', 
'chr16-', 'chr11-'), value = c(rep(1000, 3), 5832, 1000, 12324, 65738, 928042), 
grouping = c('a', 'a', 'b', 'b', 'b', 'b', 'c', 'c'))

Description

Given a DNAStringSet object, the function dereplicates reads and adds counts=X to the definition line to indicate replication.

Usage

dereplicateReads(dnaSet)

Arguments

Value

DNAStringSet object with names describing frequency of repeat.

See Also

replicateReads, removeReadsWithNs, findBarcodes, splitByBarcode

Examples

dnaSet <- c("CCTGAATCCTGGCAATGTCATCATC", "ATCCTGGCAATGTCATCATCAATGG", 
"ATCAGTTGTCAACGGCTAATACGCG", "ATCAATGGCGATTGCCGCGTCTGCA", 
"CCGCGTCTGCAATGTGAGGGCCTAA", "GAAGGATGCCAGTTGAAGTTCACAC", 
"CCTGAATCCTGGCAATGTCATCATC", "ATCCTGGCAATGTCATCATCAATGG", 
"ATCAGTTGTCAACGGCTAATACGCG", "ATCAATGGCGATTGCCGCGTCTGCA", 
"CCGCGTCTGCAATGTGAGGGCCTAA", "GAAGGATGCCAGTTGAAGTTCACAC") 
dereplicateReads(dnaSet)

Description

Given a fixed length pattern sequence and variable length subject sequences, the function roughly finds which orientation of pattern yields the most hits. The function doing the heavylifting is vcountPattern. This is an accessory function used in function listed under See Also section below.

Usage

doRCtest(
  subjectSeqs = NULL,
  patternSeq = NULL,
  qualityThreshold = 0.5,
  parallel = TRUE
)

Arguments

Value

patternSeq that aligned the best.

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, primerIDAlignSeqs

Examples

subjectSeqs <- c("CCTGAATCCTGGCAATGTCATCATC", "ATCCTGGCAATGTCATCATCAATGG", 
"ATCAGTTGTCAACGGCTAATACGCG", "ATCAATGGCGATTGCCGCGTCTGCA", 
"CCGCGTCTGCAATGTGAGGGCCTAA", "GAAGGATGCCAGTTGAAGTTCACAC")
subjectSeqs <- xscat("AAAAAAAAAA", subjectSeqs)
doRCtest(subjectSeqs, "TTTTTTTTT")

Description

Given a sampleInfo object, the function extracts a defined feature(s) for given sample or sector.

Usage

extractFeature(sampleInfo, sector = NULL, samplename = NULL, feature = NULL)

Arguments

Value

a list or list of lists depending upon which parameters were supplied.

See Also

addFeature, findPrimers, findLTRs, findLinkers, extractSeqs, trimSeqs, getSectorsForSamples

Examples

load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
samples <- c('Roth-MLV3p-CD4TMLVWell6-Tsp509I', 
'Roth-MLV3p-CD4TMLVWell6-MseI', 'Roth-MLV3p-CD4TMLVwell5-MuA')
extractFeature(seqProps, sector='2', samplename=samples, feature="primed")
extractFeature(seqProps, sector='2', samplename=samples, feature="linkered")
extractFeature(seqProps, sector='2', samplename=samples, feature="metadata")

Description

Given a sampleInfo object, the function extracts sequences for a defined feature.

Usage

extractSeqs(
  sampleInfo,
  sector = NULL,
  samplename = NULL,
  feature = "genomic",
  trim = TRUE,
  minReadLength = 1,
  sideReturn = NULL,
  pairReturn = "both",
  strict = FALSE
)

Arguments

Value

a listed DNAStringSet object structed by sector then sample. Note: when feature='genomic' or 'genomicLinkered' and when data is paired end, then "pair2" includes union of reads from both pairs which found LTR.

See Also

findPrimers, findLTRs, findLinkers, trimSeqs, extractFeature, getSectorsForSamples

Examples

load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
samples <- c('Roth-MLV3p-CD4TMLVWell6-Tsp509I', 
'Roth-MLV3p-CD4TMLVWell6-MseI', 'Roth-MLV3p-CD4TMLVwell5-MuA')
extractSeqs(seqProps, sector='2', samplename=samples, feature="primed")
extractSeqs(seqProps, sector='2', samplename=samples, feature="!primed")
extractSeqs(seqProps, sector='2', samplename=samples, feature="linkered")
extractSeqs(seqProps, sector='2', samplename=samples, feature="genomic")

Description

This function facilitates finding and trimming of long/short fragments of vector present in LM-PCR products. The algorithm looks for vector sequence present anywhere within the read and trims according longest contiguous match on either end of the read. Alignment is doing using BLAT

Usage

findAndRemoveVector(
  reads,
  Vector,
  minLength = 10,
  returnCoords = FALSE,
  parallel = TRUE
)

Arguments

Value

DNAStringSet object with Vector sequence removed from the reads. If returnCoords=TRUE, then a list of two named elements "hits" & "reads". The first element, "hits" is a GRanges object with properties of matched region and whether it was considered valid denoted by 'good.row'. The second element, "reads" is a DNAStringSet object with Vector sequence removed from the reads.

Note

If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, pslToRangedObject, blatSeqs, read.blast8, findAndTrimSeq

Description

This function facilitates finding and trimming of a short pattern sequence from a collection of subject sequences. The trimming is dictated by side parameter. For more information on the trimming process see the 'side' parameter documentation in trimSeqs. For information regarding the pattern alignment see the documentation for pairwiseAlignSeqs. This function is meant for aligning a short pattern onto large collection of subjects. If you are looking to align a vector sequence to subjects, then please use BLAT.

Usage

findAndTrimSeq(
  patternSeq,
  subjectSeqs,
  side = "left",
  offBy = 0,
  alignWay = "slow",
  ...
)

Arguments

Value

DNAStringSet object with pattern sequence removed from the subject sequences.

Note

If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, extractFeature, extractSeqs, primerIDAlignSeqs, findPrimers, findLinkers

Examples

findAndTrimSeq(patternSeq="AGACCCTTTT",
subjectSeqs=DNAStringSet(c("AGACCCTTTTGAGCAGCAT","AGACCCTTGGTCGACTCA",
"AGACCCTTTTGACGAGCTAG")), qualityThreshold=.85, doRC=FALSE, side="left", 
offBy=1, alignWay = "slow")

Description

Given a sample information object, the function reads in the raw fasta/fastq file, demultiplexes reads by their barcodes, and appends it back to the sampleInfo object. Calls splitByBarcode to perform the actual splitting of file by barcode sequences. If supplied with a character vector and reads themselves, the function behaves a bit differently. See the examples.

Usage

findBarcodes(
  sampleInfo,
  sector = NULL,
  dnaSet = NULL,
  showStats = FALSE,
  returnUnmatched = FALSE,
  dereplicate = FALSE,
  alreadyDecoded = FALSE
)

Arguments

Value

If sampleInfo is an object of SimpleList then decoded sequences are appeneded to respective sample slots, else a named list of DNAStringSet object. If returnUnmatched=TRUE, then x[["unDecodedSeqs"]] has the unmatched sequences.

See Also

splitByBarcode, dereplicateReads, replicateReads

Examples

dnaSet <- DNAStringSet(c("read1" = "ACATCCATAGAGCTACGACGACATCGACATA",
"read2"="GAATGGATGACGACTACAGCACGACGAGCAGCTACT",
"read3"="GAATGGATGCGCTAAGAAGAGA", "read4"="ACATCCATTCTACACATCT"))
findBarcodes(sampleInfo = c("ACATCCAT" = "Sample1", "GAATGGAT" = "Sample2"),
dnaSet=dnaSet, showStats=TRUE, returnUnmatched=TRUE)
## Not run: 
load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
findBarcodes(seqProps, sector = "all", showStats = TRUE)

## End(Not run)

Description

Given a SampleInfo object, the function finds integration sites for each sample using their respective settings and adds the results back to the object. This is an all-in-one function which aligns, finds best hit per read per sample, cluster sites, and assign ISU IDs. It calls blatSeqs, read.psl, getIntegrationSites, clusterSites, otuSites. here must be linkered reads within the sampleInfo object in order to use this function using the default parameters. If you are planning on BLATing non-linkered reads, then change the seqType to one of accepted options for the 'feature' parameter of extractSeqs, except for '!' based features.

Usage

findIntegrations(
  sampleInfo,
  seqType = NULL,
  genomeIndices = NULL,
  samplenames = NULL,
  parallel = TRUE,
  autoOptimize = FALSE,
  doSonic = FALSE,
  doISU = FALSE,
  ...
)

Arguments

Value

a SimpleList object similar to sampleInfo parameter supplied with new data added under each sector and sample. New data attributes include: psl, and sites. The psl attributes holds the genomic hits per read along with QC information. The sites attribute holds the condensed integration sites where genomic hits have been clustered by the Position column and cherry picked to have each site pass all the QC steps.

Note

If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

findPrimers, findLTRs, findLinkers, startgfServer, read.psl, blatSeqs, blatListedSet, pslToRangedObject, clusterSites, isuSites, crossOverCheck, getIntegrationSites, getSonicAbund, annotateSites

Examples

## Not run: 
load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
findIntegrations(seqProps, 
genomeIndices=c("hg18"="/usr/local/genomeIndexes/hg18.noRandom.2bit"), 
numServers=2)

## End(Not run)

Description

Given a sampleInfo object, the function finds 3' linkers for each sample per sector and adds the results back to the object. This is a specialized function which depends on many other functions shown in 'see also section' to perform specialized trimming of 3' primer/linker adaptor sequence found in the sampleInfo object. The sequence itself is never trimmed but rather coordinates of linker portion is recorded back to the object and used subsequently by extractSeqs function to perform the trimming. This function heavily relies on either pairwiseAlignSeqs or primerIDAlignSeqs depending upon whether linkers getting aligned have primerID in it or not.

Usage

findLinkers(
  sampleInfo,
  showStats = FALSE,
  doRC = FALSE,
  parallel = TRUE,
  samplenames = NULL,
  bypassChecks = FALSE,
  parallel2 = FALSE,
  ...
)

Arguments

Value

a SimpleList object similar to sampleInfo paramter supplied with new data added under each sector and sample. New data attributes include: linkered. If linkers have primerID then, primerIDs attribute is appended as well.

Note

• For paired end data, qualityThreshold for pair 2 is increased by 0.25 or set to 1 whichever is lower to increase quality & full match to linker sequence.

• If no linker matches are found with default options, then try doRC=TRUE.

• If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, primerIDAlignSeqs, findLTRs, findPrimers, extractFeature, extractSeqs, findAndTrimSeq, findIntegrations

Examples

## Not run: 
load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
findLinkers(seqProps, showStats=TRUE, doRC=TRUE)

## End(Not run)

Description

Given a sampleInfo object, the function finds 5' LTR following the primer for each sample per sector and adds the results back to the object. This is a specialized function which depends on many other functions shown in 'see also section' to perform specialized trimming of 5' viral LTRs found in the sampleInfo object. The sequence itself is never trimmed but rather coordinates of LTR portion is added to primer coordinates and recorded back to the object and used subsequently by extractSeqs function to perform the trimming. This function heavily relies on pairwiseAlignSeqs.

Usage

findLTRs(
  sampleInfo,
  showStats = FALSE,
  doRC = FALSE,
  parallel = TRUE,
  samplenames = NULL,
  bypassChecks = FALSE,
  parallel2 = FALSE,
  ...
)

Arguments

Value

a SimpleList object similar to sampleInfo paramter supplied with new data added under each sector and sample. New data attributes include: LTRed

Note

• For paired end data, qualityThreshold for pair 2 is decreased by 0.05 to increase chances of matching LTR sequence.

• If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, extractFeature, extractSeqs, primerIDAlignSeqs, findPrimers, findLinkers, findAndTrimSeq

Examples

## Not run: 
load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
findLTRs(seqProps, showStats=TRUE)

## End(Not run)

Description

Given a sampleInfo object, the function finds 5' primers for each sample per sector and adds the results back to the object. This is a specialized function which depends on many other functions shown in 'see also section' to perform specialized trimming of 5' primer/adaptor found in the sampleInfo object. The sequence itself is never trimmed but rather coordinates of primer portion is recorded back to the object and used subsequently by extractSeqs function to perform the trimming.

Usage

findPrimers(
  sampleInfo,
  alignWay = "slow",
  showStats = FALSE,
  doRC = FALSE,
  parallel = TRUE,
  samplenames = NULL,
  bypassChecks = FALSE,
  parallel2 = FALSE,
  ...
)

Arguments

Value

a SimpleList object similar to sampleInfo paramter supplied with new data added under each sector and sample. New data attributes include: primed

Note

• For paired end data, qualityThreshold for pair 2 is decreased by 0.10 to increase chances of matching primer sequence.

• If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, extractFeature, extractSeqs, primerIDAlignSeqs, findLTRs, findLinkers, findAndTrimSeq

Examples

## Not run: 
load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
findPrimers(seqProps, showStats=TRUE)

## End(Not run)

Description

Given a sampleInfo object, the function finds vector fragments following the LTR piece for each sample per sector and adds the results back to the object. This is a specialized function which depends on many other functions shown in 'see also section' to perform specialized trimming of 5' viral LTRs found in the sampleInfo object. The sequence itself is never trimmed but rather coordinates of vector portion is added to LTR coordinates and recorded back to the object and used subsequently by extractSeqs function to perform the trimming. This function heavily relies on blatSeqs. In order for this function to work, it needs vector sequence which is read in using 'vectorFile' metadata supplied in the sample information file in read.sampleInfo

Usage

findVector(sampleInfo, showStats = FALSE, parallel = TRUE, samplenames = NULL)

Arguments

Value

a SimpleList object similar to sampleInfo paramter supplied with new data added under each sector and sample. New data attributes include: vectored

Note

• If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

pairwiseAlignSeqs, blatSeqs, extractFeature, extractSeqs, findPrimers, findLTRs, findLinkers, findAndTrimSeq, findAndRemoveVector

Examples

## Not run: 
load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
findVector(seqProps, showStats=TRUE)

## End(Not run)

Description

Given a GRanges object from read.psl, the function uses specified filtering parameters to obtain integration sites and maintain sequence attrition. The function will remove any non-best scoring alignments from the object if not already filtered apriori.

Usage

getIntegrationSites(
  psl.rd = NULL,
  startWithin = 3,
  alignRatioThreshold = 0.7,
  genomicPercentIdentity = 0.98,
  correctByqStart = TRUE,
  oneBased = FALSE
)

Arguments

Value

a GRanges object with integration sites which passed all filtering criteria. Each filtering parameter creates a new column to flag if a sequence/read passed that filter which follows the scheme: 'pass.FilterName'. Integration Site is marked by new column named 'Position'.

See Also

startgfServer, read.psl, blatSeqs, blatListedSet, findIntegrations, pslToRangedObject, clusterSites, isuSites, crossOverCheck, read.blast8

Examples

data(psl)
psl.rd <- pslToRangedObject(psl)
getIntegrationSites(psl.rd)

Description

Given a sampleInfo object, the function gets the sectors for each samplename. This is an accessory function utilized by other functions of this package to aid sector retrieval.

Usage

getSectorsForSamples(
  sampleInfo,
  sector = NULL,
  samplename = NULL,
  returnDf = FALSE
)

Arguments

Value

If returnDf=TRUE, then a dataframe of sector associated with each samplename, else a named list of length two: x[["sectors"]] and x[["samplenames"]]

See Also

extractSeqs, extractFeature, addFeature

Examples

load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
samples <- c('Roth-MLV3p-CD4TMLVWell6-Tsp509I', 
'Roth-MLV3p-CD4TMLVWell6-MseI', 'Roth-MLV3p-CD4TMLVwell5-MuA')
getSectorsForSamples(seqProps, samplename=samples)
getSectorsForSamples(seqProps, samplename=samples, returnDf=TRUE)

Description

Given distinct fragment lengths per integration, the function calculates sonic abundance as described in sonicLength. This function is called by clusterSites and needs all individual fragments lengths per position to properly estimate the clonal abundance of an integration sites in a given population.

Usage

getSonicAbund(
  posID = NULL,
  fragLen = NULL,
  grouping = NULL,
  replicateNum = NULL,
  psl.rd = NULL,
  parallel = TRUE
)

Arguments

Value

a data frame with estimated sonic abundance shown alongside with the original input. If psl.rd parameter is defined then a GRanges object is returned with a new column 'estAbund'.

Note

For samples isolated using traditional restriction digest method, the abundance will be inaccurate as it is designed for sonicated or sheared sample preparation method.

See Also

clusterSites, otuSites, findIntegrations, getIntegrationSites, pslToRangedObject

Examples

data("A1",package='sonicLength')
A1 <- droplevels(A1[1:1000,])
bore <- with(A1, getSonicAbund(locations, lengths, "A", replicates))
head(bore)

Description

hiReadsProcessor contains set of functions which allow users to process LM-PCR products sequenced using any platform. Given an excel/txt file containing parameters for demultiplexing and sample metadata, the functions automate trimming of adaptors and identification of the genomic product. Genomic products are further processed for QC and abundance quantification.

Author(s)

Nirav V Malani

Description

Given a group of values or genomic positions per read/clone, the function tries to yield a unique ISU (Integration Site Unit) ID for the collection based on overlap of locations to other reads/clones by grouping. This is mainly useful when each read has many locations which needs to be considered as one single group of sites.

Usage

isuSites(
  posID = NULL,
  value = NULL,
  readID = NULL,
  grouping = NULL,
  psl.rd = NULL,
  maxgap = 5,
  parallel = TRUE
)

Arguments

Value

a data frame with binned values and isuID shown alongside the original input. If psl.rd parameter is defined, then a GRanges object where object is first filtered by clusterTopHit column and the isuID column appended at the end.

Note

The algorithm for making isus of sites is as follows: for each readID check how many positions are there. Separate readIDs with only position from the rest. Check if any readIDs with >1 position match to any readIDs with only one position. If there is a match, then assign both readIDs with the same ISU ID. Check if any positions from readIDs with >1 position match any other readIDs with >1 position. If yes, then assign same ISU ID to all readIDs sharing 1 or more positions.

See Also

clusterSites, isuSites, crossOverCheck, findIntegrations, getIntegrationSites, pslToRangedObject

Examples

isuSites(posID = c('chr1-', 'chr1-', 'chr1-', 'chr2+', 'chr15-', 'chr16-', 'chr11-'), 
value = c(rep(1000, 2), 5832, 1000, 12324, 65738, 928042), 
readID = paste('read', sample(letters, 7), sep = '-'), 
grouping = c('a', 'a',  'a', 'b', 'b', 'b', 'c'), parallel = FALSE)

Description

Given a group of values or genomic positions per read/clone, the function tries to yield a unique OTU (operation taxinomical unit) ID for the collection based on overlap of locations to other reads/clones by grouping. This is mainly useful when each read has many locations which needs to be considered as one single group of sites.

Usage

otuSites(
  posID = NULL,
  value = NULL,
  readID = NULL,
  grouping = NULL,
  psl.rd = NULL,
  maxgap = 5,
  parallel = TRUE
)

Arguments

Value

a data frame with binned values and otuID shown alongside the original input. If psl.rd parameter is defined, then a GRanges object.

Note

The algorithm for making OTUs of sites is as follows:

• for each grouping & posID, fix values off by maxgap parameter

• create bins of fixed values per readID

• assign arbitrary numeric ID to each distinct bins above & obtain its frequency

• perform overlap b/w readIDs with only one value (singletons) to readIDs with >1 value (non-singletons)

• - for any overlapping values, tag non-singleton readID with the ID of singleton readID

• - if non-singleton readID matched with more than one singleton readID, then pick on at random

• for any non-tagged & non-singleton readIDs, perform an overlap of values within themselves using the maxgap parameter

• - tag any overlapping positions across any readID with the ID of most frequently occuring bin

• positions with no overlap are left as is with the original arbitrary ID

See Also

clusterSites, isuSites, crossOverCheck, findIntegrations, getIntegrationSites, pslToRangedObject

Examples

otuSites(posID = c('chr1-', 'chr1-', 'chr1-', 'chr2+', 'chr15-', 'chr16-', 'chr11-'),
value = c(1000, 1003, 5832, 1000, 12324, 65738, 928042), 
readID = paste('read', sample(letters, 7), sep = '-'), 
grouping = c('a', 'a', 'a', 'b', 'b', 'b', 'c'), parallel = FALSE)

Description

Given a GRanges object, the function uses specified gaplength parameter to pair up reads where the qName column ends with "atpersand pairname atpersand" which is outputted by extractSeqs.

Usage

pairUpAlignments(
  psl.rd = NULL,
  maxGapLength = 2500,
  sameStrand = TRUE,
  parallel = TRUE
)

Arguments

Value

a GRanges object with reads paired up denoted by "paired" column. Improper pairs or unpaired reads are returned with "paired" column as FALSE.

See Also

pairwiseAlignSeqs, blatSeqs, read.blast8, read.psl, getIntegrationSites, read.BAMasPSL

Examples

## Not run: 
psl.rd <- read.BAMasPSL(bamFile=c("sample1hits.bam","sample2hits.bam"))
pairUpAlignments(psl.rd)

## End(Not run)

Description

Align a fixed length short pattern sequence (i.e. primers or adaptors) to subject sequences using pairwiseAlignment. This function uses default of type="overlap", gapOpening=-1, and gapExtension=-1 to align the patternSeq against subjectSeqs. One can adjust these parameters if prefered, but not recommended. This function is meant for aligning a short pattern onto large collection of subjects. If you are looking to align a vector sequence to subjects, then please use BLAT or see one of following blatSeqs, findAndRemoveVector

Usage

pairwiseAlignSeqs(
  subjectSeqs = NULL,
  patternSeq = NULL,
  side = "left",
  qualityThreshold = 1,
  showStats = FALSE,
  bufferBases = 5,
  doRC = TRUE,
  returnUnmatched = FALSE,
  returnLowScored = FALSE,
  parallel = FALSE,
  ...
)

Arguments

Value

• IRanges object with starts, stops, and names of the aligned sequences.

• If returnLowScored or returnUnmatched = T, then a CompressedIRangesList where x[["hits"]] has the good scoring hits, x[["Rejected"]] has the failed to match qualityThreshold hits, and x[["Absent"]] has the hits where the aligned bit is <=10% match to the patternSeq.

Note

• For qualityThreshold, the alignment score is calculated by (matches*2)-(mismatches+gaps) which programatically translates to round(nchar(patternSeq)*qualityThreshold)*2.

• Gaps and mismatches are weighed equally with value of -1 which can be overriden by defining extra parameters 'gapOpening' & 'gapExtension'.

• If qualityThreshold is 1, then it is a full match, if 0, then any match is accepted which is useful in searching linker sequences at 3' end. Beware, this function only searches for the pattern sequence in one orientation. If you are expecting to find the pattern in both orientation, you might be better off using BLAST/BLAT!

• If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

primerIDAlignSeqs, vpairwiseAlignSeqs, doRCtest, findAndTrimSeq, blatSeqs, findAndRemoveVector

Examples

subjectSeqs <- c("CCTGAATCCTGGCAATGTCATCATC", "ATCCTGGCAATGTCATCATCAATGG", 
"ATCAGTTGTCAACGGCTAATACGCG", "ATCAATGGCGATTGCCGCGTCTGCA", 
"CCGCGTCTGCAATGTGAGGGCCTAA", "GAAGGATGCCAGTTGAAGTTCACAC")
subjectSeqs <- DNAStringSet(xscat("AAAAAAAAAA", subjectSeqs))
pairwiseAlignSeqs(subjectSeqs, "AAAAAAAAAA", showStats=TRUE)
pairwiseAlignSeqs(subjectSeqs, "AAATAATAAA", showStats=TRUE, 
qualityThreshold=0.5)

Description

Align a fixed length short pattern sequence containing primerID to variable length subject sequences using pairwiseAlignment. This function uses default of type="overlap", gapOpening=-1, and gapExtension=-1 to align the patterSeq against subjectSeqs. The search is broken up into as many pieces +1 as there are primerID and then compared against subjectSeqs. For example, patternSeq="AGCATCAGCANNNNNNNNNACGATCTACGCC" will launch two search jobs one per either side of Ns. For each search, qualityThreshold is used to filter out candidate alignments and the area in between is chosen to be the primerID. This strategy is benefical because of Indels introduced through homopolymer errors. Most likely the length of primerID(s) wont the same as you expected!

Usage

primerIDAlignSeqs(
  subjectSeqs = NULL,
  patternSeq = NULL,
  qualityThreshold1 = 0.75,
  qualityThreshold2 = 0.5,
  doAnchored = FALSE,
  doRC = TRUE,
  returnUnmatched = FALSE,
  returnRejected = FALSE,
  showStats = FALSE,
  ...
)

Arguments

Value

• A CompressedIRangesList of length two, where x[["hits"]] is hits covering the entire patternSeq, and x[["primerIDs"]] is the potential primerID region.

• If returnUnmatched = T, then x[["Absent"]] is appended which includes reads not matching the first part of patternSeq.

• If returnRejected=TRUE, then x[["Rejected"]] includes reads that only matched one part of patternSeq or places where no primerID was found in between two part of patternSeq, and x[["RejectedprimerIDs"]] includes primerIDs that didn't match the correct length.

• If doAnchored=TRUE, then x[["unAnchoredprimerIDs"]] includes reads that didn't match the base before and after primer ID on patternSeq.

Note

• For qualityThreshold1 & qualityThreshold2, the alignment score is calculated by (matches*2)-(mismatches+gaps) which programatically translates to round(nchar(patternSeq)*qualityThreshold)*2

• Gaps and mismatches are weighed equally with value of -1 which can be overriden by defining extra parameters 'gapOpening' & 'gapExtension'.

• If qualityThreshold is 1, then it is a full match, if 0, then any match is accepted which is useful in searching linker sequences at 3' end. Beware, this function only searches for the pattern sequence in one orientation. If you are expecting to find the pattern in both orientation, you might be better off using BLAST/BLAT!

See Also

vpairwiseAlignSeqs, pairwiseAlignSeqs, doRCtest, blatSeqs, findAndRemoveVector

Examples

subjectSeqs <- c("CCTGAATCCTGGCAATGTCATCATC", "ATCCTGGCAATGTCATCATCAATGG", 
"ATCAGTTGTCAACGGCTAATACGCG", "ATCAATGGCGATTGCCGCGTCTGCA", 
"CCGCGTCTGCAATGTGAGGGCCTAA", "GAAGGATGCCAGTTGAAGTTCACAC")
ids <- c("GGTTCTACGT", "AGGAGTATGA", "TGTCGGTATA", "GTTATAAAAC", 
"AGGCTATATC", "ATGGTTTGTT")
subjectSeqs <- xscat(subjectSeqs, xscat("AAGCGGAGCCC",ids,"TTTTTTTTTTT"))
patternSeq <- "AAGCGGAGCCCNNNNNNNNNNTTTTTTTTTTT"
primerIDAlignSeqs(DNAStringSet(subjectSeqs), patternSeq, doAnchored = TRUE)

Description

Sample BLAT PSL file output from samples included Integration Sites Sequencing Data seqProps

Format

a data frame of 1000 rows and 21 columns

Description

Print out required fields & classes of PSL file format

Usage

pslCols(withClass = TRUE)

Arguments

Value

vector of PSL column names

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, startgfServer, blatSeqs, read.blast8, read.BAMasPSL, pslToRangedObject

Examples

pslCols()

Description

Convert psl dataframe to GRanges object using either the query or target as the reference data column.

Usage

pslToRangedObject(x, useTargetAsRef = TRUE, isblast8 = FALSE)

Arguments

Value

a GRanges object reflecting psl file type.

See Also

read.psl, read.blast8, blatListedSet

Examples

data(psl)
psl <- head(psl)
pslToRangedObject(psl)
pslToRangedObject(psl, useTargetAsRef = FALSE)

Description

Given filename(s), the function reads the BAM/SAM file, converts into a PSL like format. Any other file format will yield errors or erroneous results. This is intended to be used independently with other short read aligners.

Usage

read.BAMasPSL(bamFile = NULL, removeFile = TRUE, asGRanges = TRUE)

Arguments

Value

a GRanges or GAlignments object reflecting psl file type.

See Also

pairwiseAlignSeqs, blatSeqs, read.blast8, read.psl, pslToRangedObject, pairUpAlignments

Examples

## Not run: 
read.BAMasPSL(bamFile="processed.*.bam$")
read.BAMasPSL(bamFile=c("sample1hits.bam","sample2hits.bam"))

## End(Not run)

Description

Given filename(s), the function reads the blast8 file format from BLAT as a data frame and performs basic score filtering if indicated. Any other file format will yield errors or erroneous results.

Usage

read.blast8(
  files = NULL,
  asGRanges = FALSE,
  removeFile = TRUE,
  parallel = FALSE
)

Arguments

Value

a dataframe or GRanges object reflecting blast8 file type.

Note

If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, startgfServer, blatSeqs, read.psl

Examples

# this function works similar to read.psl #
#read.blast8(files="processed.*.blast8$")
#read.blast8(files=c("sample1hits.blast8","sample2hits.blast8"))

Description

Given filename(s), the function reads the PSL file format from BLAT as a data frame and performs basic score filtering if indicated. Any other file format will yield errors or erroneous results. Make sure there is no header row! See required columns in pslCols.

Usage

read.psl(
  pslFile = NULL,
  bestScoring = TRUE,
  asGRanges = FALSE,
  removeFile = TRUE,
  parallel = FALSE
)

Arguments

Value

a dataframe reflecting psl file type. If asGRanges=TRUE, then a GRanges object.

Note

If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, startgfServer, blatSeqs, read.blast8, read.BAMasPSL, pslToRangedObject, write.psl

Examples

## Not run: 
data(psl)
pslFile <- tempfile()
write.psl(psl, filename = pslFile)
head(read.psl(pslFile = pslFile))
# read many PSL files matching the regex #
psl <- read.psl(pslFile = "processed.*.psl$")

## End(Not run)

Description

Given a sample information file, the function checks if it includes required information to process samples present on each sector/quadrant/region/lane. The function also adds other columns required for processing with default values if not already defined ahead of time.

Usage

read.sampleInfo(sampleInfoPath = NULL, splitBySector = TRUE)

Arguments

Details

• Required Column Description:

  • sector => region/quadrant/lane of the sequencing plate the sample comes from. If files have been split by samples apriori, then the filename associated per sample without the extension. If this is a filename, then be sure to enable 'alreadyDecoded' parameter in findBarcodes, since contents of this column is pasted together with 'seqfilePattern' parameter in read.SeqFolder to find the appropriate file needed. For paired end data, this is basename of the FASTA/Q file holding the sample data from the LTR side. For example, files such as Lib3_L001_R2_001.fastq.gz or Lib3_L001_R2_001.fastq would be Lib3_L001_R2_001, and consequently Lib3_L001_R1_001 would be used as the second pair!

  • barcode => unique 4-12bp DNA sequence which identifies the sample. If providing filename as sector, then leave this blank since it is assumed that the data is already demultiplexed.

  • primerltrsequence => DNA sequence of the viral LTR primer with/without the viral LTR sequence following the primer landing site. If already trimmed, then mark this as SKIP.

  • sampleName => Name of the sample associated with the barcode

  • sampleDescription => Detailed description of the sample

  • gender => sex of the sample: male or female or NA

  • species => species of the sample: homo sapien, mus musculus, etc.

  • freeze => UCSC freeze to which the sample should be aligned to.

  • linkerSequence => DNA sequence of the linker adaptor following the genomic sequence. If already trimmed, then mark this as SKIP.

  • restrictionEnzyme => Restriction enzyme used for digestion and sample recovery. Can also be one of: Fragmentase or Sonication!

• Metadata Parameter Column Description:

  • ltrBitSequence => DNA sequence of the viral LTR following the primer landing site. Default is last 7bps of the primerltrsequence.

  • ltrBitIdentity => percent of LTR bit sequence to match during the alignment. Default is 1.

  • primerLTRidentity => percent of primer to match during the alignment. Default is .85

  • linkerIdentity => percent of linker sequence to match during the alignment. Default is 0.55. Only applies to non-primerID/random tag based linker search.

  • primerIdInLinker => whether the linker adaptor used has primerID/random tag in it? Default is FALSE.

  • primerIdInLinkerIdentity1 => percent of sequence to match before the random tag. Default is 0.75. Only applies to primerID/random tag based linker search and when primeridinlinker is TRUE.

  • primerIdInLinkerIdentity2 => percent of sequence to match after the random tag. Default is 0.50. Only applies to primerID/random tag based linker search and when primeridinlinker is TRUE.

  • celltype => celltype information associated with the sample

  • user => name of the user who prepared or processed the sample

  • pairedEnd => is the data paired end? Default is FALSE.

  • vectorFile => fasta file containing the vector sequence

• Processing Parameter Column Description:

  • startWithin => upper bound limit of where the alignment should start within the query. Default is 3.

  • alignRatioThreshold => cuttoff for (alignment span/read length). Default is 0.7.

  • genomicPercentIdentity => cuttoff for (1-(misMatches/matches)). Default is 0.98.

  • clusterSitesWithin => cluster integration sites within a defined window size based on frequency which corrects for any sequencing errors. Default is 5.

  • keepMultiHits => whether to keep sequences/reads that return multiple best hits, aka ambiguous locations.

  • processingDate => the date of processing

Value

if splitBySector=TRUE, then an object of SimpleList named by quadrant/lane information defined in sampleInfo file, else a dataframe.

See Also

read.SeqFolder, findBarcodes, splitByBarcode

Examples

runData <- system.file(file.path("extdata", "FLX_sample_run"), 
package = "hiReadsProcessor")
read.sampleInfo(file.path(runData, "sampleInfo.xlsx"))

Description

Given a sequencing folder path, sample information file path, and sequence file extension pattern, the function returns a list of variables required to process the data. The function also calls read.sampleInfo which reads in sample processing metadata and formats it if needed.

Usage

read.SeqFolder(
  sequencingFolderPath = NULL,
  sampleInfoFilePath = NULL,
  seqfilePattern = NULL
)

Arguments

Value

a SimpleList list which is used by other functions to process and decode the data.

Note

• One must make sure that each sequencing file has sector name/number prefixed at the beginning, else findBarcodes will fail trying to find the filename.

• For paired end Illumina runs, make sure the filenames include R1, R2, and I1 somewhere in the name denoting pair1, pair2, and index/barcode reads, respectively.

See Also

read.sampleInfo, findBarcodes, splitByBarcode

Examples

runData <- system.file("extdata/FLX_sample_run/", 
package = "hiReadsProcessor")
read.SeqFolder(runData, seqfilePattern=".+fna.gz$")
## Not run: 
read.SeqFolder(".", seqfilePattern = "\\.TCA.454Reads.fna$")
read.SeqFolder(".", seqfilePattern = ".+fastq$")

## End(Not run)

Description

Given a sequence reads file path, the function returns a DNAStringSet object.

Usage

read.seqsFromSector(seqFilePath = NULL, sector = 1, isPaired = FALSE)

Arguments

Value

if isPaired is FALSE, then a DNAStringSet object, else a list of DNAStringSet objects of three elements corresponding to reads from "barcode", "pair1", and "pair2". Note: "pair2" is reverse complemented!

See Also

findBarcodes, read.SeqFolder, extractSeqs

Examples

## Not run: 
load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
read.seqsFromSector(seqProps, sector="2")

## End(Not run)

Description

Given a DNAStringSet object, the function removes any reads that has either repeating or total Ns which is greater than to maxNs threshold

Usage

removeReadsWithNs(dnaSet, maxNs = 5, consecutive = TRUE)

Arguments

Value

DNAStringSet object.

See Also

dereplicateReads, replicateReads, findBarcodes, splitByBarcode

Examples

dnaSet <- c("CCTGAATCCTNNCAATGTCATCATC", "ATCCTGGCNATGTCATCATCAATGG", 
"ATCAGTTGTCAACGGCTAATACGCG", "ATCAATGGCGATTGCCGCGTCTGCA", 
"CCGNNTCTGCAATGTGNGGNCCTAN", "GAAGNNNNNNGTTGAAGTTCACAC") 
removeReadsWithNs(dnaSet)
removeReadsWithNs(dnaSet, maxNs = 4, consecutive = FALSE)

Description

Given a DNAStringSet object, the function replicates reads using counts=X marker at the end of definition line.

Usage

replicateReads(dnaSet, counts = NULL)

Arguments

Value

DNAStringSet object.

See Also

dereplicateReads, removeReadsWithNs, findBarcodes, splitByBarcode

Examples

dnaSet <- c("CCTGAATCCTGGCAATGTCATCATC", "ATCCTGGCAATGTCATCATCAATGG", 
"ATCAGTTGTCAACGGCTAATACGCG", "ATCAATGGCGATTGCCGCGTCTGCA", 
"CCGCGTCTGCAATGTGAGGGCCTAA", "GAAGGATGCCAGTTGAAGTTCACAC", 
"CCTGAATCCTGGCAATGTCATCATC", "ATCCTGGCAATGTCATCATCAATGG", 
"ATCAGTTGTCAACGGCTAATACGCG", "ATCAATGGCGATTGCCGCGTCTGCA", 
"CCGCGTCTGCAATGTGAGGGCCTAA", "GAAGGATGCCAGTTGAAGTTCACAC") 
dnaSet <- dereplicateReads(dnaSet)
replicateReads(dnaSet)

Description

Give a simple summary of major attributes in sampleInfo/SimpleList object.

Usage

sampleSummary(object, ...)

Arguments

Value

a dataframe summarizing counts of major attributes per sample and sector.

Examples

data(FLX_seqProps)
sampleSummary(seqProps)

Description

This is a processed data object containing raw sequences and respective alignments to UCSC freeze hg18 from 112 integration site samples. The object is of SimpleList class and follows a certain structural hierarchy explained by the Introductory vignette.

Format

A SimpleList object

Description

Given a character vector of barcodes/MID to sample association and a DNAStringSet object, the function splits/demultiplexes the DNAStringSet object by first few bases dictated by length of barcodes/MID supplied. This is an accessory function used by findBarcodes

Usage

splitByBarcode(
  barcodesSample,
  dnaSet,
  trimFrom = NULL,
  showStats = FALSE,
  returnUnmatched = FALSE
)

Arguments

Value

DNAStringSet object split by sample name found in barcodesSample.

See Also

findBarcodes, dereplicateReads, replicateReads

Examples

dnaSet <- DNAStringSet(c("read1" = "ACATCCATAGAGCTACGACGACATCGACATA",
"read2"="GAATGGATGACGACTACAGCACGACGAGCAGCTACT",
"read3"="GAATGGATGCGCTAAGAAGAGA", "read4"="ACATCCATTCTACACATCT"))
splitByBarcode(c("ACATCCAT" = "Sample1", "GAATGGAT" = "Sample2"), dnaSet,
showStats=TRUE)

Description

Given a vector of sequences or DNAStringSet or a FASTA filename, the function splits it into smaller pieces as denoted by totalFiles parameter.

Usage

splitSeqsToFiles(
  x,
  totalFiles = 4,
  suffix = "tempy",
  filename = "queryFile.fa",
  outDir = getwd()
)

Arguments

Value

a vector of filename names created.

See Also

blatSeqs

Examples

seqs <- DNAStringSet(sapply(sample(c(100:1000), 500),
function(size) paste(sample(DNA_BASES, size, replace = TRUE), collapse = ""))) 
splitSeqsToFiles(seqs, 5, "tempyQ", "myDNAseqs.fa", tempdir())

Description

Start or Stop a gfServer with indexed reference genome to align batch of sequences using BLAT gfServer/gfClient protocol.

Usage

startgfServer(
  seqDir = NULL,
  host = "localhost",
  port = 5560,
  gfServerOpts = c(repMatch = 112312, stepSize = 5, tileSize = 10, maxDnaHits = 10)
)

stopgfServer(host = "localhost", port = NULL)

Arguments

Value

system command status for executing gfServer command.

See Also

stopgfServer, read.psl, blatSeqs, read.blast8

Examples

#startgfServer(seqDir="/usr/local/blatSuite34/hg18.2bit",port=5560)
#stopgfServer(port=5560)

Description

This function trims a DNAStringSet object using the ranges from left, right, or middle of the sequence. This is a helper function utilized in primerIDAlignSeqs and extractSeqs. If dnaSet and coords are not the same length, then they are required to have a names attribute to perform the matched trimming.

Usage

trimSeqs(dnaSet, coords, side = "middle", offBy = 0)

Arguments

Value

a DNAStringSet object with trimmed sequences.

Note

If side is left, then any sequence following end of coords+offBy is returned. If side is right, then sequence preceding start of coords-offBy is returned. If side is middle, then sequence contained in coords is returned where offBy is added to start and subtracted from end in coords.

See Also

extractSeqs, primerIDAlignSeqs

Examples

dnaSet <- DNAStringSet(c("AAAAAAAAAACCTGAATCCTGGCAATGTCATCATC", 
"AAAAAAAAAAATCCTGGCAATGTCATCATCAATGG", "AAAAAAAAAAATCAGTTGTCAACGGCTAATACGCG",
"AAAAAAAAAAATCAATGGCGATTGCCGCGTCTGCA", "AAAAAAAAAACCGCGTCTGCAATGTGAGGGCCTAA",
"AAAAAAAAAAGAAGGATGCCAGTTGAAGTTCACAC"))
coords <- IRanges(start=1, width=rep(10,6))
trimSeqs(dnaSet, coords, side="left", offBy=1)
trimSeqs(dnaSet, coords, side="middle")

Description

Given a SampleInfo object, the function compares LTRed sequences from each sample per sector to all the linker sequences present in the run. The output is a summary table of counts of good matches to all the linkers per sample.

Usage

troubleshootLinkers(
  sampleInfo,
  qualityThreshold = 0.55,
  qualityThreshold1 = 0.75,
  qualityThreshold2 = 0.5,
  doRC = TRUE,
  parallel = TRUE,
  samplenames = NULL,
  ...
)

Arguments

Value

a dataframe of counts.

Note

If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

pairwiseAlignSeqs, vpairwiseAlignSeqs, primerIDAlignSeqs, findLTRs, findPrimers, findAndTrimSeq

Description

Align a fixed length short pattern sequence to subject sequences using vmatchPattern. This function is meant for aligning a short pattern onto large collection of subjects. If you are looking to align a vector sequence to subjects, then please use BLAT.

Usage

vpairwiseAlignSeqs(
  subjectSeqs = NULL,
  patternSeq = NULL,
  side = "left",
  qualityThreshold = 1,
  showStats = FALSE,
  bufferBases = 5,
  doRC = TRUE,
  parallel = FALSE,
  ...
)

Arguments

Value

IRanges object with starts, stops, and names of the aligned sequences.

Note

• For qualityThreshold, the alignment score is calculated by (matches*2)-(mismatches+gaps) which programatically translates to round(nchar(patternSeq)*qualityThreshold)*2.

• No indels are allowed in the function, if expecting indels then use pairwiseAlignSeqs.

• If qualityThreshold is 1, then it is a full match, if 0, then any match is accepted which is useful in searching linker sequences at 3' end. Beware, this function only searches for the pattern sequence in one orientation. If you are expecting to find the pattern in both orientation, you might be better off using BLAST/BLAT!

• If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

pairwiseAlignSeqs, primerIDAlignSeqs, doRCtest, findAndTrimSeq, blatSeqs, findAndRemoveVector

Examples

subjectSeqs <- c("CCTGAATCCTGGCAATGTCATCATC", "ATCCTGGCAATGTCATCATCAATGG", 
"ATCAGTTGTCAACGGCTAATACGCG", "ATCAATGGCGATTGCCGCGTCTGCA", 
"CCGCGTCTGCAATGTGAGGGCCTAA", "GAAGGATGCCAGTTGAAGTTCACAC")
subjectSeqs <- DNAStringSet(xscat("AAAAAAAAAA", subjectSeqs))
vpairwiseAlignSeqs(subjectSeqs, "AAAAAAAAAA", showStats=TRUE)
vpairwiseAlignSeqs(subjectSeqs, "AAAAAAAAAA", showStats=TRUE,
qualityThreshold=0.5)

Description

Given a listed DNAStringSet object return from extractSeqs, the function writes a fasta file for each sample as defined in filePath parameter.

Usage

write.listedDNAStringSet(
  dnaSet,
  filePath = ".",
  filePrefix = "processed",
  prependSamplenames = TRUE,
  format = "fasta",
  parallel = FALSE
)

Arguments

Note

• Writing of the files is done using writeXStringSet with parameter append=TRUE. This is to aggregate reads from a sample which might be present in more than one sector.

• If data is paired end, then each pair will be written separately with designations in the filename as well as in the definition line as (at)pairX(at) appended at the end.

• If parallel=TRUE, then be sure to have a parallel backend registered before running the function. One can use any of the following MulticoreParam SnowParam

See Also

findBarcodes, read.SeqFolder, extractSeqs, addListNameToReads

Examples

## Not run: 
load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
samples <- c('Roth-MLV3p-CD4TMLVWell6-Tsp509I', 
'Roth-MLV3p-CD4TMLVWell6-MseI', 'Roth-MLV3p-CD4TMLVwell5-MuA')
seqs <- extractSeqs(seqProps, sector='2', samplename=samples, feature="primed")
write.listedDNAStringSet(seqs)

## End(Not run)

Description

Given a data frame or GRanges object, the function write a tab deliminated PSL file

Usage

write.psl(x, filename = "out.psl", header = FALSE, includeOtherCols = FALSE)

Arguments

Value

name of the output PSL file

See Also

read.psl, blatSeqs, read.blast8, read.BAMasPSL, pslToRangedObject

Examples

data(psl)
pslFile <- tempfile()
write.psl(psl, filename = pslFile)