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 in here automate trimming of adaptors, removing any vector sequences, and identifying host genomic location.
The basic philosophy of this package is to detect various 'bits' of information in a sequencing read like barcodes, primers, linkers, etc and store it within a nested list in a space efficient manner. No information is duplicated once read into the object and the supplied utility functions enable addition & extraction of needed features on the fly.
Here is the workflow in a nutshell followed by few simple steps to get you started.
Please refer to following publications from Bushman Lab to obtain more information on sample/amplicon preparation
First load this package and the parallel backend of choice from BiocParallel library. Although BiocParallel is imported internally and will invoke multicore like functionality, you may have to load it to invoke snow like functionality.
library(hiReadsProcessor)
## added to avoid package build failure on windows machines ##
if(.Platform$OS.type == "windows") { register(SerialParam()) }
The package comes with an example 454 sequencing run: FLX_sample_run. In rest of this tutorial we will use this dataset to introduce functionality of this package.
A typical sequencing run will have several filetypes but among the most important files are the fasta/fastq which are created per sector/quadrant/lane. In the example dataset we have compressed fasta files for three quadrants, an excel spreadsheet holding sample metadata along with information/parameters to process that sample and vector fasta files to be trimmed if needed.
runData <- system.file("extdata/FLX_sample_run/", package = "hiReadsProcessor")
list.files(runData, recursive = TRUE)
## [1] "RunData/1.TCA.454Reads.fna" "RunData/1.TCA.454Reads.fna.gz"
## [3] "RunData/2.TCA.454Reads.fna" "RunData/2.TCA.454Reads.fna.gz"
## [5] "RunData/3.TCA.454Reads.fna" "RunData/3.TCA.454Reads.fna.gz"
## [7] "Vectors/HIV1.fa" "Vectors/MLV-vector.fa"
## [9] "sampleInfo.xls"
Function read.SeqFolder will initiate a SimpleList sample information object which will hold everything regarding the sequencing run. The object is structured to store sequencing file paths, sequence data, processed data as well as sample metadata. The function finds the required file needed to ease the automation process. It is important that somewhere in the sequencing folder there is a file called “sampleInfo” else object initialization will fail.
seqProps <- read.SeqFolder(runData, seqfilePattern=".+fna.gz$")
## Choosing /tmp/RtmpLWtU8T/Rinst778125aefde2/hiReadsProcessor/extdata/FLX_sample_run/sampleInfo.xls as sample information file.
seqProps
## List of length 6
## names(6): sequencingFolderPath seqFilePaths ... sectors callHistory
On successfully initializing the sample information object you will see that there is a hierarchy within the object. The root or top most level holds the information regarding the sequence folder. Each sample within a quadrant/lane is held within “sectors” list. Within the sectors list, there is a list of samples and the associated metadata which gets modified and appended to as the reads get trimmed and aligned.
seqProps$sectors$"1"$samples
## List of length 54
## names(54): Roth-MLV-CD4T-20100723NCMu ... Roth-MLV3p-CD4TMLVwell6-MuA
The first step of processing most sequencing run is to demultiplex reads by barcodes/MIDs. Function findBarcodes automates the demultiplexing process based on already stored data from the sample information file. Please see the documentation for read.sampleInfo for the kinds of parameters and information held in a sample information file. An example file is supplied within the FLX_sample_run dataset.
seqProps <- findBarcodes(seqProps, sector="all", showStats=TRUE)
## Decoding sector: 1
## Reading:
## /tmp/RtmpLWtU8T/Rinst778125aefde2/hiReadsProcessor/extdata/FLX_sample_run/RunData/1.TCA.454Reads.fna.gz
## Using following schema for barcode to sample associations
## barcodesSample
## TGCATCGA Roth-MLV-CD4T-20100723NCMu
## TCAGTCAG Roth-MLV-CD4T-20100723well1BMu
## ACGTACGA Roth-MLV-CD4T-20100723well2BMu1-
## CTCAGACA Roth-MLV-CD4T-20100723well2BMu10
## CTGAGTCA Roth-MLV-CD4T-20100723well2BMu11
## TCACAGAC Roth-MLV-CD4T-20100723well2BMu12
## ACTCGACA Roth-MLV-CD4T-20100723well2BMu2
## AGACAGTG Roth-MLV-CD4T-20100723well2BMu3
## CACAGACG Roth-MLV-CD4T-20100723well2BMu4
## CACTGTGA Roth-MLV-CD4T-20100723well2BMu5
## CATCTCGA Roth-MLV-CD4T-20100723well2BMu6
## CATGACGA Roth-MLV-CD4T-20100723well2BMu7
## CGATCGTA Roth-MLV-CD4T-20100723well2BMu8
## CGTAGCTA Roth-MLV-CD4T-20100723well2BMu9
## TCAGTCTC Roth-MLV-CD4T-20100723well2Mu1
## TCGTAGCA Roth-MLV-CD4T-20100723well2Mu2
## TCGTCATC Roth-MLV-CD4T-20100723well2Mu3
## TCTCACAC Roth-MLV-CD4T-20100723well2Mu4
## TGACAGTC Roth-MLV-CD4T-20100723well2Mu5
## TGCAGTAC Roth-MLV-CD4T-20100723well2Mu6
## TCACGTGA Roth-MLV3p-CD4T-20100730NC
## TGCTGATG Roth-MLV3p-CD4T-20100730Well1BstYI
## CGTGCGAC Roth-MLV3p-CD4T-20100730Well1MseI
## CAGCTGTA Roth-MLV3p-CD4T-20100730Well1NlaIII
## CAGTCTCA Roth-MLV3p-CD4T-20100730Well1Tsp509I
## AGCTCATG Roth-MLV3p-CD4T-20100730Well2BstyI
## ACACTGAC Roth-MLV3p-CD4T-20100730Well2MseI
## ACTGAGTC Roth-MLV3p-CD4T-20100730Well2NlaIII
## TCGATCGA Roth-MLV3p-CD4T-20100730Well2Tsp509I
## AGCACTAC Roth-MLV3p-CD4TMLVLot16-Mu
## TCTCAGTC Roth-MLV3p-CD4TMLVWell3-MseI
## TCTCGTCA Roth-MLV3p-CD4TMLVWell3-NlaIII
## TCGAGTAC Roth-MLV3p-CD4TMLVWell3-Tsp509I
## TGTGCTGA Roth-MLV3p-CD4TMLVWell3Harri-Mu
## ACACACTG Roth-MLV3p-CD4TMLVWell3Lot60-Mu
## ACAGTGTC Roth-MLV3p-CD4TMLVWell3Lot62-Mu
## ACGATGCT Roth-MLV3p-CD4TMLVWell3Lot64-Mu
## ACGTCATG Roth-MLV3p-CD4TMLVWell3Lot64new-Mu
## TGACTGTG Roth-MLV3p-CD4TMLVWell4-BstYI
## GCTATACA Roth-MLV3p-CD4TMLVWell4-MseI
## GAGCATGA Roth-MLV3p-CD4TMLVWell4-NlaIII
## GCATGCTA Roth-MLV3p-CD4TMLVWell4-Tsp509I
## ACTGATAC Roth-MLV3p-CD4TMLVWell5-BstYI
## GTCTGAGC Roth-MLV3p-CD4TMLVWell5-MseI
## AGCTCTGC Roth-MLV3p-CD4TMLVWell5-NlaIII
## ATACTCTC Roth-MLV3p-CD4TMLVWell5-Tsp509I
## ACTGACAC Roth-MLV3p-CD4TMLVWell6-BstYI
## TGACGTCA Roth-MLV3p-CD4TMLVWell6-MseI
## CAGTCACG Roth-MLV3p-CD4TMLVWell6-NlaIII
## TCGAGCAT Roth-MLV3p-CD4TMLVWell6-Tsp509I
## AGCTGTAC Roth-MLV3p-CD4TMLVwell3-BstYI
## TCGAGACT Roth-MLV3p-CD4TMLVwell4-MuA
## TCGACTGA Roth-MLV3p-CD4TMLVwell5-MuA
## ACAGCAGA Roth-MLV3p-CD4TMLVwell6-MuA
## Number of Sequences with no matching barcode: 196
## Number of Sequences decoded:
## sampleNames Freq
## 1 Roth-MLV-CD4T-20100723well1BMu 219
## 2 Roth-MLV-CD4T-20100723well2BMu1- 37
## 3 Roth-MLV-CD4T-20100723well2BMu10 12
## 4 Roth-MLV-CD4T-20100723well2BMu11 40
## 5 Roth-MLV-CD4T-20100723well2BMu12 9
## 6 Roth-MLV-CD4T-20100723well2BMu2 50
## 7 Roth-MLV-CD4T-20100723well2BMu3 88
## 8 Roth-MLV-CD4T-20100723well2BMu4 85
## 9 Roth-MLV-CD4T-20100723well2BMu5 86
## 10 Roth-MLV-CD4T-20100723well2BMu6 85
## 11 Roth-MLV-CD4T-20100723well2BMu7 47
## 12 Roth-MLV-CD4T-20100723well2BMu8 26
## 13 Roth-MLV-CD4T-20100723well2BMu9 28
## 14 Roth-MLV-CD4T-20100723well2Mu1 57
## 15 Roth-MLV-CD4T-20100723well2Mu2 65
## 16 Roth-MLV-CD4T-20100723well2Mu3 31
## 17 Roth-MLV-CD4T-20100723well2Mu4 22
## 18 Roth-MLV-CD4T-20100723well2Mu5 6
## 19 Roth-MLV-CD4T-20100723well2Mu6 2
## 20 Roth-MLV3p-CD4T-20100730Well1BstYI 139
## 21 Roth-MLV3p-CD4T-20100730Well1MseI 240
## 22 Roth-MLV3p-CD4T-20100730Well1NlaIII 251
## 23 Roth-MLV3p-CD4T-20100730Well1Tsp509I 313
## 24 Roth-MLV3p-CD4T-20100730Well2BstyI 324
## 25 Roth-MLV3p-CD4T-20100730Well2MseI 475
## 26 Roth-MLV3p-CD4T-20100730Well2NlaIII 371
## 27 Roth-MLV3p-CD4T-20100730Well2Tsp509I 437
## 28 Roth-MLV3p-CD4TMLVLot16-Mu 172
## 29 Roth-MLV3p-CD4TMLVWell3-MseI 468
## 30 Roth-MLV3p-CD4TMLVWell3-NlaIII 301
## 31 Roth-MLV3p-CD4TMLVWell3-Tsp509I 487
## 32 Roth-MLV3p-CD4TMLVWell3Harri-Mu 62
## 33 Roth-MLV3p-CD4TMLVWell3Lot60-Mu 36
## 34 Roth-MLV3p-CD4TMLVWell3Lot62-Mu 171
## 35 Roth-MLV3p-CD4TMLVWell3Lot64-Mu 109
## 36 Roth-MLV3p-CD4TMLVWell3Lot64new-Mu 14
## 37 Roth-MLV3p-CD4TMLVWell4-BstYI 116
## 38 Roth-MLV3p-CD4TMLVWell4-MseI 395
## 39 Roth-MLV3p-CD4TMLVWell4-NlaIII 287
## 40 Roth-MLV3p-CD4TMLVWell4-Tsp509I 318
## 41 Roth-MLV3p-CD4TMLVWell5-BstYI 148
## 42 Roth-MLV3p-CD4TMLVWell5-MseI 284
## 43 Roth-MLV3p-CD4TMLVWell5-NlaIII 286
## 44 Roth-MLV3p-CD4TMLVWell5-Tsp509I 542
## 45 Roth-MLV3p-CD4TMLVWell6-BstYI 195
## 46 Roth-MLV3p-CD4TMLVWell6-MseI 378
## 47 Roth-MLV3p-CD4TMLVWell6-NlaIII 261
## 48 Roth-MLV3p-CD4TMLVWell6-Tsp509I 490
## 49 Roth-MLV3p-CD4TMLVwell3-BstYI 211
## 50 Roth-MLV3p-CD4TMLVwell4-MuA 138
## 51 Roth-MLV3p-CD4TMLVwell5-MuA 235
## 52 Roth-MLV3p-CD4TMLVwell6-MuA 155
## Decoding sector: 2
## Reading:
## /tmp/RtmpLWtU8T/Rinst778125aefde2/hiReadsProcessor/extdata/FLX_sample_run/RunData/2.TCA.454Reads.fna.gz
## Using following schema for barcode to sample associations
## barcodesSample
## TGCATCGA Roth-MLV-CD4T-20100723NCMu
## TCAGTCAG Roth-MLV-CD4T-20100723well1BMu
## ACGTACGA Roth-MLV-CD4T-20100723well2BMu1-
## CTCAGACA Roth-MLV-CD4T-20100723well2BMu10
## CTGAGTCA Roth-MLV-CD4T-20100723well2BMu11
## TCACAGAC Roth-MLV-CD4T-20100723well2BMu12
## ACTCGACA Roth-MLV-CD4T-20100723well2BMu2
## AGACAGTG Roth-MLV-CD4T-20100723well2BMu3
## CACAGACG Roth-MLV-CD4T-20100723well2BMu4
## CACTGTGA Roth-MLV-CD4T-20100723well2BMu5
## CATCTCGA Roth-MLV-CD4T-20100723well2BMu6
## CATGACGA Roth-MLV-CD4T-20100723well2BMu7
## CGATCGTA Roth-MLV-CD4T-20100723well2BMu8
## CGTAGCTA Roth-MLV-CD4T-20100723well2BMu9
## TCAGTCTC Roth-MLV-CD4T-20100723well2Mu1
## TCGTAGCA Roth-MLV-CD4T-20100723well2Mu2
## TCGTCATC Roth-MLV-CD4T-20100723well2Mu3
## TCTCACAC Roth-MLV-CD4T-20100723well2Mu4
## TGACAGTC Roth-MLV-CD4T-20100723well2Mu5
## TGCAGTAC Roth-MLV-CD4T-20100723well2Mu6
## TCACGTGA Roth-MLV3p-CD4T-20100730NC
## TGCTGATG Roth-MLV3p-CD4T-20100730Well1BstYI
## CGTGCGAC Roth-MLV3p-CD4T-20100730Well1MseI
## CAGCTGTA Roth-MLV3p-CD4T-20100730Well1NlaIII
## CAGTCTCA Roth-MLV3p-CD4T-20100730Well1Tsp509I
## AGCTCATG Roth-MLV3p-CD4T-20100730Well2BstyI
## ACACTGAC Roth-MLV3p-CD4T-20100730Well2MseI
## ACTGAGTC Roth-MLV3p-CD4T-20100730Well2NlaIII
## TCGATCGA Roth-MLV3p-CD4T-20100730Well2Tsp509I
## AGCACTAC Roth-MLV3p-CD4TMLVLot16-Mu
## TCTCAGTC Roth-MLV3p-CD4TMLVWell3-MseI
## TCTCGTCA Roth-MLV3p-CD4TMLVWell3-NlaIII
## TCGAGTAC Roth-MLV3p-CD4TMLVWell3-Tsp509I
## TGTGCTGA Roth-MLV3p-CD4TMLVWell3Harri-Mu
## ACACACTG Roth-MLV3p-CD4TMLVWell3Lot60-Mu
## ACAGTGTC Roth-MLV3p-CD4TMLVWell3Lot62-Mu
## ACGATGCT Roth-MLV3p-CD4TMLVWell3Lot64-Mu
## ACGTCATG Roth-MLV3p-CD4TMLVWell3Lot64new-Mu
## TGACTGTG Roth-MLV3p-CD4TMLVWell4-BstYI
## GCTATACA Roth-MLV3p-CD4TMLVWell4-MseI
## GAGCATGA Roth-MLV3p-CD4TMLVWell4-NlaIII
## GCATGCTA Roth-MLV3p-CD4TMLVWell4-Tsp509I
## ACTGATAC Roth-MLV3p-CD4TMLVWell5-BstYI
## GTCTGAGC Roth-MLV3p-CD4TMLVWell5-MseI
## AGCTCTGC Roth-MLV3p-CD4TMLVWell5-NlaIII
## ATACTCTC Roth-MLV3p-CD4TMLVWell5-Tsp509I
## ACTGACAC Roth-MLV3p-CD4TMLVWell6-BstYI
## TGACGTCA Roth-MLV3p-CD4TMLVWell6-MseI
## CAGTCACG Roth-MLV3p-CD4TMLVWell6-NlaIII
## TCGAGCAT Roth-MLV3p-CD4TMLVWell6-Tsp509I
## AGCTGTAC Roth-MLV3p-CD4TMLVwell3-BstYI
## TCGAGACT Roth-MLV3p-CD4TMLVwell4-MuA
## TCGACTGA Roth-MLV3p-CD4TMLVwell5-MuA
## ACAGCAGA Roth-MLV3p-CD4TMLVwell6-MuA
## Number of Sequences with no matching barcode: 214
## Number of Sequences decoded:
## sampleNames Freq
## 1 Roth-MLV-CD4T-20100723well1BMu 215
## 2 Roth-MLV-CD4T-20100723well2BMu1- 27
## 3 Roth-MLV-CD4T-20100723well2BMu10 4
## 4 Roth-MLV-CD4T-20100723well2BMu11 41
## 5 Roth-MLV-CD4T-20100723well2BMu12 7
## 6 Roth-MLV-CD4T-20100723well2BMu2 41
## 7 Roth-MLV-CD4T-20100723well2BMu3 78
## 8 Roth-MLV-CD4T-20100723well2BMu4 76
## 9 Roth-MLV-CD4T-20100723well2BMu5 74
## 10 Roth-MLV-CD4T-20100723well2BMu6 80
## 11 Roth-MLV-CD4T-20100723well2BMu7 47
## 12 Roth-MLV-CD4T-20100723well2BMu8 15
## 13 Roth-MLV-CD4T-20100723well2BMu9 32
## 14 Roth-MLV-CD4T-20100723well2Mu1 50
## 15 Roth-MLV-CD4T-20100723well2Mu2 68
## 16 Roth-MLV-CD4T-20100723well2Mu3 21
## 17 Roth-MLV-CD4T-20100723well2Mu4 19
## 18 Roth-MLV-CD4T-20100723well2Mu5 4
## 19 Roth-MLV-CD4T-20100723well2Mu6 2
## 20 Roth-MLV3p-CD4T-20100730Well1BstYI 151
## 21 Roth-MLV3p-CD4T-20100730Well1MseI 223
## 22 Roth-MLV3p-CD4T-20100730Well1NlaIII 253
## 23 Roth-MLV3p-CD4T-20100730Well1Tsp509I 315
## 24 Roth-MLV3p-CD4T-20100730Well2BstyI 375
## 25 Roth-MLV3p-CD4T-20100730Well2MseI 428
## 26 Roth-MLV3p-CD4T-20100730Well2NlaIII 370
## 27 Roth-MLV3p-CD4T-20100730Well2Tsp509I 456
## 28 Roth-MLV3p-CD4TMLVLot16-Mu 153
## 29 Roth-MLV3p-CD4TMLVWell3-MseI 487
## 30 Roth-MLV3p-CD4TMLVWell3-NlaIII 311
## 31 Roth-MLV3p-CD4TMLVWell3-Tsp509I 469
## 32 Roth-MLV3p-CD4TMLVWell3Harri-Mu 54
## 33 Roth-MLV3p-CD4TMLVWell3Lot60-Mu 54
## 34 Roth-MLV3p-CD4TMLVWell3Lot62-Mu 165
## 35 Roth-MLV3p-CD4TMLVWell3Lot64-Mu 116
## 36 Roth-MLV3p-CD4TMLVWell3Lot64new-Mu 9
## 37 Roth-MLV3p-CD4TMLVWell4-BstYI 111
## 38 Roth-MLV3p-CD4TMLVWell4-MseI 400
## 39 Roth-MLV3p-CD4TMLVWell4-NlaIII 286
## 40 Roth-MLV3p-CD4TMLVWell4-Tsp509I 338
## 41 Roth-MLV3p-CD4TMLVWell5-BstYI 170
## 42 Roth-MLV3p-CD4TMLVWell5-MseI 319
## 43 Roth-MLV3p-CD4TMLVWell5-NlaIII 283
## 44 Roth-MLV3p-CD4TMLVWell5-Tsp509I 608
## 45 Roth-MLV3p-CD4TMLVWell6-BstYI 205
## 46 Roth-MLV3p-CD4TMLVWell6-MseI 406
## 47 Roth-MLV3p-CD4TMLVWell6-NlaIII 247
## 48 Roth-MLV3p-CD4TMLVWell6-Tsp509I 445
## 49 Roth-MLV3p-CD4TMLVwell3-BstYI 179
## 50 Roth-MLV3p-CD4TMLVwell4-MuA 125
## 51 Roth-MLV3p-CD4TMLVwell5-MuA 250
## 52 Roth-MLV3p-CD4TMLVwell6-MuA 124
## Decoding sector: 3
## Reading:
## /tmp/RtmpLWtU8T/Rinst778125aefde2/hiReadsProcessor/extdata/FLX_sample_run/RunData/3.TCA.454Reads.fna.gz
## Using following schema for barcode to sample associations
## barcodesSample
## CCGGAATT Ocwieja-HIV896-CD4TND365-InfectionI
## GATCGACT Ocwieja-HIV896-CD4TND365-InfectionII
## TCGTACAG Ocwieja-HIV896-CD4TND365-InfectionIII
## TATAGCGC Ocwieja-HIV896-CD4TND365-NoVirus
## Number of Sequences with no matching barcode: 140
## Number of Sequences decoded:
## sampleNames Freq
## 1 Ocwieja-HIV896-CD4TND365-InfectionI 3879
## 2 Ocwieja-HIV896-CD4TND365-InfectionII 3111
## 3 Ocwieja-HIV896-CD4TND365-InfectionIII 2869
## 4 Ocwieja-HIV896-CD4TND365-NoVirus 1
seqProps
## List of length 6
## names(6): sequencingFolderPath seqFilePaths ... sectors callHistory
seqProps$sectors
## List of length 3
## names(3): 1 2 3
Following the barcode sequence is the 5' viral LTR primer. Function findPrimers facilities the trimming of respective primers(primerltrsequence) for each sample. Minimum threshold for detecting the primer can be adjusted using primerLTRidentity within the sample information file.
Since it may take a while to process this kind of data, the package is equipped with processed data object which makes things easier for the tutorial. The code chunks below are not evaluated but rather references the loaded seqProps object.
load(file.path(system.file("data", package = "hiReadsProcessor"),
"FLX_seqProps.RData"))
seqProps <- findPrimers(seqProps, showStats=TRUE)
If LM-PCR products were designed to include the viral LTR (ltrBitSequence) following the primer landing site, then findLTRs confirms authenticity of the integrated virus. Absence of LTR part denotes nongenuine integration! Minimum threshold for detecting the LTR bit can be adjusted using ltrBitIdentity.
seqProps <- findLTRs(seqProps, showStats=TRUE)
If the vectorFile parameter is defined within the sample information file, function findVector tags any reads which matches the given vector file. These reads are discarded during the genomic alignment step which is covered later.
seqProps <- findVector(seqProps, showStats=TRUE)
Linker adaptors are found on the 3' end of sequences. Depending on an experiment the linkerSequence can be same or different per sample. Furthermore, some linker adaptors are designed to have primerID which can help quantify pre-PCR products. Function findLinkers makes it easy to process various samples with different linker sequences and type. If primerID technology is utilized, enabling parameter primerIdInLinker within the sample information file automates the extract of the random part within the adaptor. Thresholds for linker detection can be controlled by setting following parameters within the sample information file: linkerIdentity, primerIdInLinker, primerIdInLinkerIdentity1, primerIdInLinkerIdentity2
seqProps <- findLinkers(seqProps, showStats=TRUE, doRC=TRUE)
Once all the non-genomic parts have been detected, it is time to find the actual integration sites. Function findIntegrations makes this a breeze given that BLAT and indexed genome files are provided/in-place.
seqProps <- findIntegrations(seqProps,
genomeIndices=c("hg18"="/usr/local/genomeIndexes/hg18.noRandom.2bit"), numServers=2)
Function sampleSummary quantifies 7 basic features of this package:
sampleSummary(seqProps)
## Total sectors:1,2,3
## Warning in rbind_all(x, .id): Unequal factor levels: coercing to character
## Source: local data frame [112 x 9]
##
## Sector SampleName decoded primed LTRed vectored
## (chr) (chr) (int) (int) (int) (int)
## 1 1 Roth-MLV-CD4T-20100723NCMu NA NA NA NA
## 2 1 Roth-MLV-CD4T-20100723well1BMu 18 18 6 1
## 3 1 Roth-MLV-CD4T-20100723well2BMu1- 12 11 NA NA
## 4 1 Roth-MLV-CD4T-20100723well2BMu10 1 1 1 NA
## 5 1 Roth-MLV-CD4T-20100723well2BMu11 6 4 3 NA
## 6 1 Roth-MLV-CD4T-20100723well2BMu12 1 1 NA NA
## 7 1 Roth-MLV-CD4T-20100723well2BMu2 3 3 1 NA
## 8 1 Roth-MLV-CD4T-20100723well2BMu3 8 8 NA NA
## 9 1 Roth-MLV-CD4T-20100723well2BMu4 10 10 4 NA
## 10 1 Roth-MLV-CD4T-20100723well2BMu5 8 8 3 NA
## .. ... ... ... ... ... ...
## Variables not shown: linkered (int), psl (int), sites (int)
Before diving into functions offered by this package, lets first understand the underlying data object holding all the data. For example purposes we will refer to this as the “sampleInfo” object (although it's essentially a SimpleList object).
The figure below outlines the hierarchy of data storage within the sampleInfo object.
** THE SECTIONS BELOW ARE IN WORKS **
sessionInfo()
## R version 3.3.0 (2016-05-03)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.4 LTS
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
## [9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] hiReadsProcessor_1.8.2 hiAnnotator_1.6.2
## [3] BiocParallel_1.6.2 xlsx_0.5.7
## [5] xlsxjars_0.6.1 rJava_0.9-8
## [7] GenomicAlignments_1.8.0 Rsamtools_1.24.0
## [9] SummarizedExperiment_1.2.2 Biobase_2.32.0
## [11] GenomicRanges_1.24.0 GenomeInfoDb_1.8.2
## [13] Biostrings_2.40.0 XVector_0.12.0
## [15] IRanges_2.6.0 S4Vectors_0.10.0
## [17] BiocGenerics_0.18.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.5 RColorBrewer_1.1-2 formatR_1.4
## [4] plyr_1.8.3 bitops_1.0-6 iterators_1.0.8
## [7] tools_3.3.0 zlibbioc_1.18.0 lattice_0.20-33
## [10] evaluate_0.9 gtable_0.2.0 BSgenome_1.40.0
## [13] foreach_1.4.3 DBI_0.4-1 hwriter_1.3.2
## [16] rSFFreader_0.20.0 rtracklayer_1.32.0 stringr_1.0.0
## [19] dplyr_0.4.3 knitr_1.13 grid_3.3.0
## [22] R6_2.1.2 XML_3.98-1.4 latticeExtra_0.6-28
## [25] ggplot2_2.1.0 magrittr_1.5 splines_3.3.0
## [28] scales_0.4.0 codetools_0.2-14 ShortRead_1.30.0
## [31] assertthat_0.1 colorspace_1.2-6 sonicLength_1.4.4
## [34] stringi_1.0-1 RCurl_1.95-4.8 munsell_0.4.3