Using the sangerseqR package
29 October 2024
1 Introduction
The sangerseqR package provides basic functions for importing and
working with sanger sequencing data files. It currently functions with Scf and
ABIF files. The Scf file specification is an open source, although somewhat
limited, data file type. Several tools designed to view and or edit chromatogram
data can convert file types to Scf. The ABIF file specification is a proprietary
data storage file specification for sequencing data generated by Applied
Biosystems machines. More information on each filetype can be found at the
following sites:
- ABIF: https://projects.nfstc.org/workshops/resources/articles/ABIF_File_Format.pdf
- SCF: http://staden.sourceforge.net/manual/formats_unix_2.html
The objects and functions included in this package were developed as part of the
Poly Peak Parser web application
(http://yost.genetics.utah.edu/software.php), which automates the process
of seperating ambiguous double peaks from Sanger sequencing individuals
containing heterozygous indels. This package contains a complete working copy of
the Poly Peak Parser web application that can be run locally using the
PolyPeakParser() function. In addition to the web program, this package
also provides general objects and functions for working with Sanger sequencing
data.
This vignette will walk you through a typical workflow using two sequence files: 1) homozygous.scf and 2) heterozygous.ab1. As their names indicate, the first example contains results typical of sequencing from a PCR product of a homozygous individual or from a plasmid. The second example contains results from sequencing the same region in an individual with a small indel.
2 Loading Data
The first step of most workflows will be to upload data from a sequencing
results file. This can be done using one of three included functions:
read.abif(), read.scf() and readsangerseq(). The
first two functions directly import all of the fields into abif and
scf class objects, respectively. These classes are meant as
intermediate classes and exist to allow the user to inspect the file contents,
as file contents may vary between basecallers and sequencing machines. Users
should generally use the readsangerseq() function. This function
automatically detects and reads in the file type and then extracts the fields
necessary to create a sangerseq class object, which is used by all of
the other functions in this package.
2.1 read.abif
read.abif takes a single argument for the filename of the abif file
to be read. The resulting object contains three major parts. The header,
containing information on the file structure, the directory, containing
information on each of the data fields included in the file, and the data
fields. Here is an example:
hetab1 <- read.abif(system.file("extdata", "heterozygous.ab1", package = "sangerseqR"))
str(hetab1, list.len = 20)## Formal class 'abif' [package "sangerseqR"] with 3 slots
## ..@ header :Formal class 'abifHeader' [package "sangerseqR"] with 9 slots
## .. .. ..@ abif : chr "ABIF"
## .. .. ..@ version : int 101
## .. .. ..@ name : chr "tdir"
## .. .. ..@ number : int 1
## .. .. ..@ elementtype: int 1023
## .. .. ..@ elementsize: int 28
## .. .. ..@ numelements: int 130
## .. .. ..@ dataoffset : int 323971
## .. .. ..@ datahandle : int 0
## ..@ directory:Formal class 'abifDirectory' [package "sangerseqR"] with 7 slots
## .. .. ..@ name : chr [1:130] "AEPt" "AEPt" "APFN" "APXV" ...
## .. .. ..@ tagnumber : int [1:130] 1 2 2 1 1 1 1 1 1 1 ...
## .. .. ..@ elementtype: int [1:130] 4 4 18 19 19 19 2 5 4 4 ...
## .. .. ..@ elementsize: int [1:130] 2 2 1 1 1 1 1 4 2 2 ...
## .. .. ..@ numelements: int [1:130] 1 1 6 2 6 2 4503 1 1 1 ...
## .. .. ..@ datasize : int [1:130] 2 2 6 2 6 2 4503 4 2 2 ...
## .. .. ..@ dataoffset : int [1:130] 1113325568 1113325568 173231 838860800 163360 956301312 163366 0 65536 145752064 ...
## ..@ data :List of 130
## .. ..$ AEPt.1 : int 16988
## .. ..$ AEPt.2 : int 16988
## .. ..$ APFN.2 : chr "SeqA"
## .. ..$ APXV.1 : chr "2"
## .. ..$ APrN.1 : chr "SeqA"
## .. ..$ APrV.1 : chr "9"
## .. ..$ APrX.1 : chr "?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"yes\"?>\n<AnalysisProtocolContainer doAnalysis=\"true\" na"| __truncated__
## .. ..$ ARTN.1 : int 0
## .. ..$ ASPF.1 : int 1
## .. ..$ ASPt.1 : int 2224
## .. ..$ ASPt.2 : int 2224
## .. ..$ AUDT.1 : int [1:1370] 64 126 65 54 55 79 81 183 49 123 ...
## .. ..$ B1Pt.1 : int 2223
## .. ..$ B1Pt.2 : int 2223
## .. ..$ BCTS.1 : chr "201306-13 17:26:28 -06:00"
## .. ..$ BufT.1 : int [1:1596] -27 -27 -27 -27 -27 -27 -27 -27 -27 -27 ...
## .. ..$ CMNT.1 : chr "ID:119209><WELL:G02>"
## .. ..$ CTID.1 : chr "bdt1735"
## .. ..$ CTNM.1 : chr "bdt1735"
## .. ..$ CTOw.1 : chr "aadamson"
## .. .. [list output truncated]As you can see, the file is very long and contains a lot of Data fields (130 in this example). However, most of these contain run information and only a few are directly relevant to data analysis:
| Data Field | Description |
|---|---|
| DATA.9-DATA.12 | Vectors containing the signal intensities for each channel. |
| FWO.1 | A string containing the base corresponding to each channel. For example, if it is “ACGT”, then DATA.9 = A, DATA.10 = C, DATA.11 = G and DATA.12 = T. |
| PLOC.2 | Peak locations as an index of the trace vectors. |
| PBAS.1, PBAS.2 | Primary basecalls. PBAS.1 may contain bases edited in the original basecaller, while PBAS.2 always contains the basecaller’s calls. |
| P1AM.1 | Amplitude of primary basecall peaks. |
| P2BA.1 | (optional) Contains the secondary basecalls. |
| P2AM.1 | (optional) Amplitude of the secondary basecall peaks. |
2.2 read.scf
Like read.abif, read.scf takes a single argument with
the filename. However, the data structure of the resulting scf object
is far less complicated, containing only a header with file structure
information, a matrix of the trace data (@sample_points), a matrix of
relative probabilities of each base at each position (@sequence_probs),
basecall positions (@basecall_positions), basecalls
(@basecalls) and optionally a comments sections with the run data
(@comments). The last slot (@private) is rarely used and
impossible to interpret without knowing how it was created.
homoscf <- read.scf(system.file("extdata", "homozygous.scf", package = "sangerseqR"))
str(homoscf)## Formal class 'scf' [package "sangerseqR"] with 7 slots
## ..@ header :Formal class 'scfHeader' [package "sangerseqR"] with 14 slots
## .. .. ..@ scf : chr "scf"
## .. .. ..@ samples : int 16275
## .. .. ..@ samples_offset : int 128
## .. .. ..@ bases : int 722
## .. .. ..@ bases_left_clip : int 0
## .. .. ..@ bases_right_clip: int 0
## .. .. ..@ bases_offset : int 130328
## .. .. ..@ comments_size : int 1731
## .. .. ..@ comments_offset : int 138992
## .. .. ..@ version : num 300
## .. .. ..@ sample_size : int 2
## .. .. ..@ code_set : int 2
## .. .. ..@ private_size : int 0
## .. .. ..@ private_offset : int 140723
## ..@ sample_points : num [1:16275, 1:4] 187 190 199 220 255 304 354 389 404 402 ...
## ..@ sequence_probs : int [1:722, 1:4] 0 0 0 0 0 0 0 0 0 0 ...
## ..@ basecall_positions: int [1:722] 2 18 25 39 45 56 63 68 85 94 ...
## ..@ basecalls : chr "ARGKRAMMYWACTATAGGGCGGAATTGAATTTAGCGGCCGCGAATTCGCCCTTTGGCAAGAGAGCGACAGTCAGTCGGACTTACGAGTTGTTTTTACAGGCGCAATTCTTT"| __truncated__
## ..@ comments : chr "STRT6/21/2013\n18:04:27\nSTOP=6/21/2013\n20:02:45\nSIGN=G=124,A=134,T=204,C=159\nAEPt=16308\nAEPt=16308\nAPFN=S"| __truncated__
## ..@ private : raw [1:2] 00 312.3 readsangerseq
The readsangerseq function is a convenience function equivalent to
sangerseq(read.abif(file)) or
sangerseq(read.scf(file)). It should generally be used when the contents
of the file do not need to be directly accessed because it returns a
sangerseq object, described below.
3 Sangerseq Class Objects
The sangerseq class is the backbone of the sangerseqR package and
contains the chromatogram data necesary to perform all other functions. It can
be created in two ways: from an abif or scf object using the
sangerseq method or directly from an abif or scf file using
readsangerseq.
# from a sequence file object
homosangerseq <- sangerseq(homoscf)
# directly from the file
hetsangerseq <- readsangerseq(system.file("extdata", "heterozygous.ab1",
package = "sangerseqR"))
str(hetsangerseq)## Formal class 'sangerseq' [package "sangerseqR"] with 7 slots
## ..@ primarySeqID : chr "From ab1 file"
## ..@ primarySeq :Formal class 'DNAString' [package "Biostrings"] with 5 slots
## .. .. ..@ shared :Formal class 'SharedRaw' [package "XVector"] with 2 slots
## .. .. .. .. ..@ xp :<externalptr>
## .. .. .. .. ..@ .link_to_cached_object:<environment: 0x64cc8c912928>
## .. .. ..@ offset : int 0
## .. .. ..@ length : int 605
## .. .. ..@ elementMetadata: NULL
## .. .. ..@ metadata : list()
## ..@ secondarySeqID: chr "From ab1 file"
## ..@ secondarySeq :Formal class 'DNAString' [package "Biostrings"] with 5 slots
## .. .. ..@ shared :Formal class 'SharedRaw' [package "XVector"] with 2 slots
## .. .. .. .. ..@ xp :<externalptr>
## .. .. .. .. ..@ .link_to_cached_object:<environment: 0x64cc8c912928>
## .. .. ..@ offset : int 0
## .. .. ..@ length : int 605
## .. .. ..@ elementMetadata: NULL
## .. .. ..@ metadata : list()
## ..@ traceMatrix : int [1:16215, 1:4] 0 0 0 1 2 4 4 2 1 0 ...
## ..@ peakPosMatrix : num [1:605, 1:4] 4 13 21 31 43 58 64 73 83 98 ...
## ..@ peakAmpMatrix : int [1:605, 1:4] 380 694 836 934 1367 1063 2072 1502 1234 539 ...The slots are as follows:
| Slot | Description |
|---|---|
primarySeqID |
Identification of the primary Basecalls. |
primarySeq |
The primary Basecalls formatted as a DNAString object. |
secondarySeqID |
Identification of the secondary Basecalls. |
secondarySeq |
The secondary Basecalls formatted as a DNAString object. |
traceMatrix |
A numerical matrix containing 4 columns corresponding to the normalized signal values for the chromatogram traces. Column order = A,C,G,T. |
peakPosMatrix |
A numerical matrix containing the position of the maximum peak values for each base within each Basecall window. If no peak was detected for a given base in a given window, then “NA”. Column order = A,C,G,T. |
peakAmpMatrix |
A numerical matrix containing the maximum peak amplitudes for each base within each Basecall window. If no peak was detected for a given base in a given window, then 0. Column order = A,C,G,T. |
Accessor functions also exist for each slot in the sangerseq object. Most of the
accessors return the data in its native format, but the primarySeq
and secondarySeq accessors can optionally return the data as a
character string or a DNAString class object from the
Biostrings package by setting string=TRUE or
string=FALSE, respectively. The DNAString class contains
several convenient functions for manipulating the sequence, including
generating the reverse compliment and performing alignments. The
Biostrings package is automatically loaded with the
sangerseq package, so all methods should be available.
# default is to return a DNAString object
Seq1 <- primarySeq(homosangerseq)
reverseComplement(Seq1)## 722-letter DNAString object
## seq: TTAACCCTCACTAAAAGGGAATTAGTCCTGCAGGTT...CGCTAAATTCAATTCCGCCCTATAGTWRKKTYMCYT# can return as string
primarySeq(homosangerseq, string = TRUE)## [1] "ARGKRAMMYWACTATAGGGCGGAATTGAATTTAGCGGCCGCGAATTCGCCCTTTGGCAAGAGAGCGACAGTCAGTCGGACTTACGAGTTGTTTTTACAGGCGCAATTCTTTTTTTAGAATATTATACATTCATCTGGCTTTTTGGGTGCACCGATGAGAGATCCAGTTTTCACAGCGAACGCTATGGCTTATCACCCTTTTCACGCGCACAGGCCGGCCGACTTTCCCATGTCAGCTTTCCTTGCGGCGGCTCAACCTTCGTTCTTTCCAGCGCTCACTTTACCAGTAAACCGCTGGCGGATCATGCGCTCTCCGGTGCGGCTGAAGCTGGTTTACACGCGGCGCTTGGACATCACCACCAGGCGGCTCATCTGCGCTCTTTCAAGGGTCTCGAGCCAGAGGAGGATGTTGAGGACGATCCTAAAGTTACATTAGAAGCTAAGGAGCTTTGGGATCAATTCCACAAAATTGGAACAGAAATGGTCATCACTAAATCAGGAAGGTAAGGTCTTTACATTATTTAACCTATTGAATGCTGCATAGGGTGATGTTATTATATTACTCCGCGAAGAGTTGGGTCTATTTTATCGTAAAATATACTTTACATTATAAAATATTGCTCGGTTAAAATTCAGATGTACTGGATGCTGACATAGCATCGAAGCCTCTAARGGCGAATTCGTTTAAACCTGCAGGACTAATTCCCTTTTAGTGAGGGTTAA"4 Creating Chromatograms
Basic chromatogram plots can be made using the chromatogram
function. These plots are optimized for printing, so they contain several rows
to plot all of the data simultaneously. The downside of this is that it can give
an error if the graphics device dimensions are not large enough. If this occurs,
we suggest you provide a filename in the command to save it to a pdf
automatically sized to fit everything. Several parameters can also be set to
affect how the plot appears. These are documented in the chromatogram help file.
chromatogram(hetsangerseq, width = 80, height = 3, trim5 = 50, trim3 = 100,
showcalls = "both")
5 Making Basecalls
As shown in the chromatogram, secondary basecalls are sometimes provided in ab1
files (Scf files are unable to show them). However, the exact nature of these
calls is inconsistent. In the heterozygous.ab1 file used here, it is any peak
near the primary peak, no matter how small. For example, base 100 (first base on
the second line) has a primary call of “C” and a secondary call of “A”, even
though the A peak is very small and likely noise. In homozygous sequencing
results, these calls should simply be ignored and are hidden in the
chromatogram by default (showcalls="primary"). When heterozygous
regions of the sequence are present, the makeBaseCalls can be used
to determine whether a particular peak is homozygous or heterozygous and call
the appropriate bases.
Let’s use the chromatogram we created in the previous section as an example. The
chromatogram contains a homozygous region from bases 1 to approximately 160, but
then breaks down into a series of double peaks for the remainder of the
chromatogram. This is due to an indel in one allele of the sequenced region.
makeBaseCalls can be used to show this more clearly or to add the
secondary basecalls if the data file does not contain them. The function
essentially divides the sequence into a series of basecall windows and
identifies the tallest peak for each fluorescence channel within the window.
These peaks are converted to signal ratio to the tallest peak. A cutoff ratio is
then applied to determine if a peak is signal or noise. Peaks below this ratio
are ignored. Remaining peaks in each window are used to make primary and
secondary basecalls.
hetcalls <- makeBaseCalls(hetsangerseq, ratio = 0.33)
hetcalls## Number of datapoints: 16215
## Number of basecalls: 604
##
## Primary Basecalls: AGGCGCTGGAGTGGGTTTGACGGCGCATTCTTTTTTTAGAGTATTATACATTCATCTGGCTTTTTGGATGCACCGATGAGAGATCCAGTTTTCACAGCGAACGCTATGGCTTATCACCCTTTTCACGCGCACAGGCCGGCCGACTTTCCCATGTCAGCTTTCCTTGCGGCGGCTCAACCTTCGTTCTTTCCAGCGCTCACTTTACCACCGAACCTCAGGCGAACGCTGGGCTCTCAGGCGCGCTCCAGGCCGGCTGACGCGGGGCGACTGGACGTGCCCGCAAGGCGCCTCCAGGGGGCTCTTCCGCGCTCCTTCAGGGATATCAGGCCGTAGGGGGCGATCCTGAACTATCCTTAAATGCCATTAAGCGCTGGGGTGCTTTGCGATCAATTGCACCAAATTGGGACATCACTGAATCTCGCTGGATCGGGCTGGACATGACTTAACCTATTTAAACCTGTAGAAGGCGGCGTAATGAGATTACTCTGCATAACTCTGGGACGAGTTGATCCTATATTATCCTTTACATTACATAACATTGTAAAATATTGATTCAGATGTATTGGGAGGTGCCGTANCCTCACGATACCTATAGAAAGCCTCT
##
## Secondary Basecalls: AGGGGCMGGGGTGAATTTGAAGGCGCATTCTTTTTTTAGACTATTATACATTCATCTGGCTTTTTGGATGCACCGATGAGAGATCCAGTTTTCACAGCGAACACTATGGCTTATCACCCTTTTCACGCGCACAGGCCGGCCGACTTTCCCATGTCAGCTTTCCTTGCGGCGGCTCAACCTTCGTTCTTTCCAGCGCTCACTTTACCAGTAGGTCGCTGTAAGCTCATGCCGGATCCTGTGCTGCTGGATGTGGTTTAAACTCGTTTCTACGCGACCATTAGCCATCAGCACAKCTCCGCTCATTTAAGGGTTTCGAACCGGCGGGAGATAGTGAAGAATGTTGAAGAGGTACATAAGGATATAAGGGAATTTAAGAACAATTCGACTAAATTCGAAAAGAAATGATCAGAAATAGTCAAGAAAAATAAAGTAATTTAAGTTTTTTACATTATGTATGCTACTTAGTGTTATATTGGTTTATGTTATTATGATGAGTTGCGTATATTTTGGTGTATTATATAGTATACTATATTATATATTACTCGGTAAAACTCGGTTAAAACTCAGTTCTAATATANGATRGCTAGCCATCTAGAAAACCTCTThe resulting file now contains the maximum peak in the @primarySeq slot
and the second tallest peak, if it is above the cutoff, in the
@secondarySeq slot. If only one peak is above the cutoff ratio, then
this call matches the primary basecall. If three peaks were above the cutoff
ratio, then the peak with the maximum amplitude is the primary basecall and an
ambiguous base code is used as the secondary basecall. The resulting
chromatogram also shows this:
chromatogram(hetcalls, width = 80, height = 3, trim5 = 50, trim3 = 100,
showcalls = "both")
Chromatogram of heterozygous sequencing results after making basecalls. Primary and secondary basecalls now match for homozygous peaks
6 Parsing Alleles
Although makeBaseCalls has fixed the primary and secondary peak
calls. It still does not tell us anything about the nature of the mutation. For
this, we need to set the allele phase using a reference base sequence from an
online source or from another sequencing run on a homozygous sample. The
examples used in this vignette are from heterozygous and homozygous siblings, so
we will use the primary basecalls from the homozygous sibling (loaded earlier)
as our reference. The beginnings and ends of these sequences do not need to
match, but the reference should ideally encompass the sequenced region.
setAllelePhase will then separate the primary and secondary
basecalls into reference and non-reference bases at each position and set
(@primarySeq) to the reference and @secondarySeq to the
non-reference allele.
ref <- subseq(primarySeq(homosangerseq, string = TRUE), start = 30, width = 500)
hetseqalleles <- setAllelePhase(hetcalls, ref, trim5 = 50, trim3 = 300)
hetseqalleles## Number of datapoints: 16215
## Number of basecalls: 604
##
## Primary Basecalls: AGGSGCHGGRGTGRRTTTGAMGGCGCATTCTTTTTTTAGASTATTATACATTCATCTGGCTTTTTGGATGCACCGATGAGAGATCCAGTTTTCACAGCGAACGCTATGGCTTATCACCCTTTTCACGCGCACAGGCCGGCCGACTTTCCCATGTCAGCTTTCCTTGCGGCGGCTCAACCTTCGTTCTTTCCAGCGCTCACTTTACCAGTAAACCGCTGGCGGATCATGCGCTCTCCGGTGCGGCTGAAGCTGGTTTACACGCGGCGCTTGGACATCACCACCAGGCGGCTCATCTGCGCTCTTTCAAGGGTCTCGAGCCAGAGGAGGATGTTGAGGACGATCCTAAAGTTACATTAGAAGCTAAGGAGCTTTGGGATCAATTCCACTAAATTGGAACAGAAATGGTCATCACTAAATCAGGAAGGTAAGGTCTTTACATTATTTAACCTATTKWAWSCTRYWKARKGYKRYRTWRKKWKATKWYWYTRYRWWRMKYTGSGWMKAKTTKRKYSTATWWTATMSTWTACWWTAYWWWAYATTRYWMRRTAWWRMTYSRKWWRWAYTSRGWKSTRMYRTANSMTVRCKAKMCMTMTAGAAARCCTCT
##
## Secondary Basecalls: AGGSGCHGGRGTGRRTTTGAMGGCGCATTCTTTTTTTAGASTATTATACATTCATCTGGCTTTTTGGATGCACCGATGAGAGATCCAGTTTTCACAGCGAACACTATGGCTTATCACCCTTTTCACGCGCACAGGCCGGCCGACTTTCCCATGTCAGCTTTCCTTGCGGCGGCTCAACCTTCGTTCTTTCCAGCGCTCACTTTACCACCGGGTCTCAGTAAACCGCTGGCGGATCATGCGCTCTCCGGTGCGGCTGAAGCTGGTTTACACGCGGCGCTTGGACATCACCACCRGGCGGCTCATCTGCGCTCTTTCAAGGGTCTCGAGCCAGAGGAGGATGTTGAGGACGATCCTAAAGTTACATTAGAAGCTAAGGAGCTTTGGGATCAATTCCACAAAATTGGAACAGAAATGGTCATCACTAAATCAGGAAGGTAAGGTCTTTACATTATKWAWSCTRYWKARKGYKRYRTWRKKWKATKWYWYTRYRWWRMKYTGSGWMKAKTTKRKYSTATWWTATMSTWTACWWTAYWWWAYATTRYWMRRTAWWRMTYSRKWWRWAYTSRGWKSTRMYRTANSMTVRCKAKMCMTMTAGAAARCCTCTAt this point, we could plot the chromatogram again, but it is more informative
to align the resulting sequences to see how the alleles differ. Since
sangerseqR depends on Biostrings,
pairwiseAlignment can be used.
pa <- pairwiseAlignment(primarySeq(hetseqalleles)[1:400], secondarySeq(hetseqalleles)[1:400],
type = "global-local")
writePairwiseAlignments(pa)## ########################################
## # Program: pwalign (version 1.3.0), a Bioconductor package
## # Rundate: Tue Oct 29 19:17:38 2024
## ########################################
## #=======================================
## #
## # Aligned_sequences: 2
## # 1: P1
## # 2: S1
## # Matrix: NA
## # Gap_penalty: 14.0
## # Extend_penalty: 4.0
## #
## # Length: 410
## # Identity: 387/410 (94.4%)
## # Similarity: NA/410 (NA%)
## # Gaps: 20/410 (4.9%)
## # Score: 649.2401
## #
## #
## #=======================================
##
## P1 1 AGGSGCHGGRGTGRRTTTGAMGGCGCATTCTTTTTTTAGASTATTATACA 50
## ||||||||||||||||||||||||||||||||||||||||||||||||||
## S1 1 AGGSGCHGGRGTGRRTTTGAMGGCGCATTCTTTTTTTAGASTATTATACA 50
##
## P1 51 TTCATCTGGCTTTTTGGATGCACCGATGAGAGATCCAGTTTTCACAGCGA 100
## ||||||||||||||||||||||||||||||||||||||||||||||||||
## S1 51 TTCATCTGGCTTTTTGGATGCACCGATGAGAGATCCAGTTTTCACAGCGA 100
##
## P1 101 ACGCTATGGCTTATCACCCTTTTCACGCGCACAGGCCGGCCGACTTTCCC 150
## || |||||||||||||||||||||||||||||||||||||||||||||||
## S1 101 ACACTATGGCTTATCACCCTTTTCACGCGCACAGGCCGGCCGACTTTCCC 150
##
## P1 151 ATGTCAGCTTTCCTTGCGGCGGCTCAACCTTCGTTCTTTCCAGCGCTCAC 200
## ||||||||||||||||||||||||||||||||||||||||||||||||||
## S1 151 ATGTCAGCTTTCCTTGCGGCGGCTCAACCTTCGTTCTTTCCAGCGCTCAC 200
##
## P1 201 TTTACCA----------GTAAACCGCTGGCGGATCATGCGCTCTCCGGTG 240
## ||||||| |||||||||||||||||||||||||||||||||
## S1 201 TTTACCACCGGGTCTCAGTAAACCGCTGGCGGATCATGCGCTCTCCGGTG 250
##
## P1 241 CGGCTGAAGCTGGTTTACACGCGGCGCTTGGACATCACCACCAGGCGGCT 290
## |||||||||||||||||||||||||||||||||||||||||| |||||||
## S1 251 CGGCTGAAGCTGGTTTACACGCGGCGCTTGGACATCACCACCRGGCGGCT 300
##
## P1 291 CATCTGCGCTCTTTCAAGGGTCTCGAGCCAGAGGAGGATGTTGAGGACGA 340
## ||||||||||||||||||||||||||||||||||||||||||||||||||
## S1 301 CATCTGCGCTCTTTCAAGGGTCTCGAGCCAGAGGAGGATGTTGAGGACGA 350
##
## P1 341 TCCTAAAGTTACATTAGAAGCTAAGGAGCTTTGGGATCAATTCCACTAAA 390
## |||||||||||||||||||||||||||||||||||||||||||||| ||
## S1 351 TCCTAAAGTTACATTAGAAGCTAAGGAGCTTTGGGATCAATTCCACAAA- 399
##
## P1 391 TTGGAACAGA 400
## |
## S1 400 ---------A 400
##
##
## #---------------------------------------
## #---------------------------------------7 Conclusion
In this vignette, we have walked you through the basic functions in the
sangerseqR package. This work is a work in progress and we hope to
improve its functionality. For example, improving the base calling algorithm and
adding an interactive chromatogram function. If you have any suggestions or
requested features, please email Jonathon Hill at [email protected].