810 lines
2.2 MiB
HTML
810 lines
2.2 MiB
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<meta name="generator" content="pandoc" />
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<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
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<meta name="author" content="M. S. Benítez-Cantos" />
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<meta name="date" content="2021-01-03" />
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<title>V and J segments alignment visualization</title>
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<button type="button" class="btn btn-default btn-xs dropdown-toggle" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"><span>Code</span> <span class="caret"></span></button>
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<ul class="dropdown-menu" style="min-width: 50px;">
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<li><a id="rmd-show-all-code" href="#">Show All Code</a></li>
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<li><a id="rmd-hide-all-code" href="#">Hide All Code</a></li>
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</ul>
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</div>
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||
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|
||
|
|
<h1 class="title toc-ignore">V and J segments alignment visualization</h1>
|
||
|
|
<h4 class="author">M. S. Benítez-Cantos</h4>
|
||
|
|
<h4 class="date">01/03/2021</h4>
|
||
|
|
|
||
|
|
</div>
|
||
|
|
|
||
|
|
|
||
|
|
<p>Load libraries:</p>
|
||
|
|
<pre class="r"><code>########## LOAD LIBRARIES ###########
|
||
|
|
library(Biostrings)
|
||
|
|
library(msaR)
|
||
|
|
library(msa)
|
||
|
|
library(stringr)
|
||
|
|
library(dplyr)</code></pre>
|
||
|
|
<div id="v-and-j-alignment" class="section level1">
|
||
|
|
<h1><span class="header-section-number">1</span> V and J alignment</h1>
|
||
|
|
<div id="vj_alignment-function" class="section level2">
|
||
|
|
<h2><span class="header-section-number">1.1</span> <code>vj_alignment</code> function</h2>
|
||
|
|
<p>The <code>vj_alignment</code> function performs the V and J alignments in the TCRβ reads to delimit their HVR.</p>
|
||
|
|
<pre class="r"><code>vj_alignment <- function(reads, Vsegments, Jsegments, numCores = detectCores() - 4, gapOpening = 1){
|
||
|
|
#' Align V and J segments in a set of reads to delimit the hypervariable region (HVR)
|
||
|
|
#'
|
||
|
|
#' @param reads A \code{DNAStringSet} or a \code{QualityScaledDNAStringSet} containing the reads.
|
||
|
|
#' @param Vsegments A \code{DNAStringSet} with the V segments.
|
||
|
|
#' @param Jsegments A \code{DNAStringSet} with the J segments trimmed from the Phe codon.
|
||
|
|
#' @param numCores Number of cores to execute the function in parallel.
|
||
|
|
#' @param gapOpening Penalty for opening a gap in the alignment.
|
||
|
|
#' @return A \code{data.frame} with V and J alignment information and HVR sequence, quality and reading frame.
|
||
|
|
|
||
|
|
results <- dplyr::bind_rows(mclapply(mc.cores = numCores, X = 1:length(reads), FUN = function(i){
|
||
|
|
|
||
|
|
read <- reads[i]
|
||
|
|
|
||
|
|
# Get the read ID
|
||
|
|
read_id <- names(read)
|
||
|
|
|
||
|
|
|
||
|
|
#########################################
|
||
|
|
############## V ALIGNMENT ##############
|
||
|
|
#########################################
|
||
|
|
|
||
|
|
### Remove IMGT gaps before alignment to avoid them affecting the score ###
|
||
|
|
Vsegments_nogaps <- DNAStringSet(gsub("\\.", "", Vsegments))
|
||
|
|
names(Vsegments_nogaps) <- names(Vsegments)
|
||
|
|
|
||
|
|
### Perform 147 V alignments and get the score only (NO IMGT GAPS) ###
|
||
|
|
v_alignment1 <- pairwiseAlignment(pattern = Vsegments_nogaps,
|
||
|
|
subject = read,
|
||
|
|
type = "global-local",
|
||
|
|
scoreOnly = TRUE,
|
||
|
|
gapOpening = gapOpening)
|
||
|
|
|
||
|
|
### Select the segment with the best score ###
|
||
|
|
best_v <- Vsegments[which.max(v_alignment1)]
|
||
|
|
v_match <- names(best_v)
|
||
|
|
|
||
|
|
### Perform a complete pairwise alignment with the best match (W/ IMGT GAPS, for Cys location) ###
|
||
|
|
v_alignment2 <- pairwiseAlignment(pattern = best_v,
|
||
|
|
subject = read,
|
||
|
|
type = "global-local",
|
||
|
|
scoreOnly = FALSE,
|
||
|
|
gapOpening = gapOpening)
|
||
|
|
|
||
|
|
### Perform a complete pairwise alignment with the best match (NO IMGT GAPS) ###
|
||
|
|
best_v_nogaps <- DNAString(gsub("\\.", "", best_v))
|
||
|
|
|
||
|
|
v_alignment3 <- pairwiseAlignment(pattern = best_v_nogaps,
|
||
|
|
subject = read,
|
||
|
|
type = "global-local",
|
||
|
|
scoreOnly = FALSE,
|
||
|
|
gapOpening = gapOpening)
|
||
|
|
|
||
|
|
### Get V alignment information ###
|
||
|
|
v_score <- score(v_alignment3) # Score of alignment with no gaps
|
||
|
|
v_identity <- pid(v_alignment3, type = "PID3")
|
||
|
|
v_start <- start(Views(v_alignment3))
|
||
|
|
v_end <- end(Views(v_alignment3))
|
||
|
|
|
||
|
|
|
||
|
|
if (v_score < 0) { # If V score is negative, Cys check nor J alignment are performed
|
||
|
|
|
||
|
|
cys_check <- NA
|
||
|
|
cys_status <- NA
|
||
|
|
|
||
|
|
j_match <- NA
|
||
|
|
j_score <- NA
|
||
|
|
j_identity <- NA
|
||
|
|
j_start <- NA
|
||
|
|
j_end <- NA
|
||
|
|
phe_check <- NA
|
||
|
|
|
||
|
|
read_cys_start <- NA
|
||
|
|
HVR_seq <- NA
|
||
|
|
HVR_qual <- NA
|
||
|
|
HVR_frame <- NA
|
||
|
|
|
||
|
|
|
||
|
|
} else { # Only find Cys and align J if V score is positive
|
||
|
|
|
||
|
|
#########################################
|
||
|
|
####### FIND CYS, HANDLE INDELS #########
|
||
|
|
#########################################
|
||
|
|
|
||
|
|
# Cys coordinates in the alignment, ideally
|
||
|
|
cys_start <- 310
|
||
|
|
cys_end <- 312
|
||
|
|
|
||
|
|
# If there is any insertion in the read, Cys coordinates will be shifted in the alignment
|
||
|
|
v_ins <- unlist(deletion(v_alignment2)) # IRanges with V insertions
|
||
|
|
alg_cys_start <- cys_start + sum(width(v_ins[start(v_ins) <= cys_start])) # Cys start in alignment
|
||
|
|
alg_cys_end <- cys_end + sum(width(v_ins[start(v_ins) <= cys_end])) # Cys end in alignment
|
||
|
|
|
||
|
|
message(alg_cys_start, " ", alg_cys_end)
|
||
|
|
|
||
|
|
# Find Cys coordinates in the read
|
||
|
|
v_del <- unlist(insertion(v_alignment2)) # IRanges with deletions in subject (mostly IMGT gaps)
|
||
|
|
print(v_del)
|
||
|
|
num_v_del <- length(v_del) # Number of deletions (of any length)
|
||
|
|
shift <- c(0, cumsum(width(v_del))[-num_v_del])
|
||
|
|
v_del_sft <- shift(v_del, shift) # Fix coordinates
|
||
|
|
subject_gaps <- sum(width(v_del_sft[end(v_del_sft) < alg_cys_start])) + nchar(str_extract(alignedSubject(v_alignment2), "^-*")) # Number of gaps in subject before Cys in the alignment. The "nchar" term takes into account the gaps at the beginning of the subject, which are not considered by the "insertion" function.
|
||
|
|
|
||
|
|
read_cys_start <- v_start + alg_cys_start - subject_gaps -1 # Cys start in the read
|
||
|
|
message(read_cys_start)
|
||
|
|
|
||
|
|
# Get aligned Cys sequences in pattern and subject
|
||
|
|
cys_subject <- subseq(alignedSubject(v_alignment2), start = alg_cys_start, end = alg_cys_end) # Cys sequence in subject
|
||
|
|
message(cys_subject)
|
||
|
|
gap_subject <- unlist(gregexpr(pattern = "-", text = cys_subject)) # Gap position in subject (deletion), if any
|
||
|
|
|
||
|
|
cys_pattern <- subseq(alignedPattern(v_alignment2), start = alg_cys_start, end = alg_cys_end) # Cys sequence in pattern
|
||
|
|
gap_pattern <- unlist(gregexpr(pattern = "-", text = cys_pattern)) # Gap position in pattern (insertion), if any
|
||
|
|
|
||
|
|
# Check Cys sequence in subject
|
||
|
|
if (cys_subject %in% c("TGT", "TGC")) { # Cys codon found
|
||
|
|
|
||
|
|
cys_check <- TRUE
|
||
|
|
|
||
|
|
if (cys_subject == cys_pattern) { # Same Cys codon in pattern and subject
|
||
|
|
cys_status <- "Match"
|
||
|
|
|
||
|
|
} else { # Different Cys codon in pattern and subject
|
||
|
|
cys_status <- paste0("Pseudomatch (", cys_pattern," > ", cys_subject, ")")
|
||
|
|
}
|
||
|
|
|
||
|
|
} else if (all(gap_pattern > 0)) { # Insertion in subject, fix it by removing it
|
||
|
|
cys_status <- paste0("Insertion (", cys_pattern," > ", cys_subject, "), removed")
|
||
|
|
|
||
|
|
read_ins_pos <- read_cys_start + as.numeric(gap_pattern) -1 # Insertion position in read
|
||
|
|
|
||
|
|
# Delete insertion in read
|
||
|
|
fix_read <- as.character(read)
|
||
|
|
fix_qual <- quality(read)
|
||
|
|
stringr::str_sub(fix_read, read_ins_pos[1], read_ins_pos[length(read_ins_pos)]) <- "" # Remove Cys insertion in the read
|
||
|
|
stringr::str_sub(fix_qual, read_ins_pos[1], read_ins_pos[length(read_ins_pos)]) <- "" # Remove Cys insertion in the quality string
|
||
|
|
fix_qual <- as(fix_qual, new.quality.class) # Convert to PhredQuality class
|
||
|
|
read_id <- names(read)
|
||
|
|
read <- QualityScaledDNAStringSet(fix_read, fix_qual) # Rebuild QualityScaledDNAStringSet without Cys insertion
|
||
|
|
names(read) <- read_id
|
||
|
|
|
||
|
|
cys_codon <- subseq(read, start = read_cys_start, end = read_cys_start + 2)
|
||
|
|
cys_check <- ifelse(cys_codon %in% c("TGT", "TGC"), TRUE, FALSE)
|
||
|
|
|
||
|
|
} else if (all(gap_subject > 0)) { # Deletion in subject
|
||
|
|
cys_status <- paste0("Deletion (", cys_pattern," > ", cys_subject, ")")
|
||
|
|
cys_check <- FALSE
|
||
|
|
|
||
|
|
} else { # Mismatch
|
||
|
|
cys_status <- paste0("Mismatch (", cys_pattern," > ", cys_subject, ")")
|
||
|
|
cys_check <- FALSE
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
#########################################
|
||
|
|
############## J ALIGNMENT ##############
|
||
|
|
#########################################
|
||
|
|
|
||
|
|
### Remove V match from the read
|
||
|
|
remaining <- subseq(read, start = read_cys_start + 3) # Align J from Cys codon (not included)
|
||
|
|
|
||
|
|
### Perform 16 J alignments and get the score only
|
||
|
|
j_alignment1 <- pairwiseAlignment(pattern = Jsegments,
|
||
|
|
subject = remaining,
|
||
|
|
type = "global-local",
|
||
|
|
scoreOnly = TRUE,
|
||
|
|
gapOpening = gapOpening)
|
||
|
|
|
||
|
|
### Select the segment with the best score
|
||
|
|
best_j <- Jsegments[which.max(j_alignment1)]
|
||
|
|
j_match <- names(best_j)
|
||
|
|
|
||
|
|
### Perform a complete pairwise alignment with the best match
|
||
|
|
j_alignment2 <- pairwiseAlignment(pattern = best_j,
|
||
|
|
subject = remaining,
|
||
|
|
type = "global-local",
|
||
|
|
scoreOnly = FALSE,
|
||
|
|
gapOpening = gapOpening)
|
||
|
|
|
||
|
|
### Get J alignment information
|
||
|
|
j_score <- score(j_alignment2)
|
||
|
|
j_identity <- pid(j_alignment2, type = "PID3")
|
||
|
|
j_start <- start(Views(j_alignment2)) + read_cys_start + 2 # Get the coordinates **in the read**, not in the sequence without the V match
|
||
|
|
j_end <- end(Views(j_alignment2)) + read_cys_start + 2
|
||
|
|
|
||
|
|
### Check if the first codon is a Phe
|
||
|
|
phe_codon <- subseq(read, start = j_start, end = j_start + 2)
|
||
|
|
phe_check <- phe_codon %in% c("TTT", "TTC")
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
#########################################
|
||
|
|
############## HVR LOCATION #############
|
||
|
|
#########################################
|
||
|
|
|
||
|
|
### Get HVR sequence and sequencing quality
|
||
|
|
HVR <- subseq(read, start = read_cys_start, end = j_start + 2)
|
||
|
|
HVR_seq <- as.character(HVR)
|
||
|
|
HVR_qual <- as.character(quality(HVR))
|
||
|
|
|
||
|
|
# Find out HVR frame (0: Cys and Phe in frame; 1 OR 2: out of frame)
|
||
|
|
HVR_frame <- nchar(HVR_seq) %% 3
|
||
|
|
|
||
|
|
if (HVR_frame != 0) { # If the remainder is 1, frame = 2 and viceversa
|
||
|
|
HVR_frame <- ifelse(HVR_frame == 1, 2, 1)
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
#########################################
|
||
|
|
################# OUTPUT ################
|
||
|
|
#########################################
|
||
|
|
|
||
|
|
df <- data.frame(
|
||
|
|
read_ID = read_id,
|
||
|
|
read_length = width(read),
|
||
|
|
|
||
|
|
V_match = v_match,
|
||
|
|
V_score = v_score,
|
||
|
|
V_identity = v_identity,
|
||
|
|
V_start = v_start,
|
||
|
|
Cys_check = cys_check,
|
||
|
|
Cys_status = cys_status,
|
||
|
|
|
||
|
|
J_match = j_match,
|
||
|
|
J_score = j_score,
|
||
|
|
J_identity = j_identity,
|
||
|
|
J_end = j_end,
|
||
|
|
Phe_check = phe_check,
|
||
|
|
|
||
|
|
HVR_start = read_cys_start,
|
||
|
|
HVR_end = j_start + 2,
|
||
|
|
HVR_sequence = HVR_seq,
|
||
|
|
HVR_quality = HVR_qual,
|
||
|
|
HVR_frame = HVR_frame
|
||
|
|
|
||
|
|
) %>% mutate(read_ID = as.character(read_ID),
|
||
|
|
V_match = as.character(V_match),
|
||
|
|
J_match = as.character(J_match),
|
||
|
|
Cys_status = as.character(Cys_status),
|
||
|
|
HVR_sequence = as.character(HVR_sequence),
|
||
|
|
HVR_quality = as.character(HVR_quality))
|
||
|
|
|
||
|
|
|
||
|
|
return(df)
|
||
|
|
}))
|
||
|
|
}</code></pre>
|
||
|
|
</div>
|
||
|
|
<div id="run-alignment" class="section level2">
|
||
|
|
<h2><span class="header-section-number">1.2</span> Run alignment</h2>
|
||
|
|
<p>Load input:</p>
|
||
|
|
<ul>
|
||
|
|
<li><code>v_segments</code> and <code>j_segments</code>: both are <code>DNAStringset</code> that contains the reference sequences for V (147 sequences: all 64 segments + alleles) and J segments (16 sequences).</li>
|
||
|
|
<li><code>examples</code>: <code>QualityScaledDNAStringSet</code> that contains three example reads and their sequencing qualities. The reads have been previously reverse-complemented (<strong>!!!</strong>).</li>
|
||
|
|
</ul>
|
||
|
|
<pre class="r"><code>###############################################################################################
|
||
|
|
####################### CONVERT .fastq TO QualityScaledDNAStringSet ###########################
|
||
|
|
###############################################################################################
|
||
|
|
|
||
|
|
# curesim_fq <- ShortRead::readFastq("software/CuReSim/output/00_first_test.fastq") # Read .fq file and store in a ShortReadQ class object
|
||
|
|
# reads_sim <- ShortRead::sread(curesim_fq) # DNAStringSet with the reads
|
||
|
|
# quality_sim <- quality(curesim_fq) # BStringSet with the sequencing qualities
|
||
|
|
#
|
||
|
|
# new.quality.class <- switch(class(quality_sim), # Convert from BStringSet to XStringQuality
|
||
|
|
# SFastqQuality="SolexaQuality",
|
||
|
|
# FastqQuality="PhredQuality",
|
||
|
|
# "XStringQuality")
|
||
|
|
# quality_sim <- as(quality_sim, new.quality.class)
|
||
|
|
#
|
||
|
|
# sample_sim <- QualityScaledDNAStringSet(reads_sim, quality_sim) # Get QualityScaledDNAStringSet to use as a pairwiseAlignment input
|
||
|
|
# names(sample_sim) <- as.character(curesim_fq@id) # Character vector with the reads IDs
|
||
|
|
#
|
||
|
|
# sample_sim_rc <- reverseComplement(sample_sim) # V-CDR3-J</code></pre>
|
||
|
|
<p>(<strong>!!!</strong>) ImmuneSIM-generated reads have the following structure: V-CDR3-J. To mimic the sequencing process of our collaborators, before CuReSim, reads need to be reverse-complemented (J-CDR3-V), so that CDR3 lies at the beginning of the read, where sequencing quality is better. However, to perform V and J alignment, the reads must be reverse-complemented again (V-CDR3-J) for the alignment to perform correctly. Reverse complement can be performed with <code>Biostrings::reverseComplement()</code> function.</p>
|
||
|
|
<pre class="r"><code># Load reference sequences (Class: DNAStringSet)
|
||
|
|
v_segments <- readRDS("v_segments.rds")
|
||
|
|
j_segments <- readRDS("j_segments_phe.rds")
|
||
|
|
|
||
|
|
# Load sample (Class: QualityScaledDNAStringSet)
|
||
|
|
examples <- readRDS("example_reads.rds")</code></pre>
|
||
|
|
<pre class="r"><code># Run alignment
|
||
|
|
start_time <- Sys.time()
|
||
|
|
|
||
|
|
vj_alg <- vj_alignment(reads = examples,
|
||
|
|
Vsegments = v_segments,
|
||
|
|
Jsegments = j_segments,
|
||
|
|
numCores = detectCores() - 4) ### Alignment parallelization (alignment of 1 read vs all reference sequences per thread)
|
||
|
|
|
||
|
|
end_time <- Sys.time()</code></pre>
|
||
|
|
<pre class="r"><code># Show results
|
||
|
|
DT::datatable(vj_alg,
|
||
|
|
class = 'nowrap compact order-column',
|
||
|
|
extensions = c('Buttons', 'FixedColumns'),
|
||
|
|
options = list(dom = 'Bfrtip',
|
||
|
|
buttons = c('csv', 'excel'),
|
||
|
|
scrollX = TRUE,
|
||
|
|
fixedColumns = TRUE))</code></pre>
|
||
|
|
<div id="htmlwidget-717e4d27a18c627a7a12" style="width:100%;height:auto;" class="datatables html-widget"></div>
|
||
|
|
<script type="application/json" data-for="htmlwidget-717e4d27a18c627a7a12">{"x":{"filter":"none","extensions":["Buttons","FixedColumns"],"data":[["1","2","3"],["RCCGR:00688:05792","RCCGR:00678:05813","RCCGR:00686:05941"],[445,475,447],["TRBV27*01_F","TRBV7-8*03_(F)","TRBV27*01_F"],[528.408630371094,500.778503417969,541.732177734375],[97.9094076655052,97.5694444444444,98.6062717770035],[56,78,54],[true,true,true],["Match","Match","Match"],["TRBJ2-2*01_F","TRBJ2-6*01_F","TRBJ2-2*01_F"],[61.562313079834,61.5678253173828,61.6492309570312],[100,100,100],[392,422,394],[true,true,true],[326,353,324],[364,394,366],["TGTGCCAGCAGTCCCTCGAACACCGGGGAGCTGTTTTTT","TGTGCCAGCAGACAGGGCACTGGGGCCTACGTCCTGACTTTC","TGTGCCAGCAGTCCCAGCGGGGGGGCCCGGGGAGCTGTTTTTT"],[":???>AA@?;:;5ABA@@<AB@=A9FFEDCAA;%;;;;;",":::;7<<<<569==6>=====2@@G<==699>9<<=<=6>;@","@ACC?@?@>:9:8@89:4\";;;;;;)@@2AAA@<444%;;;;;"],[0,0,2]],"container":"<table class=\"nowrap compact order-column\">\n <thead>\n <tr>\n <th> <\/th>\n <th>read_ID<\/th>\n <th>read_length<\/th>\n <th>V_match<\/th>\n <th>V_score<\/th>\n <th>V_identity<\/th>\n <th>V_start<\/th>\n <th>Cys_check<\/th>\n <th>Cys_status<\/th>\n <th>J_match<\/th>\n <th>J_score<\/th>\n <th>J_identity<\/th>\n <th>J_end<\/th>\n <th>Phe_check<\/th>\n <th>HVR_start<\/th>\n <th>HVR_end<\/th>\n <th>HVR_sequence<\/th>\n <th>HVR_quality<\/th>\n <th>HVR_frame<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"dom":"Bfrtip","buttons":["csv","excel"],"scrollX":true,"fixedColumns":true,"columnDefs":[{"className":"dt-right","targets":[2,4,5,6,10,11,12,14,15,18]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
|
||
|
|
<pre class="r"><code>end_time - start_time</code></pre>
|
||
|
|
<pre><code>## Time difference of 2.013929 secs</code></pre>
|
||
|
|
</div>
|
||
|
|
</div>
|
||
|
|
<div id="alignment-visualization" class="section level1">
|
||
|
|
<h1><span class="header-section-number">2</span> Alignment visualization</h1>
|
||
|
|
<div id="vj_align_vis-function" class="section level2">
|
||
|
|
<h2><span class="header-section-number">2.1</span> <code>vj_align_vis</code> function</h2>
|
||
|
|
<pre class="r"><code>vj_align_vis <- function(alignmentOutput, readsDSS, vseg = v_segments, jseg = j_segments, whichSegment, gapOpening = 1, seqLogo = FALSE, widgHeight = 100){
|
||
|
|
row <- alignmentOutput
|
||
|
|
segment <- paste0(whichSegment, "_match")
|
||
|
|
|
||
|
|
read <- readsDSS[row[1, "read_ID"]]
|
||
|
|
match <- row[1, segment]
|
||
|
|
HVR_start <- row[1, "HVR_start"]
|
||
|
|
|
||
|
|
if (whichSegment == "V") {
|
||
|
|
best <- vseg[match]
|
||
|
|
} else {
|
||
|
|
best <- jseg[match]
|
||
|
|
read <- subseq(read, start = HVR_start + 3)
|
||
|
|
}
|
||
|
|
|
||
|
|
alg <- pairwiseAlignment(pattern = best,
|
||
|
|
subject = read,
|
||
|
|
type = "global-local",
|
||
|
|
scoreOnly = FALSE,
|
||
|
|
gapOpening = gapOpening)
|
||
|
|
|
||
|
|
seq <- DNAStringSet(c(alignedSubject(alg), alignedPattern(alg)))
|
||
|
|
|
||
|
|
widget <- msaR(msa = seq,
|
||
|
|
overviewbox = FALSE,
|
||
|
|
seqlogo = seqLogo,
|
||
|
|
alignmentHeight = 100,
|
||
|
|
height = widgHeight)
|
||
|
|
|
||
|
|
return(widget)
|
||
|
|
}</code></pre>
|
||
|
|
</div>
|
||
|
|
<div id="visualize-alignments" class="section level2">
|
||
|
|
<h2><span class="header-section-number">2.2</span> Visualize alignments</h2>
|
||
|
|
<p>To easily see sequence conservation enable sequence logo (Vis.elements > Show sequence logo).</p>
|
||
|
|
<pre class="r"><code>rownum <- 1 # Row of the vj_alg dataframe to visualize</code></pre>
|
||
|
|
<p><strong>V alignment</strong></p>
|
||
|
|
<pre class="r"><code>vj_align_vis(alignmentOutput = vj_alg[rownum,],
|
||
|
|
readsDSS = examples,
|
||
|
|
whichSegment = "V")</code></pre>
|
||
|
|
<div id="htmlwidget-0653b56eb9c9d8e35cf5" style="width:672px;height:100px;" class="msaR html-widget"></div>
|
||
|
|
<script type="application/json" data-for="htmlwidget-0653b56eb9c9d8e35cf5">{"x":{"alignment":">RCCGR:00688:05792\nGAAGCCCAAGTGACCCAGAACCCAAGATACCTCATCACAGTGACTGGAAAGAAGTTAACAGTGACTTGTTCTCAGAATATGAACCAT---------------------GAGTATATGTCCTGGTACCGACAAGACCCAGGGCTGGGCTTAAGGCAGATCTACTATTCAATGAAT------------GTTGAGGTGACTGATAAGGGAGATGTTCCT---GAAGGGTACAAAGTCTCTCGAAAA---GAGAGGAGGAATTTCCCCCTGATCCTGGAGTCGCCCAGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCAG----TC\n>TRBV27*01_F\nGAAGCCCAAGTGACCCAGAACCCAAGATACCTCATCACAGTGACTGGAAAGAAGTTAACAGTGACTTGTTCTCAGAATATGAACCAT.....................GAGTATATGTCCTGGTATCGACAAGACCCAGGGCTGGGCTTAAGGCAGATCTACTATTCAATGAAT............GTTGAGGTGACTGATAAGGGAGATGTTCCT...GAAGGGTACAAAGTCTCTCGAAAA...GAGAAGAGGAATTTCCCCCTGATCCTGGAGTCGCCCAGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCAGTTTATC","config":{"vis":{"conserv":false,"overviewbox":false,"seqlogo":false,"sequences":true,"markers":true,"metacell":false,"gapHeader":false,"leftHeader":true,"labels":true,"labelName":true,"labelId":true,"labelPartition":false,"labelCheckbox":false,"metaGaps":true,"metaIdentity":true,"metaLinks":true},"conf":{"dropImport":true,"registerMouseHover":false,"registerMouseClicks":true,"eventBus":true,"alphabetSize":20,"debug":false,"hasRef":false,"manualRendering":false},"colorscheme":{"scheme":"nucleotide","colorBackground":true,"showLowerCase":true,"opacity":0.6},"zoomer":{"menuFontsize":"12px","autoResize":true,"alignmentWidth":"auto","alignmentHeight":100,"columnWidth":15,"rowHeight":15,"textVisible":true,"labelIdLength":30,"labelNameLength":100,"labelPartLength":15,"labelCheckLength":15,"labelFontsize":13,"labelLineHeight":"13px","markerFontsize":"10px","stepSize":1,"markerStepSize":2,"markerHeight":20,"residueFont":"13","canvasEventScale":1,"boxRectHeight":2,"boxRectWidth":2,"overviewboxPaddingTop":10,"overviewboxWidth":"auto","overviewboxHeight":"fixed","metaGapWidth":35,"metaIdentWidth":40,"metaLinksWidth":25},"menu":{"menuFontsize":"14px","menuItemFontsize":"14px","menuItemLineHeight":"14px","menuMarginLeft":"3px","menuPadding":"3px 4px 3px 4px"}},"menu":true,"features":null},"evals":[],"jsHooks":[]}</script>
|
||
|
|
<p><strong>J alignment</strong></p>
|
||
|
|
<pre class="r"><code>vj_align_vis(alignmentOutput = vj_alg[rownum,],
|
||
|
|
readsDSS = examples,
|
||
|
|
whichSegment = "J")</code></pre>
|
||
|
|
<div id="htmlwidget-8410e81190e96b99e6ab" style="width:672px;height:100px;" class="msaR html-widget"></div>
|
||
|
|
<script type="application/json" data-for="htmlwidget-8410e81190e96b99e6ab">{"x":{"alignment":">RCCGR:00688:05792\nTTTGGAGAAGGCTCTAGGCTGACCGTACTGG\n>TRBJ2-2*01_F\nTTTGGAGAAGGCTCTAGGCTGACCGTACTGG","config":{"vis":{"conserv":false,"overviewbox":false,"seqlogo":false,"sequences":true,"markers":true,"metacell":false,"gapHeader":false,"leftHeader":true,"labels":true,"labelName":true,"labelId":true,"labelPartition":false,"labelCheckbox":false,"metaGaps":true,"metaIdentity":true,"metaLinks":true},"conf":{"dropImport":true,"registerMouseHover":false,"registerMouseClicks":true,"eventBus":true,"alphabetSize":20,"debug":false,"hasRef":false,"manualRendering":false},"colorscheme":{"scheme":"nucleotide","colorBackground":true,"showLowerCase":true,"opacity":0.6},"zoomer":{"menuFontsize":"12px","autoResize":true,"alignmentWidth":"auto","alignmentHeight":100,"columnWidth":15,"rowHeight":15,"textVisible":true,"labelIdLength":30,"labelNameLength":100,"labelPartLength":15,"labelCheckLength":15,"labelFontsize":13,"labelLineHeight":"13px","markerFontsize":"10px","stepSize":1,"markerStepSize":2,"markerHeight":20,"residueFont":"13","canvasEventScale":1,"boxRectHeight":2,"boxRectWidth":2,"overviewboxPaddingTop":10,"overviewboxWidth":"auto","overviewboxHeight":"fixed","metaGapWidth":35,"metaIdentWidth":40,"metaLinksWidth":25},"menu":{"menuFontsize":"14px","menuItemFontsize":"14px","menuItemLineHeight":"14px","menuMarginLeft":"3px","menuPadding":"3px 4px 3px 4px"}},"menu":true,"features":null},"evals":[],"jsHooks":[]}</script>
|
||
|
|
<hr />
|
||
|
|
</div>
|
||
|
|
</div>
|
||
|
|
<div id="session-info" class="section level1">
|
||
|
|
<h1><span class="header-section-number">3</span> Session info</h1>
|
||
|
|
<pre class="r"><code>sessionInfo()</code></pre>
|
||
|
|
<pre><code>## R version 3.6.1 (2019-07-05)
|
||
|
|
## Platform: x86_64-pc-linux-gnu (64-bit)
|
||
|
|
## Running under: Ubuntu 18.04.2 LTS
|
||
|
|
##
|
||
|
|
## Matrix products: default
|
||
|
|
## BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
|
||
|
|
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
|
||
|
|
##
|
||
|
|
## locale:
|
||
|
|
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
|
||
|
|
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
|
||
|
|
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
|
||
|
|
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
|
||
|
|
## [9] LC_ADDRESS=C LC_TELEPHONE=C
|
||
|
|
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
|
||
|
|
##
|
||
|
|
## attached base packages:
|
||
|
|
## [1] stats4 parallel stats graphics grDevices utils datasets
|
||
|
|
## [8] methods base
|
||
|
|
##
|
||
|
|
## other attached packages:
|
||
|
|
## [1] dplyr_1.0.2 stringr_1.4.0 msa_1.16.0
|
||
|
|
## [4] msaR_0.3.0 Biostrings_2.52.0 XVector_0.24.0
|
||
|
|
## [7] IRanges_2.18.3 S4Vectors_0.22.1 BiocGenerics_0.30.0
|
||
|
|
##
|
||
|
|
## loaded via a namespace (and not attached):
|
||
|
|
## [1] Rcpp_1.0.5 compiler_3.6.1 pillar_1.4.6 tools_3.6.1
|
||
|
|
## [5] zlibbioc_1.30.0 digest_0.6.27 jsonlite_1.7.1 evaluate_0.14
|
||
|
|
## [9] lifecycle_0.2.0 tibble_3.0.4 nlme_3.1-140 lattice_0.20-38
|
||
|
|
## [13] pkgconfig_2.0.3 rlang_0.4.8 crosstalk_1.1.0.1 yaml_2.2.1
|
||
|
|
## [17] xfun_0.19 knitr_1.30 generics_0.1.0 vctrs_0.3.4
|
||
|
|
## [21] htmlwidgets_1.5.2 grid_3.6.1 DT_0.16 tidyselect_1.1.0
|
||
|
|
## [25] glue_1.4.2 R6_2.5.0 rmarkdown_2.5 purrr_0.3.4
|
||
|
|
## [29] magrittr_1.5 htmltools_0.5.0 ellipsis_0.3.1 ape_5.4-1
|
||
|
|
## [33] stringi_1.5.3 crayon_1.3.4</code></pre>
|
||
|
|
</div>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</div>
|
||
|
|
</div>
|
||
|
|
|
||
|
|
</div>
|
||
|
|
|
||
|
|
<script>
|
||
|
|
|
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|
|
// add bootstrap table styles to pandoc tables
|
||
|
|
function bootstrapStylePandocTables() {
|
||
|
|
$('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
|
||
|
|
}
|
||
|
|
$(document).ready(function () {
|
||
|
|
bootstrapStylePandocTables();
|
||
|
|
});
|
||
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|
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</script>
|
||
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|
||
|
|
<!-- tabsets -->
|
||
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|
||
|
|
<script>
|
||
|
|
$(document).ready(function () {
|
||
|
|
window.buildTabsets("TOC");
|
||
|
|
});
|
||
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|
||
|
|
$(document).ready(function () {
|
||
|
|
$('.tabset-dropdown > .nav-tabs > li').click(function () {
|
||
|
|
$(this).parent().toggleClass('nav-tabs-open')
|
||
|
|
});
|
||
|
|
});
|
||
|
|
</script>
|
||
|
|
|
||
|
|
<!-- code folding -->
|
||
|
|
<script>
|
||
|
|
$(document).ready(function () {
|
||
|
|
window.initializeCodeFolding("show" === "show");
|
||
|
|
});
|
||
|
|
</script>
|
||
|
|
|
||
|
|
<script>
|
||
|
|
$(document).ready(function () {
|
||
|
|
|
||
|
|
// move toc-ignore selectors from section div to header
|
||
|
|
$('div.section.toc-ignore')
|
||
|
|
.removeClass('toc-ignore')
|
||
|
|
.children('h1,h2,h3,h4,h5').addClass('toc-ignore');
|
||
|
|
|
||
|
|
// establish options
|
||
|
|
var options = {
|
||
|
|
selectors: "h1,h2,h3",
|
||
|
|
theme: "bootstrap3",
|
||
|
|
context: '.toc-content',
|
||
|
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hashGenerator: function (text) {
|
||
|
|
return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
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||
|
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},
|
||
|
|
ignoreSelector: ".toc-ignore",
|
||
|
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scrollTo: 0
|
||
|
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};
|
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|
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options.showAndHide = false;
|
||
|
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options.smoothScroll = true;
|
||
|
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|
||
|
|
// tocify
|
||
|
|
var toc = $("#TOC").tocify(options).data("toc-tocify");
|
||
|
|
});
|
||
|
|
</script>
|
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</body>
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</html>
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