A validated workflow for paired total and small RNA sequencing from low-input submandibular gland biopsy specimens in de novo Parkinson's disease patients.

Code ↔ Paper

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[1] § Materials and methods › Post-sequencing quality control ↔ R/plots.R, lines 293–342 · score 0.55 · principal component, rlog transformed, PCA, gene, seq
[2] § Results › Post-sequencing quality assessment ↔ R/plots.R, lines 293–342 · score 0.51 · principal components, rlog transformed, PCA, variance, gene, seq

Paper

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The authors' code

R · 456 lines · 16 KB · no license · 2 matches

plotDispEsts.DESeqDataSet <- function( object, ymin, CV=FALSE,
genecol = "black", fitcol = "red", finalcol = "dodgerblue",
legend=TRUE, xlab, ylab, log = "xy", cex = 0.45, ... )
{
if (missing(xlab)) xlab <- "mean of normalized counts"
if (missing(ylab)) {
if (CV) {
ylab <- "coefficient of variation"
} else {
ylab <- "dispersion"
}
}
px = mcols(object)$baseMean
sel = (px>0)
px = px[sel]
# transformation of dispersion into CV or not
f <- if (CV) sqrt else I
py = f(mcols(object)$dispGeneEst[sel])
if(missing(ymin))
ymin = 10^floor(log10(min(py[py>0], na.rm=TRUE))-0.1)
plot(px, pmax(py, ymin), xlab=xlab, ylab=ylab,
log=log, pch=ifelse(py<ymin, 6, 20), col=genecol, cex=cex, ... )
# use a circle over outliers
pchOutlier <- ifelse(mcols(object)$dispOutlier[sel],1,16)
cexOutlier <- ifelse(mcols(object)$dispOutlier[sel],2*cex,cex)
lwdOutlier <- ifelse(mcols(object)$dispOutlier[sel],2,1)
if (!is.null(dispersions(object))) {
points(px, f(dispersions(object)[sel]), col=finalcol, cex=cexOutlier,
pch=pchOutlier, lwd=lwdOutlier)
}
if (!is.null(mcols(object)$dispFit)) {
points(px, f(mcols(object)$dispFit[sel]), col=fitcol, cex=cex, pch=16)
}
if (legend) {
legend("bottomright",c("gene-est","fitted","final"),pch=16,
col=c(genecol,fitcol,finalcol),bg="white")
}
}
#' Plot dispersion estimates
#'
#' A simple helper function that plots the per-gene dispersion
#' estimates together with the fitted mean-dispersion relationship.
#'
#' @docType methods
#' @name plotDispEsts
#' @rdname plotDispEsts
#' @aliases plotDispEsts plotDispEsts,DESeqDataSet-method
#'
#' @param object a DESeqDataSet, with dispersions estimated
#' @param ymin the lower bound for points on the plot, points beyond this
#' are drawn as triangles at ymin
#' @param CV logical, whether to plot the asymptotic or biological
#' coefficient of variation (the square root of dispersion) on the y-axis.
#' As the mean grows to infinity, the square root of dispersion gives
#' the coefficient of variation for the counts. Default is \code{FALSE},
#' plotting dispersion.
#' @param genecol the color for gene-wise dispersion estimates
#' @param fitcol the color of the fitted estimates
#' @param finalcol the color of the final estimates used for testing
#' @param legend logical, whether to draw a legend
#' @param xlab xlab
#' @param ylab ylab
#' @param log log
#' @param cex cex
#' @param ... further arguments to \code{plot}
#'
#' @author Simon Anders
#'
#' @examples
#'
#' dds <- makeExampleDESeqDataSet()
#' dds <- estimateSizeFactors(dds)
#' dds <- estimateDispersions(dds)
#' plotDispEsts(dds)
#'
#' @export
setMethod("plotDispEsts", signature(object="DESeqDataSet"), plotDispEsts.DESeqDataSet)
# Jan 2023 -- single function copied from `geneplotter` to reduce dependency count
# colors were changed for ease of viewing from red to blue
plotMA.dataframe <- function( object, ylim = NULL,
colNonSig = "gray60", colSig = "blue", colLine = "grey40",
log = "x", cex=0.45,
xlab="mean of normalized counts", ylab="log fold change",
... ) {
if ( !( ncol(object) == 3 & inherits( object[[1]], "numeric" ) & inherits( object[[2]], "numeric" )
& inherits( object[[3]], "logical" ) ) ) {
stop( "When called with a data.frame, plotMA expects the data frame
to have 3 columns, two numeric ones for mean and log fold change,
and a logical one for significance.")
}
colnames(object) <- c( "mean", "lfc", "sig" )
object <- subset( object, mean != 0 )
py <- object$lfc
if ( is.null(ylim) )
ylim <- c(-1,1) * quantile(abs(py[is.finite(py)]), probs=0.99) * 1.1
plot(object$mean, pmax(ylim[1], pmin(ylim[2], py)),
log=log, pch=ifelse(py<ylim[1], 6, ifelse(py>ylim[2], 2, 16)),
cex=cex, col=ifelse( object$sig, colSig, colNonSig ), xlab=xlab, ylab=ylab, ylim=ylim, ...)
abline( h=0, lwd=4, col=colLine )
}
plotMA.DESeqDataSet <- function(object, alpha=.1, main="",
xlab="mean of normalized counts", ylim,
colNonSig="gray60", colSig="blue", colLine="grey40",
returnData=FALSE,
MLE=FALSE, ...) {
res <- results(object, ...)
plotMA.DESeqResults(res, alpha=alpha, main=main, xlab=xlab, ylim=ylim, MLE=MLE)
}
plotMA.DESeqResults <- function(object, alpha, main="",
xlab="mean of normalized counts", ylim,
colNonSig="gray60", colSig="blue", colLine="grey40",
returnData=FALSE,
MLE=FALSE, ...) {
sval <- "svalue" %in% names(object)
if (sval) {
test.col <- "svalue"
} else {
test.col <- "padj"
}
if (MLE) {
if (is.null(object$lfcMLE)) {
stop("lfcMLE column is not present: you should first run results() with addMLE=TRUE")
}
lfc.col <- "lfcMLE"
} else {
lfc.col <- "log2FoldChange"
}
if (missing(alpha)) {
if (sval) {
alpha <- 0.005
message("thresholding s-values on alpha=0.005 to color points")
} else {
if (is.null(metadata(object)$alpha)) {
alpha <- 0.1
} else {
alpha <- metadata(object)$alpha
}
}
}
isDE <- ifelse(is.na(object[[test.col]]), FALSE, object[[test.col]] < alpha)
df <- data.frame(mean = object[["baseMean"]],
lfc = object[[lfc.col]],
isDE = isDE)
if (returnData) {
return(df)
}
if (missing(ylim)) {
ylim <- NULL
}
plotMA.dataframe(
df, ylim=ylim,
colNonSig=colNonSig, colSig=colSig, colLine=colLine,
xlab=xlab, main=main, ...)
}
#' MA-plot from base means and log fold changes
#'
#' A simple helper function that makes a so-called "MA-plot", i.e. a
#' scatter plot of log2 fold changes (on the y-axis) versus the mean of
#' normalized counts (on the x-axis).
#'
#' This function is essentially two lines of code: building a
#' \code{data.frame} and passing this to the \code{plotMA} method
#' for \code{data.frame}, now copied from the geneplotter package.
#' The code was modified in version 1.28 to change from red to blue points
#' for better visibility for users with color-blindness. The original plots
#' can still be made via the use of \code{returnData=TRUE} and passing the
#' resulting data.frame directly to \code{geneplotter::plotMA}.
#' The code of this function can be seen with:
#' \code{getMethod("plotMA","DESeqDataSet")}
#' If the \code{object} contains a column \code{svalue} then these
#' will be used for coloring the points (with a default \code{alpha=0.005}).
#'
#' @docType methods
#' @name plotMA
#' @rdname plotMA
#' @aliases plotMA plotMA,DESeqDataSet-method plotMA,DESeqResults-method
#'
#' @param object a \code{DESeqResults} object produced by \code{\link{results}};
#' or a \code{DESeqDataSet} processed by \code{\link{DESeq}}, or the
#' individual functions \code{\link{nbinomWaldTest}} or \code{\link{nbinomLRT}}
#' @param alpha the significance level for thresholding adjusted p-values
#' @param main optional title for the plot
#' @param xlab optional defaults to "mean of normalized counts"
#' @param ylim optional y limits
#' @param colNonSig color to use for non-significant data points
#' @param colSig color to use for significant data points
#' @param colLine color to use for the horizontal (y=0) line
#' @param returnData logical, whether to return the data.frame used for plotting
#' @param MLE if \code{betaPrior=TRUE} was used,
#' whether to plot the MLE (unshrunken estimates), defaults to FALSE.
#' Requires that \code{\link{results}} was run with \code{addMLE=TRUE}.
#' Note that the MLE will be plotted regardless of this argument,
#' if DESeq() was run with \code{betaPrior=FALSE}. See \code{\link{lfcShrink}}
#' for examples on how to plot shrunken log2 fold changes.
#' @param ... further arguments passed to \code{plotMA} if object
#' is \code{DESeqResults} or to \code{\link{results}} if object is
#' \code{DESeqDataSet}
#'
#' @author Michael Love
#'
#' @examples
#'
#' dds <- makeExampleDESeqDataSet()
#' dds <- DESeq(dds)
#' plotMA(dds)
#' res <- results(dds)
#' plotMA(res)
#'
#' @importFrom graphics abline
#'
#' @export
setMethod("plotMA", signature(object="DESeqDataSet"), plotMA.DESeqDataSet)
#' @name plotMA
#' @rdname plotMA
#' @export
setMethod("plotMA", signature(object="DESeqResults"), plotMA.DESeqResults)
plotPCA.DESeqTransform = function(object, intgroup="condition",
ntop=500, returnData=FALSE, pcsToUse=1:2)
{
message(paste0("using ntop=",ntop," top features by variance"))
# calculate the variance for each gene
rv <- rowVars(assay(object))
# select the ntop genes by variance
select <- order(rv, decreasing=TRUE)[seq_len(min(ntop, length(rv)))]
# perform a PCA on the data in assay(x) for the selected genes
pca <- prcomp(t(assay(object)[select,]))
# the contribution to the total variance for each component
percentVar <- pca$sdev^2 / sum( pca$sdev^2 )
if (!all(intgroup %in% names(colData(object)))) {
stop("the argument 'intgroup' should specify columns of colData(dds)")
}
# add the intgroup factors together to create a new grouping factor
group <- if (length(intgroup) > 1) {
intgroup.df <- as.data.frame(colData(object)[, intgroup, drop=FALSE])
factor(apply( intgroup.df, 1, paste, collapse=":"))
} else {
colData(object)[[intgroup]]
}
# assembly the data for the plot
pcs <- paste0("PC", pcsToUse)
d <- data.frame(V1=pca$x[,pcsToUse[1]],
V2=pca$x[,pcsToUse[2]],
group=group, name=colnames(object), colData(object))
colnames(d)[1:2] <- pcs
if (returnData) {
attr(d, "percentVar") <- percentVar[pcsToUse]
return(d)
}
ggplot2::ggplot(
data=d,
ggplot2::aes(
x = .data[[ pcs[1] ]],
y = .data[[ pcs[2] ]],
color=group)
) +
ggplot2::geom_point(size=3) +
ggplot2::xlab(paste0(pcs[1],": ",round(percentVar[pcsToUse[1]] * 100),"% variance")) +
ggplot2::ylab(paste0(pcs[2],": ",round(percentVar[pcsToUse[2]] * 100),"% variance")) +
ggplot2::coord_fixed()
}
#' Sample PCA plot for transformed data
#'
#' This plot helps to check for batch effects and the like.
#'
#' @docType methods
#' @name plotPCA
#' @rdname plotPCA
#' @aliases plotPCA plotPCA,DESeqTransform-method
#'
#' @param object a \code{\link{DESeqTransform}} object, with data in \code{assay(x)},
#' produced for example by either \code{\link{rlog}} or
#' \code{\link{varianceStabilizingTransformation}}.
#' @param intgroup interesting groups: a character vector of
#' names in \code{colData(x)} to use for grouping
#' @param ntop number of top genes to use for principal components,
#' selected by highest row variance
#' @param returnData should the function only return the data.frame of PC1 and PC2
#' with intgroup covariates for custom plotting (default is FALSE)
#' @param pcsToUse numeric of length 2, which PCs to plot
#'
#' @return An object created by \code{ggplot}, which can be assigned and further customized.
#'
#' @author Wolfgang Huber
#'
#' @note See the vignette for an example of variance stabilization and PCA plots.
#' Note that the source code of \code{plotPCA} is very simple.
#' The source can be found by typing \code{DESeq2:::plotPCA.DESeqTransform}
#' or \code{getMethod("plotPCA","DESeqTransform")}, or
#' browsed on github at \url{https://github.com/mikelove/DESeq2/blob/master/R/plots.R}
#' Users should find it easy to customize this function.
#'
#' @examples
#'
#' # using rlog transformed data:
#' dds <- makeExampleDESeqDataSet(betaSD=1)
#' vsd <- vst(dds, nsub=500)
#' plotPCA(vsd)
#'
#' # also possible to perform custom transformation:
#' dds <- estimateSizeFactors(dds)
#' # shifted log of normalized counts
#' se <- SummarizedExperiment(log2(counts(dds, normalized=TRUE) + 1),
#' colData=colData(dds))
#' # the call to DESeqTransform() is needed to
#' # trigger our plotPCA method.
#' plotPCA( DESeqTransform( se ) )
#'
#' @importFrom ggplot2 ggplot geom_point xlab ylab coord_fixed aes
#' @export
setMethod("plotPCA", signature(object="DESeqTransform"), plotPCA.DESeqTransform)
#' Plot of normalized counts for a single gene
#'
#' Normalized counts plus a pseudocount of 0.5 are shown by default.
#'
#' @param dds a \code{DESeqDataSet}
#' @param gene a character, specifying the name of the gene to plot
#' @param intgroup interesting groups: a character vector of names in \code{colData(x)} to use for grouping.
#' Must be factor variables. If you want to plot counts over numeric, choose \code{returnData=TRUE}
#' @param normalized whether the counts should be normalized by size factor
#' (default is TRUE)
#' @param transform whether to have log scale y-axis or not.
#' defaults to TRUE
#' @param main as in 'plot'
#' @param xlab as in 'plot'
#' @param returnData should the function only return the data.frame of counts and
#' covariates for custom plotting (default is FALSE)
#' @param replaced use the outlier-replaced counts if they exist
#' @param pc pseudocount for log transform
#' @param ... arguments passed to plot
#'
#' @examples
#'
#' dds <- makeExampleDESeqDataSet()
#' plotCounts(dds, "gene1")
#'
#' @export
plotCounts <- function(dds, gene, intgroup="condition",
normalized=TRUE, transform=TRUE,
main, xlab="group",
returnData=FALSE,
replaced=FALSE,
pc, ...) {
stopifnot(length(gene) == 1 & (is.character(gene) | (is.numeric(gene) & (gene >= 1 & gene <= nrow(dds)))))
if (!all(intgroup %in% names(colData(dds)))) stop("all variables in 'intgroup' must be columns of colData")
if (!returnData) {
if (!all(sapply(intgroup, function(v) is(colData(dds)[[v]], "factor")))) {
stop("all variables in 'intgroup' should be factors, or choose returnData=TRUE and plot manually")
}
}
if (missing(pc)) {
pc <- if (transform) 0.5 else 0
}
if (is.null(sizeFactors(dds)) & is.null(normalizationFactors(dds))) {
dds <- estimateSizeFactors(dds)
}
cnts <- counts(dds,normalized=normalized,replaced=replaced)[gene,]
group <- if (length(intgroup) == 1) {
colData(dds)[[intgroup]]
} else if (length(intgroup) == 2) {
lvls <- as.vector(t(outer(levels(colData(dds)[[intgroup[1]]]),
levels(colData(dds)[[intgroup[2]]]),
function(x,y) paste(x,y,sep=":"))))
droplevels(factor(apply( as.data.frame(colData(dds)[, intgroup, drop=FALSE]),
1, paste, collapse=":"), levels=lvls))
} else {
factor(apply( as.data.frame(colData(dds)[, intgroup, drop=FALSE]),
1, paste, collapse=":"))
}
data <- data.frame(count=cnts + pc, group=as.integer(group))
logxy <- if (transform) "y" else ""
if (missing(main)) {
main <- if (is.numeric(gene)) {
rownames(dds)[gene]
} else {
gene
}
}
ylab <- ifelse(normalized,"normalized count","count")
if (returnData) return(data.frame(count=data$count, colData(dds)[intgroup]))
plot(data$group + runif(ncol(dds),-.05,.05), data$count, xlim=c(.5,max(data$group)+.5),
log=logxy, xaxt="n", xlab=xlab, ylab=ylab, main=main, ...)
axis(1, at=seq_along(levels(group)), levels(group))
}
#' Sparsity plot
#'
#' A simple plot of the concentration of counts in a single sample over the
#' sum of counts per gene. Not technically the same as "sparsity", but this
#' plot is useful diagnostic for datasets which might not fit a negative
#' binomial assumption: genes with many zeros and individual very large
#' counts are difficult to model with the negative binomial distribution.
#'
#' @param x a matrix or DESeqDataSet
#' @param normalized whether to normalize the counts from a DESeqDataSEt
#' @param ... passed to \code{plot}
#'
#' @examples
#'
#' dds <- makeExampleDESeqDataSet(n=1000,m=4,dispMeanRel=function(x) .5)
#' dds <- estimateSizeFactors(dds)
#' plotSparsity(dds)
#'
#' @export
plotSparsity <- function(x, normalized=TRUE, ...) {
if (is(x, "DESeqDataSet")) {
x <- counts(x, normalized=normalized)
}
rs <- MatrixGenerics::rowSums(x)
rmx <- apply(x, 1, max)
plot(rs[rs > 0], (rmx/rs)[rs > 0], log="x", ylim=c(0,1), xlab="sum of counts per gene",
ylab="max count / sum", main="Concentration of counts over total sum of counts", ...)
}
# convenience function for adding alpha transparency to named colors
## col2useful <- function(col,alpha) {
## x <- col2rgb(col)/255
## rgb(x[1],x[2],x[3],alpha)
## }

plots.R at commit 9d1555f, no license · at the source

Overview

Authors: Ko-Eun Choi^1,2, Joong-Seok Kim²

Institutions: Korea University (South Korea); Catholic University of Korea (South Korea)

Journal: Frontiers in aging neuroscience, volume 18, article 1931183

Dates: received 7 July 2026; accepted 17 August 2026; published online 31 August 2026

Type: Methods article · Language: English

License: CC BY

Identifiers: DOI 10.3389/fnagi.2026.1931183 · PMID 42741392 · PMCID PMC13572653 · OpenAlex W7204756738

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), human (organism), Parkinson's (population), clinical / translational (subfield)

Methods: Smoothing, state filtering, decompositions, Connectivity

Keywords: low-input tissue, Parkinson’s disease, small RNA sequencing, submandibular gland, transcriptomics

Topic: Parkinson's Disease Mechanisms and Treatments (Neurology, Medicine), according to OpenAlex

Funding: Korean Neurological Association; Korea Health Industry Development Institute; Seoul St. Mary's Hospital, Catholic University of Korea

Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

Parkinson’s disease (PD) is characterized by progressive α-synuclein aggregation, yet the molecular mechanisms underlying this process remain incompletely understood. Transcriptomic analyses may provide important insights into the RNA-mediated regulation of PD pathogenesis, but studies of affected brain tissue are limited by the inaccessibility of living brain tissue and post-mortem RNA degradation. The submandibular gland (SMG), which exhibits α-synuclein pathology comparable to that of the substantia nigra, represents a clinically accessible peripheral tissue for investigating disease-associated transcriptomic alterations. However, ultrasound-guided core needle biopsy yields only a limited amount of tissue, making comprehensive RNA sequencing technically challenging. Here, we present and validate a workflow for paired total RNA and small RNA sequencing from low-input SMG biopsy specimens obtained from patients with de novo PD and age-matched, neurologically unaffected surgical controls. Ultrasound-guided core needle biopsy yielded specimens measuring approximately 1.2 × 7–10 mm. To enable direct tissue-to-blood comparisons, fasting peripheral blood samples were collected from the same participants on the morning of biopsy. Following immediate tissue stabilization, RNA extraction, library preparation, and next-generation sequencing, workflow performance was evaluated by assessing RNA integrity, library quality, sequencing metrics, mapping performance, and reproducibility. Despite the limited tissue input, the protocol consistently generated high-quality RNA suitable for both total RNA sequencing and small RNA sequencing. The validated workflow produced robust sequencing libraries with high reproducibility and enabled comprehensive profiling of protein-coding transcripts together with multiple classes of small non-coding RNAs, including miRNAs, piRNAs, snoRNAs, snRNAs, and tRNA-derived RNAs. This validated workflow provides a practical and reproducible approach for comprehensive transcriptomic profiling of minimally invasive SMG biopsy specimens and matched fasting peripheral blood, and may facilitate future studies of disease mechanisms, biomarker discovery, and translational applications in Parkinson’s disease.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

thelovelab/DESeq2

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 9d1555f492ef16ba4b0f5e3ba09d99f3d97aa5ca, 25 September 2026

Languages: R (49), C++ (2), Mathematica (1)

Size: 107 files, 52 scripts

Software Heritage: not archived

Found in: the text

Holds: environment (DESCRIPTION), tests, documentation, 3 notebooks

Not found: README, license file, CITATION.cff, continuous integration

Tools: DESeq2 (7 files), ggplot2 (3 files), limma (2 files), tidyverse (2 files), cowplot (1 file), edgeR (1 file), pheatmap (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

52 files

R/AllClasses.R, R, 511 lines
R/AllGenerics.R, R, 31 lines
R/RcppExports.R, R, 15 lines
R/core.R, R, 2,938 lines
R/expanded.R, R, 100 lines
R/fitNbinomGLMs.R, R, 409 lines
R/helper.R, R, 435 lines
R/lfcShrink.R, R, 531 lines
R/methods.R, R, 883 lines
R/parallel.R, R, 82 lines
R/plots.R, R, 456 lines, 2 matches
R/results.R, R, 1,301 lines
R/rlog.R, R, 287 lines
R/vst.R, R, 267 lines
R/wrappers.R, R, 119 lines
inst/script/icobra_benchmarks.R, R, 97 lines
inst/script/makeSim.R, R, 15 lines
inst/script/runScripts.R, R, 174 lines
inst/script/testsuite.Rmd, R, 173 lines
inst/script/vst.nb, Mathematica, 1,000 lines
src/DESeq2.cpp, C++, 513 lines
src/RcppExports.cpp, C++, 94 lines
tests/testthat.R, R, 3 lines
tests/testthat/test_DESeq.R, R, 20 lines
tests/testthat/test_LRT.R, R, 47 lines
tests/testthat/test_QR.R, R, 10 lines
tests/testthat/test_addMLE.R, R, 20 lines
tests/testthat/test_betaFitting.R, R, 47 lines
tests/testthat/test_collapse.R, R, 15 lines
tests/testthat/test_construction_errors.R, R, 57 lines
tests/testthat/test_design_matrix.R, R, 32 lines
tests/testthat/test_dispersions.R, R, 148 lines
tests/testthat/test_edge_case.R, R, 49 lines
tests/testthat/test_factors.R, R, 12 lines
tests/testthat/test_fpkm.R, R, 8 lines
tests/testthat/test_interactions.R, R, 30 lines
tests/testthat/test_lfcShrink.R, R, 112 lines
tests/testthat/test_linear_mu.R, R, 23 lines
tests/testthat/test_methods.R, R, 11 lines
tests/testthat/test_model_matrix.R, R, 31 lines
tests/testthat/test_nbinomWald.R, R, 83 lines
tests/testthat/test_optim.R, R, 40 lines
tests/testthat/test_outlier.R, R, 85 lines
tests/testthat/test_parallel.R, R, 70 lines
tests/testthat/test_plots.R, R, 39 lines
tests/testthat/test_results.R, R, 243 lines
tests/testthat/test_size_factor.R, R, 47 lines
tests/testthat/test_txi.R, R, 61 lines
tests/testthat/test_unmix.R, R, 58 lines
tests/testthat/test_weights.R, R, 123 lines
tests/testthat/test_zero_zero.R, R, 31 lines
vignettes/DESeq2.Rmd, R, 2,975 lines

At the source: github.com/thelovelab/DESeq2

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
52 scripts, each with its path and the digest of its content;
2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www.ncbi.nlm.nih.gov/, PRJNA1027508 (https://ncbi.nlm.nih.gov/nucleotide/PRJNA1027508).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

Funding: added Korean Neurological Association; Korea Health Industry Development Institute; Seoul St. Mary's Hospital, Catholic University of Korea

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 5 keywords, 37 references.

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This paper

Choi, K.-E., & Kim, J.-S. (2026). A validated workflow for paired total and small RNA sequencing from low-input submandibular gland biopsy specimens in de novo Parkinson's disease patients. Frontiers in aging neuroscience, 18, 1931183. https://doi.org/10.3389/fnagi.2026.1931183

BibTeX

@article{choi2026validated, author = {Choi, Ko-Eun and Kim, Joong-Seok}, title = {{A validated workflow for paired total and small RNA sequencing from low-input submandibular gland biopsy specimens in \<i\>de novo\</i\> Parkinson's disease patients}}, journal = {Frontiers in aging neuroscience}, year = {2026}, month = aug, volume = {18}, pages = {1931183}, publisher = {Frontiers Media SA}, issn = {1663-4365}, doi = {10.3389/fnagi.2026.1931183}, url = {https://doi.org/10.3389/fnagi.2026.1931183}, pmid = {42741392}, pmcid = {PMC13572653} }

RIS

TY - JOUR AU - Choi, Ko-Eun AU - Kim, Joong-Seok TI - A validated workflow for paired total and small RNA sequencing from low-input submandibular gland biopsy specimens in de novo Parkinson's disease patients T2 - Frontiers in aging neuroscience J2 - Front Aging Neurosci PY - 2026 DA - 2026/08/31 VL - 18 SP - 1931183 SN - 1663-4365 PB - Frontiers Media SA DO - 10.3389/fnagi.2026.1931183 UR - https://doi.org/10.3389/fnagi.2026.1931183 LA - en ER -

CSL-JSON

{ "id": "10.3389/fnagi.2026.1931183", "type": "article-journal", "title": "A validated workflow for paired total and small RNA sequencing from low-input submandibular gland biopsy specimens in de novo Parkinson's disease patients", "container-title": "Frontiers in aging neuroscience", "author": [ { "family": "Choi", "given": "Ko-Eun" }, { "family": "Kim", "given": "Joong-Seok" } ], "container-title-short": "Front Aging Neurosci", "volume": "18", "page": "1931183", "DOI": "10.3389/fnagi.2026.1931183", "PMID": "42741392", "PMCID": "PMC13572653", "ISSN": "1663-4365", "publisher": "Frontiers Media SA", "URL": "https://doi.org/10.3389/fnagi.2026.1931183", "language": "en", "issued": { "date-parts": [ [ 2026, 8, 31 ] ] } }

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A validated workflow for paired total and small RNA sequencing from low-input submandibular gland biopsy specimens in <i>de novo</i> Parkinson's disease patients.

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A validated workflow for paired total and small RNA sequencing from low-input submandibular gland biopsy specimens in &lt;i&gt;de novo&lt;/i&gt; Parkinson's disease patients.

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A validated workflow for paired total and small RNA sequencing from low-input submandibular gland biopsy specimens in <i>de novo</i> Parkinson's disease patients.