| Title: | Finding differentially expressed unannotated genomic regions from RNA-seq data |
|---|---|
| Description: | srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. It reads BAM files as input, and outputs a list differentially regions, together with the adjusted p-values. |
| Authors: | Zytnicki Matthias [aut, cre], Gonzalez Ignacio [aut] |
| Maintainer: | Zytnicki Matthias <[email protected]> |
| License: | GPL-3 |
| Version: | 1.25.0 |
| Built: | 2024-07-14 05:37:44 UTC |
| Source: | https://github.com/bioc/srnadiff |
The annotReg slot holds the annotated regions as a GRanges
object.
## S4 method for signature 'srnadiffExp' annotReg(object) ## S4 replacement method for signature 'srnadiffExp' annotReg(object) <- value## S4 method for signature 'srnadiffExp' annotReg(object) ## S4 replacement method for signature 'srnadiffExp' annotReg(object) <- value
object |
An |
value |
Annotated regions as a |
The regions given by the user, as a GRanges.
srnaExp <- srnadiffExample() annotReg(srnaExp)srnaExp <- srnadiffExample() annotReg(srnaExp)
The bamFiles slot holds the full paths to the BAM files as a
BamFileList.
## S4 method for signature 'srnadiffExp' bamFiles(object)## S4 method for signature 'srnadiffExp' bamFiles(object)
object |
An |
The paths to the BAM files.
require(Rsamtools) srnaExp <- srnadiffExample() path(bamFiles(srnaExp))require(Rsamtools) srnaExp <- srnadiffExample() path(bamFiles(srnaExp))
The chromosomeSizes slot holds the sizes of the chromosomes
as a named vector with chromosome names.
## S4 method for signature 'srnadiffExp' chromosomeSizes(object)## S4 method for signature 'srnadiffExp' chromosomeSizes(object)
object |
An |
A list containing the chromosome sizes.
srnaExp <- srnadiffExample() chromosomeSizes(srnaExp)srnaExp <- srnadiffExample() chromosomeSizes(srnaExp)
The countMatrix slot holds the count matrix from the DERs found.
## S4 method for signature 'srnadiffExp' countMatrix(object)## S4 method for signature 'srnadiffExp' countMatrix(object)
object |
An |
In the last step of an sRNA-diff approach, the potential DERs is assessed. Reads (including fractions of reads) that overlap each expressed region are counted to arrive at a count matrix with one row per region and one column per sample. This matrix to been used for quantify the statistic signification of the finded regions.
A matrix with the number of reads for each regions, and each sample.
srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) countMatrix(srnaExp)srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) countMatrix(srnaExp)
The coverages slot holds the coverages as a named
RleList object with one coverage vector
per seqlevel in the set of genomic alignments from the BAM files.
## S4 method for signature 'srnadiffExp' coverages(object)## S4 method for signature 'srnadiffExp' coverages(object)
object |
An |
The coverages, as a list of RleList.
srnaExp <- srnadiffExample() coverages(srnaExp)srnaExp <- srnadiffExample() coverages(srnaExp)
The normFactors slot holds the normalization factors as a
named vector with sample names.
## S4 method for signature 'srnadiffExp' normFactors(object) ## S4 replacement method for signature 'srnadiffExp,numeric' normFactors(object) <- value## S4 method for signature 'srnadiffExp' normFactors(object) ## S4 replacement method for signature 'srnadiffExp,numeric' normFactors(object) <- value
object |
An |
value |
A numeric vector, one size factor for each sample in the coverage data. |
The normFactors vector assigns to each sample coverage a value,
the normalization factor, such that count values in each sample coverage
can be brought to a common scale by dividing by the corresponding
normalization factor. This step is also called normalization, its purpose
is to render coverages (counts) from different samples, which may have
been sequenced to different depths, comparable. Normalization factors
are estimated using the median ratio method described by
Equation 5 in Anders and Huber (2010). Alternative normalization factor
estimators can also be supplied using the assignment function
sizeFactors<-.
The normalization factors, in a list.
Simon Anders, and Wolfgang Huber (2010). Differential expression analysis for sequence count data. Genome Biology, 11:106.
srnaExp <- srnadiffExample() normFactors(srnaExp)srnaExp <- srnadiffExample() normFactors(srnaExp)
The parameters slot holds the parameter values
used in an sRNA-diff approach as a named list. Default values
exist for parameters, but these can also be supplied as input
values in the useParameters argument of the srnadiff
function or using the assignment function parameters<-.
## S4 method for signature 'srnadiffExp' parameters(object) ## S4 replacement method for signature 'srnadiffExp' parameters(object) <- value ## S3 method for class 'srnadiff_par' print(x, ...)## S4 method for signature 'srnadiffExp' parameters(object) ## S4 replacement method for signature 'srnadiffExp' parameters(object) <- value ## S3 method for class 'srnadiff_par' print(x, ...)
object |
An |
value |
A named |
x |
The first element of the parameters used by an |
... |
The other elements of the parameters |
Parameters in an sRNA-diff approach.
minDepthThe cutoff to filter the base-level
coverage. Bases where at least one sample has (normalized)
coverage greater than minDepth be been retained.
Default to 10.
minSizeThe minimum size (in base-pairs) of the
regions to be found. Default to 18.
maxSizeThe maximum size (in base-pairs) of the
regions to be found. Default to 1000000.
minOverlapThis parameters is used in the construction
of the countMatrix matrix. Only reads (ranges)
with a minimum of minOverlap overlapping each expressed
region are considered to be overlapping. Default to 10.
minGapThe maximum number of different bases between
two regions. Near-identical regions are collapsed.
Only regions with at most minGap different
positions are considered identicals and are collapsed
into one single region. Default to 20.
minLogFCThe minimun sliding threshold used in the
naive and IR method. Default to 0.5.
noDiffToDiffInitial transition probability from
no differentially expressed state to differentially expressed.
Default to 0.001.
diffToNoDiffInitial transition probability from
differentially expressed state to no differentially expressed.
Default to 0.000001.
emissionEmission probability. Default to 0.9.
emissionThresholdEmission threshold. A real number
between 0 and 1. Default to 0.1.
The named list of the parameters used in the analysis.
useParameters argument in srnadiff function.
srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) print(parameters(srnaExp)) parameters(srnaExp) <- list("minSize" = 1, "maxSize" = 1500) srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) print(parameters(srnaExp))srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) print(parameters(srnaExp)) parameters(srnaExp) <- list("minSize" = 1, "maxSize" = 1500) srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) print(parameters(srnaExp))
This function plot the coverage information surrounding genomic regions while summarizing the annotation.
plotRegions( object, region, normCvg = TRUE, annot = NULL, allSignReg = TRUE, featureAnnot = NULL, flankReg = 10, colGroup = c("#0080ff", "#ff00ff"), fillReg = c("darkgreen", "darkred"), fillAnnot = "#FFD58A", type = "a", chrTitle = TRUE, trNames = c("DER", "coverage"), legend = TRUE, ... )plotRegions( object, region, normCvg = TRUE, annot = NULL, allSignReg = TRUE, featureAnnot = NULL, flankReg = 10, colGroup = c("#0080ff", "#ff00ff"), fillReg = c("darkgreen", "darkred"), fillAnnot = "#FFD58A", type = "a", chrTitle = TRUE, trNames = c("DER", "coverage"), legend = TRUE, ... )
object |
An object of class |
region |
A |
normCvg |
Boolean. If |
annot |
A |
allSignReg |
Boolean. If |
featureAnnot |
Character scalar. Feature annotation to be used, it
will be one from column names of |
flankReg |
Integer value. If |
colGroup |
Character vector of length 2. The fill colors to be used to indicate samples per group. |
fillReg |
Character vector of length 2. The fill colors to be used to indicate 'up' or 'down' regulated regions. |
fillAnnot |
Character scalar. The fill color to be used for annotated regions. |
type |
Character vector. The plot type, one of
( |
chrTitle |
Boolean or character vector of length one. Defaults
|
trNames |
Title for the tracks. By default |
legend |
Boolean triggering the addition of a legend to the (coverage) data track to indicate groups. |
... |
Additional display parameters to control the look and
feel of the plots. See the "Display Parameters" section
for functions |
This function provides a flexible genomic visualization framework by
displaying tracks in the sense of the Gviz package. Given
a region (or regions), four separate tracks are represented:
(1) GenomeAxisTrack, a horizontal axis with genomic
coordinate tickmarks for reference location to the displayed genomic
regions;
(2) GeneRegionTrack, if the annot argument is
passed, a track displaying all gene and/or sRNA annotation information
in a particular region;
(3) AnnotationTrack, regions are plotted as simple
boxes if no strand information is available, or as arrows to indicate
their direction; and
(4) DataTrack, plot the sample coverages surrounding
the genomic regions.
The sample coverages can be plotted in various different forms as well as combinations thereof. Supported plotting types are:
p: simple dot plot.
l: lines plot.
b: combination of dot and lines plot.
a: lines plot of the sample-groups average (i.e., mean)
values.
confint: confidence intervals for average values. In
combination with a type.
A list of GenomeGraph track objects, all inheriting
from class GdObject.
srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) plotRegions(srnaExp, regions(srnaExp)[1]) plotRegions(srnaExp, regions(srnaExp)[1], type = c("a", "confint"))srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) plotRegions(srnaExp, regions(srnaExp)[1]) plotRegions(srnaExp, regions(srnaExp)[1], type = c("a", "confint"))
readAnnotation reads and parses content of GFF/GTF files
and stores annotated genomic features (regions) in a GRanges
object.
readAnnotation(fileName, feature = NULL, source = NULL, tagName = NULL)readAnnotation(fileName, feature = NULL, source = NULL, tagName = NULL)
fileName |
A path, URL or connection to the GFF/GTF annotation
file. Compressed files ( |
feature |
|
source |
|
tagName |
|
feature and source can be NULL. In this case, no
selection is performed and all content into the file is imported.
If tagName is NULL, then a systematic name
(annot.N) is given to elements of the GRanges object.
A GRanges object with annotated regions information.
##----------------------------------------------------------------------- ## Extraction of miRNAs using an GTF annotation file ##----------------------------------------------------------------------- basedir <- system.file("extdata", package="srnadiff", mustWork = TRUE) gtfFile <- file.path(basedir, "Homo_sapiens.GRCh38.76.gtf.gz") annotReg <- readAnnotation(gtfFile, feature="gene", source="miRNA") annotReg ##----------------------------------------------------------------------- ## Extraction of mature miRNAs using a miRBase-formatted file ##----------------------------------------------------------------------- basedir <- system.file("extdata", package="srnadiff", mustWork = TRUE) gffFile <- file.path(basedir, "mirbase21_GRCh38.gff3") annotReg <- readAnnotation(gffFile, feature="miRNA", tagName="miRNA") annotReg ##----------------------------------------------------------------------- ## Extraction of precursor miRNAs using a miRBase-formatted file ##----------------------------------------------------------------------- basedir <- system.file("extdata", package="srnadiff", mustWork = TRUE) gffFile <- file.path(basedir, "mirbase21_GRCh38.gff3") annotReg <- readAnnotation(gffFile, feature="miRNA_primary_transcript") annotReg##----------------------------------------------------------------------- ## Extraction of miRNAs using an GTF annotation file ##----------------------------------------------------------------------- basedir <- system.file("extdata", package="srnadiff", mustWork = TRUE) gtfFile <- file.path(basedir, "Homo_sapiens.GRCh38.76.gtf.gz") annotReg <- readAnnotation(gtfFile, feature="gene", source="miRNA") annotReg ##----------------------------------------------------------------------- ## Extraction of mature miRNAs using a miRBase-formatted file ##----------------------------------------------------------------------- basedir <- system.file("extdata", package="srnadiff", mustWork = TRUE) gffFile <- file.path(basedir, "mirbase21_GRCh38.gff3") annotReg <- readAnnotation(gffFile, feature="miRNA", tagName="miRNA") annotReg ##----------------------------------------------------------------------- ## Extraction of precursor miRNAs using a miRBase-formatted file ##----------------------------------------------------------------------- basedir <- system.file("extdata", package="srnadiff", mustWork = TRUE) gffFile <- file.path(basedir, "mirbase21_GRCh38.gff3") annotReg <- readAnnotation(gffFile, feature="miRNA_primary_transcript") annotReg
This function extracts the differentially expressed regions from
srnadiffExp object ranked by p-value.
## S4 method for signature 'srnadiffExp' regions(object, pvalue = 1)## S4 method for signature 'srnadiffExp' regions(object, pvalue = 1)
object |
An |
pvalue |
Numeric cutoff value for adjusted p-values. Only regions with adjusted p-values equal or lower than specified are returned. Default to 1, all regions are returned. |
A GenomicRanges object of the selected differentially
expressed regions.
srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) regions(srnaExp, pvalue = 0.05)srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) regions(srnaExp, pvalue = 0.05)
The sampleInfo slot holds the sample information as a
data.frame with three columns labelled FileName,
SampleName and Condition. The first column is the
BAM file name (without extension), the second column the sample
name, and the third column the condition to which sample belongs.
Each row describes one sample.
## S4 method for signature 'srnadiffExp' sampleInfo(object)## S4 method for signature 'srnadiffExp' sampleInfo(object)
object |
An |
A table containing information on the samples
srnaExp <- srnadiffExample() sampleInfo(srnaExp)srnaExp <- srnadiffExample() sampleInfo(srnaExp)
srnadiff is a package that finds differently expressed regions
from RNA-seq data at base-resolution level without relying on
existing annotation. To do so, the package implements the
identify-then-annotate methodology that builds on the idea of
combining two pipelines approach: differential expressed regions detection
and differential expression quantification.
This is the main wrapper for running several key functions from this
package. It is meant to be used after that a srnadiffExp
object has been created. srnadiff implement four methods to
produce potential DERs (see Details).
Once DERs are detected, the second step in srnadiff is to
quantify the statistic signification of these.
srnadiff( object, segMethod = c("hmm", "IR"), diffMethod = "DESeq2", useParameters = srnadiffDefaultParameters, nThreads = 1 )srnadiff( object, segMethod = c("hmm", "IR"), diffMethod = "DESeq2", useParameters = srnadiffDefaultParameters, nThreads = 1 )
object |
An |
segMethod |
A character vector. The segmentation methods to use,
one of |
diffMethod |
A character. The differential expression testing
method to use, one of |
useParameters |
A named list containing the methods parameters to use.
If missing, default parameter values are supplied.
See |
nThreads |
|
The srnadiff package implements two major methods to produce
potential differentially expressed regions: the HMM and IR method.
Briefly, these methods identify contiguous base-pairs in the genome
that present differential expression signal, then these are regrouped
into genomic intervals called differentially expressed regions (DERs).
Once DERs are detected, the second step in a sRNA-diff approach is to
quantify the statistic signification of these. To do so, reads (including
fractions of reads) that overlap each expressed region are counted to
arrive at a count matrix with one row per region and one column per sample.
Then, this count matrix is analyzed using the standard workflow of
DESeq2 for differential expression of RNA-seq data, assigning a
p-value to each candidate DER. Alternatively, edgeR can be used.
The main functions for finds differently expressed regions are
srnadiffExp and srnadiff. The first one
creats an S4 class providing the infrastructure (slots) to store the
input data, methods parameters, intermediate calculations and results
of an sRNA-diff approach. The second one implement four methods to find
candidate differentially expressed regions and quantify the statistic
signification of the finded regions. Details about the implemented methods
are further described in the vignette and the manual page of the
srnadiff function.
Implemented methods to produce potential differentially expressed
regions in srnadiff are:
annotation:This method simply provides the genomic regions corresponding to the annotation file that is optionally given by the user. It can be a set of known miRNAs, siRNAs, piRNAs, genes, or a combination thereof.
hmm:This approach assumes that continuous regions of RNA
along the chromosome are either "differentially expressed" or "not".
This is captured with a hidden Markov model (HMM) with binary latent
state of each nucleotide: differentially expressed or
not differentially expressed. The observations of the HMM are
then the empirical p-values arising from the differential expression
analysis corresponding to each nucleotide position.
The HMM approach normally needs emission, transition, and starting
probabilities values (see parameters). They
can be tuned by the user. In order to finding the most likely sequence
of states from the HMM, the Viterbi algorithm is performed. This
essentially segments the genome into regions, where a region is
defined as a set of consecutive bases showing a common expression
signature.
IR:In this approach, for each base, the average from the normalized coverage is calculated across all samples into each condition. This generates a vector of (normalized) mean coverage expression per condition. These two vectors are then used to compute per-nucleotide log-ratios (in absolute value) across the genome. For the computed log-ratio expression, the method uses a sliding threshold h that run across the log-ratio levels identifying bases with log-ratio value above of h. Regions of contiguous bases passing this threshold are then analyzed using an adaptation of Aumann and Lindell algorithm for irreducibility property (Aumann and Lindell (2003)).
naive:This method is the simplest, gived a fixed threshold h, contiguous bases with log-ratio expression (in absolute value) passing this threshold are then considered as candidate differentially expressed regions.
An srnadiffExp object containing additional slots for:
regions
parameters
countMatrix
Matthias Zytnicki and Ignacio González
Aumann Y. and, Lindell Y. (2003). A Statistical Theory for Quantitative Association Rules. Journal of Intelligent Information Systems, 20(3):255-283.
regions, parameters, countMatrix
and srnadiffExp
## A typical srnadiff session might look like the following. ## Here we assume that 'bamFiles' is a vector with the full ## paths to the BAM files and the sample and experimental ## design information are stored in a data frame 'sampleInfo'. ## Not run: #-- Data preparation srnaExp <- srnadiffExp(bamFiles, sampleInfo) #-- Detecting DERs and quantifying differential expression srnaExp <- srnadiff(srnaExp) #-- Visualization of the results plotRegions(srnaExp, regions(srnaExp)[1]) ## End(Not run) srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) srnaExp## A typical srnadiff session might look like the following. ## Here we assume that 'bamFiles' is a vector with the full ## paths to the BAM files and the sample and experimental ## design information are stored in a data frame 'sampleInfo'. ## Not run: #-- Data preparation srnaExp <- srnadiffExp(bamFiles, sampleInfo) #-- Detecting DERs and quantifying differential expression srnaExp <- srnadiff(srnaExp) #-- Visualization of the results plotRegions(srnaExp, regions(srnaExp)[1]) ## End(Not run) srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp) srnaExp
List of srnadiff default parameters.
srnadiffDefaultParameterssrnadiffDefaultParameters
An object of class list of length 10.
srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp, useParameters=srnadiffDefaultParameters)srnaExp <- srnadiffExample() srnaExp <- srnadiff(srnaExp, useParameters=srnadiffDefaultParameters)
This function provides an example of a srnadiffExp object which
contains mapped reads of small RNAs extracted from SLK cells infected with
latent KSHV, compared to uninfected SLK cells.
srnadiffExample()srnadiffExample()
Raw data have been downloaded from the GEO data set GSE62830, provided in Viollet et al. (2015). Adapters were removed with fastx_clipper and mapped with bowtie2 (Salzberg and Langmead, 2012) on the human genome version GRCh38.
This example is restricted to a small locus on chr14. It uses the whole genome annotation (with coding genes, etc.) and extracts miRNAs.
An srnadiffExp object called 'srnaExp'.
Viollet, Coralie, David A. Davis, Martin Reczko, Joseph M. Ziegelbauer, Francesco Pezzella, Jiannis Ragoussis, and Robert Yarchoan (2015). "Next-Generation Sequencing Analysis Reveals Differential Expression Profiles of MiRNA-mRNA Target Pairs in KSHV-Infected Cells." PLOS ONE, 10:1–23.
Salzberg, Steven, and Ben Langmead (2012). "Fast gapped-read alignment with Bowtie 2." Nature Methods, 9:357–59.
## The 'srnadiffExp' object in this example was constructed by: ## Not run: basedir <- system.file("extdata", package="srnadiff", mustWork = TRUE) sampleInfo <- read.csv(file.path(basedir, "dataInfo.csv")) gtfFile <- file.path(basedir, "Homo_sapiens.GRCh38.76.gtf.gz") annotReg <- readAnnotation(gtfFile, feature="gene", source="miRNA") bamFiles <- file.path(basedir, sampleInfo$FileName) srnaExp <- srnadiffExp(bamFiles, sampleInfo, annotReg) parameters(srnaExp) <- srnadiffDefaultParameters save(srnaExp, file = file.path(basedir, "srnadiffExample.rda")) ## End(Not run) srnaExp <- srnadiffExample() srnaExp## The 'srnadiffExp' object in this example was constructed by: ## Not run: basedir <- system.file("extdata", package="srnadiff", mustWork = TRUE) sampleInfo <- read.csv(file.path(basedir, "dataInfo.csv")) gtfFile <- file.path(basedir, "Homo_sapiens.GRCh38.76.gtf.gz") annotReg <- readAnnotation(gtfFile, feature="gene", source="miRNA") bamFiles <- file.path(basedir, sampleInfo$FileName) srnaExp <- srnadiffExp(bamFiles, sampleInfo, annotReg) parameters(srnaExp) <- srnadiffDefaultParameters save(srnaExp, file = file.path(basedir, "srnadiffExample.rda")) ## End(Not run) srnaExp <- srnadiffExample() srnaExp
srnadiffExp is an S4 class providing the infrastructure (slots)
to store the input data, methods parameters, intermediate calculations
and results of a sRNA-diff approach.
srnadiffExp( bamFiles = NULL, sampleInfo = NULL, annotReg = NULL, diffMethod = NULL, normFactors = NULL ) ## S4 method for signature 'srnadiffExp' show(object)srnadiffExp( bamFiles = NULL, sampleInfo = NULL, annotReg = NULL, diffMethod = NULL, normFactors = NULL ) ## S4 method for signature 'srnadiffExp' show(object)
bamFiles |
A vector with the full paths to the BAM files. |
sampleInfo |
A |
annotReg |
Optional annotation information. Annotated regions as a
|
diffMethod |
A character storing the name of the method used
to compute the p-values. Can be |
normFactors |
A numeric vector, one size factor for each sample in the data. |
object |
An |
srnadiffExp load and summarize sample BAM
files into base-resolution coverage and estimate the size factors (the
effective library size) from the coverage data.
To facilitate programming pipelines, NULL values are input
for regions, parameters and countMatrix slots,
in which case the default value is used as if the argument had been
missing. These slots will be updated after differential expression
(srnadiff) approach.
srnadiffExp constructor returns an srnadiffExp
object of class S4.
The show method informatively display object contents.
bamFilesA BamFileList object
with the full paths to the BAM files.
sampleInfoA data.frame with sample and experimental
design information. Each row describes one sample.
annotRegA GRanges with annotation information.
diffMethodA character storing the name of the method used
to compute the p-values. Can be DESeq2,
or edgeR.
chromosomeSizesA named vector with the sizes of the chromosomes.
coveragesThe sample coverages, a named
RleList object.
normFactorsA vector of normalization factors.
regionsA GenomicRanges of the candidate
differentially expressed regions.
countMatrixA matrix of non-negative integer count values, one row per region and one column per sample.
parametersAn named list. The parameters for the
segmentation methods. See parameters.
basedir <- system.file("extdata", package="srnadiff", mustWork = TRUE) sampleInfo <- read.csv(file.path(basedir, "dataInfo.csv")) gtfFile <- file.path(basedir, "Homo_sapiens.GRCh38.76.gtf.gz") annotReg <- readAnnotation(gtfFile, feature="gene", source="miRNA") bamFiles <- file.path(basedir, sampleInfo$FileName) srnaExp <- srnadiffExp(bamFiles, sampleInfo, annotReg) srnaExpbasedir <- system.file("extdata", package="srnadiff", mustWork = TRUE) sampleInfo <- read.csv(file.path(basedir, "dataInfo.csv")) gtfFile <- file.path(basedir, "Homo_sapiens.GRCh38.76.gtf.gz") annotReg <- readAnnotation(gtfFile, feature="gene", source="miRNA") bamFiles <- file.path(basedir, sampleInfo$FileName) srnaExp <- srnadiffExp(bamFiles, sampleInfo, annotReg) srnaExp