sRNAtoolbox: a collection of small RNA analysis tools
sRNAtoolbox is aimed to provide small RNA researchers with several useful tools including sRNA
expression profiling from deep sequencing experiments and several downstream analysis tools.
The center piece of sRNAtoolbox is sRNAbench, which allows for expression profiling
from deep sequencing experiments.
The other tools can be either launched on sRNAbench results, or independently using the
appropriate file formats. For more information please check our publications on
Nucleic Acids Research (and
previous version)
Currently the toolbox comprises the following methods:
sRNAbench is mainly designed to be uses in model species, or at least species with
an available genome assembly or fasta-formatted annotations. Several commonly used input formats like fastq, sra, fasta or read/count are supported.
It performs expression profiling of known microRNAs and other types of small RNAs, detection and analysis of isomiRs,
outputs several graphical summaries and predicts novel microRNAs both in plants and animals.
sRNAde is a module for the detection of differentially expressed sRNA. The input is
either a number of sRNAbench output folders or a user-given expression matrix.
For the detection of differentially expressed RNAs it applies 5 widely used methods:
t-test on Reas per Million, edgeR, DEseq, DEseq2 and noiseq. Besides individual methods output, it provides a
consensus differential expression file. Additionally, this tool can perform cluster
analysis (heatmaps), isomiR analysis and it gives a summary on the sequencing statistics
of all used samples (if the input has been from sRNAbench).
Currently, microRNA deep sequencing data is available for many species that do not count
with a proper genome assembly. In such cases, frequently simple homology based detection
methods are applied. These methods can lead to false positive predictions but will also
fail to detect microRNAs that are not present in the reference database (like miRBase).
sRNAgFree is aimed for the prediction and expression profiling of microRNAs in
non-model species. It based on the duplex properties formed by the guide and passenger
strand and applies homology and learning form known binding and Drosha/Dicer processing
patterns.
This tool is principally aimed for the analysis of reads that could not be mapped in
sRNAbench or other profiling tools. The results could either point towards contamination
sources or biological meaningful information like the presence of unexpected viral or
bacterial RNA molecules.
Most microRNA target prediction tools are implemented as webservers on a limited number of
species, or do allow only the prediction of a limited number of microRNAs and/or mRNAs.
miRNAconsTargets uses a selection microRNA target prediction programs and
applies them to the user supplied sets of microRNAs and 3'UTRs. It reports both,
individual predictions and a consensus prediction.
Most microRNA target prediction tools are implemented as webservers on a limited number of
species, or do allow only the prediction of a limited number of microRNAs and/or mRNAs.
miRNAconsTargets uses a selection microRNA target prediction programs and
applies them to the user supplied sets of microRNAs and 3'UTRs or CDS sequences. It reports both,
individual predictions and a consensus prediction.
User provided small RNA sequences (fasta format) are mapped against all assemblies available
in sRNAtoolbox. The tool has two different outputs, i) The conservation depth for all small RNA input sequences, i.e
the percentage of genomes in which the sequence was found, and ii)the percentage of mapped input sequences per genome.
In several small RNA research fields, the visual exploration of small RNA expression
pattern in a genome context will be helpful. sRNAjBrowser allows the user to analyse
the expression data in a genome context by means of a jBrowser implementation.
Furthermore, this tool is connected to NGSmethDB and can therefore also display
some selected methylation tracks.
sRNAjBrowserDE extends on sRNAjBrowser as it: i) allows the comparison of two experimental
groups, i.e. it visualizes the differential expression in a genome context and ii) it
allows to visualize the expression as a function of read length. This might me particularly
interesting for plant research as 24nt long and 21/22nt long reads have very different functions.
Overrepresented functional annotations in a list of target genes might give useful hints
on the functional implications of the microRNAs. sRNAfuncTerms takes a set of microRNAs
as input, retrieves the target genes from the underlying database and detected
overrepresented GO-terms among the target genes. Apart from the complete set of microRNAs,
it tests also all microRNA modules (combinations) which allow the user to detect the microRNAs
that act on the same pathways or share the same functions.
Helper Tools
These tools are intended to help the user to either setting up a local sRNAbench database
(Ensembl Parser, NCBI Parser, RNA Central Parser, genomic tRNA Parser) or to prepare input
data for other sRNAtoolbox tools. If you use any of the generated data in your publication, please
cite the papers given at the data retrieval page (NCBI, ENSEMBL, genomic tRNA and RNA central databases).