mobileRNA

An R package for analysis of sRNA & mRNA populations in plants and animals. Specalised in the identification of RNA populations in systems with multiple genomes, such as plant graft systems.

Overview
Installation
Quick Start
Output
Advanced Analysis

Overview

mobileRNA is an R package that provides a pipeline for pre-processing and analysis of small RNA (sRNA) and messenger RNA (mRNA) sequencing data, primarily for the identification of mobile RNAs in plant graft systems. But, likely has other applications for systems containing more than one genotype, such as dual-host systems, to identify RNA molecules produced by each genotypes. mobileRNA also supports routine treatment vs control analysis to identify changes in sRNA population (abundance & production). These two workflows have be separated as mobile RNA analysis & the core RNA analysis, respectively (Figure 1).

Most available genomics approaches to identify RNA molecules produced by two different genotypes in a biological sample involve the alignment on a genotype of reference and post alignment screening of genetic variants. These methods have many possible limitations. mobileRNA aims to circumvents such problems with an alignment step which is simultaneously performed on both genomes involved. This approach considers that alignment tools already implement an algorithm ideal for identifying the best matches of reads to a given genome reference, but they do not account for potential matches to DNA sequences which are not provided as reference. The downstream analysis allows for differential analysis for the comparison in abundance, the identification of changes in RNA production and the identification of putative mobile RNA molecules via the filtering of RNA molecules which uniquely map to the mobile genome of to the other grafting partner involved, which would represent the putative mobile RNA molecules.

Figure 1: Basic diagram of mobileRNA workflows.

Installation

The latest version of the package can be install directly from this GitHub repo:

if (!require("devtools")) install.packages("devtools")
devtools::install_github("KJeynesCupper/mobileRNA", ref = "main")

To install from Bioconductor:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("mobileRNA")

Load package into R library:

library(mobileRNA)

Installation of OS dependencies

In order to utilise the mobileRNA method, please install the following OS dependencies within a Conda environment:

For sRNA data, ShortStack (>= 4.0) (Axtell 2013) is requires. Please consider that ShortStack is not available for Windows, hence, Windows users will either need to opt to use a virtual machine or Windows Subsystem for Linux In either case, both R and ShortStack will need to be installed and used on the Linux side. Please head to ShortStack to see the recommended installation instructions with Conda. This will ensure all dependencies are available within the same environment.

For mRNA data, HISAT2 (Kim 2015), HTSeq (Anders, Pyl, and Huber 2014), SAMtools (Danecek P 2021) are required within the same Conda environment [@Anaconda].

Quick Start for mobile sRNA analysis

The example data set included with mobileRNA simulates grafting between eggplant and tomato where the scion is eggplant and tomato is the rootstock. There is example mRNAseq and sRNAseq files which represent samples taken from an eggplant leaf tissue. Here we will locate sRNA produced by the tomato rootstock which have traveled into the eggplant leaves

1. Merging genome assemblies

Merge the FASTA genome assemblies of tomato and eggplant into a single reference file stored in your desired directory.

fasta_1 <- system.file("extdata","reduced_chr12_Eggplant.fa", 
                       package="mobileRNA")

fasta_2 <-system.file("extdata","reduced_chr2_Tomato.fa",
                      package="mobileRNA")
# define temporary output directory - replace with your directory
output_assembly_file <- file.path(tempfile("merged_assembly", 
                                           fileext = ".fa"))

# merge
merged_reference <- RNAmergeGenomes(genomeA = fasta_1,
                                    genomeB = fasta_2,
                                    output_file = output_assembly_file)

2. Alignment

Align sRNA sequencing reads to the merged genome using our unique alignment pipeline wrapped by the mapRNA() function.

samples <- system.file("extdata/sRNAseq",package="mobileRNA")

output_location <- tempdir()

mapRNA(input = "sRNA",
       input_files_dir = samples, 
       output_dir = output_location, 
       genomefile = output_assembly_file,
       condaenv = "/Users/user-name/miniconda3/envs/ShortStack4",
       mmap = "n")

3. Import pre-processed data into R

Import the results from the alignment step into R using the RNAimport() function. This requires the directory storing the sample output folders and the same of the samples to import from the directory.

# Directory containing results
results_dir <-  file.path(output_location,"2_alignment_results")

# Sample names and total number of reads, in the same order. 
sample_names <- c("selfgraft_demo_1", "selfgraft_demo_2", "selfgraft_demo_3",
              "heterograft_demo_1", "heterograft_demo_2", "heterograft_demo_3")


sRNA_data <- RNAimport(input = "sRNA", 
                       directory = results_dir,
                       samples = sample_names)

Now lets use the comprehensive dataset, load the pre-processed data:


data("sRNA_data")

4. Calculate the consensus dicercall

For a given sRNA cluster, each replicate has determined the dicercall, also known as the sRNA class, based on the length in nucleotides of the most abundant sRNA. This can be drawn from all samples or named samples. The output can be used as threshold values for downstream analysis, and to remove data noise depending on data quality.

sRNA_data_summary <- RNAdicercall(data = sRNA_data, tidy = TRUE )

5. Differential analysis of sRNA population

Undertake differential analysis of sRNA within the experimental design to explore changes in abundance. The function allows for two methods; edgeR or DESeq.

## sample conditions in order within dataframe
groups <- c("Selfgraft", "Selfgraft", "Selfgraft", 
            "Heterograft", "Heterograft", "Heterograft")


## Differential analysis of whole dataset: DESeq2 method 
sRNA_DESeq2 <- RNAdifferentialAnalysis(data = sRNA_data,
                                       group = groups,
                                       method = "DESeq2")
                              



                              
# save output as txt file 
write.table(sRNA_DESeq2, "./sRNA_DA_output.txt")

Summarise results:

RNAsummary(sRNA_DESeq2)

How about summarizing the sRNA population which are statistically significant:

RNAsummary(sRNA_DESeq2, alpha=0.05)

6. Identify putuative mobile sRNA

Select the putative mobile sRNA clusters using RNAmobile(). This requires supplying the function with a unique identifier of the rootstock genome. The merging step placed the prefix "B" to the tomato chromosomes.

# define control samples
controls <- c("selfgraft_1", "selfgraft_2", "selfgraft_3")

mobile_sRNA <- RNAmobile(input = "sRNA",
                         data = sRNA_DESeq2, 
                         controls = controls,
                         genome.ID = "B", 
                         task = "keep")



# save output as txt file 
write.table(mobile_sRNA, "./sRNA_mobile_output.txt")

Output

A data frame where rows represent potential mobile sRNA clusters. The columns include information on the cluster, individual sample replicates, and more.

Information on the cluster:

Locus: Name of the chromosome or scaffold, start position & end position
chr: Name of the chromosome or scaffold
start : Start position of the cluster
end : End position of the cluster

Information on each sample replicate:

Cluster: Cluster Name
Dicercall : The size of most abundant small RNA size
Count : Number of reads, default is uniquely aligned (e.g. not multi-mapping).
MajorRNA : RNA sequence of the most abundant sRNA in cluster within the sample
RPM : Reads per Million
FPKM : Fragments Per Kilobase of transcript per Million

Other information

DicerConsensus : Consensus sRNA class
DicerCounts : Number of replicates which contributed to the consensus dicercall sRNA class
CountMean : Count mean (Calculated by RNAdifferentialAnalysis())
log2FoldChange : Log2FoldChange--The effect size estimate
pvalue : P value, the probability under the assumption of no effect or no difference, of obtaining a result equal to or more extreme than what was actually observed
padjusted : A p-value adjustment
logCPM : log counts per million, measure of expression level

Mobile mRNA Analysis

For the identification of mobile mRNA, the same mobileRNA workflow can be used. For the alignment step, as well as using a merged genome assembly file (FASTA) the user will also need to generate a merged genome gene annotation file (GFF) with the same chromosome labels. As well as a dataframe contain sample data where rows represent samples, and column 1 stores the sample names, column 2 stores the fastq file name for mate 1 and, for pair-end alignment, column 3 stores the fastq file name for mate 2. While for all other downstream functions simply change some of the parameters to set the option to mRNA input data type.

Advanced Analysis

The quick start analysis allows for the retrieval of a data frame the sRNA total population in the experimental design and also the candidate mobile sRNAs. Users may want to advance the analysis and plot the data:

Exploratory and quality control analysis, such as PCA and distance matrices.
Summary values including RPM mean and Count mean across specific samples.
Plotting of the distribution of sRNA classes and the consensus dicercall across individual replicates or across the data set.
Volcano plot for mRNA data

Advanced features also include tools to assist functional analysis:

Identify genomic features associates with the sRNA clusters (ie. to explore the RNA expression of sRNA-producing genes in parallel analysis)
Extract consensus RNA sequence for target prediction analysis

References

Anders, Simon, Paul Theodor Pyl, and Wolfgang Huber. 2014. “HTSeq – A Python framework to work with high-throughput sequencing data.” Bioinformatics. http://dx.doi.org/10.1093/bioinformatics/btu638.

Axtell, MJ. 2013. “ShortStack: comprehensive annotation and quantification of small RNA genes.” Rna 19: 740–51. https://doi.org/10.1093/nar/gky316.

Danecek P, Liddle J, Bonfield JK. 2021. “Twelve years of SAMtools and BCFtools.” GigaScience 10. http://dx.doi.org/10.1186/s13059-014-0550-8.

Kim, Langmead, D. 2015. “HISAT: a fast spliced aligner with low memory requirements.” Nature Methods 12. https://www.nature.com/articles/nmeth.3317.

Last updated: 18-10-2023

KJeynesCupper / mobileRNA