Analyzing eCLIP and iCLIP data with DEWSeq

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Preface

#**Note:** if you use DEWSeq in published research, please cite:
#
#> Authors. (Year)
#> Title
#> *Genome Biology*, **15**:550.
#> [10.1186/s13059-014-0550-8](http://dx.doi.org/10.1186/s13059-014-0550-8)

Related packages

Other Bioconductor package with similar aim:

Please get familiar with this Bioconductor package before you are using DEWSeq

Acknowledgements

Wolfgang Huber and Matthias Hentze for mentoring, advice and discussion. Benjamin Lang and Gian Tartaglia for great help with functional analysis and benchmarking, as well as feedback on the vignette. Ina Huppertz for helpful feedback and language improvement on the vignette. Mike Love, Simon Anders, Bernd Klaus and Frederick Ziebell for comments and discussion.

Introduction

Introduction to eCLIP sequencing

RNA-binding proteins (RBPs) play a key role in the life-time of RNAs. They are involved in RNA synthesis, stability, degradation, transport and translation and add an important layer of regulation in the cell. Over 1,900 murine and over 1,400 human RBPs were detected in different high-throughput detection studies, many of them without known RNA-binding function [@Hentze2018].

It is of great interest to detect an RBP's binding sites to study the underlying mechanism of its regulatory potential. Individual nucleotide resolution crosslinking and immunoprecipitation (iCLIP) [@Konig2010] and the further enhanced CLIP (eCLIP) protocol [@VanNostrand2016] rely on UV crosslinking inducing covalent bonds of RNA and proteins in close proximity. When reverse transcribing the RNA fragment bound to the protein, a majority of the time the reverse transcriptase will terminate at the crosslink site. Although eCLIP introduces updates in chemistry, the use of a size-matched input (SMI) control sample is an essential addition to the protocol which can be also adapted to iCLIP or similar protocols.

knitr::include_graphics("truncation.png")

In iCLIP and eCLIP, truncation events are extracted as one nucleotide position next to the cDNA fragment (aligned read). In the classical protocols real truncations cannot be distinguished from read-through reads or other reads coming from otherwise truncated reads, which might be caused by RNA modifications or the crosslinking sites of other proteins. This might be different for each individual proteins (and the remaining polypeptide of the digested protein). Other protocols like HITS-CLIP and PAR-CLIP [@Hafner2010] rely exclusively on read-through events (although using other reverse transcriptases). While hybrid approaches exist, the technical difficulty of these protocols requires many optimizations steps, which makes them rather hard to combine.

# fig.width=200, out.width="200px"
knitr::include_graphics("truncationsite.png")

In summary, iCLIP and eCLIP protocols provide count data for single-nucleotide positions which might be the result of many heuristic events. These are described in the next chapter.

The idea behind DEWSeq

Understanding the signal

Binding modes

Unlike transcription factors, RNA-binding proteins have many different binding modes [@Hentze2018], some bind in a sequence specific manner, some have preference for structures (like stem-loops), some prefer to bind RNA modifications, others are mostly found at UTRs. A large portion of RBPs do not have a known RNA-binding domain and bind using disordered regions with unknown target preferences.

# fig.width=200, out.width="200px"
knitr::include_graphics("binding_modes.png")

All these different binding modes can result in different crosslinking patterns, hence different truncation event patterns.

Chance of crosslinking

UV crosslinking at 254 nm is used to induce covalent chemical bonds within very close proximity of RNAs to proteins. This provides a "snapshot" of the RNA binding event and ensures that no disassociation of the RNA-protein complexes takes place in further steps. Typically, crosslinking with Stratlinker(R) UV crosslinkers has a limited efficiency of around 1% or less (depending on UV lamp intensity and time of irradiation). Newer crosslinkers with lasers reach up to 5-20% crosslinking efficiency depending on the protein and experimental setup.

# fig.width=200, out.width="200px"
knitr::include_graphics("crosslinking_chance.png")

Depending on the type of protein and the binding mode, this can result in different crosslinking patterns for each protein. While crosslinking affects all RBPs, the immunoprecipitation (IP) step should only enrich for RNA fragments bound to the RBP of interest. In turn, this means that crosslinked samples show a certain background which is different to normal expression patterns measured by RNA-sequencing.

Background and antibodies

Enrichment of targets

Immunoprecipitating your (crosslinked) protein and performing sequencing without any RNA digestion is called RIP-seq (without crosslinking) or HITS-CLIP (with crosslinking). These studies nicely show that the enrichment of your protein with antibodies, recognizing protein tags or native epitops will enrich RNA fragments bound to the protein. However, the immunoprecipitated ('IP'ed) samples closely resemble a full transcriptomic background. Depending on the specificity of your antibody, purification method (e.g. type of beads) and many other factors, this degree of resemblance can vary for different proteins. The authors do not know any cases where IP steps exclusively purify targets. If you find any such cases, please contact the authors, for further discussions (we would really like to know).

Contaminants vs background

If you take a closer look at the first iCLIP analysis protocols (and lot of protocols to date), you will notice that snoRNAs, lincRNAs and other short RNAs are excluded from the analysis, although they make up a majority of the signal. They were either treated as contaminants and dismissed right away, or more recently it was recommended to compare the i/eCLIP data with an orthogonal dataset like an RNA-seq knockout data and only look at the regions of interest that are common in both. As already mentioned, it was shown that size-matched input (SMI) controls can control for different artifacts coming from crosslinking and library preparation [@VanNostrand2016]. After applying an enrichment correction it is possible to detect lincRNA, snoRNA binders.

As we previously discussed, crosslinked samples do have a different background compared to expressionlevels (as detected with RNA-seq). Therefore, it is of great importance to compare the IPed sample to a proper input control.
SMI controls can be easily applied to any CLIP protocols.

RNA fragment length and RNase concentrations

To narrow down the target RNAs to the binding site region, RNase is used to digest RNA not protected by your protein. Using different RNase concentrations will result in different RNA fragment lengths. In addition, it was shown that the crosslinking pattern will be affected by longer RNA fragments, which occurs as a result of the protein of interest binding closely together, protecting a larger RNA region from digestion [@Haberman2017].

knitr::include_graphics("digestionpatterns.png")

Read-throughs

iCLIP and eCLIP depend on the termination of the reverse transcriptase at crosslink sites. In contrast, HITS-CLIP and PAR-CLIP need reverse transcriptases reading through the crosslink sites. Both methods use different reverse transcriptases with different likelihood of truncating or reading through [@Hocq2018]. The chance of this event will also be affected by the polypeptide which is left after protein digestion of the protein of interest.

knitr::include_graphics("readthrough.png")

"Read-through" events can truncate at crosslink sites from the same protein, a different protein, an RNA modification, any other unknown events or finish at the end of an RNA fragment. Due to the low efficiency the crosslinking of multiple proteins is usually less likely, however not improbable.

Early truncations

Early truncations can also occur on a long RNA fragment with multiple crosslink sites from different proteins or multiple crosslink sites from the same protein. Again, although low crosslinking efficiency reduces the chance of having multiple crosslink sites at once, those events occur stochastically.

knitr::include_graphics("earlytruncation.png")

Method

Differentially expressed sliding windows

Because of the properties of i/eCLIP data described above, we use a sliding window approach (where different window and step sizes can be chosen in the preprocessing step) and test for significant enrichment in the IP over the control. For this, we extended r Biocpkg("DESeq2") for a one-sided test: first, we filter windows with negative wald test statistic values (windows with negative log2FoldChange) followed a right-tailed test on the remaining windows. The advantage is that the filtering step increases performance when testing millions of windows.

knitr::include_graphics("dewseqoverview.png")

Combining significant windows

In contrast to large fragments detected in ChIP-seq, the truncation sites are only one nucleotide. To combine windows in ChIP-seq dataset, r Biocpkg("csaw") defines binding regions which are then subjected to multiple hypothesis correction. In ChIP-seq many windows share coverage from large cDNA fragments and therefore r Biocpkg("csaw") performs multiple hypothesis correction on binding site with the SIMES method [@SIMES1986]. SIMES joins coverage blocks and treats them as one entity for multiple hypothesis correction. In large, SIMES punished p-values for long stretches of overlapping windows. Please refer to the r Biocpkg("csawUserGuide") for detailed description.

Single-nucleotide truncation events do not have the same property as cDNA fragments and the transcriptomic background for each gene in eCLIP or iCLIP often results in large coverage blocks. As seen in the picture below, the windows do only share information with the overlapping windows. Therefore, DEWSeq corrects the p-value for each overlapping window with the number of overlaps using Bonferroni correction.

# fig.width=200, out.width="200px"
knitr::include_graphics("overlapping_sliding_windows.png")

We then apply FDR correction on the p-values corrected for overlaps using Bonferroni correction.

Prerequisits for DEWSeq

Most importantly, replicates and input controls are required for the significance testing for DEWSeq. Currently, we only allow the use of r Biocpkg("DESeq2")'s wald test and LRT test. Please contact the authors if you would like to use more complex models in your analysis.

Input controls

Typically, CLIP-seq sa mples show a background which comes from a mix of various transcripts with different expression levels. Additionally, UV crosslinking does not affect RNAs in a linear fashion, therefore an appropriate input control is needed for the analysis of eCLIP/iCLIP data. Unfortunately, total RNA-seq does not reflect the UV crosslinked background. Also neither IgG-, empty beads, nor similar controls seem to be appropriate input controls. Mostly, IgG and empty bead controls suggest that the IP input is very clean, which is not what is seen in the data. IP sample often have a broad background signal and bias towards sno and lincRNAs, as well as other RNA types. Often, a majority of the reads in the IP come from such background, although the IgG and empty bead control samples cannot correct for that.

We recommend size-matched input controls (SMI) controls as performed in the eCLIP protocol [@VanNostrand2016]. This type of input control is not protocol specific, therefore can be easily adapted to iCLIP or other CLIP studies. It proves useful to exclude many false positives and can control for truncations caused by interferences.

knitr::include_graphics("controls.png")

Empty beads controls are usually crosslinked and control for unspecific background caused through beads, IgG controls are usually crosslinked and control additionally for background caused by the invariable region of the antibody. No crosslink (noCL) controls for background caused by the protein and the functional antibody. SMI controls are crosslinked but not enriched, it controls among others for the background caused by crosslinked RNA.

r Biocpkg("DEWSeq") uses SMI controls to calculate enrichment in the IPed samples. We recommend to use IgG or empty beads controls to flag (expression dependent) regions for optional removal.

Replicates

Biological replicates in high-throughput studies are needed in the significance testing process to ensure reproducibility of the results. Please refer to the r Biocpkg("csaw") package for discussion on this topic.

In general, we recommend to ask yourself how many replicates would you use when performing RNA-seq. Usually, the answer is, at least three for the sample and three for the control (depending on many experimental factors which also will apply for your CLIP study). As a rule of thumb, your number of replicates should not be less if you are interested in an transcriptome wide analysis of your RNA-binding protein.

Data pre-processing

DEWSeq needs a countmatrix and preprocessed annotation. The preprocessing is done in two general steps.

  1. Raw data preprocessing, alignment and PCR duplication removal
  2. Extraction of crosslink sites, preprocessing of annotation and counting.

Raw data pre-processing

From fastq to bam files

eCLIP or iCLIP data preprocessing can be time-consuming. Generally, the pre-processing steps are:

Eventually, you will end up with sorted .bam files.

Crosslink site extraction and counting

From bam and gff3 files to count matrix and annotation

htseq-clip is the preprocessing pipeline developed for the use for DEWSeq, but other tools may be used. htseq-clip performs the following steps:

knitr::include_graphics("htseq-clip.png")

In addition, htseq-clip can also:

htseq-clip can be installed via Python package installer as:

```{bash, eval = FALSE} pip install htseq-clip

Additional details on htseq-clip functions and required input files are [described here](https://htseq-clip.readthedocs.io/en/latest/)

All you need is

 * .bam files (PCR duplication removed, sorted)
 * .bam.bai index files 
 * .gff3 files (Preferrably from Gencode or similar)

An earlier, Python 2.7 compatibile version of htseq-clip is provided as a [singularity container](https://sylabs.io/) image and comes 
with a [Snakemake](https://snakemake.readthedocs.io/en/stable/) workflow for processing on local computers,
servers, or computer clusters.

Tip: You can use Sailfish, Kallisto, RSEM, Salmon or any (pseudo)aligner to 
get an estimation of the different expression levels of transcripts. Use this to 
filter the annotation before flattening it. 


# Detection of enriched regions with DEWSeq

DEWSeq uses a sliding window approach with DESeq2 for enriched binding sites. 

## Installation

DEWSeq can be installed from Bioconductor as follows:

```r
if(!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("DEWSeq")

Load the library

First step is to load the required libraries with this command

require(DEWSeq)
require(IHW)
require(tidyverse)

Importing data for DEWSeq

This example uses ENCODE SLBP data set, contains two IP and one input control (SMI) samples which is not ideal, however it is small enough as test data.

countFile <- file.path(system.file('extdata',package='DEWSeq'),'SLBP_K562_w50s20_counts.txt.gz')
annotationFile <- file.path(system.file('extdata',package='DEWSeq'),'SLBP_K562_w50s20_annotation.txt.gz')

Read data

We need to read in the

which can be prepared with htseq-clip (see above)

For this, we recommend data.table or tidyr packages to read in the data swiftly. In our experience, data.table is significantly faster than tidyr.

library(tidyverse)
library(data.table)

First we read in counts

countData <- fread(countFile, sep = "\t")

or alternatively with tidyr (slower):

countData <- read_tsv(countFile)

Then we read in the annotation data frame

annotationData <- fread(annotationFile, sep = "\t")

or alternatively with tidyr (slower):

annotationData <- read_tsv(annotationFile)

Let's take a look at the data files

head(countData)
dim(countData)
head(annotationData)
dim(annotationData)

Finally we have to create a sample description

colData <- data.frame(
  row.names = colnames(countData)[-1], # since the first column is unique_id
  type      = factor(
                c(rep("IP", 2),    ##  change this accordingly
                  rep("SMI", 1)),  ##
                levels = c("IP", "SMI"))
)

This function will parse the annotation file and create a DESeq object

ddw <- DESeqDataSetFromSlidingWindows(countData  = countData,
                                      colData    = colData,
                                      annotObj   = annotationData,
                                      tidy       = TRUE,
                                      design     = ~type)
ddw

This will return a DESeq object with the coordinates and annotation stores as rowAnnotation.

Prefiltering

Prefiltering becomes even more important with large numbers of sliding windows. Please find more details here: DESeq2 vignette on pre-filtering

keep <- rowSums(counts(ddw)) >= 10
ddw <- ddw[keep,]

Estimating Size Factors

For library normalisation, rather than modifying the raw counts, DESeq2 estimates size factors which are incorportated in the anlaysis. For more information, please refer to the DESeq2 vignette.

The standard procedure to estimate size factors is

ddw <- estimateSizeFactors(ddw)
sizeFactors(ddw)

Estimate size factors based on a specific set of RNAs

CLIP data often shows high expression levels of small RNAs like snoRNAs, lincRNAs etc, therefore you might want to normalise based on protein coding genes only.

ddw_mRNAs <- ddw[ rowData(ddw)[,"gene_type"] == "protein_coding", ]
ddw_mRNAs <- estimateSizeFactors(ddw_mRNAs)
sizeFactors(ddw) <- sizeFactors(ddw_mRNAs)
sizeFactors(ddw)

Estimate size factor for assymetric data

In general, when there is asymmetry in the data, like enrichment in IP over control, it is a good pratice to call differentially expressed features with DESeq2 and then exclude them for normalisation. This might be adjusted for your proteins.

First we identify significant windows in IP or the controls.

ddw_tmp <- ddw
ddw_tmp <- estimateDispersions(ddw_tmp, fitType = "local", quiet = TRUE)
ddw_tmp <- nbinomWaldTest(ddw_tmp)

tmp_significant_windows <- 
                results(ddw_tmp,
                    contrast = c("type", "IP", "SMI"),
                    tidy = TRUE,
                    filterFun = ihw) %>% 
                dplyr::filter(padj < 0.05) %>% 
                .[["row"]]
rm("ddw_tmp")

Then we exclude those for the estimation of the size factors

ddw_mRNAs <- ddw_mRNAs[ !rownames(ddw_mRNAs) %in% tmp_significant_windows, ]
ddw_mRNAs <- estimateSizeFactors(ddw_mRNAs)
sizeFactors(ddw) <- sizeFactors(ddw_mRNAs)

rm( list = c("tmp_significant_windows", "ddw_mRNAs"))

sizeFactors(ddw)

Differential expressed windows analysis

Estimate dispersions and wald test

The next step is to estimate the dispersions and perform the wald test with these two functions.

Please read up on fitType in the r Biocpkg("DESeq2") package and adjust according to your data set.

ddw <- estimateDispersions(ddw, fitType = "local", quiet = TRUE)
ddw <- nbinomWaldTest(ddw)

You can check the dispersion estimates with the following function:

plotDispEsts(ddw)

Significance testing

In the next step we perform a one-sided signficance test, looking for enrichment in the IP samples vs the SMI controls. Also it will perform a correction for p-values of overlapping windows: The function will determine how many overlapping windows for each window there are (this can vary at the end of features, e.g. a gene) and then perform Bonferroni for each window.

These family-wise corrected windows will corrected for multiple testing with Benjamini-Hochberg.

resultWindows <- resultsDEWSeq(ddw,
                              contrast = c("type", "IP", "SMI"),
                              tidy = TRUE) %>% as_tibble

resultWindows

You might be interested to correct for multiple hypothesis testing with IHW.

resultWindows[,"p_adj_IHW"] <- adj_pvalues(ihw(pSlidingWindows ~ baseMean, 
                     data = resultWindows,
                     alpha = 0.05,
                     nfolds = 10))

Here some basic stats about the differentially expressed windows:

resultWindows <- resultWindows %>% 
                      mutate(significant = resultWindows$p_adj_IHW < 0.01)

sum(resultWindows$significant)

r sum(resultWindows$significant) windows are significant.

Here are the top 20 from r resultWindows %>% filter(significant) %>% .[["gene_name"]] %>% unique %>% length genes:

resultWindows %>%
   filter(significant) %>% 
   arrange(desc(log2FoldChange)) %>% 
   .[["gene_name"]] %>% 
   unique %>% 
   head(20)

SLBP is an histone-RNA binding protein, so we are quite happy to see that the majority of the hits are coming from histone genes.

Combining regions

resultWindows contains information about the differential expression of windows. Now we would like to combine overlapping windows to a binding region.

resultRegions <- extractRegions(windowRes  = resultWindows,
                                padjCol    = "p_adj_IHW",
                                padjThresh = 0.01, 
                                log2FoldChangeThresh = 0.5) %>% as_tibble

Since we already corrected for the family wise error, we use the extractRegions function to combine the overlapping significant windows and provide metrics for best and worst p-adjusted value, as well as best and worst log2 fold change.

resultRegions

r nrow(resultRegions) binding regions were found which can exported as BED file so you can browse the regions in your genome browser of choice or use it for further analyses.

toBED(windowRes = resultWindows,
      regionRes = resultRegions,
      fileName  = "enrichedWindowsRegions.bed")

Further analyses which can be done is enrichment of gene types, sequence motif analysis of the regions, secondary structure analysis of binding regions and other functional analyses.

Discussion

The authors very much welcome any feedback, comments and suggestions.

Session Info

sessionInfo()

References



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DEWSeq documentation built on Feb. 7, 2020, 2 a.m.