10.3 Processing raw data and getting data into R
The rawest form of data that most users get is probably in the form of fastq files obtained from the sequencing experiments. We will describe the necessary steps and the tools that can be used for raw data processing and if they exist, we will mention their R equivalents. However, the data processing is usually done outside of the R framework, and for the following sections we will assume that the data processing is done and our analysis is starting from methylation call files.
The typical data processing step starts with a data quality check. The fastq files are first run through quality check software that shows the quality of the sequencing run. We would typically use fastQC for this. However, there are several bioconductor packages that could be of use, such as
QuasR. We have introduced how to use some of these tools for sequencing quality check in Chapter 7. Following the quality check, provided everything is OK, the reads can be aligned to the genome. Before the alignment, adapters or low-quality ends of the reads can be trimmed to increase number of alignments. Low-quality ends mostly likely have poor basecalls, which will lead to many mismatches. Reads with non-trimmed adapters will also not align to the genome. We would use adapter trimming tools such as cutadapt or flexbar for this purpose, although there are a bunch of them to choose from. Following this, reads are aligned to the genome with a bisulfite-treatment-aware aligner. For our own purposes, we use Bismark(Krueger and Andrews 2011), however there are other equally accurate aligners, and some are reviewed here. In addition, the Bioconductor package
QuasR can align BS-seq reads within the R framework.
After alignment, we need to call C->T conversions and calculate the fraction/percentage of methylation. Most of the time, aligners come with auxiliary tools that calculate per-base methylation values. Normally, they output a tabular format containing the location of the Cs and methylation value and strand. Within R,
methylKit can call methylation values from BAM files albeit with some limitations. In essence, these methylation call files can be easily read into R and downstream analysis within R starts from that point. An important quality measure at this stage is to look at the conversion rate. This simply means how many unmethylated Cs are converted to Ts. Since we expect non-CpG methylation to be rare, we can simply count the number of C->T conversions in the non-CpG context and calculate conversion rate. The best way to do this would be via spike-in sequences where we expect no methylation at all. Since non-CpG methylation is tissue specific, calculating the conversion rate using non-CpG Cs might be misleading in some cases.
Krueger, and Andrews. 2011. “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications.” Bioinformatics 27 (11): 1571–2.