RNA-seq is a powerful tool for studying gene expression and identifying novel transcripts. However, before the data can be analyzed, it must first be cleaned and processed to remove low-quality reads and adapter sequences. This process is known as trimming. In this blog post, we will walk through the steps of trimming RNA-seq data using popular software tools such as Trimmomatic and Cutadapt.
RNA-seq data needs to be trimmed for several reasons.
Firstly, during the sequencing process, artificially generated adapter sequences are added to the ends of the reads. These sequences are used to attach the RNA molecules to the sequencing platform but do not map to regions in the genome. However, if not removed, these adapter sequences can interfere with downstream analysis and lead to inaccurate results. Trimming these sequences from the reads can help to improve the quality of the data.
Secondly, RNA-seq data may contain low-quality reads that are caused by sequencing errors, PCR bias, or other factors such as poor sample quality. These reads can also interfere with downstream analysis and lead to inaccurate results. By removing these low-quality reads, researchers can ensure that the data they are analyzing is of high quality and can be used to generate accurate and reliable results.
Finally, after trimming adapters and low-quality reads, some reads may be too short to be useful for downstream analysis. Removing these reads can increase the efficiency of mapping/alignment and reduce the number of inaccurately mulit-mapped regions in the genome.
The first step in trimming RNA-seq data is to assess the quality of the raw reads. This can be done using software such as FastQC, which generates a report that includes information on read length, GC content, base quality, and sequencing adapter contamination. If the data is of poor quality, it may need to be re-sequenced or excluded from further analysis.
The next step is to remove adapter sequences from the reads. These sequences are added during library preparation and can interfere with downstream analysis. Cutadapt or Trimmomatic can be used to trim adapters from the reads by searching for the adapter sequence and removing it along with a certain number of bases from the read.
After adapter trimming, the next step is to remove low-quality reads. This can be done by setting a minimum quality threshold and removing any bases that fall below this threshold. Trimmomatic and Cutadapt both have built-in quality trimming options that can be used for this purpose.
Finally, reads that are too short after trimming can be removed. This step is important to ensure that only high-quality reads are used for downstream analysis.
Trimming RNA-seq data is an important step in the analysis process. By removing low-quality reads, adapter sequences, and bases that fall below a certain quality threshold, researchers can ensure that the data they are analyzing is of high quality and can be used to generate accurate and reliable results. There are different tools available to do the trimming process, such as Trimmomatic and Cutadapt, both are widely used and have good documentation.
Upload your raw FASTQ data to Pluto and we will run the proper pipelines, including trimming, output a QC, and be ready to analyze without managing a pipeline.
Watch a video on analyzing an RNA-Seq experiment in Pluto