Trimming and Filtering Data

learning-objectives

  • Know why data may need to be filtered or trimmed
  • Know some of the basic functionalities of Trimmomatic
  • Use Trimmomatic to trim a FastQ file

Filtering and trimming of data

As the FastQ/FastQC lesson suggests, DNA generated from a sequencing experiment will have errors. One of the reasons we want to generate millions of reads in a high-throughput sequencing experiment is so that we have more than enough data to be able to get an “average” (multiple observations at the same location). Still though, there are limitations to sequencing technologies, and if our Phred score indicates that some reads or some portions of reads are of low quality, we may want to remove those data. Also, in generating a library for DNA sequencing, we often need to attach adapter sequences as part of the PCR/cloning of the sequence. Trimming can remove these contaminating sequences as well.

Trimmomatic is a popular software for trimming sequencing reads. There are a number of operations you can perform to selectively trim individual sequences. Remember, as FastQ file may contain perhaps millions of individual sequencing reads. When you use Trimmomatic, you will usually use a quality report (like the FastQC report) to decide, what operations on average, will give you the desired result. For example, if you generally have good sequence data (high Phred score) for the first 100 bases of your reads, but then the average quality is low for the last 25 bases, you might want to trim the last 25 bases off all reads. If you have adapters on your sequence that take up the first 15 bases, you may want to remove those before analyzing your data.

Trimmomatic Functionalities

The Trimmomatic website has a manual with the full description of functionalities. Here are just a few to consider (taken from the Trimmomatic manual):

Trimmomatic Function Description Example usage
SLIDINGWINDOW Perform a sliding window trimming, cutting once the average quality within the window falls below a threshold. By considering multiple bases, a single poor quality base will not cause the removal of high quality data later in the read 
  • SLIDINGWINDOW:<windowSize>:<requiredQuality>

  • windowSize: specifies the number of bases to average across

  • requiredQuality: specifies the average quality required
HEADCROP Removes the specified number of bases, regardless of quality, from the beginning of the read 
  • HEADCROP:<length>

  • length: The number of bases to remove from the start of the read
MINLEN Removes reads that fall below the specified minimal length. If required, it should normally be after all other processing steps. Reads removed by this step will be counted and included in the „dropped reads‟ count presented in the Trimmomatic summary 
  • MINLEN:<length>

  • length: Specifies the minimum length of reads to be kept

Use Trimmomatic to trim and filter RNA-Seq reads

Using the Jupyter Notebook, you will run Trimmomatic using the SLIDINGWINDOW and MINLEN functions. Then use FastQC to see how your trim settings have changed (and hopefully improved) the quality of the data.

  1. Access the JupyterLab Lesson on CyVerse and complete Notebook 2: Trimming bad data

    Notebook Preview

    This is a preview of the notebook in this lesson. Go back to JupyterLab Lesson on CyVerse to launch and use the interactive notebook.

Questions

Question

  1. What are some reasons why sequencing data may need to be trimmed and/or filtered
  2. What does the Trimmomatic SLIDINGWINDOW function do?

Bonus

Examine your FastQC report before and after trimming. What are some differences you can spot? Did trimming help? What settings might you change next to improve the outcome?