Remove contamination and host reads

Author(s) orcid logoMina Hojat Ansari avatar Mina Hojat Ansariorcid logoBérénice Batut avatar Bérénice Batut
Overview
Creative Commons License: CC-BY Questions:

  • What preprocessing steps are required to obtain cleaned reads for downstream analysis?

  • How can we identify and remove contaminant or host-derived reads from raw sequencing data?

Objectives:

  • Identify reads originating from contaminants or host genomes.

  • Remove those reads to produce high-quality, clean metagenomic data suitable for downstream analyses.

Requirements:
Time estimation: 1 hour
Supporting Materials:
Published: Dec 18, 2025
Last modification: Dec 18, 2025
License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License. The GTN Framework is licensed under MIT
Revision: 1

Metagenomic sequencing captures all DNA present in a sample, including the microbial community, host DNA, and potential environmental or external contaminants (such as human DNA introduced during sample handling or sequencing).
Before performing taxonomic or functional analysis, it is essential to remove these host-drived or contaminant reads to avoid misleading downstream interpretations.

In this tutorial, we will learn how to identify and remove host or contaminant reads using Galaxy. We will:

  • Map raw reads to a host reference genome using Bowtie2 and extract unmapped reads.
  • Repeat the process with unmapped reads against a human reference genome to remove potential human contamination.
  • Generate a final set of clean, non-host reads suitable for downstream analyses such as assembly, binning, or profiling.

To test and illustrate the process, we use bee gut metagenome samples and focus on filtering out both host (bee) sequences and potential human contamination from the reads.

Agenda

In this tutorial, we will cover:

  1. Prepare Galaxy and data
  2. Map reads to a host genome with Bowtie2
  3. Re-pair unmapped reads
  4. Summarize mapping statistics
  5. Remove potential human contamination
  6. Conclusion

Prepare Galaxy and data

Any analysis should get its own Galaxy history. So let’s start by creating a new one:

Hands On: Data Upload

  1. Create a new history for this tutorial

    To create a new history simply click the icon at the top of the history panel:

    UI for creating new history

  2. Rename the history

    1. Click on (Edit) next to the history name (which by default is “Unnamed history”)
    2. Type the new name
    3. Click on Save
    4. To cancel renaming, click the “Cancel” button

    If you do not have the (Edit) next to the history name (which can be the case if you are using an older version of Galaxy) do the following:

    1. Click on Unnamed history (or the current name of the history) (Click to rename history) at the top of your history panel
    2. Type the new name
    3. Press Enter

Now, we need to import the data

Hands On: Import datasets

  1. Import the files from Zenodo or from the shared data library:

    https://zenodo.org/records/17829290/files/SRR24759598_1.fastq.gz
https://zenodo.org/records/17829290/files/SRR24759598_2.fastq.gz
https://zenodo.org/records/17829290/files/SRR24759616_1.fastq.gz
https://zenodo.org/records/17829290/files/SRR24759616_2.fastq.gz
    • Copy the link location

    • Click Upload at the top of the activity panel

    • Select Paste/Fetch Data

    • Paste the link(s) into the text field

    • Press Start

    • Close the window

    As an alternative to uploading the data from a URL or your computer, the files may also have been made available from a shared data library:

    1. Go into Libraries (left panel)
    2. Navigate to the correct folder as indicated by your instructor.
      • On most Galaxies tutorial data will be provided in a folder named GTN - Material –> Topic Name -> Tutorial Name.
    3. Select the desired files
    4. Click on Add to History near the top and select as Datasets from the dropdown menu

    5. In the pop-up window, choose

      • “Select history”: the history you want to import the data to (or create a new one)
    6. Click on Import

  2. Create a paired collection named ‘Raw reads’ that includes both pairs.

    • Click on Select Items at the top of the history panel Select Items button
    • Check all the datasets in your history you would like to include

    • Click n of N selected and choose Advanced Build List

      build paired collection menu item

    • You are in the collection building wizard. Choose List of Paired Datasets and click ‘Next’ button at the right bottom corner.

      collection building wizard paired list

    • Check and configure auto-pairing. Commonly matepairs have suffix _1 and _2 or _R1 and _R2. Click on ‘Next’ at the bottom.

      edit and build a paired list collection

    • Edit the List Identifier as required.
    • Enter a name for your collection
    • Click Build to build your collection
    • Click on the checkmark icon at the top of your history again

Map reads to a host genome with Bowtie2

To remove host contamination, we start by mapping the reads to the host genome using Bowtie2 to detect and remove host-derived sequences.

Hands On: Remove host reads
  1. Bowtie2 ( Galaxy version 2.5.3+galaxy1) with the following parameters:
    • “Is this single or paired library”: Paired-end Dataset Collection
      • “FASTQ Paired Dataset”: Input reads
      • “Write unaligned reads (in fastq format) to separate file(s)”: Yes
      • “Do you want to set paired-end options?”: Yes
    • “Will you select a reference genome from your history or use a built-in index?”: Use a built-in genome index
      • “Select reference genome”: A. mellifera genome (apiMel3, Baylor HGSC Amel_3.0)
    • “Set read groups information?”: Do not set
    • “Select analysis mode”: 1: Default setting only
    • “Do you want to tweak SAM/BAM Options?”: No
    • “Save the bowtie2 mapping statistics to the history”: Yes
  2. Run the tool. The outputs will include:
    • Mapping statistics report (bowtie2.log)
    • Unaligned (unmapped) forward and reverse reads
  3. These unmapped reads represent sequences not belonging to the host and will be used in the next step.
Comment: Tip

Host reference genomes vary depending on the study organism. You can upload a FASTA file of your host genome if it is not available as a built-in index.

Re-pair unmapped reads

We now combine the unmapped forward and reverse reads into a new paired-end dataset for further processing.

Hands On: Combine unmapped forward and reverse reads into a paired collection
  1. Zip collections with the following parameters:
    • “Input 1”: output_unaligned_reads_l
    • “Input 2”: output_unaligned_reads_r
    1. This step creates a new paired-end collection that represents all reads not aligned to the host genome. Rename this output collection to ‘Reads without bee reads’
    Comment: Note

    Zipping restores the normal paired-end structure, which is required for downstream tools or for rerunning the workflow on another reference.

Summarize mapping statistics

Once the host mapping is complete, we use MultiQC to summarize and visualize the mapping statistics, helping us assess how many reads were removed and how many remain.

Hands On: Evaluate host read removal results
  1. MultiQC ( Galaxy version 1.27+galaxy3) with the following parameters:
    • In “Results”:
      • “Insert Results”
        • “Which tool was used generate logs?”: Bowtie 2
          • “Output of Bowtie 2”: mapping_stats (output of Bowtie2 )
    • “Report title”: Host Removal
  2. Run the tool and open the generated HTML report.

  3. Review the mapping percentage, number of reads aligned, and number of unmapped reads.

    Comment: Tip

    Low mapping percentages in the report confirm that most host reads were successfully removed.

Question
  1. What percentage of reads mapped to the bee reference genome?
  2. What percentage of reads were removed?
  1. 2.8% for SRR24759598 and 1.1% for SRR24759616.
  2. 2.8% for SRR24759598 and 1.1% for SRR24759616.

Remove potential human contamination

After removing host reads, we can run the same workflow again to eliminate possible human contamination that may remain in the dataset.

Hands On: Rerun the workflow using the human genome as reference
  1. Use the unmapped reads Reads without bee reads as the input for this second run.
  2. In the Bowtie2 step:
    • “Will you select a reference genome from your history or use a built-in index?”: Use a built-in genome index
    • “Select reference genome”: Human (GRCh38)
    • Keep all other parameters the same as in the first run.
  3. Continue through the Zip collections and MultiQC steps as before.

  4. The output of this second run represents your final cleaned reads, free from both host and human sequences. Rename this collection to ‘Clean metagenomic reads’

    Comment: Note

    Rerunning the same workflow maintains reproducibility. Only the reference genome and the input data change between the two runs.

Question: Verify your final dataset
  1. How does the mapping percentage differ between the host and human filtering runs?
  2. How many reads have been removed?
  1. The second run (against the human genome) usually removes only a small number of additional reads.
  2. 0.1% in both samples.

Conclusion

In this tutorial, you learned how to:

  • Identify and remove reads originating from host or contaminant genomes using Bowtie2.
  • Combine unmapped forward and reverse reads into a paired collection for reuse.
  • Summarize mapping statistics and verify host-read removal using MultiQC.
  • Rerun the same workflow with a human reference genome to remove residual human contamination.

The resulting clean reads are now ready for downstream metagenomic analyses such as:

  • Assembly
  • Binning
  • Functional or taxonomic profiling

These preprocessing steps are essential to ensure accurate microbial community reconstruction without interference from host DNA.