HMP-DACC CloVR Human Contaminant Screening Walkthrough


This walkthrough provides a simple example of how to set-up and run the Human Contaminant Screening pipeline using the web-browser accessible CloVR dashboard. The pipeline uses the NCBI BMTagger (Best Match Tagger) tool to identify and remove human reads in metagenomic sequences. For this walkthrough, we shall utilize a mock dataset which consists of 50:50 mix of human contaminant-screened reads from an HMP project and filtered human reads from a 1000 genomes project.

This walkthrough uses the cloud for computational support. Specifically, we demonstrate how to run the pipeline using the academic cloud DIAG, which is free for researchers. Alternatively, you could run the pipeline on Amazon EC2 or using other cloud computing providers.

The Human Contaminant Screening SOP provides a detailed description of this pipeline.

Note: This pipeline has high memory requirements. The underlying programs require about 8.5Gb memory and three times as much harddisk space for index data. Disk space needed for temporary files depends on input, and is typically the same size as that of the input for metagenomic datasets. If your local machine does not have this capacity, consider running the pipeline on DIAG or Amazon EC2.


Getting started with CloVR

Installing and setting up CloVR is a one-time process. If you have done this before, you may skip to the next step – Setting up input dataset.

Install CloVR

CloVR is run using a local desktop client. Visit the Getting started with CloVR page to download and install the client. Once the CloVR virtual machine is set up and launched, you should see a screen similar to Figure 1.

Figure 1. CloVR desktop client


Start the CloVR web interface

First check the CloVR desktop window for the IP address of your virtual machine (VM). Then enter this IP address in a web browser as shown in Figure 2.

Figure 2. Accessing the CloVR web interface


Add cloud credentials to the pipeline

Visit the Adding Credentials page for steps on how to add DIAG credentials. Once the your DIAG credentials are setup, you should see it listed within the credentials tab as shown in Figure 3.

Figure 3. Credentials


Setting up input dataset

Prepare input datasets

The input to this pipeline can be either FASTA or FASTQ read files. Also, input could be either a single-end reads file or set of paired-end reads files.

The first step is to move your input data file(s) into the user_data folder located within the clovr-standard-* image directory. This will enable you to easily access the data through the CloVR dashboard.

Figure 4. Move input files to “user_data” folder. In this example, the input is a set of paired reads files- seq.1.fastq and seq.2.fastq


Add datasets to the pipeline

Before starting a pipeline, you must add your datasets to the CloVR VM as “Tags”.  To add tags, click “Add” on the web interface.

Figure 5. Adding new tags

Then click on “Select file from image”, which will open a sub-window where you can select one or multiple FASTA or FASTQ files for upload into the VM. For a paired-end dataset, you should select exactly two files – the files will be screened as pair. For a single-end dataset, you could select one or multiple files. If you select multiple files, each file will be screened individually.

Alternatively, you can use “Browse” in the “Upload File” window to find and select files from anywhere on your local computer, but multiple files have to be uploaded in separate steps.

Select “Nucleotide FASTA”  or “Nucleotide FASTQ” (depending of your file type) from the “File Type” drop-down menu and name your dataset, e.g. as “HCS_paired_1”. Add an optional description of your dataset. Click “Tag” to upload the data to CloVR. A “Completed Successfully” window should appear to indicate that your dataset was added to the CloVR VM.

Figure 6. Adding a fasta or fastq dataset


The new dataset will be listed under the “Data Sets” tab on the CloVR web interface. Multiple files will listed under the same “Tag” name.

Figure 7. Tagged Dataset


Pipeline setup and execution

To initialize a new pipeline run, click on the “Other Protocols” drop-down as shown in the figure below. Then select “clovr_human_contaminant_screening_paired” (for paired-end dataset) or “clovr_human_contaminant_screening_single” (for single-end dataset).


Figure 8. Starting a new pipeline


This will open the pipeline configuration window. Select the Tag corresponding to the input dataset file(s). Select “fasta” or “fastq” as the input format.

Select “DIAG” credentials from the “Account” drop-down menu.

Provide a name to recognize your pipeline in the web interface home page as “Pipeline Description”, e.g. “hcs_paired_test1″.

Figure 9. Configuring a human contaminant screening (paired) pipeline


Check your input by clicking “validate”. If the validation is successful, start the pipeline by clicking “Run”.

After a successful pipeline submission, the web interface will change to the “Home” page where the new pipeline will be listed as “Status: running.”


Monitoring the pipeline

Your pipeline should now appear in the Pipelines window in the CloVR dashboard along with its status. Occasionally, the pipeline may idle for a minute or two before running. You can click on the pipeline to get a description, input parameters, and hyperlinks to more advanced workflow interfaces like Ergatis. Clicking on the [Pipeline #] headers in the “Pipeline Information” window will open the Ergatis “Workflow creation and monitoring interface” in a separate browser window, which provides useful information for troubleshooting of failed pipeline runs.

Figure 10. Pipeline status


Accessing the outputs

Once the pipeline completes, the results can be downloaded from this CloVR dashboard by clicking on the Outputs tab (see Figure below). All results files are created as compressed archives (.tar.gz), which can be extracted using the Finder in Mac OS X, the Tar utility in Unix or programs such as WinZip or WinRAR, in Windows.

Figure 11. Accessing output file



Examiming the outputs

The CloVR-Human Contaminant Screening pipeline outputs the following files:

Output Description
screened_files Output fasta or fastq file(s) resulting after human sequences have been removed.
screened_ids A list of the sequence IDs that were removed.