Identify and Annotate Variants (WES)

The Identify and Annotate Variants (WES) template workflow should be used to identify and annotate variants in one sample. The workflow is a combination of the Identify Variants and the Annotate Variants workflows.

The workflow starts with mapping the sequencing reads to the human reference sequence, followed by a local realignment to improve the variant detection that is run afterwards. After the variants have been detected, they are annotated with gene names, amino acid changes, conservation scores, information from relevant variants present in the ClinVar database, and information from common variants present in the common dbSNP Common, HapMap, and 1000 Genomes database. Furthermore, a detailed targeted region coverage report is created to inspect the overall coverage and mapping specificity.

Before starting the workflow, you will need to import in the CLC Workbench a file with the genomic regions targeted by the amplicon or hybridization kit. Such a file (a BED or GFF file) is usually available from the vendor of the enrichment kit and sequencing machine. Use the Import | Tracks tool to import it in your Navigation Area.

Run the Identify and Annotate Variants (WES) workflow

To run the Identify and Annotate Variants (WES) workflow, go to:

        Toolbox | Template Workflows | Biomedical Workflows (Image biomedical_twf_folder_open_16_n_p) | Whole Exome Sequencing (Image exome_sequencing_closed_16_n_p) | Somatic Cancer (Image somatic_folder_closed_16_n_p) | Identify and Annotate Variants (WES) (Image identify_annotate_var_wes_16_n_p)

  1. Double-click on the workflow name to start the analysis. If you are connected to a server, you will first be asked where you would like to run the analysis.

  2. First select the sequencing reads from the sample that should be analyzed (figure 20.38).

    Image annotate_and_filter_variants_wizardstep1_wes
    Figure 20.38: Select all sequencing reads from the sample to be analyzed.

    If several samples should be analyzed, the tool has to be run in batch mode. This is done by checking "Batch" and selecting the folder that holds the data you wish to analyze.

  3. In the Target regions dialog (figure 20.39), you can specify the target regions track. Only variants within the specified regions will be detected.

    Image identify_and_annotate_variants_step6_wes
    Figure 20.39: Specify the target regions.

  4. In the next dialog, you have to select which reference data set should be used to identify and annotate variants (figure 20.40).

    Image identify_annotate_variants_wes
    Figure 20.40: Choose the relevant reference Data Set to identify and annotate.

  5. In the next wizard step (figure 20.41) you can select the population from the 1000 Genomes project that you would like to use for annotation.

    Image identify_and_annotate_variants_step3_wes
    Figure 20.41: Select the population from the 1000 Genomes project that you would like to use for annotation.

  6. In the next dialog (figure 20.42), you have to specify the parameters for the variant detection.

    Image identify_and_annotate_variants_step5_wes
    Figure 20.42: Specify the parameters for variant calling.

    For a description of the different parameters that can be adjusted, see If you click on "Locked Settings", you will be able to see all parameters used for variant detection in the template workflow.

  7. In the QC for Target Sequencing step (figure 20.43) you can specify the minimum read coverage that should be present in the targeted regions.

    Image identify_and_annotate_variants_step4_wes
    Figure 20.43: Set the coverage threshold for the QC of the Targeted sequencing.

  8. Finally, select a population from the HapMap database (figure 20.44). This will add information from the Hapmap database to your variants.

    Image identify_and_annotate_variants_step8_wes
    Figure 20.44: Select a population from the HapMap database to add information from the Hapmap database to your variants.

  9. In the last wizard step you can check the selected settings by clicking on the button labeled Preview All Parameters. In the Preview All Parameters wizard you can only check the settings, and if you wish to make changes you have to use the Previous button from the wizard to edit parameters in the relevant windows.

  10. Choose to Save your results and click Finish.

Output from the Identify and Annotate Variants (WES) workflow

The Identify and Annotate Variants (WES) workflow produces several outputs.

  1. Read Mapping (Image read_track_16_n_p) The mapped sequencing reads. The reads are shown in different colors depending on their orientation, whether they are single reads or paired reads, and whether they map unambiguously (see

  2. Target Regions Coverage (Image annotation_track_16_n_p) The target regions coverage track shows the coverage of the targeted regions. Detailed information about coverage and read count can be found in the table format, which can be opened by pressing the table icon found in the lower left corner of the View Area.

  3. Target Regions Coverage Report (Image proteinreport_16_n_p) The report consists of a number of tables and graphs that in different ways provide information about the targeted regions.

  4. Three variant tracks (Image variant_track_16_n_p): Two from the Variant Caller: the Unfiltered Variants is output before the filtering steps, the Variants passing filters is the one used in the Genome Browser View (see . for a definition of the variant table content). The third is the Indels indirect evidence track produced by the Structural Variant Caller. This is also available in the Genome Browser View. The variants can be shown in track format or in table format. When holding the mouse over the detected variants in the Track List, a tooltip appears with information about the individual variants. You will have to zoom in on the variants to be able to see the detailed tooltip.

  5. Amino acid changes Adds information about amino acid changes caused by the variants.

  6. Genome Browser View (Image trackset_16_n_p) A collection of tracks presented together. Shows the annotated variant track together with the human reference sequence, genes, transcripts, coding regions, amino acid changes, the mapped reads, the identified variants, and the indels indirect evidence variants (see figure 20.5).

Please do not delete any of the produced files alone as some of them are linked to other outputs. Please always delete all of them at the same time.

A good place to start is to take a look at the mapping report to see whether the coverage is sufficient in the regions of interest (e.g. > 30 ). Furthermore, please check that at least 90% of the reads are mapped to the human reference sequence. In case of a targeted experiment, please also check that the majority of the reads are mapping to the targeted region.

Next, open the Genome Browser View (see figure 20.45).

The Genome Browser View includes a track of the identified annotated variants in context to the human reference sequence, genes, transcripts, coding regions, targeted regions, mapped sequencing reads, relevant variants in the ClinVar database as well as common variants in common dbSNP Common, HapMap, and 1000 Genomes databases.

Image annotate_and_filter_variants_result1_wes
Figure 20.45: Genome Browser View to inspect identified variants in the context of the human genome and external databases.

To see the level of nucleotide conservation (from a multiple alignment with many vertebrates) in the region around each variant, a track with conservation scores is added as well.

By double-clicking on the annotated variant track in the Genome Browser View, a table will be shown that includes all variants and the added information/annotations (see figure 20.46).

Image annotate_and_filter_variants_result2_wes
Figure 20.46: Genome Browser View with an open track table to inspect identified somatic variants more closely in the context of the human genome and external databases.

The added information will help you to identify candidate variants for further research. For example can common genetic variants (present in the HapMap database) or variants known to play a role in drug response or other relevant phenotypes (present in the ClinVar database) easily be seen.

Not identified variants in ClinVar, can for example be prioritized based on amino acid changes (do they cause any changes on the amino acid level?). A high conservation level on the position of the variant between many vertebrates or mammals can also be a hint that this region could have an important functional role and variants with a conservation score of more than 0.9 (PhastCons score) should be prioritized higher. A further filtering of the variants based on their annotations can be facilitated using the table filter on top of the table.

If you wish to always apply the same filter criteria, the Create new Filter Criteria tool should be used to specify this filter and the Identify and Annotate Variants (WES) workflow should be extended by the Identify Candidate Tool (configured with the Filter Criterion). See the reference manual for more information on how preinstalled workflows can be edited.

Please note that in case none of the variants are present in ClinVar or dbSNP Common, the corresponding annotation column headers are missing from the result.

In case you like to change the databases as well as the used database version, please use the Reference Data Manager.