Power Tools

• Run Name: This is a unique name for each run of pipelines in your project
• Organism: Reference Genome used for alignment
• Reference Genome Annotation: Annotation that should be used for determining gene and transcript counts.
• Input Folder: This is the folder that contains all of the fastq files that will be used in this analysis
• Sample Description File
• This file matches the sequence files to samples; sequence data from multiple runs will be merged if they have the same SampleID
• RunID should be a part of the the fastq files.
• GroupID denotes samples to be analyzed jointly
• File Format

RunID	SampleID	GroupID	SampleType
SRR994739	SAMEA9454349	Tumor A	Tumor
SRR994740	SAMEA9454349	Normal A	Normal
SRR994741	SAMEA9454341	Tumor B	Tumor
SRR994742	SAMEA9454342	Family B	Normal

• Capture Bedfile
• The intervals in capture BED file indicate regions where alignments are expected based on the target capture kit.
• Make sure that there is no column names present in the file.
• The fourth column can indicate a region name and used to determine poorly capture regions.

SeqName	Start	End	Name
chr1	1787293	1787413	GNB1:GNB1_chr1:1718769-1718876:chr1:1718769-1718876_1
chr1	1787353	1787473	GNB1:GNB1_chr1:1718769-1718876:chr1:1718769-1718876_2
chr1	1789040	1789160	GNB1:GNB1_chr1:1720491-1720708:chr1:1720491-1720708_1
chr1	1789160	1789280	GNB1:GNB1_chr1:1720491-1720708:chr1:1720491-1720708_2
chr1	1790375	1790495	GNB1:GNB1_chr1:1721833-1722035:chr1:1721833-1722035_1
chr1	1790495	1790615	GNB1:GNB1_chr1:1721833-1722035:chr1:1721833-1722035_2
chr1	1793187	1793307	GNB1:GNB1_chr1:1724683-1724750:chr1:1724683-1724750_1
chr1	1793247	1793367	GNB1:GNB1_chr1:1724683-1724750:chr1:1724683-1724750_2
chr1	1804380	1804500	GNB1:GNB1_chr1:1735857-1736020:chr1:1735857-1736020_1
chr1	1804500	1804620	GNB1:GNB1_chr1:1735857-1736020:chr1:1735857-1736020_2
chr1	1806416	1806536	GNB1:GNB1_chr1:1737913-1737977:chr1:1737913-1737977_1
chr1	1806476	1806596	GNB1:GNB1_chr1:1737913-1737977:chr1:1737913-1737977_2

• Variants (genomevariants)
• DeepVariant VCF
• MAF

1. Navigate to the DeepSomatic launcher card. You can use the search bar at the top right corner, or use the Google DeepOmics tags to find the workflow card.

2. Select the version from the dropdown versioning menu in the top right corner. On this page, you can find information about the workflow analysis. When ready to begin, click “Run Workflow”.

3. Select the sequencer platform that was used to generate the data, Illumina, PacBio, or Oxford Nanopore. Also provide the directory containing the files to be analyzed as well as a file containing RunIDs, LibraryIDs and SampleIDs (this table can be created within the workflow itself).

4. On the next tab, provide a BED file containing the genomic regions to be analyzed. You may also optionally upload a BED file detailing genomic regions of note.

5. Finally, give your workflow run a unique name, and review the input data and run parameters. When ready to submit, click “Run Workflow”.

1. To view results for your DeepSomatic workflow, first find your workflow run from the Activity tab of the platform. You can use the search bar to search for it. Select your workflow run for more information.

2. Upon selection, results from your workflow run are summarized in the Results tab.

3. Navigate to the All Files tab to view and download analysis outputs in the output folder. These folders are also available in the File Explorer.

1. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2012).

• Run Name: This is a unique name for each run of pipelines in your project
• Organism: Reference Genome used for alignment
• Reference Genome Annotation: Annotation that should be used for determining gene and transcript counts.
• Input Folder: This is the folder that contains all of the fastq files that will be used in this analysis
• Sample Description File
• This file matches the sequence files to samples; sequence data from multiple runs will be merged if they have the same SampleID
• RunID should be a part of the the fastq files.
• GroupID denotes samples to be analyzed jointly
• File Format

RunID	SampleID	Group ID	SampleType
SRR994739	SAMEA9454349	Family A	Proband
SRR994740	SAMEA9454349	Family A	Parent
SRR994741	SAMEA9454341	Family A	Parent

• Capture Bedfile
• The intervals in capture BED file indicate regions where alignments are expected based on the target capture kit.
• Make sure that there is no column names present in the file.
• Forth column can indicate a region name and used to determine poorly capture regions.

SeqName	Start	End	Name
chr1	1787293	1787413	GNB1:GNB1_chr1:1718769-1718876:chr1:1718769-1718876_1
chr1	1787353	1787473	GNB1:GNB1_chr1:1718769-1718876:chr1:1718769-1718876_2
chr1	1789040	1789160	GNB1:GNB1_chr1:1720491-1720708:chr1:1720491-1720708_1
chr1	1789160	1789280	GNB1:GNB1_chr1:1720491-1720708:chr1:1720491-1720708_2
chr1	1790375	1790495	GNB1:GNB1_chr1:1721833-1722035:chr1:1721833-1722035_1
chr1	1790495	1790615	GNB1:GNB1_chr1:1721833-1722035:chr1:1721833-1722035_2
chr1	1793187	1793307	GNB1:GNB1_chr1:1724683-1724750:chr1:1724683-1724750_1
chr1	1793247	1793367	GNB1:GNB1_chr1:1724683-1724750:chr1:1724683-1724750_2
chr1	1804380	1804500	GNB1:GNB1_chr1:1735857-1736020:chr1:1735857-1736020_1
chr1	1804500	1804620	GNB1:GNB1_chr1:1735857-1736020:chr1:1735857-1736020_2
chr1	1806416	1806536	GNB1:GNB1_chr1:1737913-1737977:chr1:1737913-1737977_1
chr1	1806476	1806596	GNB1:GNB1_chr1:1737913-1737977:chr1:1737913-1737977_2

• Variants (genomevariants)
• DeepVariant VCF
• MAF

1. Navigate to the DeepTrio launcher card. You can use the search bar at the top right corner, or use the Google DeepOmics tags to find the workflow card.

2. Select the version from the dropdown menu in the top right corner. On this page, you can find information about the workflow analysis. When ready to begin, click “Run Workflow”.

3. Select the sequencer platform that was used to generate the data, Illumina, PacBio, or Oxford Nanopore. Choose whether or not to split large fastq files into smaller files by checking the “Whole Genome Sequencing Parallelization” box (warning: may be more costly). Also provide the directory containing the files to be analyzed as well as a file containing RunIDs, LibraryIDs and SampleIDs (this table can be created within the workflow itself).

4. On the next tab, select a reference genome to which the input data will be compared. You may also optionally upload a BED file detailing genomic regions of note.

5. Finally, give your workflow run a unique name, and review the input data and run parameters. When ready to submit, click “Run Workflow”.

1. To view results for your DeepTrio workflow, first find your workflow run from the Activity tab of the platform. You can use the search bar to search for it. Select your workflow run for more information.
2. Upon selection, results from your workflow run are summarized in the Results tab.

3. Navigate to the All Files tab to view and download analysis outputs in the output folder. These folders are also available in the File Explorer.

1. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2012).

• Run Name: This is a unique name for each run of pipelines in your project
• Organism: Reference Genome used for alignment
• Reference Genome Annotation: Annotation that should be used for determining gene and transcript counts.
• Input Folder: This is the folder that contains all of the fastq files that will be used in this analysis
• Sample Description File
• This file matches the sequence files to samples; sequence data from multiple runs will be merged if they have the same SampleID
• RunID should be a part of the the fastq files.
• GroupID denotes samples to be analyzed jointly
• File Format

RunID	SampleID	GroupID
SRR994739	SAMEA9454349	Family A
SRR994740	SAMEA9454349	Family A
SRR994741	SAMEA9454341	Family A
SRR994742	SAMEA9454342	Family B

• Capture Bedfile
• The intervals in capture BED file indicate regions where alignments are expected based on the target capture kit.
• Make sure that there is no column names present in the file.
• Forth column can indicate a region name and used to determine poorly capture regions.

SeqName	Start	End	Name
chr1	1787293	1787413	GNB1:GNB1_chr1:1718769-1718876:chr1:1718769-1718876_1
chr1	1787353	1787473	GNB1:GNB1_chr1:1718769-1718876:chr1:1718769-1718876_2
chr1	1789040	1789160	GNB1:GNB1_chr1:1720491-1720708:chr1:1720491-1720708_1
chr1	1789160	1789280	GNB1:GNB1_chr1:1720491-1720708:chr1:1720491-1720708_2
chr1	1790375	1790495	GNB1:GNB1_chr1:1721833-1722035:chr1:1721833-1722035_1
chr1	1790495	1790615	GNB1:GNB1_chr1:1721833-1722035:chr1:1721833-1722035_2
chr1	1793187	1793307	GNB1:GNB1_chr1:1724683-1724750:chr1:1724683-1724750_1
chr1	1793247	1793367	GNB1:GNB1_chr1:1724683-1724750:chr1:1724683-1724750_2
chr1	1804380	1804500	GNB1:GNB1_chr1:1735857-1736020:chr1:1735857-1736020_1
chr1	1804500	1804620	GNB1:GNB1_chr1:1735857-1736020:chr1:1735857-1736020_2
chr1	1806416	1806536	GNB1:GNB1_chr1:1737913-1737977:chr1:1737913-1737977_1
chr1	1806476	1806596	GNB1:GNB1_chr1:1737913-1737977:chr1:1737913-1737977_2

• Variants (genomevariants)
• DeepVariant VCF
• MAF

1. Navigate to the DeepVariant launcher card. You can use the search bar at the top right corner, or use the Google DeepOmics tags to find the workflow card.

2. Select the version from the dropdown versioning menu in the top right corner. On this page, you can find information about the workflow analysis. When ready to begin, click “Run Workflow”.

3. Select the sequencer platform that was used to generate the data, Illumina, PacBio, or Oxford Nanopore. Choose which algorithm to run based on the input data, Parabricks DeepVariant, DeepVariant for RNAseq, or DeepVariant. Choose whether or not to split large fastq files into smaller files by checking the “Whole Genome Sequencing Parallelization” box (warning: may be more costly).

4. On the same tab, name the variant result output. Also provide the directory containing the files to be analyzed as well as a file relating SampleIDs to GroupIDs (this table can be created within the workflow itself).

5. On the next tab, select a reference genome to which the input data will be compared. You may also optionally upload a BED file detailing genomic regions of note.

6. Finally, give your workflow run a unique name, and review the input data and run parameters. When ready to submit, click “Run Workflow”.

1. To view results for your DeepVariant workflow, first find your workflow run from the Activity tab of the platform. You can use the search bar to search for it. Select your workflow run for more information.

2. Upon selection, results from your workflow run are summarized in the Results tab.

3. Navigate to the All Files tab to view and download analysis outputs in the output folder. These folders are also available in the File Explorer.

1. Yun, T. et al. Accurate, scalable cohort variant calls using DeepVariant and GLnexus. (2020) doi:10.1101/2020.02.10.942086.
2. Lin, M. F. et al. GLnexus: Joint variant calling for large cohort sequencing. (2018) doi:10.1101/343970.
3. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2012).

• Run Name
• This is a unique name for each run of pipelines in your project
• SRAList
• File with SRA Run, Sample or Project IDs: SRR/ERR, SAM, PRJ
• File with GEO Sample ides: GSM
• Recount3 Project ID
• Search Projects IDs

• For SRA FastQ Files, 1 per RunID
• RunID.fastq.gz
• Sample to Run IDs

RunID	SampleID
SRR994739	SAMEA9454349
SRR994740	SAMEA9454349
SRR994741	SAMEA9454341
SRR994742	SAMEA9454348
SRR994743	SAMEA9454348
SRR994744	SAMEA9454342

• For Recount3 and GEO, there are a variety of files available depending on sample/project.

1. Navigate to the Download Public Data Files workflow. You can use the search bar at the top-right corner to find the workflow, or use the External Data or Power Tools filter on the left-hand side.
2. Select version from the dropdown menu in the top right corner. When ready to begin analysis, click the “Run Workflow” button.

3. To start, select the type of data you wish to retrieve - either Short Read Archive (SRA) data, Gene Omnibus Expression (GEO) data, or Recount3 data. Depending on the resource you wish to access, you will be asked to provide a file containing the IDs of the data to retrieve - SRA IDs, GEO IDs, or the Recount3 project ID respectively.

4. Give the workflow a unique name, and review the workflow inputs and parameters. When ready to submit, click “Run Workflow”.

1. To begin, find the workflow run in the Activity tab. Select your workflow.
2. On this page, you can view an array of information about your workflow run. To find your downloaded files, select the Files tab.

3. In the Files tab, select the Output folder to view all output files.

4. Alternately, you can find retrieved files in the pipeline-outputs folder.

5. Once in the folder, select getncbi.

6. Find the folder corresponding to your workflow run, then select output to view files.

• Run Name
• This is a unique name for each run of pipelines in your project
• List File
• List of Gene Symbols
• Genome Regions File
• List of Genomics Regions (remember that chromosome names must match)

• Sequences from the gene or genomic region
• Gene sequences include genomic with the window, cds and protein

1. Navigate to the Extract Sequences from Genome launcher card. You can use the search bar at the top right corner, or use the Power Tool tag to find the workflow card.

2. Select the version from the dropdown menu in the top right corner. On this page, you can find information about the workflow analysis. When ready to begin, click “Run Workflow”.

3. Select the type of data source, gene sequence or gene region, then select a reference genome and assembly version. Next, determine the gene window size which is the length upstream/downstream of the intended gene to add onto the gene as an input sequence. Finally, provide a file containing the genes to be extracted.

4. Give your workflow run a unique name, and review the input data and run parameters. When ready to submit, click “Run Workflow”.

1. To view results for your Extract Sequences from Genome workflow, first find your workflow run from the Activity tab of the platform. You can use the search bar to search for it. Select your workflow run for more information.
2. Upon selection, results from your workflow run are summarized in the Results tab. Previews of the output files can be viewed here.

3. Navigate to the All Files tab to view and download analysis outputs in the output folder. These folders are also available in the File Explorer.

1. Navigate to the FastQC launcher card. You can use the search bar at the top right corner, or use the Power Tool or Genomics tags to find the workflow card.

2. Select the version from the dropdown menu in the top right corner. On this page, you can find information about the workflow analysis. When ready to begin, click “Run Workflow”.

3. Select the directory where the fastQ files for your analysis are located. Accepted file formats are .fq.gz or fastq.gz.

4. Give your workflow run a unique name, and review the input data and run parameters. When ready to submit, click “Run Workflow”.

1. To view results for your FastQC workflow, first find your workflow run from the Activity tab of the platform. You can use the search bar to search for it. Select your workflow run for more information.
2. Upon selection, results from your workflow run are summarized in the Results tab. HTML output files can be previewed or opened from here.

3. Navigate to the All Files tab to view and download analysis outputs in the output folder. These folders are also available in the File Explorer.

1. Andrews, S. et al. FastQC. (2012).

1. Navigate to the Kraken 2 launcher card. You can use the search bar at the top right corner, or use the Power Tool or Genomics tags to find the workflow card.

2. Select the version from the dropdown menu in the top right corner. On this page, you can find information about the workflow analysis. When ready to begin, click “Run Workflow”.

3. Select the directory where the fastQ files for your analysis are located. Accepted file formats are .fq.gz or fastq.gz. Determine how you want any contaminants to be filtered.

4. Give your workflow run a unique name, and review the input data and run parameters. When ready to submit, click “Run Workflow”.

1. To view results for your Kraken 2 workflow, first find your workflow run from the Activity tab of the platform. You can use the search bar to search for it. Select your workflow run for more information.
2. Upon selection, results from your workflow run are summarized in the Results tab. HTML output files can be previewed or opened from here.

3. Navigate to the All Files tab to view and download analysis outputs in the output folder. These folders are also available in the File Explorer.

1. Wood, D. E. & Salzberg, S. L. Kraken: Ultrafast metagenomic sequence classification using exact alignments. Genome Biology 15, R46 (2014).

• FastA file
• Algorithm
• Database or Genome

Sequence Alignment Output

1. Navigate to the Sequence Similarity Search workflow. You can use the search tool at the top right corner, or find it using the Sequence Alignment filter on the left side.
2. Select the version from the dropdown menu in the top right corner. You can view use cases, a summary, and inputs/outputs on this page. When ready to begin analysis, click “Run Workflow”.

3. Launcher Tabs
1. Provide a FastA file containing the query sequence(s). Then, select the type of sequence search (nucleotide to nucleotide, nucleotide to protein, etc), the search algorithm, and the database to search.

2. Determine how you want to the output file (XML, HTML, etc), and tune additional search parameters depending on your chosen algorithm.

3. Review workflow parameters and inputs, and give the workflow run a unique name. When ready to submit the job, click “Run Workflow”.


Results Walkthrough

4. To view the results of your Sequence Similarity Search workflow run, first find and select your workflow run from the Activity Tab.
5. Navigate to the Files tab, then find workflow outputs under the output folder.

1. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. Journal of Molecular Biology 215, 403–410 (1990).
2. Pearson, W. R. & Lipman, D. J. Improved tools for biological sequence comparison. Proceedings of the National Academy of Sciences of the United States of America 85, 2444–2448 (1988).
3. Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nature Methods 18, 366–368 (2021).
4. Wrpearson/Fasta36 at master.
5. Protein-to-genome alignment with miniprot | Bioinformatics | Oxford Academic.

Optional Inputs

• Barcode Sequence
• used to separate multiplexed samples by sequence barcodes or indexes
• MASseq Adapters
• used in masseq applications

FastQ files per Sample

1. Navigate to the Sequencer Raw Data to FastQ launcher card. You can use the search bar at the top right corner, or use the Google DeepOmics tags to find the workflow card.

2. Select the version from the dropdown versioning menu in the top right corner. On this page, you can find information about the workflow analysis. When ready to begin, click “Run Workflow”.

3. Select the sequence input type from the drop-down menu. Also provide the directory containing the files to be analyzed as well as a file containing the barcode sequence for each sample (this table can be created within the workflow itself).

4. On the next tab, choose the data assay type (or ONT basecalling model) based on the sequence input type that was selected on the first tab. Configure any additional parameters. These parameters will differ for Illumina vs PacBio vs ONT fast5 input types:

Illumina parameter interface:



PacBio parameter interface:



ONT fast5 parameter interface:


5. Finally, give your workflow run a unique name, and review the input data and run parameters. When ready to submit, click “Run Workflow”.

1. To view results for your Sequencer Raw Data to FastQ workflow, first find your workflow run from the Activity tab of the platform. You can use the search bar to search for it. Select your workflow run for more information.

2. Upon selection, results from your workflow run are summarized in the Results tab.

3. Navigate to the All Files tab to view and download analysis outputs in the output folder. These folders are also available in the File Explorer.

1. Travers, K. J., Chin, C.-S., Rank, D. R., Eid, J. S. & Turner, S. W. A flexible and efficient template format for circular consensus sequencing and SNP detection. Nucleic Acids Research 38, e159–e159 (2010).
2. Baid, G. et al. DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer. Nature Biotechnology 41, 232–238 (2023).
3. Chris Seymour, Joyjit Daw, Mike Vella & Mark Bicknell. Dorado is a high-performance, easy-to-use, open source basecaller for Oxford Nanopore reads.
4. Bcl2fastq.