Technical Note
The BaseJumperTM Bioinformatics Platform
Authors
Victor Weigman, PhD
Senior Director
Bioinformatics
BioSkryb Genomics

Viren Amin, PhD
Data Engineer
Bioinformatics
BioSkryb Genomics

Enabling Rapid, Multi-omic Analyses and Interactive
Visualization from Anywhere
Translating the molecular alterations, especially those from a single cell, can be a
powerful tool to understand underlying mechanisms of disease and therapies. Alongwith managing the data volume from sequencing technologies, the complexity of thesechanges requires a multi-dimensional approach to first compute and identify the variancefound within the cells of a study and then display these effects concomitantly. The
BaseJumper platform is designed to enable the researcher with powerful computing andvisualization tools that put them in control of driving their own analysis and interpretation.

Corey Culler

Figure 1. The BioSkryb BaseJumper
cloud-based bioinformatic platform. The
platform was designed to operate on standard
laboratory computers with internet access.
Researchers' sequencing data can be directly
uploaded securely and processed basedon pre-loaded analysis pipelines through astandard web portal. BaseJumper automates
many of the functions typically requiring
bioinformatics staff time, deep computational
power, access to comprehensive annotationand iteration of visualization. Convenient
dashboards organize projects, link analysis
pipelines and provide reports that can be
downloaded and shared.

Director
Information Technologies
BioSkryb Genomics

Accessing and Uploading Data
Leveraging any computer typically found in alaboratory (Figure 1), new researchers to the
BaseJumper Platform can request their own
account at the initial landing page. This initial step
will immediately allow access to a demonstration
workspace of exemplar BioSkryb datasets andprojects. Through the information provided,
BaseJumper administrators will link the accountto an existing "Organization" space, or create
a new one. This critical step ensures sensitive
genomic information is only shared to authorizedindividuals. During this setup, Organizations areable to assign administrators to their space andallow access-granting to additional subgroups,
team "Workspaces," and assign roles to otherresearchers.
Getting sequencing data into BaseJumper
enables researchers to leverage existing datasets
held in the Illumina BaseSpaceTM Sequence

Hub and from other cloud or local repositories.
Accessing the latter is enabled through Globus1,
where the researchers' existing data endpoints
can be transferred directly to BaseJumper's datadelivery portal.
During the project creation process, a researcher
identifies the sequencing runs or file directories
that comprise the experiment. Mechanisms
for performing sample filters on file names canrapidly search through large data volumes to
identify the correct files. During this process, theresearcher applies metadata, such as chemistry
and library preparation workflows. The metadata
collection is a valuable step which can later beleveraged to determine workflow-specific biases.
Following this, data paths for all experimental
samples will be aggregated, copied and stampedwith date and size within the project and can beused for later workflows. An email is created andsent to the researcher to denote initiation andcompletion of projects.
For Research Use Only. Not for use in diagnostic procedures.

BaseJumperTM Bioinformatics Platform

2

Figure 2. The ABCs of the BaseJumper Workflow. Researchers create accounts as a part of an Organization and are assigned into one or more of that
Organization's workspaces. Once in a workspace, they can create projects, pulling data from Illumina BaseSpace and/or their own share. Bioinformatics
pipelines can then be queued to immediately launch after data transfer to BaseJumper. Researchers control which modules and parameters are passed topipelines set for DNA or RNA workflows. Output from these pipelines can then be fed into dynamic visualization applications and exported into individual's
specific data endpoints.

Analysis Workflow

Analysis Pipelines

Project creation is just the first and most fundamental part ofleveraging the BaseJumper platform. While enabling rich,
dynamic visualizations is at the core of BaseJumper's mission,
the project is the extension of the experiment and careful setup
is the foundation of meaningful downstream interpretation.
In Figure 2, each of the steps performed by the BaseJumper
platform are outlined and make up the core features researchers
are able to perform:

After projects are created and samples' sequencing files(FASTQs) have been identified, the researcher can selectfrom analysis pipelines suitable to highlight the genomic
and transcriptomic variability found in the samples within
the projects. Pipeline modules and parameters can be
configured to allow flexibility to match prior analyses.

 Access - The platform puts control of genomic information in the hands of each Organization. Researchers request or are assigned roles specific to their Organization's
access policies.
 Build - Create a project from previous sequencing data.
This can be done through existing access of BaseSpaceor from other data endpoints, accessible through Globus.
 Compute - Researchers can determine whether one or
multiple bioinformatics pipelines can be executed (or asubset of samples within a project).
 Display - Projects feature a visualization section with several application portals to display and manipulate pipeline generated results.
 Export - Data generated by BaseJumper can be exported either by direct download of files (or graphics) or at a
project-level into a researchers' data endpoint.

Currently, BaseJumper provides these pipeline capabilities:
 QC - Metrics such as library complexity, error rates, chromosomal coverage, library anomalies and read numbers
are returned. This can be run to do rapid (down-sampled)
determination of the quality of your sequencing results
prior to longer, more expensive analyses.
 DNA-based - The genomic pipeline performs analyses
to report variation in: single nucleotide variants (SNVs),
small insertions or deletions (Indels), and copy number
variants (CNVs). Regions of interest can be provided toframe these results against exome or other targeted panel capture technologies.
 RNA-based - The transcriptomic pipeline makes the mostof isoform and gene-level counting and normalization
suitable for BioSkryb's full transcript length results or
end-tagging data. Results include gene-level counts, isoform-level counting, count normalization and cell identification.

The BaseJumper platform is designed in such a way to becomplementary to your organization's existing bioinformatics
team. You can choose one or more of these steps that best
fits your internal workflows.
Projects and visualization
results can not only be shared internally but to external
collaborators as needed and authorized by researcher.

Annotation of results is vitally important to provide thecontext of results to known features. While the RNAbased workflows provide counts specific to individual
splice forms and common HGNC2 gene symbols,
identification of cellular information is provided to predict:

To facilitate understanding the status of each step, a messaging
system within BaseJumper leverages the email within aresearcher's profile to notify when each step begins andcompletes, enhancing the efficiency of the researcher's time.

 Primary cell / progenitor (Human Primary Cell Atlas HPCA3)

 Cell cycle state (G1, S, G2M)

 Tissue of origin (GTeX4)
 Associated cancer tissue (TCGA, multiple cancers)
For Research Use Only. Not for use in diagnostic procedures.

BaseJumperTM Bioinformatics Platform

3

Figure 3. The BaseJumper
Visualization Portal.
Visualization of project-level
datasets is accessed throughdifferent embedded views.
Within each embedded application there are manipulatable representations ofgenomic and transcriptomic
data. A key component ofeach application is the selection and storing of geneand sample sets which canbe used to label and group
datasets in linked applications to enable data overlays. Each graphic can be
downloaded directly to auser's computer for sharing
and the configuration filesaved to reuse on a separate
project - enabling consistency in interpretation.

For variants identified through the DNA-based pipelines there
are more complex annotations for DNA-based variation:
 Polymorphic annotations - gnomAD5
 Pathogenic designations (ClinVar6, COSMIC7)
 Prevalent Cancer Mutations (frequency in TCGA) Effect on Protein8
Predicting the impact of a variant on a protein function
reports values from: Sorting Intolerant From Tolerant (SIFT)9,
Likelihood ratio test (LRT)10, FATHM11, Protein Variation EffectAnalyzer (PROVEAN)12 and MetaSVM/MetaLR13.

Visualization
Complex and detailed visualization is at the heart of elucidating
discovery for the researcher, across applications spaces. The
BaseJumperTM bioinformatics platform was designed toprovide a streamlined solution to immediately visualize andinterpret pipeline data. This can be done from data generated
within the BaseJumper pipeline system or through anothersolution.
Within a project is a portal of applications that can produce
a dynamic view (Figure 3) of pipeline results. Within theapplication, the researcher is free to select subsets of the datato perform a more detailed interpretation. This can be done
across the scale of sample and gene-level to individuallyqueried mutations or transcripts.

Visualizations currently served by BaseJumper include: Genome Views - The genome variability within thesamples of a project can be freely navigated. In addition, specific loci or markers can be viewed specifically Copy Number - Levels of aneuploidy across the dataset
are reported to identify common areas of gain or loss.
Selecting an area provides a list of the genes that occupy those regions
 Variant Filtering - The genetic variation of samples
across the project can be simultaneously viewed intabular format, along with meaningful annotation. The
researcher can then chose a myriad of filtering options
for meaningful or actionable changes and observe theextent of heterogeneity across the project Sample Similarity - Correlation of samples that sharesimilar genomic or transcript variation can be identified
to select for subsets
 Variability of Expression - Heatmaps at the gene andtranscript level can be annotated for provided gene/
pathway membership along with sample-related annotation (such as cellular ID) identified through othervisualization applications
 Differential Expression - The same sample characteristics can be used to perform supervised analysis acrossgroups and new gene lists associated to biologicalgroups identified
For Research Use Only. Not for use in diagnostic procedures.

Each of the applications' visualizations can be exported anddownloaded directly to the researcher's computer.

Exporting Results
Researchers can decide to export results at any stage of theanalysis workflow. After a pipeline has been run, there is a file
explorer available to download individual graphical reports,
metadata or result files. Each graphical application within thevisualization portal has a direct image download to capture
screenshots and add to any presentation. Bulk project-level
data can be flagged for transfer and passed directly to users'
data endpoints (through Globus).

Summary
BaseJumper is a scalable and secure platform that allows
the interpretation of large datasets and fuels discovery of
new biomarkers within single cell sequencing data. Modular
workflows enable the researcher to control which parts of theplatform can be leveraged to complement and enhance internal
capabilities. Visualization applications can take datasets acrossgenomic and transcriptomic results and can be manipulated
based on expert interpretation of the researcher to maximize
biological leverage. Compiling all of this within a browser
maximizes accessibility of data so you can perform analyses
anywhere inspiration takes you.

9.

Sim, N.L., et al., SIFT web server: predicting effects of amino acidsubstitutions on proteins. Nucleic Acids Res, 2012. 40(Web Server issue):p. W452-7.

10.

Qian, M. and Y. Shao, A likelihood ratio test for genome-wide association
under genetic heterogeneity. Ann Hum Genet, 2013. 77(2): p. 174-82.

11.

Shihab, H.A., et al., An integrative approach to predicting the functional
effects of non-coding and coding sequence variation. Bioinformatics, 2015.
31(10): p. 1536-43.

12.

Shihab, H.A., et al., Predicting the functional consequences of cancerassociated amino acid substitutions. Bioinformatics, 2013. 29(12): p. 150410.

13.

Shihab, H.A., et al., Predicting the functional, molecular, and phenotypicconsequences of amino acid substitutions using hidden Markov models.
Hum Mutat, 2013. 34(1): p. 57-65.

14.

Shihab, H.A., et al., Ranking non-synonymous single nucleotide
polymorphisms based on disease concepts. Hum Genomics, 2014. 8: p. 11.

15.

Choi, Y., et al., Predicting the functional effect of amino acid substitutions
and indels. PLoS One, 2012. 7(10): p. e46688.

16.

Dong, C., et al., Comparison and integration of deleteriousness prediction
methods for nonsynonymous SNVs in whole exome sequencing studies.
Hum Mol Genet, 2015. 24(8): p. 2125-37.

17.

Rebhan, M., et al., GeneCards: integrating information about genes,
proteins and diseases. Trends Genet, 1997. 13(4): p. 163.

18.

Howe, K.L., et al., Ensembl 2021. Nucleic Acids Res, 2021. 49(D1): p.
D884-D891.

References1.

Foster, I., "Globus Online: Accelerating and Democratizing Science throughCloud-Based Services," Internet Computing, IEEE , vol. 15, no. 3, pp. 70,73,
May-June 20112.

Tweedie S, Braschi B, Gray KA, Jones TEM, Seal RL, Yates B, Bruford EA.
Genenames.org: the HGNC and VGNC resources in 2021. Nucleic Acids Res.

3.

D. Aran, A.P. Looney, L. Liu, E. Wu, V. Fong, A. Hsu, et al. Reference-based
analysis of lung single-cell sequencing reveals a transitional profibrotic
macrophage Nat Immunol, 20 (2019), pp. 163-172

4.

GTEx Consortium (2013). The Genotype-Tissue Expression (GTEx) project.
Nature genetics, 45(6), 580–585.

5.

Karczewski, K.J., Francioli, L.C., Tiao, G. et al. The mutational constraint
spectrum quantified from variation in 141,456 humans. Nature 581, 434–
443 (2020).

6.

Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart
J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu C, Maddipatla Z, Malheiro
A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott
DR. ClinVar: improving access to variant interpretations and supporting
evidence. Nucleic Acids Res . 2018 Jan 4. PubMed PMID: 29165669 .

7.

John G Tate, Sally Bamford, Harry C Jubb, Zbyslaw Sondka, David M Beare,
Nidhi Bindal, Harry Boutselakis, Charlotte G Cole, Celestino Creatore,
Elisabeth Dawson, Peter Fish, Bhavana Harsha, Charlie Hathaway, Steve C
Jupe, Chai Yin Kok, Kate Noble, Laura Ponting, Christopher C Ramshaw,
Claire E Rye, Helen E Speedy, Ray Stefancsik, Sam L Thompson, Shicai Wang,
Sari Ward, Peter J Campbell, Simon A Forbes, COSMIC: the Catalogue Of
Somatic Mutations In Cancer, Nucleic Acids Research, Volume 47, Issue
D1, 08 January 2019, Pages D941–D947, Robinson, J.T., et al., Integrative
genomics viewer. Nat Biotechnol, 2011. 29(1): p. 24-6.

8.

A program for annotating and predicting the effects of single nucleotide
polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogasterstrain w1118; iso-2; iso-3.", Cingolani P, Platts A, Wang le L, Coon M, Nguyen
T, Wang L, Land SJ, Lu X, Ruden DM. Fly (Austin). 2012 Apr-Jun;6(2):80-92.

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