Archer™ FusionPlex™ NGS Data Analysis, Algorithms and Methods: Hypothesis-Free Gene Fusion Detection

AGBT Meeting, February 25-28, 2015

Authors

Aaron Berlin, Erik Reckase, Joseph Heimiller, Doug Wendel, Michael Banos, Jeremy Widmann, Thon DeBoer, Brian Kudlow, Brady Culver, Jason Myers, Josh Stahl and Abel Licon

ArcherDX Inc., Boulder, CO



Discovered Novel Fusions Using Hypothesis-Free Fusion Detection

Figure 1 Novel Fusions
  • Oncogenes can fuse to multiple partners
  • Designing primers to all possible pairs is not scalable
  • Archer Anchored Multiplex PCR (AMP™) technology requires only a single primer on the target gene
  • ANY possible fusion partner can now be detected

Minimize Sequencing Error with Fusion Consensus

Figure 2 Minimize Sequencing Error
  • Each read has an 8-bp molecular barcode appended to it. Knowing that each read comes from the same molecule, sequencing error can be minimized by using a SNP caller to create a consensus read
  • Each compressed bin then contributes to a final fusion consensus. By running a SNP caller, we can remove sequencing artifacts of small molecular bins

Align and Annotate Fusion Partners

Figure 3 Align and Annotate Fusion Partners
  • Partial alignments to the targeted gene are fusion candidates
  • Align partial alignments to human genome to determine fusion partner (i.e., hypothesis-free)
  • Annotate transcripts with BLAST

Remove Mispriming Artifacts (False Fusions)

By aligning the primer to the fusion partner (LTK), we can remove false fusions that have high homology to the target gene (ALK)

Figure 4 Remove False Fusions

Annotate Known Fusions with Quiver™ Database

Figure 5 Annotate Kown Fusions with Quiver Database
  • The Quiver database is the aggregation of available gene fusion resources (COSMIC1, Mitelman2, ChimerDB3, dbCRID4, TICdb5, and ChiTars6) and internally curated data
  • Quiver increases confidence in a fusion call by associating it with published evidence

Determine Protein Translation of Fusion Transcript

Compute all possible fusion transcript proteins and determine reading frame

Figure 6 Determine Protein Translation of Fusion Transcript

Visualize Fusion Transcript in JBrowse

Visualize fusion consensus transcript in a single view across chromosomes using JBrowse

Figure 7 Visualize Fusion Transcript in JBrowse

User-Friendly Parallel Web Application

Figure 8 User Friendly Web Application
  • Download Virtual Machine for local and completely private install
  • Upload FASTQ files and start the analysis
  • Run multiple samples in parallel


Resources

  1. Sanger Institute Catalogue of Somatic Mutations in Cancer website, http://www.sanger.ac.uk/cosmic Bamford et al (2004). The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer, 91, 355-358.
  2. Mitelman Database of Chromosome Abberrations and Gene Fusions in Cancer (2014). Mitelman F, Johansson B and Mertens F (Eds.).
  3. ChimerDB 2.0 - A knowledge base for fusion genes updated. Nucleic Acids Res. 38 (Database issue), D81-85. (2010 Jan).
  4. dbCRID: A database of chromosomal rearrangements in human diseases. Nucleic Acids Res. 2011 Jan; 39 (Database issue): D895-900. doi: 10.1093/nar/gkq1038.
  5. TICdb: A collection of gene-mapped translocation breakpoints in cancer. BMC Genomics 2007, 8:33 doi:10.1186/1471-2164-8-33.
  6. ChiTars: A database of human, mouse, and fruit fly chimeric transcripts and RNA-sequencing data. Nucleic Acids Research 41 (D1): D142-D151. doi:10.1093/nar/gks1041.

For Research Use Only. Not for use in diagnostic procedures. For Research Use Only. Not for use in diagnostic procedures.

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For Research Use Only. Not for use in diagnostic procedures. For Research Use Only. Not for use in diagnostic procedures.