Transcriptomic Structural Variant Detection (TSV)
Detect Transcriptomic Structural Variants using Ocean’s platform and advanced annotations to discover new insights.
Transcriptomic Structural Variants are large sequence variants present in the trancriptome. They can result in a disease phenotype and effect individual health by contributing to disease progression or genesis. In diseases such as cancer, it is not the SV at the genomic level, but the expression regulation at the RNA level that results in their biological and clinical significance.1
Some Transcriptomic Structural Variants are associated with specific diseases and can be used as biomarkers and some are known to be pathogenic, causing deadly diseases such as brain tumors.2 There are FDA approved and NCCN recommended TSV biomarkers used for detection of diseases like adenocarcinoma, large cell, squamous cell, NSCLS NOS.3
Ocean’s solution is backed by expert services and a powerful platform.
Lead by renowned experts in transcriptomics, Ocean provides state-of-the-art transcriptomics solutions including Transcriptomic Structural Variant detection and annotation from RNA-seq. TSV detection using txome.ai is >50% more precise than other tools such as DELLY2, LUMPY, and TransABySS with Gmap2;4 and detects 50% fewer false positive extragenic fusions than deFUSE.5 Using the Ocean platform is the best approach to detect both gene-fusions and extragenic fusions.
Learn how to detect TSVs to discover new insights.
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- Mittal, V. and McDonald, J., 2015. Integrated sequence and expression analysis of ovarian cancer structural variants underscores the importance of gene fusion regulation. BMC Medical Genomics, 8(1).
- Szulzewsky,F. et al. (2020) Comparison of tumor-associated YAP1 fusions identifies a recurrent set of functions critical for oncogenesis. Genes & Development, 34, 1051-1064.
- El‐Deiry,W. et al. (2019) The current state of molecular testing in the treatment of patients with solid tumors, 2019. CA: A Cancer Journal for Clinicians.
- The performance was compared on simulated data consisting of different number of structural variants (200, 500 and 800). All types of structural variants were used including insertion, deletions, translocation, inversions, and duplications.
- The comparison was done on two cancer cell lines HCC1954 and HCC1395.