Informatics

(I001) Evaluation of T2T-CHM13 as a reference genome for somatic variant detection

(I002) Large-scale mining of more than 65,000 karyotypes in the Mitelman Database identifies unexpected differences and similarities between lymphoid, myeloid, and solid tumors

(I003) Methylation profiling differentiates subtypes of breast cancer with prognostic significance

(I004) Developing Bespoke Genomic Reference Standards for Validating Somatic Mutation Detection in Clinical Whole Exome Sequencing Assays

(I005) An anchor-based read-origin inference method enhanced SMN1/2 copy number variation and conversion event detection in Spinal Muscular Atrophy patients using Oxford Nanopore Technology long reads

(I006) Comparative Analysis of Gene Fusion Detection using Multiple Platforms: TruSeq RNA Exome, Total RNASeq and Archer FUSIONPlex

(I007) Evaluation of a High-Throughput CDR3 Identification Algorithm for 1136 Clinical Samples Targeting the IGHV Leader, IGH FR1, and TRG Loci.

(I008) Artifact and Background Calculator Identifies Patterns of False Positive Variant Calls in Clinical NGS Panels

(I009) An Event-Driven Architecture to Simplify Systems Communication in the Molecular Laboratory

(I010) Evaluation of Assay-Specific Single Nucleotide Polymorphism B-allele Frequency Performance Metrics Informs Accurate Allele-Specific Copy Number Variant Calling in Tumor Only Panel NGS Data

(I011) Robust detection of Copy Number Amplifications using Pillar’s oncoReveal™ Core LBx panel with low input from liquid biopsy samples

(I012) Customization of the CAP Lung Cancer Biomarker Reporting Template to Facilitate Direct Population of Genomic Test Results by Molecular Laboratories

(I013) Comparison Study Between Multiple Variant Annotation Software Solutions

(I014) Normalization of Real-World Sequence Variant Data from Heterogeneous Sources

(I015) Application of an advanced data analytics platform leveraging artificial intelligence to identify RNA biomarkers

(I016) The Adoption of a Single Bioinformatics Platform (AUGMET) for Harmonization of Data Analysis Standardizes and Increases Automation in Clinical NGS Workflows

(I017) Benchmarking the performance of tumor-only somatic variant detection algorithms with the G4 sequencing platform

(I018) Tumor Mutational Burden (TMB) Scoring in Pediatric Tumors Using a Comprehensive Clinical Genomics Sequencing Approach

(I019) Deep Learning Applications Using H&E Images Improve Clinical Sequencing Workflows

(I020) Differentially Expressed Genes in Atopic Dermatitis: bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus

(I021) MPath LymphoClone: A Clinical Software Portal for Analysis, Visualization and Report Generation of Next-Generation Sequencing (NGS) Based Clonality Testing

(I022) Impact of Down-sampling T-Cell Receptor Sequencing Reads on Clonality Interpretation

(I023) Investigation of Critical RNA Sequencing Variables and Their Impact on Fusion Calling

(I024) Using Tumor/Germline paired data to recalibrate rescue of somatic variants in Tumor-only NGS

(I025) Understanding California's COVID-19 Variant Data through Unsupervised Machine Learning Analysis

(I026) Laboratory-developed bioinformatics pipeline for detection of copy number variations and microsatellite instability using next generation sequencing-bases assays

(I027) Overcoming Sample Quality and Quantity Limitations for NGS Analysis of Clinically Relevant Samples

(I028) Leveraging the JAX Clinical Knowledgebase (CKB) and the Disease Ontology to Analyze Therapeutic Responses Across Cancer Subtypes

(I029) Genome integrity assessment by optical genome mapping for research in cell and gene therapy, stem cell, and bioprocessing applications

(I030) Automated Variant Classification for Myeloid Neoplasm Next-Generation Sequencing Test using Machine Learning

(I031) Robust bioinformatic method for estimating tumor content in cfDNA using denoised target sequencing

(I032) Pan-cancer Analysis of Biomarkers Using Clinical Tumor-only Whole Exome Sequencing Data

(I033) Creation of an Automated VCF Positive Control Truth Set to Facilitate Quality Control Review

(I034) Investigating the Feasibility of Detecting Metastatic Lymph Nodes Using Pre-Trained EfficientNet Neural Networks with Minimal Datasets