Aim 1: To develop and refine algorithms that analyze and integrate transcriptomic and genomic sequencing data to identify genetic modifiers of the HPAH phenotype in our cohort.

Objective

To enhance our understanding of genetic factors contributing to the HPAH phenotype by developing and refining analytical methods for integrating transcriptomic and genomic data.

Sub-Aim 1.1: To identify potential genetic modifiers for HPAH.

• Objective: Perform variant analysis to identify genetic modifiers in HPAH, adhering to ACMG guidelines.
• References:
  • [Reference 1] (e.g., Author, Year, Title, Journal, DOI)
  • [Reference 2]
• Methodology:
  • [Variant Analysis Pipeline] (e.g., Custom pipeline adhering to ACMG guidelines)
  • [Software Tools] (e.g., GATK, VEP)
• Tools:
  • [GATK] (Genome Analysis Toolkit)
  • [VEP] (Variant Effect Predictor)
• Datasets:
  • [Cohort Genomic Data] (e.g., Whole-exome sequencing, HPAH cohort)
  • [Public Genomic Databases] (e.g., dbGAP, ClinVar)
• Preliminary Data:
  • Initial variant analysis revealed X number of potential genetic modifiers linked to HPAH.
  • [Figures, tables, or other data visuals can be linked or described here.]
• Progress:
  • What Worked: Successfully identified variants adhering to ACMG guidelines.
  • What Didn’t Work: Challenges in variant filtering due to low coverage in some regions.

Sub-Aim 1.2: To extract protein-protein interactions (PPIs) networks.

• Objective: Utilize newly identified and previously validated genes to extract comprehensive PPIs networks.
• References:
  • [Reference 1] (e.g., Author, Year, Title, Journal, DOI)
  • [Reference 2]
• Methodology:
  • [PPI Network Extraction] (e.g., Integration of multiple PPI databases)
  • [Database Integration] (e.g., STRING, BioGRID)
• Tools:
  • [STRING] (Search Tool for the Retrieval of Interacting Genes/Proteins)
  • [BioGRID] (Biological General Repository for Interaction Datasets)
• Datasets:
  • [PPI Datasets] (e.g., Comprehensive PPI sets from multiple databases)
  • [Gene Lists] (e.g., Newly identified genes, Previously validated genes)
• Preliminary Data:
  • Preliminary PPI networks have been constructed, revealing key interactions among candidate genes.
  • [Figures, tables, or other data visuals can be linked or described here.]
• Progress:
  • What Worked: Successfully integrated data from multiple PPI databases.
  • What Didn’t Work: Difficulty in validating low-confidence interactions.

Sub-Aim 1.3: To generate a network of hub genes.

• Objective: Reanalyze transcriptomic datasets and apply weighted gene co-expression network analysis (WGCNA) to generate a network of hub genes and functional disease modules.
• References:
  • [Reference 1] (e.g., Author, Year, Title, Journal, DOI)
  • [Reference 2]
• Methodology:
  • [WGCNA] (e.g., Weighted Gene Co-Expression Network Analysis)
  • [Data Normalization] (e.g., Reanalysis and normalization of transcriptomic data)
• Tools:
  • [WGCNA] (Software for gene co-expression network analysis)
  • [R/Bioconductor] (R packages for data normalization and analysis)
• Datasets:
  • [Cohort Transcriptomic Data] (e.g., RNA-seq data from HPAH cohort)
  • [dbGAP Transcriptomic Data] (e.g., Publicly available transcriptomic datasets)
  • [GEO Transcriptomic Data] (e.g., Publicly available transcriptomic datasets)
• Preliminary Data:
  • Initial WGCNA analysis has identified several hub genes potentially involved in HPAH pathogenesis.
  • [Figures, tables, or other data visuals can be linked or described here.]
• Progress:
  • What Worked: Identification of key hub genes and functional modules.
  • What Didn’t Work: Reanalysis revealed inconsistencies in data normalization.