G2H Scope of Services and Quotation

Scope of Services and Quotation

Introduction

Gene to Health Limited is pleased to present our comprehensive Scope of Services and Quotation tailored to support your research laboratory's data analysis and omics research needs. Leveraging our expertise in advanced genetic and bioinformatics analyses, we are committed to delivering PhD-level, high-quality, cost-effective solutions to enhance your research capabilities.

Quotation

All base prices are in Hong Kong Dollars (HKD) and are subject to uprise change based on project scope and specific requirements.

Total Estimated Cost: Varies based on selected services

Special Package Offer: For clients opting for multiple (>3) services, a 10% discount is applicable.

Terms and Conditions

1. Payment Terms: 30% upfront upon agreement, 70% upon project completion. Or by milestones upon negotiations. All payments shall be settled within 30 days against invoice date.
2. Delivery Timeframe: Dependent on project scope; estimated between 2-12 weeks.
3. Revisions: Includes up to two rounds of major revisions and five rounds of minor revisions per deliverable.
4. Confidentiality: All client data will be handled with strict confidentiality.
5. Validity: Quotation valid for 30 days from the date of issuance.
6. Deliverability: All analyzed data, software outputs, pipeline instructions and deliverable results (such as figures, tables, trained models, parameters) will be returned to clients. The availability of generated scripts is negotiable, depending on contract scope, client’s demands, and involvement of key intellectual properties.

Scope of Services

Bioinformatic Services

1. GWAS-based Statistical Genetic Analysis

Description: Comprehensive genome-wide association analyses (GWAS) of genetic data to identify associations between genetic variants and various phenotypic traits.

Tools & Software:

• Primary: PLINK, REGENIE, GCTA, GATK
• Additional: LDSC, FUMA, BOLT-LMM, EIGENSOFT, SNPTEST, EMMAX, METAL, FINEMAP

Pipeline & Processes:

• Data Cleaning & Quality Control: Ensuring data integrity by removing low-quality samples and variants.
• Population Stratification: Correcting for population structure using principal component analysis (PCA).
• Genome-Wide Association Studies (GWAS): Identifying significant genetic variants associated with specific traits.
• Heritability Estimation: Calculating the proportion of variance explained by genetic factors.
• Imputation: Enhancing genotype data completeness using reference panels.
• Meta-Analysis: Combining results from multiple studies to increase statistical power.

2. Single-Cell Omics Analysis

Description: Detailed analysis of single-cell genomic and transcriptomic data to understand cellular heterogeneity and complex biological processes.

Tools & Software:

• Primary: Partek Flow
• Additional: Seurat, Scanpy, CellRanger, Monocle, Drop-seq Tools, SingleR

Pipeline & Processes:

• Data Preprocessing: Quality control, normalization, and scaling of single-cell data.
• Clustering & Dimensionality Reduction: Identifying distinct cell populations using PCA, t-SNE, or UMAP.
• Differential Expression Analysis: Detecting genes with significant expression changes across clusters.
• Trajectory Analysis: Inferring cellular development pathways and lineage relationships.
• Integration of Multi-Modal Data: Combining single-cell RNA-seq with other data types (e.g., ATAC-seq).

3. Polygenic Risk Score (PRS) Training, Modeling, and Testing

Description: Development and validation of polygenic risk scores to predict individual susceptibility to various health conditions based on genetic data.

Tools & Software:

• Primary: PRSice, LDpred, PRS-CS
• Additional: PLINK, LASSOSUM, SBayesR

Pipeline & Processes:

• SNP Selection: Identifying relevant genetic variants for the risk score.
• Weighting: Assigning effect sizes to each SNP based on GWAS results.
• Score Calculation: Aggregating weighted SNPs to compute individual PRS.
• Validation: Testing the predictive accuracy of PRS in independent cohorts.
• Optimization: Refining models to improve prediction performance.

4. Epidemiological Statistical Models

Description: Application of statistical models to study the distribution and determinants of health-related states and events within populations.

Tools & Software:

• Primary: R (e.g. survival package)
• Additional: Python (e.g. statsmodels)

Pipeline & Processes:

• Cox Proportional Hazards Models: Assessing the impact of variables on time-to-event outcomes.
• Logistic Regression: Modeling binary outcomes.
• Linear Regression: Analyzing continuous traits.
• Mixed-Effects Models: Handling hierarchical or longitudinal data structures.
• Survival Analysis: Estimating survival functions and hazard ratios.

5. Supervised Machine Learning Models

Description: Implementation of machine learning techniques to predict outcomes and identify patterns within your data.

Tools & Software:

• Primary: Scikit-learn (Python), Random Forest, Support Vector Machines (SVM), Neural Networks
• Additional:
• Boosting Algorithms: XGBoost, LightGBM, AdaBoost, CatBoost
• Deep Learning Frameworks: PyTorch
• Ensemble Methods: Gradient Boosting Machines (GBM), Extra Trees, Bagging
• Regression Models: ElasticNet, Lasso, Ridge Regression
• Other Algorithms: Naive Bayes, Decision Trees

Pipeline & Processes:

• Data Preparation: Feature selection, engineering, and data splitting into training and testing sets.
• Model Training: Building models using algorithms like Random Forest, SVM, Gradient Boosting, and Neural Networks.
• Model Evaluation: Assessing performance using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC.
• Hyperparameter Tuning: Optimizing model parameters for improved performance.
• Model Deployment: Integrating models into existing workflows or systems.

6. Next Generation Sequencing (NGS) Analysis

Description: Comprehensive analysis of next-generation sequencing (NGS) data to provide insights into genomic, transcriptomic, and epigenomic landscapes.

Tools & Software:

• Primary: Illumina BaseSpace, Bowtie2, STAR, HISAT2
• Additional: BWA, SAMtools, BEDTools, DESeq2, EdgeR, FastQC, MultiQC

Pipeline & Processes:

• Quality Control: Assessing sequencing quality using FastQC and MultiQC.
• Read Alignment: Mapping reads to reference genomes using BWA, Bowtie2, or STAR.
• Variant Calling: Identifying SNPs and indels with tools like GATK and SAMtools.
• Transcriptome Assembly: Assembling RNA-seq data using HISAT2 and StringTie.
• Differential Expression Analysis: Utilizing DESeq2 and EdgeR for identifying differentially expressed genes.
• Functional Annotation: Annotating variants and genes using ANNOVAR and SnpEff.
• Data Integration: Combining NGS data with other omics datasets for multi-dimensional analysis.

7. Long Read Sequencing Analysis

Description: Advanced analysis of long-read sequencing data to resolve complex genomic regions, detect structural variants, and enhance genome assemblies.

Tools & Software:

• Primary: SMRT Analysis (PacBio), Nanopore Tools (ONT)
• Additional: Canu, Flye, Minimap2, Longshot, Porechop, Guppy, Racon, Pilon, SVIM, Sniffles, Prokka, MAKER

Pipeline & Processes:

• Basecalling: Converting raw sequencing data into nucleotide sequences using Guppy or similar tools.
• Read Filtering: Removing low-quality reads and adapters with Porechop and Fastp.
• Alignment: Mapping reads to reference genomes using Minimap2 or BWA-MEM.
• De Novo Assembly: Constructing genome assemblies from long reads with Canu or Flye.
• Hybrid Assembly: Combining long reads with short reads for improved assembly accuracy.
• Error Correction: Refining assemblies using Racon for consensus polishing and Pilon for further error correction.
• Structural Variant Detection: Identifying large insertions, deletions, inversions, and translocations using tools like SVIM and Sniffles.
• Annotation: Annotating assembled genomes with functional and structural information using Prokka and MAKER.
• Comparative Genomics: Comparing assemblies across different samples or conditions to identify genomic variations.

8. Post-GWAS Analyses

Description: Post-GWAS analyses help interpret genetic associations identified in Genome-Wide Association Studies (GWAS), linking statistical results to biological mechanisms. They pinpoint causal variants, explore genetic architectures, and assess functional impacts.

Key Analyses:

1. Fine-Mapping
• Objective: Identify likely causal variants in associated loci.
• Tools: CAVIAR, FINEMAP
• Process: Integrate GWAS statistics with LD data, perform statistical fine-mapping, prioritize variants for validation.
2. Functional Annotation
• Objective: Understand the biological functions of variants and their impact on gene regulation or protein function.
• Tools: ANNOVAR, VEP
• Process: Annotate variants with genomic locations, regulatory elements, and gene associations.
3. Pathway Enrichment
• Objective: Identify biological pathways enriched with associated genes.
• Tools: MAGMA, DAVID
• Process: Map variants to genes, perform enrichment analysis, interpret relevant pathways.
4. Colocalization Analysis
• Objective: Assess if the same variant affects multiple traits.
• Tools: COLOC, SMR
• Process: Integrate GWAS with eQTL data, test shared genetic architecture.
5. Gene-Based Testing
• Objective: Evaluate the cumulative effect of gene variants on traits.
• Tools: MAGMA, VEGAS2
• Process: Aggregate variant associations within genes, perform statistical tests.
6. Heritability Partitioning
• Objective: Estimate contributions of functional categories to trait heritability.
• Tools: LDSC
• Process: Assign SNPs to functional categories and estimate their heritability.
7. eQTL Analysis
• Objective: Link GWAS variants with gene expression.
• Tools: GTEx, FastQTL
• Process: Cross-reference GWAS variants with eQTL databases, explore gene regulation mechanisms.

9. Mendelian Randomization Study

Description: Mendelian Randomization (MR) uses genetic variants as instrumental variables to infer causal links between risk factors and health outcomes, reducing confounding and reverse causality.

Tools & Software:

• Primary: MRBase, MVMR, TwoSampleMR, MRInstruments
• Additional: GCTA, PLINK

Pipelines & Processes:

1. Instrumental Variables Selection
• Objective: Identify SNPs st