Trusted Research Environment Usage

This document outlines the usage of the Trusted Research Environment (TRE).

If you haven't already, please request and connect to your environment first.

Desktop Environment

Once connected, you will see a Linux desktop environment:

Terminal Application:

Working with Data

Shared data will be available in designated directories (typically /data/common)

Data Location:

• Your approved cohort data will be pre-loaded in designated directories
• Clinical data is typically in structured formats (CSV, TSV, or database files)
• Genomic data may be in standard formats (VCF, BAM, FASTQ, etc.)

Please configure your scripts to write any outputs to /data/output

In order to conserve cloud resources, raw sequencing data is made available 'on-demand' by default. For downloading raw data within the TRE, please refer to the API client guide

Running Analysis

cd /data/common/scripts
R < example_script.r --no-save

Available Analysis Tools

The Trusted Research Environment comes with a suite of pre-installed tools for various types of analysis:

General Purpose Tools

• RStudio: An integrated development environment (IDE) for R.
• R: A language and environment for statistical computing and graphics, with various packages installed, including Bioconductor.
• Python: A versatile programming language widely used for data analysis, with common scientific libraries.
• Jupyter Notebooks: An interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text.

Genomic Analysis Tools

• PLINK 1.9: A whole-genome association analysis toolset.
• PLINK 2.0: A next-generation whole-genome association analysis toolset, offering improved performance and new features.
• IGV (Integrative Genomics Viewer): A high-performance visualization tool for genomic data.
• BCFtools: A set of utilities for variant calling and manipulating VCF and BCF files.
• VCFTools: A program package designed to work with VCF files, including filtering, merging, and comparing.
• BEDTools: A versatile toolset for genomic feature manipulation.
• GATK (Genome Analysis Toolkit): A comprehensive toolkit for variant discovery in high-throughput sequencing data, including tools for Somatic Variant (SV), Copy Number Variant (CNV), and Pathogen Sequencing (Pathseq) analysis.
• HLA Caller: Tools for Human Leukocyte Antigen (HLA) typing (e.g., xHLA, HiBAG).
• samtools: Utilities for interacting with and post-processing sequence alignments in SAM, BAM, and CRAM formats.
• tabix: A generic indexer for TAB-delimited genome position files.
• htslib: A C library for reading/writing high-throughput sequencing data formats.
• minconda: A free minimal installer for conda.
• annovar: A tool for annotating genetic variants.
• METAL: A tool for meta-analysis of genome-wide association scans.

R Packages

The R environment includes a comprehensive set of packages for statistical analysis and bioinformatics:

• data.table: Extension of data.frame for faster data manipulation.
• DESeq2: Differential gene expression analysis based on the negative binomial distribution.
• GWASTOOLS: Tools for quality control and analysis of genome-wide association studies.
• biomaRt: R interface to BioMart online biological data resources.
• gtools: Various R programming tools.
• dplyr: A grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges.
• ggplot2: A system for declaratively creating graphics, based on The Grammar of Graphics.
• SKAT: Sequence Kernel Association Test for genetic association studies.
• robustSKAT: Robust Sequence Kernel Association Test.
• HIBAG: HLA Imputation Based on Allele Gene.
• stringr: A consistent, simple, and easy-to-use set of wrappers for common string operations.
• foreach: Provides a mechanism for iterating over elements in a collection, without the need for explicit loop counters.
• SNPRelate: Parallel computing toolset for genome-wide association studies.
• readxl: Read Excel files into R.
• tidyverse: An opinionated collection of R packages designed for data science.
• ggpubr: ggplot2 based publication ready plots.
• survplot: Tools for plotting survival data.
• All their respective dependencies.