FunGen-xQTL Computational Protocol

Developed for reproducible & reusable molecular QTL analyses for the NIH/NIA Alzheimer's Disease Sequencing Project (ADSP) Functional Genomics xQTL (FunGen-xQTL) Project.

Overview of the protocol

Standardized reference data

Reference data are standardized and curated by the ADSP FGC Standardization Workgroup in coordination with NIAGCADS. Please find reference data specifications on ADSP Dashboard.

Software environment

We use a set of packages from the Conda ecosystem to deploy our software. Most packages are from conda-forge and bioconda, along with a custom channel for software unavailable from those repositories.

Pipeline execution

Pipelines in this repository are written in the Script of Scripts (SoS) workflow language. Like most other workflow languages, SoS workflows can distribute and execute computing jobs directly in High Performance Computing cluster. Unlike most other workflow languages, SoS workflows are created using SoS Notebooks (based on Ipython Notebook and developed in Jupyter) which allow for both scientific narrative and pipeline scripts in the same document. Unlike typical Jupyter Notebooks intended for interactive data analysis, SoS workflows written in Jupyter Notebooks can be executed directly as command line scripts either on a local computer or in a HPC environment.

We provide this toy example for running SoS pipeline on a typical HPC cluster environment. First time users are encouraged to try it out in order to help setting up the computational environment necessary to run the analysis in this protocol.

Source code

• Source code of pipelines implemented in this repository are available at https://github.com/statfungen/xqtl-protocol/tree/main/code.

How to use the resource

Organization of the resource

The website https://statfungen.github.io/xqtl-protocol is generated from files under the code folder of the source code repository. The pipeline folder contains symbolic links automatically generated for pipeline files under code. The logic of the entire xQTL analysis workflow is roughly reflected on the left sidebar:

• The GETTING STARTED section serves as the main landing page or index of the xQTL protocol, guiding users through the various pipelines implemented in this repository. It's structured to mirror the logic of the xQTL analysis we've crafted. Because this page provides pointers to other sections, users can primarily focus here without having to sift through the rest of the pages pages in this repository.
• The COMMAND GENERATOR section is designed as a one-stop hub for "push button" commands, enabling users to generate the full QTL analysis pipeline workflow scripts from a straightforward configuration file. Notebooks within this section are intended to be executed as command line software for data analysis command generation. Users can then execute these generated commands directly to conduct all preset analyses. Alternatively, they can tweak the commands to cater to particular analysis requirements. The configuration file serves a dual purpose: streamlining control and maintaining a record of executed workflows.
• Other sections in bold fonts provide an array of available analyses, presented roughly from upstream to downstream processes. Most of these sections feature mini-protocols, represented as clickable, non-bold text under each analysis category, leading to specific notebooks. These notebooks detail the commands necessary for the analyses defined in the respective mini-protocols. Predominantly tutorial-based, they are designed to be executed interactively in Jupyter or via the command terminal, allowing users to navigate through the SoS pipelines step by step. A few of these sections serve as actual pipeline modules which we'll discuss next (see below).
• Mini-protocols, as mentioned earlier, can be expanded by clicking the downward arrows, revealing the SoS implementations of pipeline modules. These represent the crux of the pipeline implementations and are intended to be executed as command line software. They're also self-contained, allowing for reusability beyond the specific context of xQTL data analysis.

Computing environment setup

• In order to run the xQTL protocol on your computer (or a High Performance Computing cluster), please see our pixi setup repository at https://github.com/StatFunGen/pixi-setup for instructions to install software. Native support is only provided for Linux and macOS (Intel and Apple Silicon) - Windows users will need to install Windows Subsystem For Linux (WSL) to locally install the software.
• We have provided example data-sets in this Synapse folder. For guidance on downloading the data programmatically, refer to this documentation. If you need to set up a Synapse client, once you have installed, pixi, use pixi global install -c biconda -c conda-forge synapseclient.
  • Within the test_data folder, datasets prefixed with MWE (Minimal Working Example) are provided. These are used for unit testing each module, ensuring the integrity of the code.
  • The protocol_data folder houses a comprehensive set of data, illustrating the full extent of our protocol. This is showcased in this notebook, with the source code available for reference.
• Please clone this repository https://github.com/statfungen/xqtl-protocol onto your computer. This is the source code for this resource. All pipelines are symbolic links in the pipeline folder. Users are encouraged to execute from the root of the repository folders by typing sos run pipeline/<pipeline_file>.ipynb that is, executing the symbolic links directly to perform the analysis.

See Also

• Analysis from FunGen-xQTL consortium using this protocol can be found at https://github.com/cumc/fungen-xqtl-analysis

Our team

This repository is developed by the Analysis Working Group of the NIA FunGen-xQTL consortium.

Developers

Lead developers

• Hao Sun, Department of Neurology, Columbia University
• Gao Wang, Department of Neurology, Columbia University

Main contributors (largely based on GitHub Pull Requests)

Name	Affiliation
Xuanhe Chen	Department of Biostatistics, Columbia University
Wenhao Gou	Department of Biostatistics, Columbia University
Liucheng Shi	Department of Biostatistics, Columbia University
Haochen Sun	Department of Biostatistics, Columbia University
Zining Qi	Department of Biostatistics, Columbia University
Yifei Wang	Department of Neurology, Columbia University
Ru Feng	Department of Neurology, Columbia University
Alexandre Pelletier	Department of Medicine, Boston University
Travyse Edwards	Mount Sinai & University of Pennsylvania
Daniel Nachun	Department of Pathology, Stanford University
Jiacheng Li	Department of Neurology, Columbia University
Mintao Lin	Department of Medicine, Boston University

Leadership

FunGen-AD

Name	Affiliation
Philip De Jager	Department of Neurology, Columbia University
Carlos Crunchaga	Department of Psychiatry, Neurology and Genetics, Washington University in St. Louis

FunGen-xQTL Analysis Working Group

Name	Affiliation
Gao Wang	Department of Neurology, Columbia University
Xiaoling Zhang	Departments of Medicine and Biostatistics, Boston University
Edoardo Marcora	Departments of Neuroscience, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai
Fanny Leung	Department of the Pathology and Laboratory Medicine, University of Pennsylvania
Julia TCW	Department of Pharmacology and Bioinformatics, Boston University
Kushal K. Dey	Memorial Sloan Kettering
Alan Renton	Departments of Neuroscience, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai
Stephen Montgomery	Department of Pathology, Stanford University
Xiaoquan Wen	Department of Biostatistics, University of Michigan