Immune Repertoire Analysis on High-Performance Computing Using VDJServer V1: A Method by the AIRR Community.

BackgroundNext generation sequencing (NGS) technologies enable studies and analyses of the diversity of both T and B cell receptors (TCR and BCR) in human and animal systems to elucidate immune functions in health and disease. Over the last few years, several algorithms and tools have been developed to support respective analyses of raw sequencing data of the immune repertoire. These tools focus on distinct aspects of the data processing and require a strong bioinformatics background. To facilitate the analysis of T and B cell repertoires by less experienced users, software is needed that combines the most common tools for repertoire analysis.ResultsWe introduce a graphical user interface (GUI) providing a complete analysis pipeline for processing raw NGS data for human and animal TCR and BCR clonotype determination and advanced differential repertoire studies. It provides two applications. ClonoCalc prepares the raw data for downstream analyses. It combines a demultiplexer for barcode splitting and employs MiXCR for paired-end read merging and the extraction of human and animal TCR/BCR sequences. ClonoPlot wraps the R package tcR and further contributes self-developed plots for the descriptive comparative investigation of immune repertoires.ConclusionThis workflow reduces the amount of programming required to perform the respective analyses and supports both communication and training between scientists and technicians, and across scientific disciplines.The Open Source development in Java and R is modular and invites advanced users to extend its functionality. Software and documentation are freely available at https://bitbucket.org/ClonoSuite/clonocalc-plot.

High Performance Computing (HPC) applications commonly use Message Passing Interface (MPI) to scale complex workloads on distributed memory architectures, while Graphical User Interface (GUI) is the primary basis for pre-processing and post-processing. Containerization enables portability and reproducibility for applications. Docker is a widely used containerization platform but cannot be easily adopted in secure HPC environments due to the need for root privileges to run Docker containers and the ensuing security issues, as well as the lack of native support for Message Passing Interface (MPI) and HPC workload managers. In addition to offering portability and reproducibility to scientific computing, Singularity and Sarus are container runtimes that enable containers to run on HPC with user credentials by removing security concerns and allowing reuse of Docker containers without any changes. This work presents approaches to creating and running MPI and GUI applications in an isolated container environment on HPC in the cloud. We provide performance evaluation of application containers transformed from Docker to Singularity and Sarus in the context of native on HPC in the cloud.

Accurate detection is still a challenge in machine learning (ML) for Alzheimer's disease (AD). Class imbalance in imbalanced AD data is another big challenge for machine-learning algorithms working under the assumption that the data are evenly distributed within classes. Here, we present a hyperparameter tuning workflow with high-performance computing (HPC) for imbalanced data related to prevalent mild cognitive impairment (MCI) and AD in the Health and Aging Brain Study-Health Disparities (HABS-HD) project. We applied a single-node multicore parallel mode to hyperparameter tuning of gamma, cost, and class weight using a support vector machine (SVM) model with 10 times repeated fivefold cross-validation. We executed the hyperparameter tuning workflow with R's bigmemory, foreach, and doParallel packages on Texas Advanced Computing Center (TACC)'s Lonestar6 system. The computational time was dramatically reduced by up to 98.2% for the high-performance SVM hyperparameter tuning model, and the performance of cross-validation was also improved (the positive predictive value and the negative predictive value at base rate 12% were, respectively, 16.42% and 92.72%). Our results show that a single-node multicore parallel structure and high-performance SVM hyperparameter tuning model can deliver efficient and fast computation and achieve outstanding agility, simplicity, and productivity for imbalanced data in AD applications.

High-throughput single cell RNA-seq (scRNA-seq) has transformed our understanding of complex and heterogenous immune populations. New advances in scRNA-seq are expanding the molecules that can be profiled at the single cell level, such as oligo-conjugated antibody technologies that enable protein expression profiling alongside mRNA. Although the ability to sequence the immune repertoire can provide crucial insights into understanding the complexities of the adaptive immune system and advancing discoveries in immuno-oncology, the ability to extract this information from single cells requires new technology to profile regions of the mRNA that are missed by conventional 3′ scRNA-seq. In this study we utilized an ex vivo antigen stimulation system to measure antigen-specific T-cell activation and clonal amplification. Stimulated T cells from two donors were loaded onto the BD Rhapsody™ Single-Cell Analysis System to extract immune repertoire information in addition to gene and protein expression information from the same cells. In a single workflow, we profiled a panel of 400 mRNA targets, 20 BD® AbSeq protein markers associated with different status of T-cell activation, and the hypervariable region of T-cell receptors at the single cell resolution. The study is a proof of concept of combining mRNA, protein and immune repertoire analysis at the single cell level to deconvolute the heterogeneity and different activation of stimulated T cells. For Research Use Only. Not for use in diagnostic or therapeutic procedures. BD, the BD Logo, and Rhapsody are trademarks of Becton, Dickinson and Company or its affiliates. © 2019 BD. All rights reserved.

In adaptive immune receptor repertoire analysis, determining the germline variable (V) allele associated with each T- and B-cell receptor sequence is a crucial step. This process is highly impacted by allele annotations. Aligning sequences, assigning them to specific germline alleles, and inferring individual genotypes are challenging when the repertoire is highly mutated, or sequence reads do not cover the whole V region. Here, we propose an alternative naming scheme for the V alleles, as well as a novel method to infer individual genotypes. We demonstrate the strengths of the two by comparing their outcomes to other genotype inference methods. We validate the genotype approach with independent genomic long-read data. The naming scheme is compatible with current annotation tools and pipelines. Analysis results can be converted from the proposed naming scheme to the nomenclature determined by the International Union of Immunological Societies (IUIS). Both the naming scheme and the genotype procedure are implemented in a freely available R package (PIgLET https://bitbucket.org/yaarilab/piglet). To allow researchers to further explore the approach on real data and to adapt it for their uses, we also created an interactive website (https://yaarilab.github.io/IGHV_reference_book).

This report summarizes the final product developed for the US DOE Small Business Innovation Research (SBIR) Phase II grant made to Engineering Mechanics Corporation of Columbus (Emc2) between April 16, 2014 and August 31, 2016 titled ‘Adoption of High Performance Computational (HPC) Modeling Software for Widespread Use in the Manufacture of Welded Structures’. Many US companies have moved fabrication and production facilities off shore because of cheaper labor costs. A key aspect in bringing these jobs back to the US is the use of technology to render US-made fabrications more cost-efficient overall with higher quality. One significant advantage that has emerged in the US over the last two decades is the use of virtual design for fabrication of small and large structures in weld fabrication industries. Industries that use virtual design and analysis tools have reduced material part size, developed environmentally-friendly fabrication processes, improved product quality and performance, and reduced manufacturing costs. Indeed, Caterpillar Inc. (CAT), one of the partners in this effort, continues to have a large fabrication presence in the US because of the use of weld fabrication modeling to optimize fabrications by controlling weld residual stresses and distortions and improving fatigue, corrosion, and fracture performance. This report describes Emc2’s DOE SBIR Phase II final results to extend an existing, state-of-the-art software code, Virtual Fabrication Technology (VFT®), currently used to design and model large welded structures prior to fabrication - to a broader range of products with widespread applications for small and medium-sized enterprises (SMEs). VFT® helps control distortion, can minimize and/or control residual stresses, control welding microstructure, and pre-determine welding parameters such as weld-sequencing, pre-bending, thermal-tensioning, etc. VFT® uses material properties, consumable properties, etc. as inputs. Through VFT®, manufacturing companies can avoid costly design changes after fabrication. This leads to the concept of joint design/fabrication where these important disciplines are intimately linked to minimize fabrication costs. Finally service performance (such as fatigue, corrosion, and fracture/damage) can be improved using this product. Emc2’s DOE SBIR Phase II effort successfully adapted VFT® to perform efficiently in an HPC environment independent of commercial software on a platform to permit easy and cost effective access to the code. This provides the key for SMEs to access this sophisticated and proven methodology that is quick, accurate, cost effective and available “on-demand” to address weld-simulation and fabrication problems prior to manufacture. In addition, other organizations, such as Government agencies and large companies, may have a need for spot use of such a tool. The open source code, WARP3D, a high performance finite element code used in fracture and damage assessment of structures, was significantly modified so computational weld problems can be solved efficiently on multiple processors and threads with VFT®. The thermal solver for VFT®, based on a series of closed form solution approximations, was extensively enhanced for solution on multiple processors greatly increasing overall speed. In addition, the graphical user interface (GUI) was re-written to permit SMEs access to an HPC environment at the Ohio Super Computer Center (OSC) to integrate these solutions with WARP3D. The GUI is used to define all weld pass descriptions, number of passes, material properties, consumable properties, weld speed, etc. for the structure to be modeled. The GUI was enhanced to make it more user-friendly so that non-experts can perform weld modeling. Finally, an extensive outreach program to market this capability to fabrication companies was performed. This access will permit SMEs to perform weld modeling to improve their competitiveness at a reasonable cost.

The chief impediment to widespread usage of parallel computing is the difficulty in programming high performance computers (HPCs). Furthermore, most users work from a Windows PC, so that learning UNIX as a prerequisite to parallel programming is a further obstruction. What is needed is a smooth workflow that simplifies both the programming task and the remote execution management. VISION/HPC is a Python-based, drag-and-drop visual-programming environment that reduces sophisticated programming tasks to dropping and connecting icons in a graphical user interface (GUI) flowchart. This is important for productivity, which is dominated by the time spent studying results versus the time spent writing maintainable code to generate those results. As a Python-based open-source package, it encapsulates scientific and parallel programming Python modules that are accessed through the visual interface. VISION/HPC runs on a local Windows PC and manages jobs on a remote backend. This means that the graphic intensive graphical user interface (GUI) runs on the local workstation and does not push individual pixels through a busy network connection.

ObjectivesThis study investigated the characteristics of the immune repertoire in normal Chinese individuals of different ages.Materials and MethodsIn this study, all seven receptor chains from both B and T cells in peripheral blood of 16 normal Chinese individuals from two age groups were analyzed using high‐throughput sequencing and dimer‐avoided multiplex PCR amplification. Normal in this study is defined as no chronic, infectious or autoimmune disease within 6 months prior to blood draw.ResultsWe found that compared with the younger group, the clonal expression of T‐cell receptor repertoire increased in the older group, while diversity decreased. In addition, we found that the T‐cell receptor repertoire was more significantly affected by age than the B‐cell receptor repertoire, including significant differences in the use of the unique TCR‐alpha and TCR‐beta V‐J gene combinations, in the two groups of normal participants. We further analyzed the degree of complementarity determining region 3 sequence sharing between the two groups, and found shared TCR‐alpha, TCR‐gamma, immunoglobulin‐kappa and immunoglobulin‐lambda chain complementarity determining region 3 sequences in all subjects.ConclusionTaken together, our study gives us a better understanding of the immune repertoire of different normal Chinese people, and these results can be applied to the treatment of age‐related diseases. Immune repertoire analysis also allows us to observe participant's wellness, aiding in early‐stage diagnosis.

Classical Molecular Dynamics (MD) simulates the dynamical evolution of biological systems at the atomic level. Using MD in conjunction with high-performance computing (HPC) architectures, we can evaluate the possible interactions between a ligand library against one protein target to find a drug that can influence a protein target to cure a disease. Simultaneously, we can also obtain information about their dynamic evolution. One of the primary software packages for MD simulations is Desmond, which employs Maestro for the setup, execution, and analysis of MD through a graphical user interface (GUI), which is suitable even for non-expert users. However, using the GUI, users can typically run only one short (less than 1000 ns) MD each time. Our work aims to create a method/protocol to run several MD simulations simultaneously on a remote HPC cluster within Maestro-Desmond. In this work, we provide TOLEDO (Throughput Optimization of Ligand-Protein Systems Exploration through Dynamics simulation in Optimized HPC systems) to overcome such limitations and run several MD simulations simultaneously. The best feature of TOLEDO is its independence from the usual time constraints of many clusters, with storage space being the only limitation. To run TOLEDO, we prepare/set up the protein-ligand complex before running MD via Maestro GUI. Next, we run the main TOLEDO script for several MD simulations on a supercomputer. When TOLEDO finishes, users obtain reports and graphics. The obtained results are easily interpretable. In essence, TOLEDO significantly enhances MD throughput beyond the capabilities of the Maestro GUI.

The diversity of T-cell receptors (TCRs) and B-cell receptors (BCRs) underpins the adaptive immune system’s ability to recognize and respond to a wide array of antigens. Recent advancements in RNA sequencing have expanded its application beyond transcriptomics to include the analysis of immune repertoires, enabling the exploration of TCR and BCR sequences across various physiological and pathological contexts. This review highlights key methodologies and considerations for TCR and BCR repertoire analysis, focusing on the technical aspects of receptor sequence extraction, data processing, and clonotype identification. We compare the use of bulk and single-cell sequencing, discuss computational tools and pipelines, and evaluate the implications of examining specific receptor regions such as CDR3. By integrating immunology, bioinformatics, and clinical research, immune repertoire analysis provides valuable insights into immune function, therapeutic responses, and precision medicine approaches, advancing our understanding of health and disease.

Image based Computational Fluid Dynamics (CFD) simulation of the cardiovascular system is increasingly becoming important and its application in everyday medical practice can already be envisaged. The goal of this workshop is to address all the factors involved in the development of a computational framework/software for modelling and analyses of the cardiovascular system and provide examples. The development of such framework, requires integration, management and interpretation of data from several technology areas such as a) feature detection and extraction of arterial geometry from imaging data, b) adaptive grid generation techniques for 3-D asymmetric geometries c) hemodynamic modelling, disparate length-scale model, and fluid-tissue interaction with high-performance computing, d) CFD data validation, e) feature extraction/detection and visualisation algorithms, f) graphical user interface to allow remote visualisation of post processed data. These computational tools are employed to study flow in specific problem sites in the vascular tree such as the carotid, femoral, coronary and abdominal arteries. Such studies provide understanding of the factors involved in the initialisation and evolution of arterial disease due to altered flow conditions (as a result of plaque formation) such as flow separation and reversal, and low and oscillatory wall shear stress. It is also used to study the effect of various clinical procedures such as the implantation of stents, vascular grafts, vascular prostheses and artificial valve implants on local and global hemodynamics. This workshop will address a new emerging paradigm in clinical practice known as predictive medicine for effective surgical planning and post surgical rehabilitation. The workshop will also address the difficulties in the implementation of some of the technology areas in this application with examples of carotid, femoral, and abdominal artery simulations.

This presentation was given at GCC2021, see the video here: https://sched.co/jm7V Abstract: Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses with unparalleled precision. The capacity of machine learning (ML) to identify complex sequence patterns renders it an ideal approach for AIRR-based diagnostics and therapeutic discovery. To date, widespread AIRR ML adoption has been inhibited by a lack of accessibility, reproducibility, transparency, and interoperability. Existing tools are often challenging to install, support only command-line interfaces, and rely on a variety of non-standardized file formats. The immuneML Galaxy portal (galaxy.immuneml.uio.no) addresses these concerns through an ecosystem of tools that covers the steps from initial processing of AIRR sequencing data to training ML models and performing post-analysis interpretations. We cater to immunologists by providing Galaxy tool interfaces with guided selection for core aspects in the AIRR domain. Also, immuneML ensures full interoperability between Galaxy and command-line use of the same platform by auto-generating a YAML-specification file for command-line use when analyses are specified in the Galaxy tool, and by allowing the YAML files of command-line users to be used directly as input in an alternative Galaxy tool for running immuneML.

In environmental data analysis, source apportionment can be an important approach to extract useful information that might otherwise be hidden within the data. The United States Environmental Protection Agency (EPA) has developed an open-source python package, the Environmental Source Apportionment Toolkit (ESAT), which enables source apportionment modeling and error estimation workflows. ESAT is intended to replace Positive Matrix Factorization v5 (PMF5) that has substantial data size limitations. ESAT is currently in alpha testing with development plans for enhanced functionality and support of large datasets, High-performance Computing (HPC) execution through a command line interface (CLI), and a standalone desktop graphical user interface (GUI). The alpha product of ESAT is publicly available and offers a complete application programming interface (API) to replicate the workflows and functionality of PMF5, with examples provided through Jupyter Notebooks. The ESAT computing module currently contains two non-negative matrix factorization (NMF) algorithms for model training, with the module designed for other algorithms to be easily added. The two algorithms currently available are the least-squares NMF (LS-NMF) and weighted-semi NMF (WS-NMF). Each algorithm offers different benefits depending on project or data requirements. The ESAT python codebase has been optimized to run in a highly parallelized manner, with most of the numerical computations implemented in Rust, a low-level language comparable in performance to C. ESAT replicates the model error estimation methods of PMF5, namely bootstrap, displacement, and a hybrid method. To facilitate experimentation and testing, ESAT contains a synthetic dataset generator and model simulator that can evaluate how well ESAT can recreate synthetic factors and contributions. Continuous development of new features are tested and added to the python package on a regular basis. One such feature is the addition of an uncertainty perturbation workflow, which will run a collection of models while slightly perturbing the uncertainty matrix, and then evaluating the impact on the solution profiles and contributions. The alpha version of the ESAT python package is available for installation from pypi at https://pypi.org/project/esat/. Further testing and development of the alpha version will proceed to a full release in late 2025. The development of a GUI desktop application is currently planned to begin after the ESAT full release.

VERAIO is a set of utility codes used to provide a common set of input and outputs to the Virtual Environ ment for Reactor Applications (VERA). VERA is a collection of several different computer codes that all have a common input and output. This prevents the need to manage input and output from each individual code, allowing for ease of use and reducing errors associated with code operability. The VERAIO utilities include VERAIn, VERAView, and VERARun. Each of these utilities is described below. VERAIn is an input processor that reads an ASCII input file generated by users, parses the file, performs some error checking, and writes an XML file to be read by other VERA codes. The main purpose of VERAIn is to provide a common input to all of the VERA codes so users only need to learn one input. VERAIn is written in Perl and uses YAML configuration files to provide flexibility. VERAView is a graphical user interface (GUI) that reads a VERA HDF output file and allows users to visualize results. VERAView is written in Python. VERARun is a script that drives the VERA execution in a high performance computing (HPC) environment. Work performed at the code level supports the Quality Assurance Program Plan (QAPP) (VERA-QA-001), and VERA Software Quality Assurance Plan (VERA-QA-002).

This paper is an overview of Power System Simulation Toolbox (psst). psst is an open-source Python application for the simulation and analysis of power system models. psst simulates the wholesale market operation by solving a DC Optimal Power Flow (DCOPF), Security Constrained Unit Commitment (SCUC) and a Security Constrained Economic Dispatch (SCED). psst also includes models for the various entities in a power system such as Generator Companies (GenCos), Load Serving Entities (LSEs) and an Independent System Operator (ISO). psst features an open modular object oriented architecture that will make it useful for researchers to customize, expand, experiment beyond solving traditional problems. psst also includes a web based Graphical User Interface (GUI) that allows for user friendly interaction and for implementation on remote High Performance Computing (HPCs) clusters for parallelized operations. This paper also provides an illustrative application of psst and benchmarks with standard IEEE test cases to show the advanced features and the performance of toolbox.