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Immunogenetics databases aid genetic research in disease and drug development research. Lab mice (Mus musculus) are crucial for in-vivo research and constitute most non-human vertebrate data. The IMGT database's completeness in representing diverse mice strains is unclear, leading to disparities in the representation of different strains in immunogenetics databases. We assessed the IMGT database's representation of four lab mice strains (C57BL6, C57BL6/J, BALB/c, and NOD) by analyzing 181 TCR-seq samples from the SRA using MiXCR software. MiXCR aligns TCR reads to the IMGT database and compares them to the reference reads. Results revealed that C57BL/6 mice are more representative of the V gene in the IMGT database (0.66 ± 8.71), while BALB/c mice are more representative of J gene (0.08 ± 1.58). Our study represents the first study to comprehensively evaluate the completeness of the IMGT database for diverse mice strains, and demonstrate that the database is severely incomplete for various mice strains and provide appealing evidence for the urgent need to diversify the databases. We identify underrepresented mice strains in the database and emphasize the importance of diverse immunogenetics databases for understanding the immune responses in different mouse strains.

This study was motivated by the difficulty to identify the patients that will react beneficially to anti-tumor treatments, in particular for targeted and immuno-therapies. In particular, there is currently a lack of a database specifically dedicated to support the development of AI models to help doctors in this mission. The Data Lakehouse architecture, which we have implemented with open source Delta Lake technology, brings together the best features of Data Lake and Data Warehouse.

In the context of the European project KATY (GA: 101017453), we prototyped the development of a Data Lakehouse by integrating three research studies that generated molecular profiling data from cohorts of tumor tissues taken from patients included in drug clinical trials. We will present the challenges related to the creation of this data architecture as well as the ongoing developments on data governance and secure access.

This Data Lakehouse will allow three types of access for the KATY consortium: querying with data analytics approaches, targeted data extraction for the development of AI models and feeding a Knowledge Graph to structure experimental data and a priori biological and clinical knowledge.

Hypothyroidism is a common endocrine disorder. The disease is manifested when the thyroid gland fails to produce sufficient thyroid hormones. In many cases, the low levels of thyroid hormones are due to environmental factors. Congenital hypothyroidism (CH) results from thyroid development abnormalities. A large-scale study was conducted to identify functional genes associated with increased or decreased risk for hypothyroidism. The study used the UK-Biobank database, which reports on 13,687 cases of European ancestry with a prevalence of 7.5% and 2.0% for females and males, respectively. Using the gene-based proteome-wide association study (PWAS) method, we identified a ranked list of 77 statistically significant genes. We observed that many of the genes involved in hypothyroidism function in the recognition and response of immune cells, with a strong signature of autoimmunity. Expanding the analysis to additional genetic association protocols (e.g., TWAS, Open Targets Genetics, and coding-GWAS) revealed a complex etiology of hypothyroidism with genes explaining the CH developmental program, autoimmunity, and expression dysregulation in target tissues. No sex-dependent genetic effects were found. The study highlights the importance of applying complementary genome-based association methods to complex diseases. We conclude that an integration of established association methods can improve interpretability and clinical utility.

Antimicrobial Resistance (AMR) is an urgent threat to human health worldwide as microbes have developed resistance to even the most advanced drugs. In this year’s CAMDA challenge, we focused on exploring the metagenomic surveillance data from a selection of 144 isolates from six US cities (Baltimore, Denver, Minneapolis, New York, Sacramento, and San Antonio) provided by MetaSUB International Consortium. We found that the AMR marker genes identified from the Metagenomic data could be used to distinguish different city origins. Given the query AMR markers, we successfully identified the city of origin as New York.

Antimicrobial resistance (AMR) detection is of medical and social relevance. Several algorithms are able to detect sequences associated to organisms known to present some degree of AMR. In this contribution, we approach the problem from a different perspective. We aim to characterize the codon usage of all samples available from a city. Then, we link the codon usage of all samples in a city to the histogram of the most frequent resistance mechanisms and AMR gene families. We followed an exploratory data analysis path, and in this report, we briefly sketch the steps. As a preliminary result, we have found a discrete correlation between the codon usage and the relative frequency of the most common resistance mechanisms

Taxonomic and Anti Microbial Resistance (AMR) patterns arise in different cities. Every year the Community of Interest Critical Assessment of Massive Data Analysis (CAMDA) provides a challenge that helps scientists to build capacities and good extensive data practices. We explored microbiome data from 15 cities. Samples from 2016 and 2017 were supplied by MetaSUB, aiming to identify a mysterious city given an AMR pattern. Here we address both 1) the forensic geolocalization challenge, i.e., given a training set to predict the city label of a test set, and 2) Discovering the mysterious city given the AMR profile. These are preliminary results; we will hold a hackathon from 2-7 June to work on the challenges.
Our work is divided into 1)Antibiotic profiling, 2)Preliminary data exploration, 3)Classification algorithms, 4) Variance reduction, and 5) Hypothesis testing. Antibiotic profiling shows NYC as the city with more antibiotic resistance mechanisms. We utilized logistic regression and neural networks for the classification problem. We will expand our analyses by incorporating Support Vector machines, Random Forests, etc. We used Negative Binomial regression to address the variance reduction by identifying differentially abundant OTUs, using its results to diminish the number of OTUs and reduce the sparsity in the dataset.

The CAMDA2023 Anti-Microbial Resistance Prediction and Forensics Challenge features resistance profiles of clinical isolates as well as environmental meta-genomics sequences. The goal is to identify resistance conferring genes in read samples collected from urban transport locations, and from metadata (AMR markers) associated with isolates collected in a hospital. The specific goal is to predict the city where the hospital is located. In this paper we describe KISS, a novel implementation of a tool for the filtering of reads against a reference database, and the prediction of the location of the hospital. The abundance of reference sequences from the CARD database in the environmental meta-genomics samples collected in various US cities is used to deduce the hospital location. A prototype implementation is compared to Bowtie2, and produces comparable results in less time.