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Karagiannis, T.

Publications and source records attributed to Karagiannis, T..

3 recordsLinked to original sources

Multi-omic Longitudinal Analysis of Canine Osteosarcoma Identifies Inter-Patient Heterogeneity and Immune Enrichment in Metastatic Lesions

Osteosarcoma (OS) exhibits substantial genomic complexity and inter-patient heterogeneity, necessitating longitudinal, patient-matched analyses to understand acquired features of tumor evolution. However, most published OS data is limited to primary tumor samples, limiting insight into patient-specific resistance mechanisms. To address this, we characterized the genomic landscape of paired primary and metastatic tumor samples from dogs with spontaneous OS. Whole-genome and single-cell RNA sequencing reveal mutation and gene expression profiles that are predominantly organized by patient identity. Mutational burden and pathway alterations such as those involving PI3K, NOTCH, TP53, MAPK, RAS and epigenetic regulation differ between primary and metastatic samples. Variants present in tumor tissue are readily detectable in paired cfDNA samples, demonstrating the utility of this assay for identifying tumor-specific alterations associated with treatment resistance. Analysis of bulk RNA-seq data to estimate cell-type composition shows greater immune cell representation in metastases, underscoring the importance of immune signaling pathways in OS. These findings exemplify the presence of patient-specific alterations in genomic architecture over the course of tumor progression, linking CNV amplification, pathway reprogramming, and immune evasion in metastatic OS.

genomics↗

Bayesian Differential Analysis of Cell Type Proportions

The analysis of cell type proportions in a biological sample should account for the compositional nature of the data but most analyses ignore this characteristic with the risk of producing misleading conclusions. The recent method scCODA appropriately incorporates these constraints by using a Bayesian Multinomial-Dirichlet model that requires a reference cell type to normalize the distribution of all cell types. However, a reference cell type that is stable across biological conditions may not always be available. Here, we present an approach that uses a Bayesian multinomial regression for the analysis of single cell distribution data without the need for a reference cell type. We show an implementation example using the rjags package within the R software.

bioinformatics↗

Pipeliner: A Nextflow-based framework for the definition of sequencing data processing pipelines

The advent of high-throughput sequencing technologies has led to the need for flexible and user-friendly data pre-processing platforms. The Pipeliner framework provides an out-of-the-box solution for processing various types of sequencing data. It combines the Nextflow scripting language and Anaconda package manager to generate modular computational workflows. We have used Pipeliner to create several pipelines for sequencing data processing including bulk RNA-seq, single-cell RNA-seq (scRNA-seq), as well as Digital Gene Expression (DGE) data. This report highlights the design methodology behind Pipeliner which enables the development of highly flexible and reproducible pipelines that are easy to extend and maintain on multiple computing environments. We also provide a quick start user guide demonstrating how to setup and execute available pipelines with toy datasets.

bioinformatics↗