Biology subjects

Mesirov, J.

Publications and source records attributed to Mesirov, J..

4 recordsLinked to original sources

bioRxiv2024

AmpliconSuite: an end-to-end workflow for analyzing focal amplifications in cancer genomes

Focal amplifications in the cancer genome, particularly extrachromosomal DNA (ecDNA) amplifications, are emerging as a pivotal event in cancer progression across diverse cancer contexts, presenting a paradigm shift in our understanding of tumor dynamics. Simultaneously, identification of the various modes of focal amplifications is bioinformatically challenging. We present AmpliconSuite, a collection of tools that enables robust identification of focal amplifications from whole-genome sequencing data. AmpliconSuite includes AmpliconSuite- pipeline; utilizing the AmpliconArchitect (AA) method, and AmpliconRepository.org; a community- editable website for the sharing of focal amplification calls. We also describe improvements made to AA since its initial release that improve its accuracy and speed. As a proof of principle, we utilized publicly available pan-cancer datasets encompassing 2,525 tumor samples hosted on AmpliconRepository.org to illustrate important properties of focal amplifications, showing ecDNA has higher copy number, and stronger oncogene enrichment, compared to other classes of focal amplifications. Finally, we illustrate how AmpliconSuite-pipeline enables delineation of the various mechanisms by which ecDNA forms.

bioinformatics↗

bioRxiv2023

Deciphering the Functional Roles of Individual Cancer Alleles Across Comprehensive Cancer Genomic Studies

Cancer genome data has been growing in both size and complexity, primarily driven by advances in next-generation sequencing technologies, such as Pan-cancer data from TCGA, ICGC, and single-cell sequencing. Yet, discerning the functional role of individual genomic lesions remains a substantial challenge due to the complexity and scale of the data. Previously, we introduced REVEALER, which identifies groups of genomic alterations that significantly associate with target functional profiles or phenotypes, such as pathway activation, gene dependency, or drug response. In this paper, we present a new mathematical formulation of the algorithm. This version (REVEALER 2.0) is considerably more powerful than the original, allowing for rapid processing and analysis of much larger datasets and facilitating higher-resolution discoveries at the level of individual alleles. REVEALER 2.0 employs the Conditional Information Coefficient (CIC) to pinpoint features that are either complementary or mutually exclusive but still correlate with the target functional profile. The aggregation of these features provides a better explanation for the target functional profile than any single alteration on its own. This is indicative of scenarios where several activating genomic lesions can initiate or stimulate a key pathway or process. We replaced the initial three-dimensional kernel estimation with multiple precomputed one-dimensional kernel estimations, resulting in an approximate 150x increase in speed and efficiency. This improvement, combined with its efficient execution, makes REVEALER 2.0 suitable for much larger datasets and a more extensive range of genomic challenges.

bioinformatics↗

bioRxiv2022

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. Still, inferring biological processes requires additional post hoc statistics and annotation for interpretation of features learned from software packages developed for NMF implementation. Here, we aim to introduce a suite of computational tools that implement NMF and provide methods for accurate, clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations, and open questions in the field is followed by three vignettes for the Bayesian NMF algorithm CoGAPS (Coordinated Gene Activity across Pattern Subsets). Each vignette will demonstrate NMF analysis to quantify cell state transitions in public domain single-cell RNA-sequencing (scRNA-seq) data of malignant epithelial cells in 25 pancreatic ductal adenocarcinoma (PDAC) tumors and 11 control samples. The first uses PyCoGAPS, our new Python interface for CoGAPS that we developed to enhance runtime of Bayesian NMF for large datasets. The second vignette steps through the same analysis using our R CoGAPS interface, and the third introduces two new cloud-based, plug-and-play options for running CoGAPS using GenePattern Notebook and Docker. By providing Python support, cloud-based computing options, and relevant example workflows, we facilitate user-friendly interpretation and implementation of NMF for single-cell analyses.

systems biology↗

bioRxiv2017

A unified web platform for network-based analyses of genomic data

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to quantitatively compare the signal-to-noise ratio of different networks, the biology they describe, and to identify the optimal network to interpret a particular genetic dataset. Via GeNets users can train a machine-learning model (Quack) to make such comparisons; and they can execute, store, and share analyses of genetic and RNA sequencing datasets.

genomics↗