Biology subjects

Liefeld, T.

Publications and source records attributed to Liefeld, T..

6 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

NMFClustering: Accessible NMF-based clustering utilizing GPU acceleration

SummaryNon-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible in silico research for non-programmers. Availability and ImplementationNMFClustering is freely available on the public GenePattern server at https://genepattern.ucsd.edu. Code for the NMFClustering is available under a BSD style license on github at https://github.com/genepattern/nmf-gpu. ContactTed Liefeld, jliefeld@cloud.ucsd.edu Supplementary InformationSupplementary data are available at Bioinformatics online and at https://datasets.genepattern.org/?prefix=data/test_data/NMFClustering/.

bioinformatics↗

bioRxiv2023

Genomics to Notebook (g2nb): extending the electronic notebook to address the challenges of bioinformatics analysis

We present Genomics to Notebook (g2nb), an environment that combines the JupyterLab notebook system with widely-used bioinformatics platforms. Galaxy, GenePattern, and the JavaScript versions of IGV and Cytoscape are currently available within g2nb. The analyses and visualizations within those platforms are presented as cells in a notebook, making thousands of genomics methods available within the notebook metaphor and allowing notebooks to contain workflows utilizing multiple software packages on remote servers, all without the need for programming. The g2nb environment is, to our knowledge, the only notebook-based system that incorporates multiple bioinformatics analysis platforms into a notebook interface.

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↗

bioRxiv2020

NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA-Seq Data

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility and reusability of pipeline data, to provide a template for data processing of future spaceflight-relevant datasets, and to encourage cross-analysis of data from other databases with the data available in GeneLab.

bioinformatics↗

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↗