ORCID Profile
0000-0003-2725-0694
Current Organisation
City University of New York
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Publisher: American Association for Cancer Research (AACR)
Date: 14-11-2012
DOI: 10.1158/1078-0432.CCR-12-1915
Abstract: Purpose: More than 20 million archival tissue s les are stored annually in the United States as formalin-fixed, paraffin-embedded (FFPE) blocks, but RNA degradation during fixation and storage has prevented their use for transcriptional profiling. New and highly sensitive assays for whole-transcriptome microarray analysis of FFPE tissues are now available, but resulting data include noise and variability for which previous expression array methods are inadequate. Experimental Design: We present the two largest whole-genome expression studies from FFPE tissues to date, comprising 1,003 colorectal cancer (CRC) and 168 breast cancer s les, combined with a meta-analysis of 14 new and published FFPE microarray datasets. We develop and validate quality control (QC) methods through technical replication, independent s les, comparison to results from fresh-frozen tissue, and recovery of expected associations between gene expression and protein abundance. Results: Archival tissues from large, multicenter studies showed a much wider range of transcriptional data quality relative to smaller or frozen tissue studies and required stringent QC for subsequent analysis. We developed novel methods for such QC of archival tissue expression profiles based on s le dynamic range and per-study median profile. This enabled validated identification of gene signatures of microsatellite instability and additional features of CRC, and improved recovery of associations between gene expression and protein abundance of MLH1, FASN, CDX2, MGMT, and SIRT1 in CRC tumors. Conclusions: These methods for large-scale QC of FFPE expression profiles enable study of the cancer transcriptome in relation to extensive clinicopathological information, tumor molecular biomarkers, and long-term lifestyle and outcome data. Clin Cancer Res 18(22) 6136–46. ©2012 AACR.
Publisher: Springer Science and Business Media LLC
Date: 04-2019
Publisher: Cold Spring Harbor Laboratory
Date: 27-03-2019
DOI: 10.1101/590562
Abstract: Recent developments in experimental technologies such as single-cell RNA sequencing have enabled the profiling a high-dimensional number of genome-wide features in in idual cells, inspiring the formation of large-scale data generation projects quantifying unprecedented levels of biological variation at the single-cell level. The data generated in such projects exhibits unique characteristics, including increased sparsity and scale, in terms of both the number of features and the number of s les. Due to these unique characteristics, specialized statistical methods are required along with fast and efficient software implementations in order to successfully derive biological insights. Bioconductor - an open-source, open-development software project based on the R programming language - has pioneered the analysis of such high-throughput, high-dimensional biological data, leveraging a rich history of software and methods development that has spanned the era of sequencing. Featuring state-of-the-art computational methods, standardized data infrastructure, and interactive data visualization tools that are all easily accessible as software packages, Bioconductor has made it possible for a erse audience to analyze data derived from cutting-edge single-cell assays. Here, we present an overview of single-cell RNA sequencing analysis for prospective users and contributors, highlighting the contributions towards this effort made by Bioconductor.
Publisher: Springer Science and Business Media LLC
Date: 11-2021
Publisher: Springer Science and Business Media LLC
Date: 29-03-2012
Abstract: With over 20 million formalin-fixed, paraffin-embedded (FFPE) tissue s les archived each year in the United States alone, archival tissues remain a vast and under-utilized resource in the genomic study of cancer. Technologies have recently been introduced for whole-transcriptome lification and microarray analysis of degraded mRNA fragments from FFPE s les, and studies of these platforms have only recently begun to enter the published literature. The Emerging Technologies for Translational Bioinformatics symposium on gene expression profiling for archival tissues featured presentations of two large-scale FFPE expression profiling studies (each involving over 1,000 s les), overviews of several smaller studies, and representatives from three leading companies in the field (Illumina, Affymetrix, and NuGEN). The meeting highlighted challenges in the analysis of expression data from archival tissues and strategies being developed to overcome them. In particular, speakers reported higher rates of clinical s le failure (from 10% to 70%) than are typical for fresh-frozen tissues, as well as more frequent probe failure for in idual s les. The symposium program is available at fpe . Multiple solutions now exist for whole-genome expression profiling of FFPE tissues, including both microarray- and sequencing-based platforms. Several studies have reported their successful application, but substantial challenges and risks still exist. Symposium speakers presented novel methodology for analysis of FFPE expression data and suggestions for improving data recovery and quality assessment in pre-analytical stages. Research presentations emphasized the need for careful study design, including the use of pilot studies, replication, and randomization of s les among batches, as well as careful attention to data quality control. Regardless of any limitations in quantitave transcriptomics for FFPE tissues, they are often the only biospecimens available for large patient populations with long-term history and clinical follow-up. Current challenges can be expected to remain as RNA sequencing matures, and they will thus motivate ongoing research efforts into noise reduction and identification of robust, translationally relevant biological signals in expression data from FFPE tissues.
Publisher: Cold Spring Harbor Laboratory
Date: 24-06-2020
DOI: 10.1101/2020.06.24.167353
Abstract: Human microbiome research is a growing field with the potential for improving our understanding and treatment of diseases and other conditions. The field is interdisciplinary, making concise organization and reporting of results across different styles of epidemiology, biology, bioinformatics, translational medicine, and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. A multidisciplinary group of microbiome epidemiology researchers reviewed elements of available reporting guidelines for observational and genetic studies and adapted these for application to culture-independent human microbiome studies. New reporting elements were developed for laboratory, bioinformatic, and statistical analyses tailored to microbiome studies, and other parts of these checklists were streamlined to keep reporting manageable. STORMS is a 17-item checklist for reporting on human microbiome studies, organized into six sections covering typical sections of a scientific publication, presented as a table with space for author-provided details and intended for inclusion in supplementary materials. STORMS provides guidance for authors and standardization for interdisciplinary microbiome studies, facilitating complete and concise reporting and augments information extraction for downstream applications. The STORMS checklist is available as a versioned spreadsheet from www.stormsmicrobiome.org/ .
Location: United States of America
No related grants have been discovered for Levi Waldron.