Biology subjects

Fertig, E.

Publications and source records attributed to Fertig, E..

5 recordsLinked to original sources

bioRxiv2018

Comprehensive analysis of retinal development at single cell resolution identifies NFI factors as essential for mitotic exit and specification of late-born cells.

Precise temporal control of gene expression in neuronal progenitors is necessary for correct regulation of neurogenesis and cell fate specification. However, the extensive cellular heterogeneity of the developing CNS has posed a major obstacle to identifying the gene regulatory networks that control these processes. To address this, we used single cell RNA-sequencing to profile ten developmental stages encompassing the full course of retinal neurogenesis. This allowed us to comprehensively characterize changes in gene expression that occur during initiation of neurogenesis, changes in developmental competence, and specification and differentiation of each of the major retinal cell types. These data identify transitions in gene expression between early and late-stage retinal progenitors, as well as a classification of neurogenic progenitors. We identify here the NFI family of transcription factors (Nfia, Nfib, and Nfix) as genes with enriched expression within late RPCs, and show they are regulators of bipolar interneuron and Muller glia specification and the control of proliferative quiescence.

developmental biology↗

bioRxiv2018

Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico

Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/

systems biology↗

bioRxiv2017

Enter the matrix: Interpreting unsupervised feature learning with matrix decomposition to discover hidden knowledge in high-throughput omics data

Omics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge--answering questions from high-dimensional data that we have not yet thought to ask.

systems biology↗

bioRxiv2017

Epigenetic Regulation of Gene Expression in Cancer: Techniques, Resources, and Analysis

Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, non-coding RNAs, post-translational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence. These epigenetic alterations, once thought to be crucial only for the malignant phenotype maintenance, are now recognized as critical also for disrupting essential pathways that protect the cells from uncontrolled growth, longer survival and establishment in distant sites from the original tissue. In this review, we focus on DNA methylation and chromatin structure in cancer. While associated with cancer, the precise functional role of these alterations is an area of active research using emerging high-throughput approaches and bioinformatics analysis tools. Therefore, this review describes these high-throughput measurement technologies, public domain databases for high-throughput epigenetic data in tumors and model systems, and bioinformatics algorithms for their analysis. Advances in bioinformatics data integration techniques that combine these epigenetic data with genomics data are essential to infer the function of specific epigenetic alterations in cancer, and are therefore also a focus of this review. Future studies using these emerging technologies will elucidate how alterations in the cancer epigenome cooperate with genetic aberrations to cause tumorigenesis initiation and progression. This deeper understanding is essential to future studies that will precisely infer patients prognosis and select patients who will be responsive to emerging epigenetic therapies.

cancer biology↗

bioRxiv2016

PatternMarkers and Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS) for data-driven detection of novel biomarkers via whole transcriptome Non-negative matrix factorization (NMF)

SummaryNon-negative Matrix Factorization (NMF) algorithms associate gene expression with biological processes (e.g., time-course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers. Therefore, we developed a novel PatternMarkers statistic to extract genes for biological validation and enhanced visualization of NMF results. Finding novel and unbiased gene markers with PatternMarkers requires whole-genome data. However, NMF algorithms typically do not converge for the tens of thousands of genes in genome-wide profiling. Therefore, we also developed Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS), the first robust whole genome Bayesian NMF using the sparse, MCMC algorithm, CoGAPS. This software contains analytic and visualization tools including a Shiny web application, patternMatcher, which are generalized for any NMF. Using these tools, we find granular brain-region and cell-type specific signatures with corresponding biomarkers in GTex data, illustrating GWCoGAPS and patternMarkers ascertainment of data-driven biomarkers from whole-genome data.\n\nAvailabilityPatternMarkers & GWCoGAPS are in the CoGAPS Bioconductor package (3.5) under the GPL license.\n\nContactgsteinobrien@jhmi.edu; ccolantu@jhmi.edu; ejfertig@jhmi.edu

bioinformatics↗