Showing papers by "James B. Brown published in 2016"

De novoIdentification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing

Abstract: Advances in nanopore sequencing technology have enabled investigation of the full catalogue of covalent DNA modifications. We present the first algorithm for the identification of modified nucleotides without the need for prior training data along with the open source software implementation, nanoraw. Nanoraw accurately assigns contiguous raw nanopore signal to genomic positions, enabling novel data visualization, and increasing power and accuracy for the discovery of covalently modified bases in native DNA. Ground truth case studies utilizing synthetically methylated DNA show the capacity to identify three distinct methylation marks, 4mC, 5mC, and 6mA, in seven distinct sequence contexts without any changes to the algorithm. We demonstrate quantitative reproducibility simultaneously identifying 5mC and 6mA in native E. coli across biological replicates processed in different labs. Finally we propose a pipeline for the comprehensive discovery of DNA modifications in any genome without a priori knowledge of their chemical identities.

Centromere and kinetochore gene misexpression predicts cancer patient survival and response to radiotherapy and chemotherapy

Abstract: Chromosomal instability (CIN) is a hallmark of cancer that contributes to tumour heterogeneity and other malignant properties. Aberrant centromere and kinetochore function causes CIN through chromosome missegregation, leading to aneuploidy, rearrangements and micronucleus formation. Here we develop a Centromere and kinetochore gene Expression Score (CES) signature that quantifies the centromere and kinetochore gene misexpression in cancers. High CES values correlate with increased levels of genomic instability and several specific adverse tumour properties, and prognosticate poor patient survival for breast and lung cancers, especially early-stage tumours. They also signify high levels of genomic instability that sensitize cancer cells to additional genotoxicity. Thus, the CES signature forecasts patient response to adjuvant chemotherapy or radiotherapy. Our results demonstrate the prognostic and predictive power of the CES, suggest a role for centromere misregulation in cancer progression, and support the idea that tumours with extremely high CIN are less tolerant to specific genotoxic therapies.

Daphnia magna transcriptome by RNA-Seq across 12 environmental stressors

Abstract: The full exploration of gene-environment interactions requires model organisms with well-characterized ecological interactions in their natural environment, manipulability in the laboratory and genomic tools. The waterflea Daphnia magna is an established ecological and toxicological model species, central to the food webs of freshwater lentic habitats and sentinel for water quality. Its tractability and cyclic parthenogenetic life-cycle are ideal to investigate links between genes and the environment. Capitalizing on this unique model system, the STRESSFLEA consortium generated a comprehensive RNA-Seq data set by exposing two inbred genotypes of D. magna and a recombinant cross of these genotypes to a range of environmental perturbations. Gene models were constructed from the transcriptome data and mapped onto the draft genome of D. magna using EvidentialGene. The transcriptome data generated here, together with the available draft genome sequence of D. magna and a high-density genetic map will be a key asset for future investigations in environmental genomics.

MODA: MOdule Differential Analysis for weighted gene co-expression network

Abstract: Gene co-expression network differential analysis is designed to help biologists understand gene expression patterns under different condition. By comparing different gene co-expression networks we may find conserved part as well as condition specific set of genes. Taking the network as a collection as modules, we use a sample-saving method to construct condition-specific gene co-expression network, and identify differentially expressed subnetworks as conserved or condition specific modules which may be associated with biological processes. We have implemented the method as an R package which establishes a pipeline from expression profile to biological explanations. The usefulness of the method is also demonstrated by synthetic data as well as Daphnia magna gene expression data under different environmental stresses.

MODA: MOdule Differential Analysis for weighted gene co-expression network

Abstract: Gene co-expression network differential analysis is designed to help biologists understand gene expression patterns under different conditions. We have implemented an R package called MODA (Module Differential Analysis) for gene co-expression network differential analysis. Based on transcriptomic data, MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes. The usefulness of the method is also demonstrated by synthetic data as well as Daphnia magna gene expression data under different environmental stresses.

Frontiers in RNA biology: Advances from a small fly Drosophila melanogaster

Abstract: Author(s): Brown, JB; Celniker, SE | Abstract: In this article, we discuss emerging frontiers in RNA biology from a historical perspective. The field is currently undergoing yet another transformative expansion. RNA-seq has revealed that splicing, and, more generally, RNA processing is far more complex than expected, and the mechanisms of regulation are correspondingly sophisticated. Our understanding of the molecular machines involved in RNA metabolism is incomplete and derives from small sample sizes. Even if we manage to complete a catalogue of molecular species, RNA isoforms and the ribonucleoprotein complexes that drive their genesis, the horizons of molecular dynamics and cell-type-specific processing mechanisms await. This is an exciting time to enter into the study of RNA biology; analytical tools, wet and dry, are advancing rapidly, and each new measurement modality brings into view another new function or activity of versatile RNA. Since the dawn of sequence-based RNA biology, we have come a long way.