Temporal Expression Profiles Reveal Potential Targets during Postembryonic Development of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae)

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Sarcophaga peregrina (Robineau-Desvoidy, 1830) is a species of medical and forensic importance. In order to investigate the molecular mechanism during postembryonic development and identify specific genes that may serve as potential targets, transcriptome analysis was used to investigate its gene expression dynamics from the larval to pupal stages, based on our previous de novo-assembled genome of S. peregrina. Totals of 2457, 3656, 3764, and 2554 differentially expressed genes were identified. The specific genes encoding the structural constituent of cuticle were significantly differentially expressed, suggesting that degradation and synthesis of cuticle-related proteins might actively occur during metamorphosis. Molting (20-hydroxyecdysone, 20E) and juvenile (JH) hormone pathways were significantly enriched, and gene expression levels changed in a dynamic pattern during the developmental stages. In addition, the genes in the oxidative phosphorylation pathway were significantly expressed at a high level during the larval stage, and down-regulated from the wandering to pupal stages. Weighted gene co-expression correlation network analysis (WGCNA) further demonstrated the potential regulation mechanism of tyrosine metabolism in the process of puparium tanning. Moreover, 10 consistently up-regulated genes were further validated by qRT-PCR. The utility of the models was then examined in a blind study, indicating the ability to predict larval development. The developmental, stage-specific gene profiles suggest novel molecular markers for age prediction of forensically important flies.

Related collections

Most cited references 65

Record: found
Abstract: found
Article: found

Is Open Access

Trimmomatic: a flexible trimmer for Illumina sequence data

Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)

Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.

0 comments Cited 18555 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P Tamayo, V K Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

0 comments Cited 14893 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .