\u3ci\u3eFundulus\u3c/i\u3e as the premier teleost model in environmental biology: Opportunities for new insights using genomics

A strong foundation of basic and applied research documents that the estuarine fish Fundulus heteroclitus and related species are unique laboratory and field models for understanding how individuals and populations interact with their environment. In this paper we summarize an extensive body of work examining the adaptive responses of Fundulus species to environmental conditions, and describe how this research has contributed importantly to our understanding of physiology, gene regulation, toxicology, and ecological and evolutionary genetics of teleosts and other vertebrates. These explorations have reached a critical juncture at which advancement is hindered by the lack of genomic resources for these species. We suggest that a more complete genomics toolbox for F. heteroclitus and related species will permit researchers to exploit the power of this model organism to rapidly advance our understanding of fundamental biological and pathological mechanisms among vertebrates, as well as ecological strategies and evolutionary processes common to all living organisms

Fundulus as the premier teleost model in environmental biology : opportunities for new insights using genomics

Author Posting. © Elsevier B.V., 2007. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Comparative Biochemistry and Physiology Part D: Genomics and Proteomics 2 (2007): 257-286, doi:10.1016/j.cbd.2007.09.001.A strong foundation of basic and applied research documents that the estuarine fish Fundulus heteroclitus and related species are unique laboratory and field models for understanding how individuals and populations interact with their environment. In this paper we summarize an extensive body of work examining the adaptive responses of Fundulus species to environmental conditions, and describe how this research has contributed importantly to our understanding of physiology, gene regulation, toxicology, and ecological and evolutionary genetics of teleosts and other vertebrates. These explorations have reached a critical juncture at which advancement is hindered by the lack of genomic resources for these species. We suggest that a more complete genomics toolbox for F. heteroclitus and related species will permit researchers to exploit the power of this model organism to rapidly advance our understanding of fundamental biological and pathological mechanisms among vertebrates, as well as ecological strategies and evolutionary processes common to all living organisms.This material is based on work supported by grants from the National Science Foundation DBI-0420504 (LJB), OCE 0308777 (DLC, RNW, BBR), BES-0553523 (AW), IBN 0236494 (BBR), IOB-0519579 (DHE), IOB-0543860 (DWT), FSML-0533189 (SC); National Institute of Health NIEHS P42-ES007381(GVC, MEH), P42-ES10356 (RTD), ES011588 (MFO); and NCRR P20 RR-016463 (DWT); Natural Sciences and Engineering Research Council of Canada Discovery (DLM, TDS, WSM) and Collaborative Research and Development Programs (DLM); NOAA/National Sea Grant NA86RG0052 (LJB), NA16RG2273 (SIK, MEH,GVC, JJS); Environmental Protection Agency U91620701 (WSB), R82902201(SC) and EPA’s Office of Research and Development (DEN)

Assessment of Nextera Long Mate-Pair Libraries: A Rapid, Low-Input Method for Mate-Pair Library Construction Yields Improved Assemblies

Long mate-pair libraries are invaluable tools for genome assembly. However, traditional methods of long mate-pair library construction require large (20 g) quantities of DNA and several days of hands-on time. Illuminas Nextera Long Mate-Pair (LMP) method is rapid and requires only 1 to 4 micrograms of input material. Here we present an initial assessment of the method for both gel-free and gel size-selected libraries using microbial, fungal, and plant samples. We observed uniform read coverage and high read uniqueness for Nextera LMP libraries. Assembly using ALLPATHS-LG generated low contig and scaffold numbers even with relatively low mate-pair coverage

Appendix B. Statistical analyses of soft-coral genets.

Statistical analyses of soft-coral genets

Appendix A. Soft-coral grid maps and sample provenances.

Soft-coral grid maps and sample provenances

MAGI: A method for metabolite, annotation, and gene integration

AbstractMetabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (i.e. gene annotations). Metabolite, Annotation, and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network. This is calculated by a method that emphasizes consensus between metabolites and genes via biochemical reactions. To demonstrate the potential of this method, we applied MAGI to integrate sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. Our findings suggest that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations in this organism. MAGI also made biochemical predictions for poorly annotated genes that were consistent with the extensive literature on this important organism. This limited analysis suggests that using metabolomics data has the potential to improve annotations in sequenced organisms and also provides testable hypotheses for specific biochemical functions. MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with source code available at https://github.com/biorack/magi

Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly bar-coded transposons.

UnlabelledTransposon mutagenesis with next-generation sequencing (TnSeq) is a powerful approach to annotate gene function in bacteria, but existing protocols for TnSeq require laborious preparation of every sample before sequencing. Thus, the existing protocols are not amenable to the throughput necessary to identify phenotypes and functions for the majority of genes in diverse bacteria. Here, we present a method, random bar code transposon-site sequencing (RB-TnSeq), which increases the throughput of mutant fitness profiling by incorporating random DNA bar codes into Tn5 and mariner transposons and by using bar code sequencing (BarSeq) to assay mutant fitness. RB-TnSeq can be used with any transposon, and TnSeq is performed once per organism instead of once per sample. Each BarSeq assay requires only a simple PCR, and 48 to 96 samples can be sequenced on one lane of an Illumina HiSeq system. We demonstrate the reproducibility and biological significance of RB-TnSeq with Escherichia coli, Phaeobacter inhibens, Pseudomonas stutzeri, Shewanella amazonensis, and Shewanella oneidensis. To demonstrate the increased throughput of RB-TnSeq, we performed 387 successful genome-wide mutant fitness assays representing 130 different bacterium-carbon source combinations and identified 5,196 genes with significant phenotypes across the five bacteria. In P. inhibens, we used our mutant fitness data to identify genes important for the utilization of diverse carbon substrates, including a putative d-mannose isomerase that is required for mannitol catabolism. RB-TnSeq will enable the cost-effective functional annotation of diverse bacteria using mutant fitness profiling.ImportanceA large challenge in microbiology is the functional assessment of the millions of uncharacterized genes identified by genome sequencing. Transposon mutagenesis coupled to next-generation sequencing (TnSeq) is a powerful approach to assign phenotypes and functions to genes. However, the current strategies for TnSeq are too laborious to be applied to hundreds of experimental conditions across multiple bacteria. Here, we describe an approach, random bar code transposon-site sequencing (RB-TnSeq), which greatly simplifies the measurement of gene fitness by using bar code sequencing (BarSeq) to monitor the abundance of mutants. We performed 387 genome-wide fitness assays across five bacteria and identified phenotypes for over 5,000 genes. RB-TnSeq can be applied to diverse bacteria and is a powerful tool to annotate uncharacterized genes using phenotype data