Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

FAST: FAST Analysis of Sequences Toolbox.

FAST (FAST Analysis of Sequences Toolbox) provides simple, powerful open source command-line tools to filter, transform, annotate and analyze biological sequence data. Modeled after the GNU (GNU's Not Unix) Textutils such as grep, cut, and tr, FAST tools such as fasgrep, fascut, and fastr make it easy to rapidly prototype expressive bioinformatic workflows in a compact and generic command vocabulary. Compact combinatorial encoding of data workflows with FAST commands can simplify the documentation and reproducibility of bioinformatic protocols, supporting better transparency in biological data science. Interface self-consistency and conformity with conventions of GNU, Matlab, Perl, BioPerl, R, and GenBank help make FAST easy and rewarding to learn. FAST automates numerical, taxonomic, and text-based sorting, selection and transformation of sequence records and alignment sites based on content, index ranges, descriptive tags, annotated features, and in-line calculated analytics, including composition and codon usage. Automated content- and feature-based extraction of sites and support for molecular population genetic statistics make FAST useful for molecular evolutionary analysis. FAST is portable, easy to install and secure thanks to the relative maturity of its Perl and BioPerl foundations, with stable releases posted to CPAN. Development as well as a publicly accessible Cookbook and Wiki are available on the FAST GitHub repository at https://github.com/tlawrence3/FAST. The default data exchange format in FAST is Multi-FastA (specifically, a restriction of BioPerl FastA format). Sanger and Illumina 1.8+ FastQ formatted files are also supported. FAST makes it easier for non-programmer biologists to interactively investigate and control biological data at the speed of thought

Open Science principles for accelerating trait-based science across the Tree of Life.

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges

Character Selection During Interactive Taxonomic Identification: “Best Characters”

Software interfaces for interactive multiple-entry taxonomic identification (polyclaves) sometimes provide a “best character” or “separation” coefficient, to guide the user to choose a character that could most effectively reduce the number of identification steps required. The coefficient could be particularly helpful when difficult or expensive tasks are needed for forensic identification, and in very large databases, uses that appear likely to increase in importance. Several current systems also provide tools to develop taxonomies or single-entry identification keys, with a variety of coefficients that are appropriate to that purpose. For the identification task, however, information theory neatly applies, and provides the most appropriate coefficient. To our knowledge, Delta-Intkey is the only currently available system that uses a coefficient related to information theory, and it is currently being reimplemented, which may allow for improvement. We describe two improvements to the algorithm used by Delta-Intkey. The first improves transparency as the number of remaining taxa decreases, by normalizing the range of the coefficient to [0,1]. The second concerns numeric ranges, which require consistent treatment of sub-intervals and their end-points. A stand-alone Bestchar program for categorical data is provided, in the Python and R languages. The source code is freely available and dedicated to the Public Domain

A CD-ROM Based Agricultural Information Retrieval System

An information retrieval system for agricultural extension was developed using CD-ROM technology as the primary medium for information delivery. Object-oriented database techniques were used to organize the information. Conventional retrieval techniques including hypertext, fulltext searching, and relational databases, and decision support programs such as expert systems were integrated into a complete package for accessing information stored on the CDROM. A multimedia user interface was developed to provide a variety of capabilities including computer graphics and high-resolution digitized images. Information for the disk was gathered and entered using extension publications which were tagged using an SGML-based document markup language. The fully operational CD-ROM system has been implemented in all 67 county extension offices in Flori

Evaluation and Optimization of Bioinformatic Tools for the Detection of Human Foodborne Pathogens in Complex Metagenomic Datasets

Foodborne human pathogens pose a significant risk to human health as each year one in six Americans becomes sick from one of over 31 known human foodborne pathogens. Due to the differences in their growth requirements, current detection assays can only detect one to a few of these pathogens per single assay. Metagenomics, an emerging field, allows for an entire community of organisms to be analyzed from DNA or RNA sequence data generated from a single sample, and therefore has the potential to detect any and all foodborne pathogens present in a single complex matrix. However, currently available bioinformatic pipelines for metagenomic sequence analysis require extensive time and high computer power inputs, often with unreliable results. The objectives of this study are 1) to evaluate community profiling bioinformatic pipelines, mapping pipelines and a novel pipeline created at Oklahoma State University, E-probe Diagnostic Nucleic-acid Analysis (EDNA), for the detection of S. enterica (as a model foodborne pathogen) in metagenomic data, 2) to optimize EDNA pipeline for sensitive detection of the S. enterica in metagenomic data, and 3) to simultaneously detect multiple foodborne pathogens from a single metagenomic sample. EDNA was able to detect S. enterica in metagenomic data in approximately five minutes compared to the other pipelines, which took between 2-500 hours. The optimized parameters for the EDNA pipeline were limited to using cleaned Illumina data with a read depth of one. The minimum BLAST E-value was set to 10^-3 for curation. For detection the minimum percent identity was set to 95% and the minimum query coverage to 90% with an E-probe length of 80 nt. These new parameters significantly improved the sensitivity of the assay 100-fold, from 10^3 S. enterica cells detected by the original EDNA pipeline to just 10 cells. In the simultaneous detection of multiple foodborne pathogens, EDNA detected three additional pathogens Listeria monocytogenes, Campylobacter jejuni and Shiga toxin producing Escherichia coli at ten contamination levels in less than ten minutes and provided new detection insights into read abundance as it corresponds to pathogen cell numbers

DoOPSearch: a web-based tool for finding and analysing common conserved motifs in the promoter regions of different chordate and plant genes

BACKGROUND: The comparative genomic analysis of a large number of orthologous promoter regions of the chordate and plant genes from the DoOP databases shows thousands of conserved motifs. Most of these motifs differ from any known transcription factor binding site (TFBS). To identify common conserved motifs, we need a specific tool to be able to search amongst them. Since conserved motifs from the DoOP databases are linked to genes, the result of such a search can give a list of genes that are potentially regulated by the same transcription factor(s). RESULTS: We have developed a new tool called DoOPSearch for the analysis of the conserved motifs in the promoter regions of chordate or plant genes. We used the orthologous promoters of the DoOP database to extract thousands of conserved motifs from different taxonomic groups. The advantage of this approach is that different sets of conserved motifs might be found depending on how broad the taxonomic coverage of the underlying orthologous promoter sequence collection is (consider e.g. primates vs. mammals or Brassicaceae vs. Viridiplantae). The DoOPSearch tool allows the users to search these motif collections or the promoter regions of DoOP with user supplied query sequences or any of the conserved motifs from the DoOP database. To find overrepresented gene ontologies, the gene lists obtained can be analysed further using a modified version of the GeneMerge program. CONCLUSION: We present here a comparative genomics based promoter analysis tool. Our system is based on a unique collection of conserved promoter motifs characteristic of different taxonomic groups. We offer both a command line and a web-based tool for searching in these motif collections using user specified queries. These can be either short promoter sequences or consensus sequences of known transcription factor binding sites. The GeneMerge analysis of the search results allows the user to identify statistically overrepresented Gene Ontology terms that might provide a clue on the function of the motifs and genes