EcoCyc is a bioinformatics database (DB) available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project was to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli . EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli . The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed via EcoCyc.org. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc is also available. This review outlines the data content of EcoCyc and the procedures by which this content is generated.

Introduction:Operon prediction is a valuable component of microbial-genome annotation because operon organization can yield inferences about gene function, and because knowledge of operon structure can aid the interpretation of gene expression data. Methods:We present a number of improvements to the existing Pathway Tools operon predictor based mostly on 7 new features that we hypothesized would increase its performance. The new features include shared Gene Ontology biological process terms, similarity of codon usage and GC content, correlated gene expression, and shared protein complex. Results:We evaluated the proposed 7 new features and found that the addition of 6 of them improved the performance of the operon predictor from 79.55% to 83.49%, a decrease in error rate of 19.3%. When gene expression data was not included, the accuracy decreased to 82.547, still an improvement of 14.7%. One of the proposed features as well as a previously used feature had no effect and were removed. Discussion:Although some of the new features had strong predictive value individually, when combined with the other features they did not have a large impact on predictive accuracy, suggesting that they were not independent from the other features.

Background Enrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets. The key idea behind enrichment analysis is: given a set of significantly expressed genes (or metabolites), use that set to infer a smaller set of perturbed biological pathways or processes, in which those genes (or metabolites) play a role. Enrichment computations rely on collections of defined biological pathways and/or processes, which are usually drawn from pathway databases. Although practitioners of enrichment analysis take great care to employ statistical corrections (e.g., for multiple testing), they appear unaware that enrichment results are quite sensitive to the pathway definitions that the calculation uses. Results We show that alternative pathway definitions can alter enrichment p -values by up to nine orders of magnitude, whereas statistical corrections typically alter enrichment p -values by only two orders of magnitude. We present multiple examples where the smaller pathway definitions used in the EcoCyc database produces stronger enrichment p -values than the much larger pathway definitions used in the KEGG database; we demonstrate that to attain a given enrichment p -value, KEGG-based enrichment analyses require 1.3–2.0 times as many significantly expressed genes as does EcoCyc-based enrichment analyses. The large pathways in KEGG are problematic for another reason: they blur together multiple (as many as 21) biological processes. When such a KEGG pathway receives a high enrichment p -value, which of its component processes is perturbed is unclear, and thus the biological conclusions drawn from enrichment of large pathways are also in question. Conclusions The choice of pathway database used in enrichment analyses can have a much stronger effect on the enrichment results than the statistical corrections used in these analyses.

MOTIVATION:Biological systems function through dynamic interactions among genes and their products, regulatory circuits and metabolic networks. Our development of the Pathway Tools software was motivated by the need to construct biological knowledge resources that combine these many types of data, and that enable users to find and comprehend data of interest as quickly as possible through query and visualization tools. Further, we sought to support the development of metabolic flux models from pathway databases, and to use pathway information to leverage the interpretation of high-throughput data sets.RESULTS:In the past 4 years we have enhanced the already extensive Pathway Tools software in several respects. It can now support metabolic-model execution through the Web, it provides a more accurate gap filler for metabolic models; it supports development of models for organism communities distributed across a spatial grid; and model results may be visualized graphically. Pathway Tools supports several new omics-data analysis tools including the Omics Dashboard, multi-pathway diagrams called pathway collages, a pathway-covering algorithm for metabolomics data analysis and an algorithm for generating mechanistic explanations of multi-omics data. We have also improved the core pathway/genome databases management capabilities of the software, providing new multi-organism search tools for organism communities, improved graphics rendering, faster performance and re-designed gene and metabolite pages.AVAILABILITY:The software is free for academic use; a fee is required for commercial use. See http://pathwaytools.com.CONTACT:pkarp@ai.sri.com.SUPPLEMENTARY INFORMATION:Supplementary data are available at Briefings in Bioinformatics online.

Microbial genome web portals have a broad range of capabilities that address a number of information-finding and analysis needs for scientists. This article compares the capabilities of the major microbial genome web portals to aid researchers in determining which portal(s) are best suited to their needs. We assessed both the bioinformatics tools and the data content of BioCyc, KEGG, Ensembl Bacteria, KBase, IMG, and PATRIC. For each portal, our assessment compared and tallied the available capabilities. The strengths of BioCyc include its genomic and metabolic tools, multi-search capabilities, table-based analysis tools, regulatory network tools and data, omics data analysis tools, breadth of data content, and large amount of curated data. The strengths of KEGG include its genomic and metabolic tools. The strengths of Ensembl Bacteria include its genomic tools and large number of genomes. The strengths of KBase include its genomic tools and metabolic models. The strengths of IMG include its genomic tools, multi-search capabilities, large number of genomes, table-based analysis tools, and breadth of data content. The strengths of PATRIC include its large number of genomes, table-based analysis tools, metabolic models, and breadth of data content.

MetaCyc (https://MetaCyc.org) is a comprehensive reference database of metabolic pathways and enzymes from all domains of life. It contains more than 2570 pathways derived from >54 000 publications, making it the largest curated collection of metabolic pathways. The data in MetaCyc is strictly evidence-based and richly curated, resulting in an encyclopedic reference tool for metabolism. MetaCyc is also used as a knowledge base for generating thousands of organism-specific Pathway/Genome Databases (PGDBs), which are available in the BioCyc (https://BioCyc.org) and other PGDB collections. This article provides an update on the developments in MetaCyc during the past two years, including the expansion of data and addition of new features.

BioCyc.org is a web portal that contains Pathway/Genome Databases for over 14,500 organisms, including 2400 human microbiome members. The BioCyc databases are unique in integrating a diverse range of data and providing a high level of curation for important microbes as well as model eukaryotic organisms and Homo sapiens. Each Pathway/Genome Database describes the genome and proteome of an organism, as well as its biochemical pathways and (for a small number of organisms) its regulatory network. BioCyc bioinformatics tools combine unparalleled breadth and user friendliness and include a unique set of visualization tools to speed comprehension of its extensive and complex data. BioCyc also includes MetaCyc, the largest collection of metabolic pathways and enzymes currently available, with over 2600 pathways. It contains the equivalent of about 9,400 textbook-pages of mini-review summaries for enzymes and pathways. The data in MetaCyc has been manually curated from over 58,000 published papers. Analysis tools in BioCyc include comparisons of pathways and genomes among multiple organisms, alignment of orthologous genes in a multi-genome browser, and searching for minimal-cost metabolic network routes that connect two substrates. Tools are also provided for visualizing omics data – such as metabolomics and gene expression data – on individual pathway diagrams, on personalized groups of multiple pathway diagrams called pathway collages, and on diagrams depicting the full metabolic network of an organism. A new visualization tool in BioCyc is the Omics Dashboard, an interactive tool for exploration and analysis of gene-expression and metabolomics datasets through a hierarchy of cellular systems and subsystems. This highly visual tool enables the user to survey their data at a very high level of abstraction, and to drill down to examine details at the pathway, subsystem, and gene levels. Patterns in the data can often be perceived quickly and easily. Another new tool is the MultiOmics Explainer, which helps researchers understand and interpret the results of their omics experiments in the context of what is known about an organism's metabolic and regulatory network. At this time this tool is not available online and requires installation of the software on a local computer. Another recent addition to BioCyc involves manual curation of databases for important pathogens. So far, the project includes Mycobacterium tuberculosis (H37Rv), Staphylococcus aureus (NCTC 8325) and Salmonella enterica (14028S) The BioCyc databases can be accessed through the BioCyc.org web site and can be downloaded. In addition, the Pathway Tools software can be downloaded and installed locally to create BioCyc-like databases for more genomes, curate existing databases, or perform additional analyses that are not available online. Access to MetaCyc and EcoCyc is unrestricted. Unlimited access to other databases requires a subscription. Support or Funding Information National Institute of General Medical Sciences of the National Institutes of Health (NIH) [GM080746, GM077678, GM75742] A portion of the initial display of the Omics Dashboard, showing the average gene expression levels of genes, at different time points, in pathways from different subsystems. Another example output of the Omics Dashboard, showing gene-expression profiles at the regulon level. A typical pathway diagram from a BioCyc database. The Multi-Genome Browser enables comparison of orthologous regions from different organisms. Genes in the Genome Browser are color-coded to indicate transcription units. Reaction diagrams provide color-coded atom mapping between reactants and products. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

MOTIVATION:The increasing availability of annotated genome sequences enables construction of genome-scale metabolic networks, which are useful tools for studying organisms of interest. However, due to incomplete genome annotations, draft metabolic models contain gaps that must be filled in a time-consuming process before they are usable. Optimization-based algorithms that fill these gaps have been developed, however, gap-filling algorithms show significant error rates and often introduce incorrect reactions.RESULTS:Here, we present a new gap-filling method that computes the costs of candidate gap-filling reactions from a universal reaction database (MetaCyc) based on taxonomic information. When gap-filling a metabolic model for an organism M (such as Escherichia coli), the cost for reaction R is based on the frequency with which R occurs in other organisms within the phylum of M (in this case, Proteobacteria). The assumption behind this method is that different taxonomic groups are biased toward using different metabolic reactions. Evaluation of the new gap-filler on randomly degraded variants of the EcoCyc metabolic model for E.coli showed an increase in the average F1-score to 99.0 (when using the variable weights by frequency method at the phylum level), compared to 91.0 using the previous MetaFlux gap-filler and 80.3 using a basic gap-filler. Evaluation on two other microbial metabolic models showed similar improvements.AVAILABILITY AND IMPLEMENTATION:The Pathway Tools software (including MetaFlux) is free for academic use and is available at http://pathwaytools.com. Additional code for reproducing the results presented here is available at www.ai.sri.com/pkarp/pubs/taxgap/supplementary.zip.SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Interpreting changes in metabolite abundance in response to experimental treatments or disease states remains a major challenge in metabolomics. Pathway Covering is a new algorithm that takes a list of metabolites (compounds) and determines a minimum-cost set of metabolic pathways in an organism that includes (covers) all the metabolites in the list. We used five functions for assigning costs to pathways, including assigning a constant for all pathways, which yields a solution with the smallest pathway count; two methods that penalize large pathways; one that prefers pathways based on the pathway's assigned function, and one that loosely corresponds to metabolic flux. The pathway covering set computed by the algorithm can be displayed as a multi-pathway diagram ("pathway collage") that highlights the covered metabolites. We investigated the pathway covering algorithm by using several datasets from the Metabolomics Workbench. The algorithm is best applied to a list of metabolites with significant statistics and fold-changes with a specified direction of change for each metabolite. The pathway covering algorithm is now available within the Pathway Tools software and BioCyc website.

BioCyc.org is a microbial genome Web portal that combines thousands of genomes with additional information inferred by computer programs, imported from other databases and curated from the biomedical literature by biologist curators. BioCyc also provides an extensive range of query tools, visualization services and analysis software. Recent advances in BioCyc include an expansion in the content of BioCyc in terms of both the number of genomes and the types of information available for each genome; an expansion in the amount of curated content within BioCyc; and new developments in the BioCyc software tools including redesigned gene/protein pages and metabolite pages; new search tools; a new sequence-alignment tool; a new tool for visualizing groups of related metabolic pathways; and a facility called SmartTables, which enables biologists to perform analyses that previously would have required a programmer's assistance.

The EcoCyc model-organism database collects and summarizes experimental data for Escherichia coli K-12. EcoCyc is regularly updated by the manual curation of individual database entries, such as genes, proteins, and metabolic pathways, and by the programmatic addition of results from select high-throughput analyses. Updates to the Pathway Tools software that supports EcoCyc and to the web interface that enables user access have continuously improved its usability and expanded its functionality. This article highlights recent improvements to the curated data in the areas of metabolism, transport, DNA repair, and regulation of gene expression. New and revised data analysis and visualization tools include an interactive metabolic network explorer, a circular genome viewer, and various improvements to the speed and usability of existing tools.

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology.Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate.One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments.Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods.Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology.While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines.In this manuscript, we present 1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, 2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and 3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer, and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings, and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. The software creates and manages a type of organism-specific database called a Pathway/Genome Database (PGDB), which the software enables database curators to interactively edit. It supports web publishing of PGDBs and provides a large number of query, visualization, and omics-data analysis tools. Scientists around the world have created more than 45,000 PGDBs by using Pathway Tools, many of which are curated databases for important model organisms. Those PGDBs can be exchanged using a peer-to-peer database-sharing system called the PGDB Registry.