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Large volumes of metagenomic samples are being processed and submitted to PATRIC for analysis as reads or assembled contigs. Effective analysis of these samples requires solutions to a number of problems, including the binning of assembled, mixed, metagenomically-derived contigs into taxonomic units. Description: The PATRIC metagenome binning service utilizes the PATRIC database to furnish a large, diverse set of reference genomes. Reference genomes are assigned based on the presence ofdoi:10.1101/2019.12.13.874651 fatcat:diyrlalajjayjfr2obe2hzekvm
more »... opy universal marker proteins in the sample, and contigs are assigned to the bin corresponding to the most similar reference genome. Each set of binned contigs represents a draft genome that will be annotated by RASTtk in PATRIC. A structured-language binning report is provided containing quality measurements and taxonomic information about the contig bins. Conclusion: We provide a new service for rapid and interpretable metagenomic contig binning and annotation in PATRIC.
Large amounts of metagenomically-derived data are submitted to PATRIC for analysis. In the future, we expect even more jobs submitted to PATRIC will use metagenomic data. One in-demand use case is the extraction of near-complete draft genomes from assembled contigs of metagenomic origin. The PATRIC metagenome binning service utilizes the PATRIC database to furnish a large, diverse set of reference genomes. We provide a new service for supervised extraction and annotation of high-quality,doi:10.1371/journal.pone.0250092 pmid:33857229 fatcat:mkzl5vls4fd2faqeyduon7qebq
more »... mplete genomes from metagenomically-derived contigs. Reference genomes are assigned to putative draft genome bins based on the presence of single-copy universal marker roles in the sample, and contigs are sorted into these bins by their similarity to reference genomes in PATRIC. Each set of binned contigs represents a draft genome that will be annotated by RASTtk in PATRIC. A structured-language binning report is provided containing quality measurements and taxonomic information about the contig bins. The PATRIC metagenome binning service emphasizes extraction of high-quality genomes for downstream analysis using other PATRIC tools and services. Due to its supervised nature, the binning service is not appropriate for mining novel or extremely low-coverage genomes from metagenomic samples.
Recent advances in high-volume sequencing technology and mining of genomes from metagenomic samples call for rapid and reliable genome quality evaluation. The current release of the PATRIC database contains over 220,000 genomes, and current metagenomic technology supports assemblies of many draft-quality genomes from a single sample, most of which will be novel. We have added two quality assessment tools to the PATRIC annotation pipeline. EvalCon uses supervised machine learning to calculate andoi:10.1186/s12859-019-3068-y pmid:31581946 pmcid:PMC6775668 fatcat:ertmvmsb4nbpfkar6qyt6w4gva
more »... annotation consistency score. EvalG implements a variant of the CheckM algorithm to estimate contamination and completeness of an annotated genome.We report on the performance of these tools and the potential utility of the consistency score. Additionally, we provide contamination, completeness, and consistency measures for all genomes in PATRIC and in a recent set of metagenomic assemblies. EvalG and EvalCon facilitate the rapid quality control and exploration of PATRIC-annotated draft genomes.
In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, thedoi:10.1093/nar/gkt1226 pmid:24293654 pmcid:PMC3965101 fatcat:oayl74ppbbgplfhsh6wle6nqdm
more »... SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast. nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources.
i = N i -F i , in thresholds for each experiment, then finding the 25th percentile of D i across all experiments, D 25th . ... For any experiment i where D i < D 25th , set F i = N i -D 25th . ... Copyright © 2016 Faria, Davis, Edirisinghe, Taylor, Weisenhorn, Olson, Stevens, Rocha, Rocha, Best, DeJongh, Tintle, Parrello, Overbeek and Henry. ...doi:10.3389/fmicb.2016.01819 pmid:27933038 pmcid:PMC5121216 fatcat:24zzala32fegnhnnwksy2blmui
the next step, the code uses the ''simulate_model_growth'' function to run a standard FBA on the SEED E. coli model, maximizing the model growth rate in simulated glucose minimal media (called Carbon-D-Glucose ...doi:10.1371/journal.pone.0048053 pmid:23110173 pmcid:PMC3480482 fatcat:xfze4aq3bbbn3lvhayzqry4xvu
The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. Indoi:10.1038/srep08365 pmid:25666585 pmcid:PMC4322359 fatcat:6wmo7ui4izfuxf5wqbykx7lfke
more »... his paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception. T he last two decades of research have brought vast changes to the field of genomics. The sequencing of a genome and the subsequent annotation of gene functions that were originally performed by teams of researchers expending thousands of man-hours of labor has become a standard laboratory technique that can be performed by a single person in one day. As sequencing technology has advanced and the cost has dropped, the number of genomes being deposited into the public databases has outpaced Moore's Law 1,2 . This has shifted the bottlenecks in genomic analysis from the sequencing per se to the tools that are used for annotation and genomic analysis. In 2008, the RAST server (Rapid Annotation using Subsystem Technology) was developed to annotate microbial genomes 3,4 . It works by projecting manually curated gene annotations from the SEED database onto newly submitted genomes 5-7 . The key to the consistency and accuracy of the RAST algorithm has been the carefully structured annotation data in the SEED, which are organized into subsystems (sets of logically related functional roles) 5 . As a result, RAST has become one of the most popular sources for consistent and accurate annotations for microbial genomes. The RAST community currently consists of ,10,000 active users who have contributed an average of 1,170 microbial genomes per week in the last year. It is also being used as the foundation for maintaining consistency for automated metabolic modeling in the ModelSEED 8 and KBase (kbase.us), and for comparative genomics in the bacterial pathogen database, PATRIC 9,10 . RAST and other annotation engines encapsulate software for identifying and annotating specific genomic features into a standard annotation pipeline       . This approach has several advantages including offering speed, convenience and consistency to the user. In order to annotate with RAST, users submit their contigs to the server where the computation is performed. This frees users from having to download and install multiple programs, or to perform intensive computations. However, despite these advantages, this approach also has limitations. For OPEN SUBJECT AREAS: COMPARATIVE GENOMICS BIOINFORMATICS
but not D-glucose, a primary carbon source for most lactobacilli (2) . ... L. florum is most closely related to Lactobacillus lindneri and Lactobacillus sanfranciscensis, but it differs from these species because L. florum is fructophilic and exhibits a preference for D-fructose ...doi:10.1186/1471-2164-9-75 pmid:18261238 pmcid:PMC2265698 fatcat:5g42373tjfecxp44yqns7qwzoe
The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user-created genome-groups, with detailed summaries and views of thedoi:10.1093/nar/gkw1017 pmid:27899627 pmcid:PMC5210524 fatcat:amtvn252r5gixdpbsxatrltt3m
more »... data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by 'virtual integration' to any of PATRIC's public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.
The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology - predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computingdoi:10.1101/096354 fatcat:ojeek6qm4nca5c2wtapougjlm4
more »... cture; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.
When a Narrative is shared or cop- OPEN C O R R E S P O N D E N C E tion for that app so credit can be given to the contributor. ... For reproducibility, all apps in KBase C O R R E S P O N D E N C E sis workflows have the potential to empower scientists in a broad range of application areas for systems biology, including environmental ...doi:10.1038/nbt.4163 pmid:29979655 fatcat:5hbmayan3bfijaijmmsueufweq
Looney (Reno, NV) 89j:68144 68T20 68Q25 90B35 Parrello, Bruce D. (1-NW); Kabat, Waldo C. (1-NW); Wos, L. (1-ANL) Job-shop scheduling using automated reasoning: a case study of the car-sequencing problem ...
The anomalous extension problem in default reasoning. 89j:68143 Parrello, Bruce D. ... (Not in MR) Greenberg, Harvey J. see Glover, Fred, (Not in MR) Iwasaki, Yumi see Friedland, Peter E., 89j:68141 Kabat, Waldo C. see Parrello, Bruce D.; et al., 89j:68144 Kasymova, Zh. S. ...
(with Parrello, Bruce D.; Wos, Larry) Job-shop scheduling using automated reasoning: a case study of the car-sequencing problem. J. Automat. Reason. 2 (1986), no. 1, 1-42. ... D. Donevski) 89j:94006 94A12 — (with Kraus, F.; Anderson, B. D. O.; Mansour, M.) On the robustness of low-order Schur polynomials. JEEE Trans. Circuits and Systems 35 (1988), no. 5, 570-577. ...
(From the text) 89g:28028 28D05 (47A35) Wos, Larry (with Parrello, Bruce D.; Kabat, Waldo C.) Job-shop scheduling using automated reasoning: a case study of the car-sequencing problem. J. Automat. ... (see 89m:03002) 03A05 Wright, D. J. see Wright, Donald J. Wright, David C. (with Rokhsar, Daniel S.; Mermin, N. ...
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