Demo Data from EDBT 2026 / reviewers in the wild / expert
James M. Tiedje
dblp:64/1330
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0002-8992-6218ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 3
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
3 papers |
Bioinformatics and computational biology · 100% |
Topics — the 6 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
metagenomics |
0.6 | 2 | 2018 | ARGs-OAP v2.0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes · Bioinform. 2018 ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database · Bioinform. 2016 |
Bioinformatics and computational biology › metagenomics
antimicrobial resistance gene detection |
0.2 | 1 | 2016 | ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database · Bioinform. 2016 |
Bioinformatics and computational biology › genome annotation
gene annotation |
0.2 | 1 | 2016 | ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database · Bioinform. 2016 |
Bioinformatics and computational biology › sequence analysis
sequence annotation |
0.2 | 1 | 2016 | ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database · Bioinform. 2016 |
Bioinformatics and computational biology
sequence analysis |
0.1 | 1 | 2018 | ARGs-OAP v2.0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes · Bioinform. 2018 |
Bioinformatics and computational biology › computational microbiology › microbiome analysis
microbial community profiling |
0.0 | 1 | 2009 | BEsTRF: a tool for optimal resolution of terminal-restriction fragment length polymorphism analysis based on user-defined primer-enzyme-sequence databases · Bioinform. 2009 |
Methods — techniques the papers use, named apart from their topics
similarity search · 0.3profile HMM · 0.3hidden markov model · 0.3sequence similarity search · 0.2sequence database search · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | ARGs-OAP v2.0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomesabstractMotivation: Much global attention has been paid to antibiotic resistance in monitoring its emergence, accumulation and dissemination. For rapid characterization and quantification of antibiotic resistance genes (ARGs) in metagenomic datasets, an online analysis pipeline, ARGs-OAP has been developed consisting of a database termed Structured Antibiotic Resistance Genes (the SARG) with a hierarchical structure (ARGs type-subtype-reference sequence). Results: The new release of the database, termed SARG version 2.0, contains sequences not only from CARD and ARDB databases, but also carefully selected and curated sequences from the latest protein collection of the NCBI-NR database, to keep up to date with the increasing number of ARG deposited sequences. SARG v2.0 has tripled the sequences of the first version and demonstrated improved coverage of ARGs detection in metagenomes from various environmental samples. In addition to annotation of high-throughput raw reads using a similarity search strategy, ARGs-OAP v2.0 now provides model-based identification of assembled sequences using SARGfam, a high-quality profile Hidden Markov Model (HMM), containing profiles of ARG subtypes. Additionally, ARGs-OAP v2.0 improves cell number quantification by using the average coverage of essential single copy marker genes, as an option in addition to the previous method based on the 16S rRNA gene. Availability and implementation: ARGs-OAP can be accessed through http://smile.hku.hk/SARGs. The database could be downloaded from the same site. Source codes for this study can be downloaded from https://github.com/xiaole99/ARGs-OAP-v2.0. Supplementary information: Supplementary data are available at Bioinformatics online. Xiaole Yin, Xiaotao Jiang, Benli Chai, Liguan Li, James R. Cole, James M. Tiedje |
Bioinform. | 7 |
| 2016 | ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-databaseabstractMOTIVATION: Environmental dissemination of antibiotic resistance genes (ARGs) has become an increasing concern for public health. Metagenomics approaches can effectively detect broad profiles of ARGs in environmental samples; however, the detection and subsequent classification of ARG-like sequences are time consuming and have been severe obstacles in employing metagenomic methods. We sought to accelerate quantification of ARGs in metagenomic data from environmental samples. RESULTS: A Structured ARG reference database (SARG) was constructed by integrating ARDB and CARD, the two most commonly used databases. SARG was curated to remove redundant sequences and optimized to facilitate query sequence identification by similarity. A database with a hierarchical structure (type-subtype-reference sequence) was then constructed to facilitate classification (assigning ARG-like sequence to type, subtype and reference sequence) of sequences identified through similarity search. Utilizing SARG and a previously proposed hybrid functional gene annotation pipeline, we developed an online pipeline called ARGs-OAP for fast annotation and classification of ARG-like sequences from metagenomic data. We also evaluated and proposed a set of criteria important for efficiently conducting metagenomic analysis of ARGs using ARGs-OAP. AVAILABILITY AND IMPLEMENTATION: Perl script for ARGs-OAP can be downloaded from https://github.com/biofuture/Ublastx_stageone ARGs-OAP can be accessed through http://smile.hku.hk/SARGs CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Xiaotao Jiang, Benli Chai, Liping Ma, Anni Zhang, James R. Cole, James M. Tiedje |
Bioinform. | 8 |
| 2009 | BEsTRF: a tool for optimal resolution of terminal-restriction fragment length polymorphism analysis based on user-defined primer-enzyme-sequence databasesabstractSUMMARY: BEsTRF (Best Estimated T-RF) provides a standalone environment for analyzing primers-enzymes-gene section combinations used in terminal-restriction fragment length polymorphism (T-RFLP) for its optimal resolution. User-defined sequence databases of several hundred thousand DNA sequences can be explored and the resolution of user-specified sets of primers and restriction endonucleases can be analyzed on either forward or reverse terminal fragments. Sequence quality, primer mismatches, insertions and deletions can be controlled and each primer pair-specific sequence collections can be exported for downstream analyses. The configuration for a novel T-RFLP population profiling using rpoB gene (DNA-directed RNA polymerase, beta subunit) on forward fluorescently labeled primer are presented. AVAILABILITY: BEsTRF is freely available at http://lie.fe.uni-lj.si/bestrf and can be downloaded from the same site. The online protocol, numerous primer and enzyme dictionaries, sequence collections and results generated during this work for various genes are available at our website http://lie.fe.uni-lj.si/bestrf. Blaz Stres, James M. Tiedje, Bostjan Murovec |
Bioinform. | 2 |