The document summarizes the Bacterial Bioinformatics Resource Center (BRC), which merges several bioinformatics resources to support infectious disease research. The BRC contains over 130,000 microbial genomes with uniform annotations across genes, proteins, pathways, and more. It also includes curated data on antimicrobial resistance for over 15,000 genomes. The BRC enables users to analyze genomes, perform comparative analyses, and has deployed machine learning to predict antimicrobial resistance phenotypes from genomic data.

3. July 2018

4.  NIAID-funded Bacterial Bioinformatics Resource Center (BRC)
designed to support infectious disease research
 Special emphasis on 22 pathogenic genera
◦ Bacillus, Bartonella, Borrelia, Brucella, Burkholderia, Campylobacter,
Chlamydophila, Clostridium, Coxiella, Ehrlichia, Escherichia, Francisella,
Helicobacter, Listeria, Mycobacterium, Rickettsia, Salmonella, Shigella,
Staphylococcus, Streptococcus, Vibrio, and Yersinia
 Merger of PATRIC, RAST, SEED and other resources built by
teams at ANL, UC, FIG, and Virginia Tech
 Usage:
◦ >30,000 users
◦ >4,000 citations

5.  > 130,000 public microbial genomes – more added every month
◦ 10 host genomes and their annotations
 Uniform genome annotations across all genomes
◦ Genes, RNAs, proteins, protein functions, GO, EC, protein families
◦ AMR genes, virulence factors, drug targets, essential genes
◦ Biochemical pathways and metabolic models
◦ Annotations of all public genomes updated every 3-4 months
 Curated genome metadata and AMR phenotypes
◦ Disease, isolation, phenotype, clinical and environmental
◦ AMR phenotype data: >15,000 genomes and >100 antibiotics
 Transcriptomics data (>800 datasets)
 Protein-protein and host-pathogen interactions
 Proteomics, metabolomics and Tn-seq data

6. 0
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7.  Using curated AMR phenotype data in PATRIC as
training sets, build machine learning classifiers
 Predict the antimicrobial resistance (AMR)
phenotypes for new genomes
 Predict the genomic regions associated with AMR
 Use these predictions to identify new AMR genes
and enhance our understanding of AMR
mechanisms
 To date, 40 AMR phenotype prediction classifiers
have been deployed.

8.  Genome Assembly
◦ Many Assemblers (short, long reads), Compare Assembly Output
 Genome Annotation
◦ High-speed genome annotation using RASTtk and controlled vocabulary from
SEED project
◦ Specialized annotation modules - New
 Prediction of AMR phenotype and AMR genes
 Prediction of gene essentiality
 Similar Genome Finder - New
◦ Find genomes that are most similar to a genome of interest
 Proteome Comparison
◦ Compare up to 8 genomes to a reference using bi-directional BLAST hits
 Variation Analysis - New
◦ Identify SNPs, SNVs, and insertion / deletion

9.  Comparative Analysis and Visualizations
◦ Protein family and metabolic pathway comparisons
◦ Gene list, gene set, projections, heatmaps
◦ Transcriptome analysis, up/down fold changes
◦ Metadata, disease, and PPI data
 Comprehensive Searching
◦ AMR genes (ARDB, CARD PATRIC AMR db), genome features,
external ID mapping, similarity, gene pages, gene collections,
correlated genes, genome finder, transcriptome, EC, GO, etc.

31. • Comparison of thousands of protein families across hundreds of genomes

32. • Comparison of thousands of protein families across hundreds of genomes

33. • Comparison of thousands of protein families across hundreds of genomes

34. • Comparison of metabolic pathways across hundreds of genomes

46.  Seeking NIAID funding – funding extension
 A possible pre-Evergreen 2019 workshop
 Pushing for community annotation
◦ Undergraduate students (I have about 20 in
training)
 PLEASE MAKE YOUR VOICE HEARD: What’s
on your wish list?
◦ What do we need to improve phage therapy
resources, in particular?

47. Robert A. Edwards, PhD
 RASTtk and PhiRAST development:
Ross Overbeek, Robert Olson, Jim Davis, Gordon
Pusch, Terry Disz, Bruce Parrello
 Phage annotators (Phantomers):
Bhakti Dwivedi, Mya Breitbart, et al.
 FIG and all SEED annotators:
VeronikaV, SvetaG, OlgaV/Z, et al.
$$
&
NSF
Katelyn McNair

48.  SEED, RAST, myRAST, phiRAST:
◦ RAST: Aziz et al., BMC Genomics 2008
◦ SEED servers: Aziz RK,, et al. (2012) PLoS ONE 7(10):
e48053.
◦ Nucleic Acids Res. 2014 Jan;42(Database issue):D206-14
 PATRIC: Antonopolus et al., Brief. Bioinf. 2017 Jul
31; Online early