Team I Webserver Group

Members: Devishi Kesar, Shuheng Gan, Winnie Zheng, Priya Narayanan, Aaron Pfennig

Introduction

Objective

• Provide a comprehensive, automated platform to analyze E.coli isolates in order to predict virulence factors and outbreak cluster
• Functionalities of the webserver:
  • Identify virulence factors/microbial resistance and outbreak response for provided isolates
  • Allow data upload at each step of outline pipeline
  • Visualize findings in a comprehensible way
• Design
  • Intuitive usage
  • Provide only essential options

WebServer

• Structure
• Access to Webserver

Here is Link to access our webserver:

Functionalities

Genome Assembly

• Performs de-novo assembly with FastQ files as input
• Runs following tools by default:
  • fastp: read pre-processing
  • Unicycler: Genome assembly
• Options:
  • Perform read preprocessing
  • Kmer-size
  • Spades as alternative assembly method
• The input FastQ files must be paired-end reads
• Outputs as FASTA file
• Visualisation: Quast output
• For more details to visit: Team1_Genome_Assembly

Gene Prediction

• Gene finding in assembled isolates or provided FASTA fileTakes FastQ files as input
• Runs following tools by default:
  • CDS: Prodigal
  • tRNA: Aragorn
  • rRNA: barrnap
• Options:
  • GeneMarkS-2 as alternative tool for CDS predictions
  • tRNAscan-SE as alternative tool for tRNA predictions
  • RNAmmer as alternative tool for rRNA predictions
• Outputs as *.gff file, *_cds.fna file, *_protein.faa file and *_rna.fna file
• For more details to visit: Team1_Gene_Prediction

Functional Annotation

• Obtain functional information about predicted genes
• Input: FASTA file
• Cluster Tool: usearch
  • Output: centroid.fasta
• Homology Tools:
  • General annotation: InterProScan, EggNOGmapper
  • Antibiotic resistance gene: DeepARG
• Abinitio Tools:
  • Signal Peptides: SignalP 5.0
  • Transmembrane Proteins: TMHMM
  • CRISPR Sites: PilerCR
• Output: *.tsv file
• For more details to visit: Team1_Functional_Annotation

Comparative Genomics

• Comparison of genomic features of input files to identify outbreak cluster
• Input: FASTA file, prodigal training file(chewBBACA)
• Tools used:
  • MUMmer 4.0
  • chewBBACA
  • kSNP 3.0
  • FigTree
• Options:
  • Parsimony tree, maximum likelihood and neighbour joining trees as option for kSNP
  • k-mer size option for kSNP
• Output: .tsv file(for chewBBACA, MUMmer), .png(kSNP)
• Visualisation: Phylogenetic tree for identified SNP’s, phylogenetic tree for MLST, graph for epidemiological data visualisation

• For more details to visit: Team1_Comparative_Genomics

Webserver Demo

• Choice One: Running General Pipline
• Choice Two: Running each step separately