Team I Webserver Group

Web Server

Introduction

Background

The goal of our predictive webserver is to process biological data​ and output the results of the analysis in a user-friendly format. We will provide information about an input sample's antibiotic resistance and other biological traits such as genus, species, and strain.

Goals

• Assemble input reads​
• Analyze assemblies​
• Visualize results​
• Implement a way for results to be downloaded

Technologies Used

PHP was used because it is a universal web language that has numerous useful libraries and is easy to integrate with HTML. This was used with Laravel to allow for rapid application development.

Functionalities

De Novo Genome Assembly

FastQC was used to perform quality control checks on the raw input sequence data. Then, de novo sequencing was used in our pipeline because no reference sequence is needed in this case. Sequence reads are assembled as contigs, and the coverage quality of de novo sequence data depends on the size and continuity of the contigs. We used Skesa for de novo genome assembly. This tool is currently unpublished.

Species & Strain Typing

MEGA, GenomeTester4 and StrainSeeker were used to constructs a list of specific k-mers for each node of any given Newick-format tree and enables the identification of bacterial isolates in 1–2 min. MEGA7 was used to align the sequences and construct neighbor-joining tree. Then StrainSeeker was used to build a custom database using the 258 Klebsiella genomes​ we were given. To build a custom database, the tree generated by MEGA7 was used to function as the guide tree, describing the relationships between given strains. Then StrainSeeker was used to detect novel strains that are related to strains in the database.

perl builder.pl -n refseq_guide_tree.nwk -d strain_fasta_directory -w 32 -o my_database

perl seeker.pl -i sample_file.fastq -d ss_db_w32 -o sample_result.txt

A pre-build database is used by the StrainSeeker for species identification. Strainseeker is a tool which lets you rapidly and accurately makes as assessment of the species and strain of a bacterial assembly. It works in a matter of minutes and can be customized to use a user-created database. It works on paired-end reads and can even identify novel strains and place them near their close relatives on the phylogeny tree. It is therefore a useful tool for further assessment of a sample of unknown origin.

For KAREN, we are specifically concerned only with Klebsiella spp.. When testing the results using the pre-built database, our results showed it was seemed accurate at analyzing the Klebsiella strains. For this reason, we choose to use the pre-built database for finding species and strain identification.

Genome Database

We created a curated list of genes that are included in the 258 Klebsiella genomes​ we were given, performed a literature review to find genes that may indicate colistin resistance​, and built a gene panel to help us find phenotypic indicators in our assembled genomes. We will be using a MySQL database that will show 0 (absent) or 1 (present) for both antibiotic resistance genes and virulence factor genes.

Team

Dongjo Ban, Genevieve Brandt, Saurabh Gulati, Yuntian He, Ryan Place, Nirav Shah, Casey Smith, Mohit Thakur, Stephen Wist