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=Web Server=
==Introduction==
==Introduction==


===Background===
===Background===


The goal of '''K'''lebsiella '''A'''ntibiotics '''RE'''sistance Predicitio'''N''' (KAREN) webserver is to assemble and annotate genome of ''Klebsiella spp.'' and provide the results to the user in an user-friendly format. KAREN could also be used to assemble genomes of other bacteria, however the server has been currently designed to annotate only '''Klebsiella''' genomes.
The objectives of our BIOL 7210: Computational Genomics teams were to, given unassembled genome sequence data from the Weiss Lab at the Emory University School of Medicine, proceed through five distinct stages of analysis and interpretation of that data: genome assembly, gene prediction, functional annotation, comparative genomics, and production of a predictive webserver. At the last stage, our goal was to create a predictive webserver that performed the functionalities of some, if not all, of the work from previous groups.


The objective of the BIOL7210: Computational Genomics this year was to perform genome assembly and functionally annotate 258 genomes. Recent studies have shown the emergence of colistin and fosfomycin resistance within ''Klebsiella spp.''.
===Goals===
 
KAREN is able to perform the following analyses with the input of raw sequence reads.


- TO DO:
Our goals for a predictive webserver were as follows:
- TO DO:
- TO DO:
 
===Goals===


*Assemble input reads​
*Assemble input reads​
*Analyze assemblies​
*Analyze assemblies​
*Visualize results​
*Visualize results​ in user-friendly format
*Implement a way for results to be downloaded
*Implement a way for results to be downloaded
===KAREN===
'''K'''lebsiella '''A'''ntibiotics '''RE'''sistance Predicitio'''N''' (KAREN) is a culmination of these objectives and is able to perform the following analyses given an input of raw sequence reads:
* De novo assembly
* Species identification
* Strain identification
* Average Nucleotide Identity
* Computational phenotyping
* Visualization of results


===Technologies Used===
===Technologies Used===
For the creation and development of this webserver, we used PHP framework for server-side programming. PHP provides a strong frameworks to support MySQL and Apache Server. Also PHP provides the feasibility of the development of Model-View-Controller framework, which provides a simpler user-interface. There are many such frameworks available, among which we used Laravel.


Laravel was created by Taylor Otwell and is based on Symfony which provides three important features we wanted to implement within our webserver - 1. Blade Templates (User Interface), 2. Migrations (Database Management) and 3. Job Chainings. This webserver is built on PHP v7.0.0 and Laravel v5.5.
[[File:laravel.png]]
 
 
For the creation and development of this webserver, we used PHP framework for server-side programming. PHP provides a strong frameworks to support MySQL and Apache Server. Also, PHP provides the feasibility of the development of Model-View-Controller (MVC) framework, which provides a more simple user-interface. There are many MVC frameworks available, among which we used Laravel. Laravel was used because of its extensive documentation and a large community of developers. It also currently is one of the most widely used PHP frameworks. For more information, please visit their [https://laravel.com/ website]
 
 
This webserver is built on PHP v7.0.0 and Laravel v5.5.


==Functionalities==
==Functionalities==
===de novo Genome Assembly===
===De novo Genome Assembly using SKESA===
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.
We used SKESA for de novo genome assembly. The input to the assembler was raw reads (forward & reverse) retrieved using SRA accession numbers. The output contigs then were scaffolded using a tool called SSPACE. For more information on the assembly pipeline, refer to [http://compgenomics2018.biosci.gatech.edu/Team_I_Genome_Assembly_Group Genome Assembly team]. SKESA is currently unpublished.
 
 
[[File:Assembly_flowchart.png]]


===Species & Strain Typing by StrainSeeker===
===Species & Strain Typing by StrainSeeker===
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.
Strainseeker is a tool which lets you rapidly and accurately make an assessment of the species and strain of a bacterial assembly. StrainSeeker has a pre-built database that is uses for species identification and works on paired-end reads to identify strain type. It has the ability to identify novel strains and is therefore a useful tool for further assessment of a sample of unknown origin.  


<code>perl builder.pl -n refseq_guide_tree.nwk -d strain_fasta_directory -w 32 -o my_database</code>
<code>perl builder.pl -n refseq_guide_tree.nwk -d strain_fasta_directory -w 32 -o my_database</code>
Line 39: Line 48:
<code>perl seeker.pl -i sample_file.fastq -d ss_db_w32 -o sample_result.txt</code>
<code>perl seeker.pl -i sample_file.fastq -d ss_db_w32 -o sample_result.txt</code>


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.
The pipeline works by taking a FASTA file as an input, which will then be processed by StrainSeeker. The output will be parsed by one of the scripts in the pipeline and visualization with tables containing information is generated.
 
[[File:Strainseeker flowchart.png]]
 
===Computational Phenotyping using CARD and VFDB Databases===
The Comprehensive Antibiotic Resistance Database (CARD) includes information on resistant genes, proteins coded by those genes, and their associated phenotypes. Since we want to understand the cause of heteroresistance and/or heterosusceptibility, we performed computational phenotyping against the CARD database to determine which antibiotic genes were present within the genome assembly.  
 
The Virulence Factors Database (VFDB) is a reference database that holds information on virulence factors of pathogenic bacteria. They hold about 2,353 virulence factors including bacterial toxins, cell surface proteins, cell surface carbohydrates, and hydrolytic enzymes that may contribute to the pathogenicity of the bacterium. Computational phenotyping was performed against the VFDB database as well.
 
 
[[File:Card flowchart.png]]
[[File:Vfdb.png]]
 
 
There are two pipelines for processing input FASTA file. For CARD, we use BLAST and filter its results based on % coverage and % identity to categorize antibiotic resistance genes as "High", "Medium", "Low". These labels represent our confidence in the BLAST results being antibiotic resistance genes. This output is parsed further to categorize each gene into drug and resistance mechanism category. The final outputs for the CARD pipeline are barcharts that shows the number of counts for antibiotics and resistance mechanisms for a given input FASTA file.
 
 
Similar to CARD, we use BLAST to retrieve information for a given FASTA file. This pipeline displays a table of virulence factor found in the genome.
 
===pyani===
In order to calculate the Average Nucleotide Identity (ANI) between genomes, we implemented the python tool pyani. ANI is a measure of genome relatedness, and it shows how many nucleotides are identical between two genomes. The ANI value is related to DNA-DNA hybridization values, which traditionally indicate the microbial species definition. ANI values above 95% indicate that two genomes are the same species.
 
We implemented pyani through using a very quick alignment tool - mummer. In our server, we run pyani between six genomes. The user is able to choose among 20 reference genomes and any genome that a user has uploaded. So, ANI can be used to see similarities and differences between a dataset and also to get an idea of identity to ''Klebsiella'' references. The result from pyani is then parsed to generate a heatmap with the input genomes.
 
[[File:Pyani_updated.png]]
 
==Webserver ([http://predict2018a.biosci.gatech.edu/ Link])==
 
 
===Upload File===
 
 
This is where the users can upload files to analyze, such as genome FASTA file. The users will be given an access code, which will be used later to retrieve their uploaded files for analysis available on the webserver.
 
===Assembly===
 
 
Aside from performing assembly, under dropdown menu for Assembly, there is a download page from which the users can access their assembled files. Users will be sent an email and download code when the job is complete.
 
===Predictions===
 
By clicking the dropdown menu, the prediction pipelines that were made available for the webserver is shown. Currently available ones include CARD/VFDB analysis, pyani, and StrainSeeker.
 
===Screenshots===
 
 
[[File:Home team1.PNG]]
 
 
 
[[File:Assembly team1.PNG]]
 
 


===CARD Database===
[[File:Pyani team1.PNG]]
The Comprehensive Antibiotic Resistance Database includes information on resistant genes, the proteins coded by those genes and their associated phenotypes. As one of the objectives of the class was to understand the cause of hetero-resistance and hetero-susceptibility, we performed computational phenotyping - to determine the antibiotic genes present within the genome assembly created by the webserver against the CARD database.


<Image>
=References=
Andrews, S. (2010). ''FastQC: a quality control tool for high throughput sequence data''. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc


The graph above describes the counts of the genes found and and the efflux mechanism that they possess. As '''Kleibsiella spp.''' are one of the bacteria known to develop multi-drug resistance, this information can be useful for interpretation and get a brief idea on the organism that was assembled.
Bolger, A. M., Lohse, M., & Usadel, B. (2014). ''Trimmomatic: A flexible trimmer for Illumina Sequence Data''. Bioinformatics, btu170.


===VFDB Database===
Chen, L., Yang, J., Yu, J., Yao, Z., Sun, L., Shen, Y., Jin, Q. (2005). ''VFDB: a reference database for bacterial virulence factors'' .Nucleic Acids Res. 33:D325-8.
The Virulence Factors Database is a reference database that holds information on virulent factors of pathogenic bacteria. They hold about 2,353 virulence factors including bacterial toxins, cell surface proteins, cell surface carbohydrates, and hydrolytic enzymes that may contribute to the pathogenicity of the bacterium.


<Image>
Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, et al. (2007). ''DNA-DNA hybridization values and their relationship to whole-genome sequence similarities''. Int J Syst Evol Micr 57: 81-91. doi:10.1099/ijs.0.64483-0.


Jia et al. (2017). ''CARD 2017: expansion and model-centric curation of the Comprehensive Antibiotic Resistance Database''. Nucleic Acids Research, 45, D566-573


Leighton, Pritchard: The James Hutton Institute (2015). ''PyANI''. https://github.com/widdowquinn/pyani


===PyANI===
Roosaare et al. (2017). ''StrainSeeker: fast identification of bacterial strains from raw sequencing reads using user-provided guide trees''. PeerJ 5:e3353

Latest revision as of 11:31, 28 April 2018

Introduction

Background

The objectives of our BIOL 7210: Computational Genomics teams were to, given unassembled genome sequence data from the Weiss Lab at the Emory University School of Medicine, proceed through five distinct stages of analysis and interpretation of that data: genome assembly, gene prediction, functional annotation, comparative genomics, and production of a predictive webserver. At the last stage, our goal was to create a predictive webserver that performed the functionalities of some, if not all, of the work from previous groups.

Goals

Our goals for a predictive webserver were as follows:

  • Assemble input reads​
  • Analyze assemblies​
  • Visualize results​ in user-friendly format
  • Implement a way for results to be downloaded

KAREN

Klebsiella Antibiotics REsistance PredicitioN (KAREN) is a culmination of these objectives and is able to perform the following analyses given an input of raw sequence reads:

  • De novo assembly
  • Species identification
  • Strain identification
  • Average Nucleotide Identity
  • Computational phenotyping
  • Visualization of results

Technologies Used


For the creation and development of this webserver, we used PHP framework for server-side programming. PHP provides a strong frameworks to support MySQL and Apache Server. Also, PHP provides the feasibility of the development of Model-View-Controller (MVC) framework, which provides a more simple user-interface. There are many MVC frameworks available, among which we used Laravel. Laravel was used because of its extensive documentation and a large community of developers. It also currently is one of the most widely used PHP frameworks. For more information, please visit their website


This webserver is built on PHP v7.0.0 and Laravel v5.5.

Functionalities

De novo Genome Assembly using SKESA

We used SKESA for de novo genome assembly. The input to the assembler was raw reads (forward & reverse) retrieved using SRA accession numbers. The output contigs then were scaffolded using a tool called SSPACE. For more information on the assembly pipeline, refer to Genome Assembly team. SKESA is currently unpublished.


Species & Strain Typing by StrainSeeker

Strainseeker is a tool which lets you rapidly and accurately make an assessment of the species and strain of a bacterial assembly. StrainSeeker has a pre-built database that is uses for species identification and works on paired-end reads to identify strain type. It has the ability to identify novel strains and is therefore a useful tool for further assessment of a sample of unknown origin.

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


The pipeline works by taking a FASTA file as an input, which will then be processed by StrainSeeker. The output will be parsed by one of the scripts in the pipeline and visualization with tables containing information is generated.

Computational Phenotyping using CARD and VFDB Databases

The Comprehensive Antibiotic Resistance Database (CARD) includes information on resistant genes, proteins coded by those genes, and their associated phenotypes. Since we want to understand the cause of heteroresistance and/or heterosusceptibility, we performed computational phenotyping against the CARD database to determine which antibiotic genes were present within the genome assembly.

The Virulence Factors Database (VFDB) is a reference database that holds information on virulence factors of pathogenic bacteria. They hold about 2,353 virulence factors including bacterial toxins, cell surface proteins, cell surface carbohydrates, and hydrolytic enzymes that may contribute to the pathogenicity of the bacterium. Computational phenotyping was performed against the VFDB database as well.



There are two pipelines for processing input FASTA file. For CARD, we use BLAST and filter its results based on % coverage and % identity to categorize antibiotic resistance genes as "High", "Medium", "Low". These labels represent our confidence in the BLAST results being antibiotic resistance genes. This output is parsed further to categorize each gene into drug and resistance mechanism category. The final outputs for the CARD pipeline are barcharts that shows the number of counts for antibiotics and resistance mechanisms for a given input FASTA file.


Similar to CARD, we use BLAST to retrieve information for a given FASTA file. This pipeline displays a table of virulence factor found in the genome.

pyani

In order to calculate the Average Nucleotide Identity (ANI) between genomes, we implemented the python tool pyani. ANI is a measure of genome relatedness, and it shows how many nucleotides are identical between two genomes. The ANI value is related to DNA-DNA hybridization values, which traditionally indicate the microbial species definition. ANI values above 95% indicate that two genomes are the same species.

We implemented pyani through using a very quick alignment tool - mummer. In our server, we run pyani between six genomes. The user is able to choose among 20 reference genomes and any genome that a user has uploaded. So, ANI can be used to see similarities and differences between a dataset and also to get an idea of identity to Klebsiella references. The result from pyani is then parsed to generate a heatmap with the input genomes.

Webserver (Link)

Upload File

This is where the users can upload files to analyze, such as genome FASTA file. The users will be given an access code, which will be used later to retrieve their uploaded files for analysis available on the webserver.

Assembly

Aside from performing assembly, under dropdown menu for Assembly, there is a download page from which the users can access their assembled files. Users will be sent an email and download code when the job is complete.

Predictions

By clicking the dropdown menu, the prediction pipelines that were made available for the webserver is shown. Currently available ones include CARD/VFDB analysis, pyani, and StrainSeeker.

Screenshots



References

Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina Sequence Data. Bioinformatics, btu170.

Chen, L., Yang, J., Yu, J., Yao, Z., Sun, L., Shen, Y., Jin, Q. (2005). VFDB: a reference database for bacterial virulence factors .Nucleic Acids Res. 33:D325-8.

Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, et al. (2007). DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Micr 57: 81-91. doi:10.1099/ijs.0.64483-0.

Jia et al. (2017). CARD 2017: expansion and model-centric curation of the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research, 45, D566-573

Leighton, Pritchard: The James Hutton Institute (2015). PyANI. https://github.com/widdowquinn/pyani

Roosaare et al. (2017). StrainSeeker: fast identification of bacterial strains from raw sequencing reads using user-provided guide trees. PeerJ 5:e3353

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