Graph_Genome

Introduction

This pipeline is developed for detecting rare pathogens by aligning raw reads from Illumina platform to graph reference genome. This graph based approach allows to use multiple reference genome in sequencing. Currently, this pipeline is developed for detecting subtypes of Enterovirus species.

Vsearch algorithm is used to cluster matured peptides of all known subtypes of Enterovirus. This clustering technique is protein content-based approach.

Groot Graphs is used to index database for searching and build graph for visualizing the variants between all subtypes within Enterovirus species.

Pipeline

Building Clustering Database

• Fasta file including all known subtypes is provided.
• From this fasta file, obtain the list of accession_no for each subtype then download genbank files contain completem genome for each subtype
• Extract matured peptides of each subtypes and prepare pre-clustered files (under format '*.fna')
• Run vsearch to get the clustered database
• Index the database for building graph by running groot

Fastq file processing

• Raw reads in fastq files are trimmed out by FastQC with default Q = 20, then all reads with length 100 +/- 10 bp will be used to align to genome graph
• Visualisation of number of reads before and after quality check can be provided

Alignment reads

After multiple sequence alignment database is built up, alignment reads from fastq files can be started with shell script to run python script, a table result for read alignment is extracted with information include:

Building graph

• Multiple reference graph is visualised by Bandage

Cluster Analysis

List of metrics

Visualisation

Graph genome is visualised by Bandage to get the overview about variation, hitting when aligning the query sequence.

Simulation data

Wgsim tools

Simulated fastq files will be created from fasta files:

• simulation for the read length
• simulation for the error rate/ mutation rate
• simulation for the contaminant reads
• simulation for ...

Estimating the FDR

One query sample versus graph

Multiple query samples versus graph

Adding mutation rate to query samples

Estimated sensitivity and specificity

Estimating TP and FN

Estimating FP and TN

Calculating sensitivity and specificity