Team II Functional Annotation Group

Introduction

Functional Annotation

Functional Annotations are processes that identify the locations of genes and all coding regions in the genome and determine the function of these genes.This biological information can be biological functions, biochemical functions or gene expression regulation.

Data

We are given 258 assembled genomes and predicted genes of Klebsiella spp.

Approach

Functional Annotation utilizes computational methods to functionally annotate 258 Klebsiella genomes. The approach should be scalable, reduce query size and reduce database size. Two classes of methods are generally adopted: ab-initio and homology-based.

Protein-Coding Regions

Signaling Peptides

Transmembrane Regions

LipoP is used for both Transmembrane regions and lipoproteins. It is based on Hidden Markov Model(HMM). These 4 clases are predicted SpI: signal peptide (signal peptidase I) SpII: lipoprotein signal peptide (signal peptidase II) TMH: n-terminal transmembrane helix. This is generally not a very reliable prediction and should be tested. This part of the model is mainly there to avoid tranmembrane helices being falsely predicted as signal peptides. CYT: cytoplasmic. It really just means all the rest. For technical reasons (see paper) the score for CYT is always the same.

perl LipoP -short <input FASTA> > <output GFF>

Lipoproteins

Operons

Pathways

eggNog-mapper

Public database consisting of Orthologous Groups (OGs) of proteins at taxonomic levels. The recent version (4.5) expands functional annotation of OGs to KEGG pathways (KO), SMART/Pfam domains,
and Gene Ontology (GO) terms from Gene Ontology Consortium.

The OG database contains annotations called eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) to produce orthology.
It consists of 2031 and 1655 prokaryotic mappings, respectively. In addition to the command-line tool, eggNOG v4.5 is available through both a web interface and RESTful API.

Command line:

python emapper.py -i input_file.faa --output output_file -m [diamond|hmmer] -d bact

Non-coding RNA

rRNA, tRNA, and sRNA

Please refer to "Team II Gene Prediction Group - RNA Prediction" [1]

CRISPR

CRISPRs (Clustered Regularly Interspaced Short Palindromic Repeats) are found in approximately 40% of sequenced bacterial genomes. They are reported to be related to Bacterial immunity regulation, cell defense mechanism, DNA rearrangement, replication, and regulation. And Klebsiella has an unusually high proportion of self-targeting spacers

Tool

Version: Piler-CR 1.06
PILER-CR is a program which specifically designed for the identification and analysis of CRISPR repeats. The program executes rapidly and has both high sensitivity and high specificity.

Input: FASTA

Output: Piler-CR specific report format

Performance by using reference genome:

Run-time: ~5 seconds for a 5Mb genome
CRISPR found:2

Others

Antibiotic Resistance

Virulence Factors

Prophage Genes

Prophage genes are a bacteriophage genetic materials integrated into bacterial DNA genome or existing plasmid. This requires phages in latent phase that the viral genes are present in the bacterium without causing disruption of the bacterial cell. In fact, prophage genes are one of the major source of new genes and functions in bacterial genomes[2], such as antibiotic resistance[3], virulence factor[4], and biofilm formation[5].

PHASTER

PHASTER (PHAge Search Tool Enhanced Release)[6] is a server for the rapid identification and annotation of prophage sequences in bacterial genomes and plasmids. It takes both assembled genome (with or without contigs) or GenBank file and compares to their viral and bacterial databases[7].

Input: FASTA or GenBank
Output: PHASTER specific report format
Upload Query to PHASTER Server: wget --post-file="Input_File" "http://phaster.ca/phaster_api" -O "Output_Status.txt"
Check Status: wget "http://phaster.ca/phaster_api?acc="query_ID"" -O "Output_Status.txt"
Download Results: wget phaster.ca/submissions/query_ID.zip -O "Output_Results.zip"