Team II Functional Annotation Group

Team 2: Functional Annotation

Team Members: Danielle Temples, Courtney Astore, Rhiya Sharma, Ujani Hazra, Sooyoun Oh

Introduction

What is Functional Annotation?

The practice of putting biological meaning to coding genes (genes that encode proteins) and their corresponding protein sequences. Such annotations can be derived using homology and ab initio based approaches, which will be further explained in subsequent sections.

Objective: Perform a full functional annotation on the genes and proteins determined by the Gene Prediction group that is relevant to C. jejuni

Homology Approaches

• Determine function via sequence similarity to already functionally annotated sequences
• Limited by what we already know.

Ab Initio Approaches

• Determine function via predictive model without comparing to existing sequences
• Based on laws of nature
• Difficult to verify without experiments

Data Overview

We received 50 fna and 50 faa files from the gene prediction group. The 50 fna files are multifasta files representing each genome. The 50 faa files are multifasta files representing each proteome.

Clustering

• Significant sequence similarity implies shared ancestry that often leads to shared function
• Clustering such sequences can reduce repeat queries in homology-based annotations
• Reducing repeats improves speed and storage costs

CD-HIT

• Widely used program for clustering and comparing protein or nucleotide sequences
• Very fast and can handle extremely large databases

Homology Methods

Categories

Prophage:

• Play an important role in the evolution of bacterial genomes and their pathogenicity​
• Can change or knock out gene functions; alter gene expression

Virulence:

• A pathogen's ability to infect or damage a host
• Ex: toxins, surface coats that inhibit phagocytosis, surface receptors that bind to host cells

Fully Automated Functional Annotation:​

• Tools that annotate a spectrum of features related to the function

Antibiotic Resistance

• When bacteria develop the ability to defeat the drugs designed to kill them
• Leads to higher medical costs, prolonged hospital stays, and increased mortality​

Operons:

• A functional unit of transcription and genetic regulation​
• Identifying these may enhance our knowledge of gene regulation & function which is a key addition to genome annotation

ProphET

• PROPHage Estimation Tool
• Identifies prophages in bacterial genomes with high precision and offers a fast, highly scalable alternative
• Uses three steps: similarity search, calculation of the density of prophage genes, and edge refinement

VFDB

• Virulence Factor DataBase
• Provide virulence structure features, functions, and mechanisms used to allow pathogens to conquer new niches and circumvent host defense mechanisms
• BLAST based identification of virulence genes

PANNZER2

• Protein ANNotation with Z-scoRE​
• Provides both Gene Ontology (GO) annotations and free text description predictions​
• Uses SANSparallel to perform high-performance homology searches​
• Updated on a monthly schedule

BLAST

• Basic Local Alignment Search Tool​
• A database is searched for high-scoring local alignments with a query​
• The annotations on the sequence that score the highest alignment are assigned to the query sequence, provided the alignment score passes a threshold

CARD

• Comprehensive Antibiotic Resistance Database​
• Provides data, models, and algorithms relating to the molecular basis of antimicrobial resistance​
• Can be used for the analysis of genome sequences using the Resistance Gene Identifier

Ab Initio Methods

Categories

Transmembrane Proteins (Cell Membrane and Outer Membrane):​

• Bacteria have the ability to export effector proteins in membranes of eukaryotic host
• Integral membrane protein that function as gates or docking sites that allow or prevent the entry or exit of materials across the cell membrane​

Signal Peptides:

• Guide secretory proteins to find their correct locations outside the cell membrane for signal transduction 

CRISPR:​

• Provides immunity to the bacteria against Bacteriophages
• Contributes to the Virulence and Pathogenicity of the bacteria

TBBpred

• Uses neural networks/SVM approach
• Predicts the transmembrane Beta barrel regions in a given protein sequence

TMHMM2

• Uses Hidden Markov Model​ approach
• Outputs the number of transmembrane helices predicted, the position of each residue with respect to the cell membrane (outside/inside the cell, transmembrane segment), and optional graphical output visualizing the predicted helix​

PilerCR

• Rapid identification and classification of CRISPR repeats
• High sensitivity and high specificity

SignalP

• Uses a deep neural network algorithm
• Predicts presence of signal peptides and location of cleavage sites
• Does not determine lipoproteins

Initial Pipeline