Team I Functional Annotation Group

Introduction

Background

Functional annotation is the process of locating genes and identifying their functions (biochemical functions, regulatory functions, etc.) in the genome.

Objective

• Fully annotate 258 genomes from Gene Prediction group, focusing on antibiotic resistance
• Provide Comparative Genomics group with data required to perform Genome Wide Association Study(GWAS)

Tools

Prokka

Command:

prokka --outdir <output_directory> --kingdom <species' kingdom> --genus <species' genus> --gram <> --prefix <output_file> --rfam --rnammer <input_file> 

• Runtime: ~ 16mins /genome

eggNOG

Eggnog performs functional annotation of genes and proteins using orthology assignments from pre-computed clusters and phylogenies from eggnog database. The database contains Orthologous Groups of proteins at taxonomic levels.

Command:

python emapper.py -i <input_file> --output <output_file> -m [diamond,hmm] --usemem -d <database_name>

• --usemem: for loading the database in RAM memory

PilerCR

PilerCR identifies and analyzes CRISPR repeats

Command:

pilercr -in <input_file> -out <output_file>

• Runtime: <5 sec/genome

Phobius

Phobius predicts transmembrane topology and signal peptides from amino acid sequences, it was a challenging problem because of high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions.

Phobius is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states, which allows it to have a higher accuracy rate.

Command:

phobius.pl -<output_format> <input_file> > <output_file>

• Runtime: 12-16mins /genome

LipoP

LipoP predicts lipoprotein signal peptides in Gram-negative bacteria, its hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins.

Command:

LipoP -<output_format> -<input_file> <output_file>

• Runtime: ~2mins /genome

TMHMM

Command:

tmhmm -<output_format> -<input_file> <output_file>

• Runtime: ~6mins /genome

SignalP

Command:

signalp -t <organism_type> -f <output_format> <input_file>

• Runtime: ~ 4mins /genome

DeepARG

DeepARG is a machine learning solution that uses deep learning to characterize and annotate antibiotic resistance genes in metagenomes. It contains two models for different inputs, short sequence reads from Next Generation Sequencing and gene-like sequences

Command:

• Runtime: 3min27s /genome

Interproscan

InterProScan runs the scanning algorithms from the InterPro database, which uses predictive models, known as signatures, provided by member databases, in an integrated way.

Command:

interproscan.sh -appl <application_you_want> -iprlookup -pa -i <input_file> -f <output_format> 

• -iprlookup: include lookup of corresponding InterPro annotation in the TSV and GFF3 format
• -pa: lookup of corresponding pathway annotation
• Runtime: 1min/genome, depends on applications you choose

DOOR2

DOOR2 (Database of PrOkaryotic OpeRons) is the largest operon database as of April 2018. Operons are important because they play a role in regulation of genes that perform protein synthesis.

Workflow:

-Download operon tables from DOOR2.
-Download Fasta files based on GID
-Make a blast database and perform blastp
-Filter results and requery the operon table

(Percent identity and query coverage were filtered to be 80% or greater)

-makeblastdb -in <fasta file> -dbtype <prot or nucl>
-blastp -db <database> -query <input fasta> -out <output file> -max_target_seqs <int> -max_hsps <int> -num_threads <int> -outfmt "<int followed by information you would like presented from search>"

• Runtime: <5sec/genome, with multicore

Reference

• LukasKäll, AndersKrogh, Erik L.LSonnhammer. "A Combined Transmembrane Topology and Signal Peptide Prediction Method"Journal of Molecular Biology 14 May 2004, Pages 1027-1036.