Team I Functional Annotation Group: Difference between revisions
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'''Phobius''' | '''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: | Command: | ||
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'''DeepARG''' | '''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 and gene-like sequences | |||
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: | Command: | ||
<pre></pre> | <pre></pre> | ||
Revision as of 15:32, 9 April 2018
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)
Pipeline
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
PilerCR
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
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
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