Team I Functional Annotation Group

Team 1 Functional Annotation

Team members: Maria Ahmad, Shuheng Gan, Manasa Vegesna, Hyeonjeong Cheon, and Kenji Gerhardt

Introduction

Background

Functional annotation is the process of associating predicted genes with their functional role in a cell. This includes the type of gene (translated/untranslated), their location within the cell, and their chemical and biological roles. These can be derived both by comparison to similar, already annotated genes with known (or anticipated) functions, or through ab initio annotation that relies on existing models, but which does not rely on databases of other annotated genes.

Objective

Our goal in this step is to functionally annotate the genes supplied to us by the gene prediction group.

Data

The genomes supplied to us were identified during assembly as E. coli. E. coli is the most highly studied microorganism: at present, NCBI contains 1072 complete E. coli genomes in its genome database, along with thousands of additional chromosomes, contigs, and scaffolds. This depth of study extends to its genes, where multiple pathogenic strains are identified and characterized well.

Pipeline

Pipeline image will be inserted

Methods

Ab-initio Approach

Ab-Initio Tools predict and annotate different regions of the prokaryotic genome using 1) sequence composition, 2) likelihoods within the gene models, 3) gene content, and 4) ignal Detection. Ab-Initio Approach can be used for finding new genes, and no external data or evidence is needed for the prediction. However, it is limited by the presence of False Positives in the predicted data as well as over-prediction of small genes.

PSORTb

remove if not needed

SignalP

remove if not needed

LipoP

remove if not needed

TatP

remove if not needed

TMHMM

remove if not needed

Phobius

remove if not needed

CRISPRCasFinder

remove if not needed

PILER-CR

remove if not needed

CRT

remove if not needed

Homology-based Tools

Homology between genes means they share ancestry. Homologous genes that have recently diverged usually share function. We are looking to transfer annotation on known genes to our predicted genes by finding homology genes. Gene databases are collections of annotated genes. When we search a gene against a database, the search is looking for homology between our gene sequences and those in the database to determine what our genes’ function will be.

Homology-based tools are more accurate and reliable than Ab-initio tools, and can be targeted for specific purposes, e.g. antibiotic resistance genes. However, homology tools are dependent on existing annotations and on what databases are being searched.

Prokka

• Prokka is a command line software tool for the rapid annotation of prokaryotic genomes in about 10 min on a desktop computer.
• Features include coding sequences (Prodigal), rRNA (RNAmmer), tRNA (Aragorn), signal peptides (SignalP), and non-coding RNA (Infernal) and including these options are flexible.
• The typical input file is FASTA format and output contains 12 output files: .err, .fsa, .sqn, .val, .faa, .gbf, .tbl, .ffn, .gff, .tsv, .fna, .log, .txt

Command:

prokka <input_file> --outdir <output directory> --prefix <file prefixes> --kingdom Bacteria --locustag --norrna --notrna

EggNOG-mapper

• Performes fast functional annotation of genes and proteins
• Uses precomputed orthologous groups and phylogenies from the eggNOG database which consists of Orthologous Groups( OGs) of proteins at taxonomic levels.
• Has a mode recommended for large datasets called DIAMOND. Diamond mode searches for the best seed ortholog located in the eggNOG protein database, and is faster than the other mode HMM. The HMM mode is comprised of a collection of precompiled hidden Markov models.

To download bacteria databases:

python2.7 download_eggnog_data.py bact

Command:

python emapper.py -i <input_file> --output <output_file> -m diamond -d bact -o <output directory>

InterProScan

• InterProScan is function annotation tool that scans sequences against InterPro protein signature databases. It classifies proteins into families and predict domains and important sites.

• Signatures are predictive models constructed from multiple sequence alignments that can be used to classify proteins. Using protein signatures is often a more sensitive way of identifying protein function than pairwise sequence similarity searches, such as BLAST.

• InterPro combines four types of protein signatures (patterns, profiles, fingerprints and HMMs) from multiple databases.

Download (https://github.com/ebi-pf-team/interproscan/wiki/HowToDownload)

1. download interproscan and most database

wget ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/5.42-78.0/interproscan-5.42-78.0-64-bit.tar.gz wget ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/5.42-78.0/interproscan-5.42-78.0-64-bit.tar.gz.md5 md5sum -c interproscan-5.42-78.0-64-bit.tar.gz.md5

1. download Panther

cd [InterProScan5 home]/data/ wget ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/data/panther-data-14.1.tar.gz wget ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/data/panther-data-14.1.tar.gz.md5 md5sum -c panther-data-14.1.tar.gz.md5

Requirement (https://github.com/ebi-pf-team/interproscan/wiki/InstallationRequirements)

- Perl 5.22

- Python 3

- Java 11

Command

/interproscan.sh -i <input_file_name> -dp -d <output_directory> 

default: run all 14 databases default outputs: input_file_name.gff3 input_file_name.tsv input_file_name.xml

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