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

Clustering

Protein clustering is the task of reducing a large number of sequences down to a smaller set of cluster representatives. A cluster representative is a single sequence whose functional characteristics are similar/the same as the other sequences contained within the cluster. The point of clustering is to produce a set of fewer, and thereby computationally easier/quicker to annotate, representative sequences whose subsequent annotation can then be applied to the other members of the cluster they represent.

In order to cluster sequences, the tool we selected for our final pipeline (USearch) sets a boundary based on percent identity between sequences in a dataset. This boundary at which sequences are clustered is the minimum threshold of difference between the two sequences for them to be included in the same cluster; sequences more dissimilar than the boundary will either be clustered with a different sequence, or will become their own cluster if they fail to cluster with any other sequences. As a result, the ideal boundary is one which captures sequences sharing function within single clusters, and does not cluster sequences with different functions in the same cluster.

In practice, such an ideal boundary will always be beset with problems: very similar sequences may have different functions, very disparate sequences may share function, and the necessary similarity to share function between proteins will vary by protein. Further, due to the degeneracy of the translation between nucleotides and amino acids, sequences which are similar at the protein level may be highly divergent at the nucleotide level, excluding the untranslated untranslated proteins. As a result, performance will be imperfect, and a decision between accuracy of clustering results and efficacy of clustering has to be made.

For the purposes of our work, we decided to cluster amino acids rather than nucleotide sequences for the majority of our tasks. Protein function is ultimately dictated by amino acid sequence, at least insofar as the data that annotation tools receive from a user. Although most tools are designed to work both with nucleotide sequences and amino acid sequences, clustering nucleotide sequences with the aim of clustering protein function is a largely misguided effort.

NCBI guidelines [28] indicate that protein functional similarity is very likely at 40% amino acid identity. However, since our task is performed in the context of 50 samples of the same species, we decided upon a much more conservative 70% amino acid identity boundary to cluster our sequences. This produces more clusters than is likely necessary, but is less likely to cluster proteins with disparate functions than a more lenient standard.

USearch

The tool our pipeline uses is called USearch. Among other functions, USearch performs sequence clustering using a greedy agglomerative algorithm, meaning it creates clusters and assigns cluster identity in the order of input sequences.

While it is possible that this order-dependent behavior may result in a sequence being assigned to a cluster that it does not ideally join, the conservative cutoff for sequence similarity that we selected makes it unlikely that this will affect function. More probably, a sequence will be assigned to one of a few similar clusters which properly should be joined, as the multiple clusters share the same function.

The result of USearch is pair of files: one tab-separated file listing the cluster representative, the sequences belonging to that representative and their identity to the representative, and a second listing just the representative sequences in FASTA format, which is friendly to annotation tools.

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)

# 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

# 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> -appl <databases_you_choose> -f <output_format> -t <sequence_type>

parameters:

-d: optional, default current directory [./]

-appl: optional, default all 14 databases [CDD-3.17,Coils-2.2.1,Gene3D-4.2.0,Hamap-2020_01,MobiDBLite-2.0,PANTHER-14.1,Pfam-32.0,PIRSF-3.02,

PRINTS-42.0,ProSitePatterns-2019_11,ProSiteProfiles-2019_11,SFLD-4,SMART-7.1,SUPERFAMILY-1.75,TIGRFAM-15.0]

-f: optional, default [gff3, tsv, and xml for protein | gff3 and xml for nucleotide]

-t: optional, n/p, default protein [p]

runtime of default setting: ~12h

DeepARG

DeepARG is a deep learning tool that annotate antibiotic resistance genes in metagenomes. It is composed of two models for two types of input: DeepARG-SS for short sequence reads from Next Generation Sequencing (NGS) technologies such as Ilummina and DeepARG-LS for long gene-like sequences from assembled samples.

Download

git clone https://bitbucket.org/gusphdproj/deeparg-ss

Requirement

- Python 2.7

Command

python ./deepARG.py --align --genes --type prot --input <gene-like_sequences_fasta_file> --out <output_file_name>

output:

- output_file_name.align.daa, DIAMOND alignment archive

- output_file_name.align.daa.tsv, tabular format. Default: qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore

- output_file_name.mapping.ARG, contains the sequences with a probability >= --prob (0.8 default)

- output_file_name.mapping.potential.ARG, contains the sequences with a probability < --prob (0.8 default)

Reference

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