From Sequence to Knowledge (Tools for Phage Genome Annotation)

From Sequence to Knowledge
Computational tools for
phage genome annotation
A helping hand through
The Annotation Bottleneck
Ramy K. Aziz
Professor of Microbiology and Immunology,
Children’s Cancer Hospital (Egypt 57357) &
Faculty of Pharmacy, Cairo University
Twitter: @azizrk

PRELUDE
6 August 2021 Phage Genomics - Evergreen 2021

A bit of history…
• Since 2009, a Genomics Workshop has
become an essential part of the world-
famous Biennial Evergreen phage meeting
• The challenge was: how to meet
needs/expectations that are so many and
so diverse, in ~4 hours
• The next-level challenging request =
objectively keeping up with all excellent
tools that are being developed

MOTIVATION
WHY?

“The analysis bottleneck”
• Observation:
– We generate more data than we can analyze.
– We generate sequence data faster
than we can analyze them.
• Opinion:
– Bottlenecks are not
created equal!
– It is important to define the question(s)
before working on the answer(s)!

6 August 2021 Phage Genomics - Evergreen 2021 Roux et al. Nature Biotech 2019

• The Lavigne paradox (2013)

EXPECTATIONS

Attendees’ expectations
• How many Evergreen/Annotation workshops you attended?
• Have you:
– annotated at least a phage genome?
– compared several phage genomes?
– worked on a viral metagenome?
– used the command line (Unix, Linux,
Mac Terminal) for sequence analysis?
• To optimize the content, let’s
take this survey on SOCRATIVE
(http://socrative.com)
– Enter ROOM: AZIZ15

What biologists want?
• A flawless, fully automated machine that reads
scientists’ mind, takes sequence as input and
converts it into publishable knowledge
Charlie Chaplin - Feeding Machine - Modern Times

What life offers?

What working in genomics
really is: “It takes two to tango”
Biologist
(aka human)
Computer
(aka machine)
Give me
everything
tonight!
Garbage IN
 Garbage
OUT

DEFINING THE QUESTION(S)
“Begin with the end in mind”

What you want …... is
from genome from metagenome
Incomplete
frameshift
- complete
- accurate
Credit: Andrew Kropinski Credit: Bas Dutilh
faulty assembly

What you want …... is
from genome from metagenome
6 August 2021
Incomplete faulty assembly
frameshift
- complete
- accurate
Phage Genomics - Evergreen 2021
Credit: Andrew Kropinski Credit: Bas Dutilh

A process of reconstruction

• Experimentally
DNA
GTCTCTCTNNNTCTCTTG

• Experimentally
• Computationally
DNA
GTCTCTCTNNNTCTCTTG
GTCTCTCTNNNTCTCTTG

• Experimentally
• Computationally
“Any phage
one can get!”
“eDNA”
GTCTCTCTNNNTCTCTTG
GTCTCTCTNNNTCTCTTG

THE PROCESS / PIPELINE

Assembly
Gene finding/
ORF calling
tRNA calling
Annotation
(Assigning
functions)
orienting
Validation
Fixing frameshifts
Introns and Inteins Subsystem
assignment
Refinement/
Secondary
annotation
loop
Special purpose:
toxins, morons, integrases,
lifestyle prediction
Regulatory elements
(promoters, terminators)
Output: files and graphics
From raw sequence data to
genome submission/ publication

Countless tools and databases

Countless tools and databases
• Galaxy Apollo (Jason Gill et al.)
– https://cpt.tamu.edu/galaxy-pub/
• VIGA (by Enrique Gonzalez Tortuero)
– https://github.com/EGTortuero/viga
– (https://www.biorxiv.org/content/10.1101/277509v1)
– Email: E.GonzalezTortuero@salford.ac.uk

Material/Resources
• Data & links:
– http://egybio.net/tutorial
• Slides
– http://bit.ly/annotation2018, http://bit.ly/phageRAST2018
– http://bit.ly/phagePATRIC
– Old tutorials (more detailed, but missing latest ):
• Evergreen 2011: http://slidesha.re/phantome1
• http://slidesha.re/phiRAST1 (by Karin Holmfeldt)
• Evergreen 2013: http://bit.ly/phantome2
• Evergreen 2015: http://bit.ly/phantome3
• VoM 2016: http://bit.ly/annotation2016, http://bit.ly/phantome4
• Evergreen 2017:
http://bit.ly/phigenomics2017 , http://bit.ly/phantome2017

Material/Resources

I. ANNOTATION OVERVIEW

Desired outcome
Well characterized genome, in which, ideally we
know:
 the location & function of all the genes
 the location of promoters & terminators
 the correct taxonomy
PstI PstI
20
21
22
23
24
25
26
26A
27 28 29
30
31
32
33
30.0 kb
Viruses; dsDNA viruses, no RNA stage; Caudovirales; Siphoviridae;
T1virus

Desired outcome:
Create GenBank submission
• Complete, accurate description of the
genome and its taxonomy

Desired outcome (2)

Desired outcome (3)

Desired outcome (4)
 Protein products of concern, particularly
for those interested in phage therapy:
 Integrases
 Toxins
 Antimicrobial resistance genes
PstI PstI
20
21
22
23
24
25
26
26A
27 28 29
30
31
32
33
30.0 kb

II. CLASSIFICATION

Classification
• The phage sequence space (Lima-Mendez et al.)
• The phage proteomic tree (Edwards & Rohwer)
• New: VIP tree http://www.genome.jp/viptree

III. AUTOMATED ANNOTATION

THE CONCEPTS BEHIND THE TOOLS
How to?

It is all about
Matching/ Comparing Classifying
From:
Current Opinion in Biotechnology
2003, 14:303–310

What to count?

6 August 2021
What to count? How to bin?
How to classify these?

6 August 2021
Assembly or long-reads

6 August 2021
“Truth”

6 August 2021
Similarity, variability, and functional prediction

6 August 2021
Counting genes/ gene families (protein families)…
Counting domains/ motifs

Assembly
Gene finding/
ORF calling
tRNA calling
Annotation
(Assigning
functions)
orienting
Validation (segmenter)
Fixing frameshifts
Introns and Inteins Subsystem
assignment
Refinement/
Secondary
annotation
loop
Special purpose:
toxins, morons, integrases,
lifestyle prediction
Regulatory elements
(promoters, terminators)
Output: files and graphics
From raw sequence data to
genome submission/ publication

Nomenclature Sins
 hypothetical protein  DNA polymerase with no
or poor quality evidence is far worse than:
 DNA polymerase  hypothetical protein
 Be cautious about using BLASTP hits in naming
gps – is there additional evidence to back the
designation up?

 All of these describe homologs of the
product of the coliphage T4 rIIA gene!
rIIA protector from prophage-induced early lysis
protector from prophage-induced early lysis
protector from prophage-induced early lysis rIIA
membrane-associated affects host membrane ATPase
rIIA membrane-associated affects host membrane ATPase
phage rIIA lysis inhibitor
rIIA protector
rIIA
rIIA protein
membrane integrity protector
hypothetical protein
unnamed protein product !!!!!!
protein of unknown function
Consistent Nomenclature

What do I call my gene product
(i.e. protein)?
 “phage hypothetical protein” – redundant
 “gp87” (gp = gene product)  hypothetical protein
 gp200 describes radically different proteins in
Listeria, Enterococcus, Mycobacterium,
Rhodococcus, Sphingomonas, Pseudomonas,
• Bacillus and Synechococcus phage genomes
 Add /note=“similar to gp43 of Escherichia coli
phage T4”

Bottom line:
Manual vs. Automated
• “Tortoises can tell you more about the road
than rabbits… ” Khalil Gibran
• “… but they may never reach the end!”
• The best approach?
– Human expert-based annotation

PATRIC/SEED/RAST: Main concept
One genome
All genomes

PATRIC/SEED/RAST: Main concept
One genome
All genomes
“Subsystems-based technologies were developed in the SEED with the view that
the interpretation of one genome can be made more efficient and consistent if
hundreds of genomes are simultaneously annotated in one subsystem at a time”

RAST: automated annotation

Subsystems-based tools
(Extended RAST family)
• (At least) Five ways to annotate a genome via RAST:
– RAST (http://rast.nmpdr.org)
• annotates online, saves your genome on server
– Use your favorite gene caller then upload gbk file to RAST
– myRAST (local)
• uses the server but you can edit offline)
– RASTtk (second-generation RAST)
• modular
• batch upload
– PATRIC
– Phanotator

RAST (http://rast.nmpdr.org)

RASTtk (RAST toolkit)

RAST Video demos available
• Find & watch:
– http://tutorial.theseed.org

PATRIC (https://www.patricbrc.org/)

IV. COMPARATIVE GENOMICS

Genomic pairwise comparisons
 EMBOSS Stretcher: http://emboss.bioinformatics.nl/cgi-
bin/emboss/stretcher N.B. genomes must be collinear
 BLASTN - NCBI
 ANI (Average Nucleotide Identity): http://enve-
omics.ce.gatech.edu/ani/
 GGDC 2.0 (Genome to Genome Distance Calculator):
http://ggdc.dsmz.de/distcalc2.php
 jSpeciesWS – ANI:
http://jspecies.ribohost.com/jspeciesws/

Proteomic pairwise comparisons
 CoreGenes
http://binf.gmu.edu:8080/CoreGenes3.5/
 tBLASTX
 Remember that protein sequence is more
conserved than DNA sequence; probably
useful for more distant relationships.

V. METADATA

Standards for metadata reporting
Roux, et al.

VI. VISUALIZATION

Servers & software
 BLAST Ring Image Generator (http://brig.sourceforge.net)
 CGView (http://wishart.biology.ualberta.ca/cgview)
 CGView Comparison Tool:
http://stothard.afns.ualberta.ca/downloads/CCT
 Circos (http://circos.ca)
 DNAPlotter:
(http://www.sanger.ac.uk/science/tools/dnaplotter)
 Easyfig (http://mjsull.github.io/Easyfig/)
 GenomeVx (http://wolfe.ucd.ie/GenomeVx)
 GView Server (https://server.gview.ca)
 progressiveMauve and ACT

EasyFig

CGView Comparison Tool

BLAST Ring Image Generator

Conclusion: The Kropinski wisdom

The Kropinski wisdom
1. Always use more than one tool.
2. Never blindly trust any automated (or manual)
process.
3. Use your eyes and hands: visual inspection/
manual proofreading, re-annotation
– Every apparently complicated file is still editable on
your favorite text editor (e.g., NotePad).
4. If you don’t know a gene’s function (if you
can’t convince your grandma), better keep it
unnamed than contribute to error propagation.

From Sequence to Knowledge (Tools for Phage Genome Annotation)

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