From Sequence to Knowledge: The Art & Science of Phage Genome Annotation. Ramy K. Aziz Cairo University

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1 From Sequence to Knowledge: The Art & Science of Phage Genome Annotation Ramy K. Aziz Cairo University

2 A helping hand through The Annotation Bottleneck From Sequence to Knowledge: PhAnToMe, RAST, and the Ultimate Kropinski Toolkit Compiled by: Andrew Kropinski and Ramy Aziz

3 Data & links: Online material Slides Old tutorials (more detailed, but missing latest ): Evergreen 2011: (Karin) Evergreen 2013: Evergreen 2015:

4 INTRODUCTION

5 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 The analysis bottleneck The Lavigne paradox

7 The analysis bottleneck The Lavigne paradox

8 Quick group activity Defining the question(s): How many of you have annotated a genome? How many phage genomes have you sequenced (or are in the process of sequencing)? a) None b) 1-5 c) 5-50 d) > 50 What is the single most pressing question you want to answer from genome analysis?

9 Begin with the end in mind (Covey, the 7 habits) DEFINING THE QUESTION(S)

10 What You Want Frameshift Incomplete: genome termini The goal: complete accurate Faulty assembly chimeric fragments

11 A process of reconstruction

12 Annotation à Reconstruction from genome from metagenome - complete - accurate frameshift Incomplete faulty assembly Credit: Andrew Kropinski Credit: Bas Dutilh

13 Annotation à Reconstruction from genome from metagenome - complete - accurate frameshift Incomplete faulty assembly Credit: Andrew Kropinski Credit: Bas Dutilh 21 July 2016 Phage Genomics - VoM 2016

14 A process of reconstruction Experimentally DNA TGATTGTGTGTTTGCGCAATGCG TGATTGGTCTNNNTCTCTTGCGCAATGCG ATGTGTATATATAGTGAGCTTGCCC GTCTCTCTNNNTCTCTTG

ATGTGTATATATAGTGAGCTTGCCC GTCTCTCTNNNTCTCTTG Computationally

15 A process of reconstruction Experimentally DNA TGATTGTGTGTTTGCGCAATGCG TGATTGGTCTNNNTCTCTTGCGCAATGCG ATGTGTATATATAGTGAGCTTGCCC GTCTCTCTNNNTCTCTTG Computationally TGATTGTGTGTTTGCGCAATGCG TGATTGGTCTNNNTCTCTTGCGCAATGCG ATGTGTATATATAGTGAGCTTGCCC GTCTCTCTNNNTCTCTTG

Assembly trna calling Validation (segmenter) From Sequence to Knowledge From raw sequence data to genome submission/ publication orienting Fixing frameshifts Gene finding/ ORF calling Annotation

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

17 Countless tools

18 Authority figures Andrew Kropinski Rob Lavigne Rob Edwards

19 General outline Part I: The Kropinski toolkit Tools approved and recommended by Andrew Kropinski ( from seq to pub Part II: SEED-based tools: The RAST family The PhAnToMe database/portal

20 The Kropinski Toolkit

21 What we want, according to Andrew Well characterized genome, in which, ideally we know: the location & function of all the genes the location of promoters & terminators the correct taxonomy PstI A PstI kb Viruses; dsdna viruses, no RNA stage; Caudovirales; Siphoviridae; T1virus

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

23 Desired outcome (2)

24 Desired outcome (3)

25 Desired outcome (4) Protein products of concern, particularly for those interested in phage therapy: Integrases Toxins PstI PstI kb A 30 32

26 Processes and Steps I. Primary analysis (QC/ pre-annotation proofreading: e.g., orient with BLASTN) II. Genome annotation Gene finding (ORF calling) Automated annotation Massaging (edition, functional assignment) III. Second dimension (regulatory elements) IV. Comparative genomics V. Metadata VI. Visualization

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

28 II. Genome Annotation AUTOMATED ANNOTATION

29 RAST (subsystems-based tools) Will be the major focus of this short tutorial The goal is: 1. Quick demo how to use RAST 2. Quick preview batch annotation in RAST 3. Optimize RAST for phage annotation 4. Demonstrate & discuss how to improve RAST output

30 RAST (subsystems-based tools) But, before getting there

31 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 2 Aug 2015 Phage Genomics - Evergreen 2015

32 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

33 What do I call my gene product (i.e. protein)? /product= UboA ; NrdA ; hypothetical protein SA5_0153/152 ; ORF184 (as bad as gp184); RNAP1 ; "32 kda protein Bad because they don`t mean anything to the casual (or informed) reader. Unless you are a bioinformatician or biostatistician be conservative in recording hits. Could you convince your grandmda?, if not list as a hypothetical protein but do take a stand putative DNA polymerase is cowardly

34 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

35 Consistent Nomenclature 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

36 Bottom line: Manual vs. Automated Turtles know the road better than rabbits Khalil Gibran but they may never reach the end! The best approach? Human expert-based annotation 2 Aug 2015 Phage Genomics - Evergreen 2015

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

38 IV. COMPARATIVE GENOMICS

39 Genomic pairwise comparisons EMBOSS Stretcher: N.B. genomes must be collinear BLASTN - NCBI ANI (Average Nucleotide Identity): GGDC 2.0 (Genome to Genome Distance Calculator): jspeciesws ANI:

40 Proteomic pairwise comparisons CoreGenes ( TBLASTX Remember protein sequence is more conserved than DNA sequence; probably useful for more distant relationships

41 VI. POLISH IT TO PUBLISH IT

functions) Introns and Inteins Subsystem assignment Refinement/ Secondary annotation loop Special purpose:

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

43 Servers & software BLAST Ring Image Generator ( CGView ( CGView Comparison Tool: Circos ( DNAPlotter: ( Easyfig ( GenomeVx ( GView Server ( progressivemauve and ACT

44 EasyFig

45 CGView Comparison Tool

46 BLAST Ring Image Generator

Why learn sequence database searching? Searching Molecular Databases with BLAST

Why learn sequence database searching? Searching Molecular Databases with BLAST What have I cloned? Is this really!my gene"? Basic Local Alignment Search Tool How BLAST works Interpreting search results