1. Assembly & Annotation at iPlant
Josh Stein (CSHL)
Matt Vaughn (TACC)
Dian Jiao (TACC)
Zhenyuan Lu (CSHL)
Nirav Merchant (U. Arizona)
Michael Schatz (CSHL)
Roger Barthelson (CSHL)
Cantarel et al Genome Research 18:188
Holt & Yandell BMC Bioinformatics 12:491

2. Maize Genome Project Genome Strategy 9 PI’s 2500 Mb 10 chromosomes
3-yr NSF funded project -- $30 M
Mapping
U. Arizona
Genome
2500 Mb
10 chromosomes
50,000 genes
Strategy
BAC-by-BAC
17,000 clones
Finish genic regions
9 PI’s
FPC map
Min. tiling path
BAC selection
Sequencing
Washington U.
6X shotgun
Auto finish
Manual finishing
GenBank c
Annotation
Repeat analysis
Gene prediction
Database
Browser
CSHL
Maizesequence.org

3. Technology Lowering Barriers

4. Assembly & Annotation at iPlant

5. Science. 2009 Nov 20;326(5956):1112-5. doi: 10.1126/science.1178534.
Complexity of Genomes
Science Nov 20;326(5956): doi: /science

6. Assembling a Genome 1. Shear & Sequence DNA
2. Construct assembly graph from overlapping reads
…AGCCTAGGGATGCGCGACACGT
GGATGCGCGACACGTCGCATATCCGGTTTGGTCAACCTCGGACGGAC
CAACCTCGGACGGACCTCAGCGAA…
3. Simplify assembly graph
4. Detangle graph with long reads, mates, and other links

7. Ingredients for a good assembly
Coverage
High coverage is required
Oversample the genome to ensure every base is sequenced with long overlaps between reads
Biased coverage will also fragment assembly
Read Coverage
Expected Contig Length
Read Length
Reads & mates must be longer than the repeats
Short reads will have false overlaps forming hairball assembly graphs
With long enough reads, assemble entire chromosomes into contigs
Quality
Errors obscure overlaps
Reads are assembled by finding kmers shared in pair of reads
High error rate requires very short seeds, increasing complexity and forming assembly hairballs
Amount of oversampling depends of read length, genome complexity
Current challenges in de novo plant genome sequencing and assembly
Schatz MC, Witkowski, McCombie, WR (2012) Genome Biology. 12:243

8. N50 size
Def: 50% of the genome is in contigs as large as the N50 value
Example: 1 Mbp genome
N50 size = 30 kbp
(300k+100k+45k+45k+30k = 520k >= 500kbp)
Note:
N50 values are only meaningful to compare when base genome size is the same in all cases
50%
1000
300
100 45 45 30 20 15 15 10 . . . . .

9. Attempt to answer the question: “What makes a good assembly?”
Organizers provided sequence data to assembly experts around the world
Assemblathon 1: ~100Mbp simulated genome
Assemblathon 2: 3 vertebrate genomes each ~1GB
Results demonstrate trade-offs assemblers must make
organized by UC Davis and UC Santa Cruz
“good framing problem”
Assemblathon 1: A competitive assessment of de novo short read assembly methods.
Earl, DA, et al. (2011) Genome Research. doi: /gr
Assemblathon 2: Evaluating de novo methods of genome assembly in three vertebrate species
Bradnam, KR. et al (2013) GigaScience 2:10 doi: / X-2-10

10. Final Rankings
organized by UC Davis and UC Santa Cruz
“good framing problem”
ALLPATHS and SOAPdenovo came out neck-and-neck followed closely behind by Celera Assembler, SGA, and ABySS
My recommendation for “typical” short read assembly is to use ALLPATHS
Single molecule sequencing becoming extremely attractive if you have access

11. Apps in Discovery Environment
Genome Assembly
Allpaths-LG
Soapdenovo2
ABySS
Velvet
Newbler
Ray
Contig analysis tools
With or without reference
sequence for comparison

12. Assembly Workflow Upload Reads Quality Assessment De novo Assembly
Minutes to Months
Quality Assessment
Minutes to Hours
An unfamiliar problem with familiar data
De novo Assembly
Hours to Days
Assembly Assessment
Minutes to Hours

13. Apps in Discovery Environment
(for sequencing studies)
Sequence Quality Control
FastQC
Fastx Toolkit
Suffixerator/Tallymer/mkindex
Sabre, Scythe, Sickle (paired end trimming)
SGA cleanup (paired end quality trimming)
Future plans
Sequence induction, assessment, and trimming pipeline
Mira contaminant detection and removal

14. QC: FastQC
An unfamiliar problem with familiar data

15. QC: Read Coverage
Reference:
Reads:
Errors
Coverage
Repeats

16. Wheat Genome (A. tauschi / CSHL)

17. QC: Mer counts Frag1.fq Frag2.fq FASTX_fastq-to-fasta
An unfamiliar problem with familiar data
Suffixerator
Suffixerator-Tallymer-mkindex
A new method to compute K-mer frequencies and its application to annotate large repetitive plant genomes
Kurtz S. Narechania A, Stein JC, Ware D. (2008) BMC Genomics. 9:517

18. Using Allpaths LG You must have at least 2 libraries
One overlapping fragment library, e.g. 100 bp reads with 180 bp spacing
One jumping mate-pair library, e.g bp spacing

19. reads unipaths assembly corrected reads doubled reads localized data
How ALLPATHS-LG works
reads
See Youtube:
corrected reads
doubled reads
localized data
local graph assemblies
global graph assembly
Oversimplified, actually fifty modules developed over the past six years and a bit cluttered. Laden with opportunities for improvement!
unipaths
assembly
Sante Gnerre et al (2010) PNAS 1513–1518, doi: /pnas

20. Where is the sample data?
ALLPATHS-LG in DE
180 bp
3500 bp
Data Source: GAGE Project

21. Where is the Allpaths LG App?
ALLPATHS-LG in DE
Where is the Allpaths LG App?

22. Fragment Reads
ALLPATHS-LG in DE

23. Jumping Reads
ALLPATHS-LG in DE

24. ALLPATHS-LG in DE
Run Settings

25. Running ALLPATHS-LG
An unfamiliar problem with familiar data

26. Parra G, Bradnam K, Korf I. (2007) Bioinformatics. 23 (9): 1061-1067.
Post-QC: CEGMA
An unfamiliar problem with familiar data
CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes
Parra G, Bradnam K, Korf I. (2007) Bioinformatics. 23 (9):

27. Resources iPlant Assembly Competitions
Assembly Competitions
Assemblathon:
GAGE:
Assembler Websites:
ALLPATHS-LG:
SOAPdenovo:
Celera Assembler:
Tools:
FastQC:
Tallymer:
CEGMA:

28. What Are Annotations?
Annotations are descriptions of features of the genome
Structural: exons, introns, UTRs, splice forms etc.
Coding & non-coding genes
Expression, repeats, transposons
Annotations should include evidence trail
Assists in quality control of genome annotations
Examples of evidence supporting a structural annotation:
Ab initio gene predictions
ESTs
Protein homology
It is especially important that all genome annotations include with themselves an evidence trail that describes in detail the evidence that was used to both suggest and support each annotation. This assists in quality csontrol and downstream management of genome annotations.
Now while many of you are likely already familiar with this, I will explain anyway

29. Secondary Annotation Protein Domains GO and other ontologies
InterPro Scan: combines many HMM databases
GO and other ontologies
Pathway mapping
E.g. BioCyc Pathway tools

30. Challenges in Plant Genome Annotation
Genomes are BIG
Highly repetitive
Many pseudogenes
Assembly contamination
Incomplete evidence
No method is 100% accurate

31. Options for Protein-coding Gene Annotation
Yandell & Ence. Nature Reviews Genetics 13, (May 2012) | doi: /nrg3174

32. Typical Annotation Pipeline
Contamination screening
Repeat/TE masking
Ab initio prediction
Evidence alignment (cDNA, EST, RNA-seq, protein)
Evidence-driven prediction
Chooser/combiner
Evaluation/filtering
Manual curation

33. MAKER-P Automated Pipeline
MPI-enabled to allow parallel operation on large compute clusters
Repeat Library
Ab initio prediction
Evidence
Collaboration with Yandell Lab

34. What is a GFF File?
Generic Feature Format

35. Quality Control evaluation of the MAKER-P and TAIR10 datasets using Annotation Edit Distance (AED).
Figure 1.MAKER-P provides automated means for QC. MAKER-P provides methods for automatic management and quality control of genome annotations, using metrics developed by the Sequence Ontology project. One of these metrics is AED. AED is calculated in the same manner as SN and SP, but in place of a reference gene model, the coordinates of the union of the aligned evidence is used instead. AED = 1 – AC, where AC = (SN + SP)/2. An AED of 0 indicates that the annotation is in perfect agreement with its evidence, whereas an AED of 1 indicates a complete lack of evidence support for the annotation. The left panel of this figure illustrates hypothetical cumulative AED distributions for 3 different annotated genomes. 95% of the annotations in a very well annotated genome, for example, have an annotation edit distance (AED) of less than 0.5 (illustrated in left panel above). This is true, for example of the human genome annotations. In the current release of the Arabidopsis annotations (TAIR10) 88% of the annotations have an AED of less than 0.5 (navy line)(see above right); thus the TAIR10 annotations are already quite good, but could be further improved. This value is increased to 98% when only TAIR10’s 4- and 5-star rated transcripts are considered in the analysis (blue line, right panel). When all TAIR10 gene models are passed to MAKER-P and processed using its update functionality to automatically revise them to better fit the evidence, AEDs drop (green line vs. navy line), indicating improvements in quality. De novo annotation with MAKER produced an annotation set in which 97% of the annotations have an AED of 0.5 (red line).
Better Quality Worse

36. MAKER-P at iPlant TACC Lonestar Supercomputer PAG 2014:
22,656 CPU cores on1,888 nodes
Genome
Assembly
Size (Mb)
CPU
Run Time
Arabidopsis thaliana
TAIR10
120
600
2:44
1500
1:27
Zea mays
RefGen_v2
2067
2172
2:53
Campbell et al. Plant Physiology. December 4, 2013, DOI: /pp
PAG 2014:
W559 - Annotation of the Lobolly Pine Megagenome—Jill Wegrzyn
20.15 Gb assembly—split into 40 jobs—216 CPU/job (8640 CPU total)—17 hours
P157 - Disease Resistance Gene Analysis on Chromosome 11 Across Ten Oryza Species
10 rice species (each w/12 chromosome pseudomolecules)
96 CPU per chromosome (1152 CPU total) ~ 2hr per genome

37. MAKER-P at iPlant Atmosphere: MAKER_2.28 (emi-F13821D0) Virtual image
MPI-enabled for parallel computing
Check out with up to 16 CPU
Tested with 4 CPU instance
Completed rice chr 1 in 8 hr 45 min

38. MAKER-P Tutorial

39. Annotation Post-Analysis
AED threshold
InterProScan
Comparative analysis, e.g. BLAST vs RefSeq proteins

40. Annotation Post-Analysis
InterProScan

41. Assembly & Annotation at iPlant

42. Additional MAKER-P Resources
MAKER-P: lab.org/software/maker-p.html
Repeat Library contstuction: iki/index.php/Repeat_Library_Construction-- Advanced
Pseudogene identification: .php/Protocol:Pseudogene