GCAT-SEEK Eukaryotic Genomics Workshop
Eukaryotic Genomics Breakout Session
Outline
Tues, Day 2
AM
Module 1. Genome assembly: Overview and Experimental design
Module 2. Linux tutorial: Some basics and software installation
Module 3. Genome assembly: Data quality and genome size estimation using Jellyfish
Module 4. Genome assembly: Data quality and error correction
Module 5. Genome assembly: Assembly algorithms
PM
Module 6. Genome annotation: Overview and manual annotation of a eukaryotic gene
Module 7. Genome annotation with Maker: Introduction and repeat finding
Module 8. Genome annotation with Maker: Gene finder training
Module 9. Genome annotation with Maker: Putting it all together
Weds, Day 3
AM
Module 10. Genome annotation with Maker: Processing results
Module 11. Genome annotation: Using a genome browser to observe evolutionary patterns
PM
TBA
Thurs, Day 4
AM
TBA
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Module 1. Genome assembly: Overview &
experimental design
Background
The ultimate goal of genome assembly is to completely construct intact chromosomes. Rapidly
decreasing cost per bp has made it possible to randomly sequence even entire large mammalian
genomes like that of human ( >3GBp) by densely covering the genome with 100X coverage in short
sequence reads. This is analogous to snow falling down on your yard, randomly coating all exposed
surfaces.
The fundamental problem of genome assembly is sequencing through repetitive DNA. One cannot
sequence through a repeat with reads that are shorter or the same size as the length of the repeat.
Here, using four 5 bp reads one can not determine whether the correct path is (1) or (2) above.
However, using paired end data one can determine a unique sequence because the insert size (15bp)
exceeds the length of the repeat (5bp). Note that given the data above, connecting the two contigs into
a single scaffold resulted in placement of two Ns.
Some repeats are long and abundant in eukaryotic genomes. Long interspersed nuclear elements
(Lines) are about 6Kbp long and comprise over 15% of the genome in humans. Short nuclear elements
(e.g. such as Alu elements) are just as abundant in humans, but are only about 350bp long.
In practice, in a genome sequencing project one aims for the attaining the largest and fewest DNA
pieces (known as contigs) with as few errors as possible. Errors in sequencing and assembly may result
in misjoins (or chimeras), where disparate parts of the genome are connected erroneously. Contigs may
be connected by paired read data to form scaffolds. Often there is a closely estimated distance between
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paired reads, but without information to completely fill in the gaps, resulting in stretches of Ns, or
unknown nucleotide sequence.
Recent genomes are considered “good” having N50 scores in the millions of bp. An N50 is the size of the
smallest scaffold such that 50% of the genome is contained in scaffolds of size N50 or larger (Salzberg et
al. 2012). Assemblies should have scaffolds with N50 sizes big enough to contain entire genes for the
organism of interest that can then be subject to comprehensive annotation. Gene size is proportional to
genome size as shown in the figure below from Yandell and Ence (2012). The figure shows that for a
species with a 1Gbp genome, gene size will be about 5Kbp. One would expect an assembly with an N90
of 5Kbp to contain about 90% of genes on single scaffolds in this case.
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Genome projects are of great usefulness in providing a centralized location for the research community
for that organism to map information. The following view of the UCSC genome browser
(http://genome.ucsc.edu) shows a slice of the human genome project. Many different tracks of data
have been mapped to this location, the beta globin gene, by the research community. In Module 11 you
will go to the UCSC genome browser and investigate evolutionary patterns at the Beta globin gene.
Draft genomes are more appropriate for some uses than others. Draft genomes are appropriately used
for determining gene sequences, including promoters, exons, introns, and other regulatory sequences.
They are also appropriate for determining variable sites such as single nucleotide polymorphisms and
insertion/deletions. They are less appropriate for determining copy number of genes because the
assembled sequence may not be complete, and repeats tend to “collapse” into one sequence given that
different sequences look the same to an assembler. Duplicated areas of genomes can be
underrepresented. For this reason the amount of assembled DNA may underestimate true genome
length. Because draft genomes come in many small pieces, determining gene order is difficult.
When planning a genome sequencing project some considerations regarding the genomes itself are
important in determining sequencing strategy. Planning for adequate sequencing coverage of the
genome in raw data is essential (>100x). Genome size is such an important consideration in designing a
sequencing project because of the cost of obtaining that much sheer data. Because the location of any
given sequence read is random, 100X coverage is necessary to ensure coverage of all regions of the
genome, except for difficult to sequence regions. To make matters worse, because organisms’ genes get
bigger as the genome gets bigger, better assemblies are needed in organisms with larger genomes to
achieve comprehensive gene annotation.
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Understanding %GC content is helpful to determine necessary coverage because some commercial
sequencing platforms, such as Illumina, have biases against certain nucleotides, (Illumina is somewhat
biased against AT coverage; Dohm et al. 2008). Different sequencing platforms have different biases in
error rates. One study showed that Illumina has highest A to C error rates, which was partly explained
because both are called by a red detection laser; Dohm et al. 2008). Illumina has low overall error rates
of about 0.1%, but error rates of 3% were estimated at the 3’ end of reads (Dohm et al. 2008).
Imbalance in GC content in genome content or in sequencing coverage will result in the need for higher
overall coverage (Kenney et al. 2010).
Species vary remarkably in percent of repetitive DNA, with some plants having greater than 80% of DNA
in repeats, leading to very large, difficult-to-sequence genomes. Species with larger genomes with more
repetitive DNA will require special library construction involving “jumping” or “mate-pair” libraries that
sequence the ends of large fragments, in addition to shorter paired-end libraries. Mate-pair sequencing
is important in connecting relatively distant contigs and making them into scaffolds. This is an essential
step to produce long scaffolds (millions of bp) in genomes with a large fraction of repetitive DNA. The
Mate-Paired sequencing strategy connects contigs across repetitive regions. The figure below shows the
difference between paired-end and mate-pair genomic DNA libraries. Sequencing of large mammalian
and plant genomes typically involves high (>40x) coverage using a series of fragment and mate pair
libraries of different sizes. For example for Giant Panda, the first large mammalian genome, fragment
sizes of 150bp, 500bp, 2kbp, 5kbp and 10kbp were used to produce 96x coverage of a 2.4Gbp genome
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(Li et al 2010; see Schatz et al. 2010 for a review of early shotgun assemblies).
Fragment library paired-end sequencing
DEF
ABC
180bp
Produces the sequence reads
Forward: ABC
Reverse: FED
Orientation: “Innie” or “Normal Forward/Reverse”
Mate paired sequencing
ABC
DEF
3500 bp
Produces the sequence reads
Forward: DEF
Reverse: CBA
Orientation: Outie or “Reverse”
Circularize
DEF ABC
DEF ABC
Cut
DEF
ABC
Sequence
ABC
DEF
Orientation: Outie
F IGURE 1. F ORMATION OF PAIRED - END AND MATE - PAIR LIBRARIES . P AIRED - END LIBRARIES PRODUC E FRAGMENTS IN NORMAL
FORWARD - REVERSE OR “ INNIE ” ORIENTATION AND MATE - PAIR LIBRARIES PRODUCE FRAGMENTS IN AN ORIENTATION THAT IS REVERSED
COMPARED TO PAIRED - END LIBRARIES , CALLED “ OUTIE .”
Other genome sequencing experimental design considerations include cost per bp, error rates, error
types, single end, paired end, or mate pair sequencing sequencing, and read length. For deNovo
genome assembly mate pairs are essential to get past repeats. Long reads (from Pac Bio) or jumping
libraries are often combined with short read data sets to aid in increasing assembly. For bacterial-sized
genomes, decent assemblies can be performed from 2X250bp MiSeq Runs (Magoc, Pabinger, Canzar et
al. 2013).
A summary of important characteristics for different sequencing platforms is summarized at the
following web page: http://www.molecularecologist.com/next-gen-fieldguide-2013/. So far it has been
updated every year. Go there now. As of 2013, Tables 1a and 1b show that for plant and animal
genome sequencing and resequencing, the Illumina HiSeq and Miseq get the highest grades. This is due
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to low cost per bp, large throughput, and low error rates. The long reads of the MiSeq would make it
the favored choice for small eukaryotic genomes due to reduced cost concerns and ability to sequence
through small repeats like SINEs. Table 2 shows that Illumina platforms have the lowest cost per bp.
Table 3 shows that Illumina data has among the lowest error rates of current machines, but not the
absolute lowest.
Goals


Choose and justify the appropriate methods for whole genome sequencing using Next Gen
sequencing technology
Apply NextGen sequencing methodologies to solve their own research questions
V&C core competencies addressed
Apply process of science: Design of genome sequencing approach
GCAT-SEEK sequencing requirements
None
Computer/program requirements for data analysis
Web browser
Protocols
None
Assessment
See essays below.
Time line of module
One hour of lecture.
Discussion topics for class
Q. Does a nucleotide of sequence using the Illumina platform cost closer to 1 cent per bp or 1 cent per
million bp?
Q. Which sequencing platform has the absolute lowest sequencing error rates? Why do you think it is
not winning the sequencing battle to Illumina?
Q. Design a genome sequencing project for a mammal for 100 X coverage. Include as many details
about platform and cost as possible.
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Q. Design a sequencing project for a bacteria for 100 X coverage. Include as many details about
platform and cost as possible.
Q. Look up repeat content for several bacteria, plants, animals, and fungi. How would this affect a
genome sequencing approach?
Q. Why is it important to sequence genomes?
References
Literature cited
Dohm JC, Lottaz C, Borodina T, Himmelbauer H. 2008. Substantial biases in ultra-short read data sets
from high-throughput DNA sequencing. Nucleic Acids Res, 36:e105+.
Kenney DR, Schatz MC, Salzberg SL. 2010. Quake:quality-aware detection and correction of sequencing
errors. Genome Biology 11:R116
Li et al. 2010. The sequence and de novo assembly of the giant panda genome. Nature. 463: 311-317
Magoc T, Pabinger S, Canzar S, et al. 2013. An evaluation of genome assemblers for bacterial organisms.
Bioinformatics online advanced access. [The GAGE-B project]
Salzberg SL, Phillippy AM, Zimin A, et al. 2012. GAGE: a critical evaluation of genome assemblies and
assembly algorithms. Genome Res 22: 557-567. [important online supplements!]
Shatz MC, Delcher AL, Salzberg SL. 2010. Assembly of large genomes using second-generation
sequencing. Genome Res 20: 1165-1173.
Yandell M, Ence D. 2012. A beginner's guide to eukaryotic genome annotation. Nature Reviews
Genetics 13:329-342.
Further reading
ASSEMBLATHON
The Assemblathon (http://assemblathon.org)
Earl D, Bradnam K, St John J, et al. 2011. Assemblathon 1: a competitive assessment of de novo short
read assembly methods. Genome Res 21:2224-2241
Bradnam KR, Fass JN, Alexandrov A, et al. Assemblathon 2: evaluating de novo methods of genome
assembly in three vertebrate species. arXiv preprint arXiv:1301.5406
Other Assembly Reviews
Alkan C, Sajjadian S, Eichler E Limitations of next-generation genome sequence assembly. 2011. Nat
Methods 8:61-65
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Birney E. 2011. Assemblies: The good, the bad, the ugly. Nature Methods. 8:59-60.
Compeau PEC, Pevzner PA, Tesler G. 2011. How to apply de Bruijn graphs to genome assembly. Nature
Biotechnology. 29:987-991.
Miller JR, Koren S, Sutton G. 2010. Assembly algorithms for next-generation sequencing data.
Genomics 95: 315-327.
Paszkiewicz K, Studholme DJ. 2010. De novo assembly of short sequence reads. Briefings in
Bioinformatics. 11: 457-472.
Pop M. 2009. Genome assembly reborn: recent computational challenges. Briefings in Bioinformatics.
10:354-366.
Zhang et al. 2011. A practical comparison of De Novo genome assembly software tools for nextgeneration sequencing technologies. PLos ONE 6: e17915.
Sequencing Technology Updates
http://www.molecularecologist.com/next-gen-fieldguide-2013/
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