1. Very important to know the difference between the trees!
Species tree vs. gene/protein tree
Trees can be very different, since genes can have their own histories
Very important to know the difference between the trees!
a. Gene tree is based a set of orthologous genes (i.e. related by a common ancestor)
Often (but certainly not always) the gene tree is similar to the species tree
b. Species tree is meant to represent the historical relationship between species.
Want to build on characters that reflect time since divergence:
In the genomic age, often use as many genes as possible (hundreds to thousands)
to generate a species tree: Phylogenomics

2. Phylogenomics: Using Whole-genome information to reconstruct
the Tree of Life
Several approaches:
1. Concatonate many gene sequences and treat as one
Use a ‘super matrix’ of variable sequence characters
2. Construct many separate trees, one for each gene, and then compare
Often construct a ‘super tree’ that is built from all single trees
3. Incorporate non-sequence characters like synteny, intron structure, etc.
The goal is to use many different # and types of characters
to avoid being mislead about the
relationship between species.
Now recognized that different regions of the genome can have distinct histories.

3. A few other key basic concepts:
Selection acts on phenotypes, based on their fitness cost/advantage, to affect
the population frequencies of the underlying genotypes.
In the case of DNA sequence:
Neutral substitutions = no effect on fitness, no effect on selection
Given a ~constant mutation rate, can convert the # of substitutions into
time of divergence since speciation = molecular clock theory.
Deleterious substitutions = fitness cost
* These are removed by purifying (negative) selection
Advantageous substitutions = fitness advantage
* These alleles are enriched for through adaptive (positive) selection

4. Evolutionary genomics relies on one or more quality genome sequences
Quality of a genome sequence can dramatically affect evolutionary interpretations
Bad genome = bad evolutionary inference
Therefore, it’s important to know what makes a good genome sequence

5. Anatomy of a Genome Project
Sequencing
De novo vs. ‘resequencing’
Sanger WGS versus ‘next generation’ sequencing
High versus low sequence coverage
Assembly
Draft assembly
Gap closure
Annotation
Gene, intron, RNA prediction
De novo vs. homology-based prediction
Assessing confidence
Comparison
Comparing gene content, lineage specific gene loss, gain, emergence
Comparing genome structure (chromosomes, breakpoints, etc)
Comparing evolutionary rates of change (rates of amino-acid, nucleotide substitution)

6. Sequencing Approaches
Old school: Sanger Whole Genome Shotgun (WGS)

7. The coverage of a genome = average coverage across all base pairs
Overlapping sequencing ‘reads’
are assembled into a ‘contig’
10-fold
representation
at this point
2-fold
representation
at this point
The coverage of a genome = average coverage across all base pairs
8 - >10-fold is typically considered high coverage
1-3-fold is considered low coverage
** Even high average coverage can include ‘gaps’ (i.e. regions with NO coverage)
See Lander-Waterman formula (poisson distribution that incorporates the
number and length of reads, size of genome, coverage, and amount of overlap between reads)
For 500 Mb target, 600bp read length, 5X coverage: ~29k gaps; 10X coverage: 393 gaps

8. Sequencing Approaches
Advantages of Sanger Whole Genome Shotgun (WGS)
* High quality sequence data
* Individual sequence reads are long (~1,000 bp)
* WGS is less work than map-based sequencing
Disadvantages of Sanger Whole Genome Shotgun (WGS)
* Still a lot of processing involved
* Sanger sequencing is expensive and slow (gel-based sequencing)
* True WGS sequencing requires good sequencing to get assembly to work

9. Sequencing Approaches
New technology: ‘Next generation’ sequencing
Includes ‘454’, Illumina/Solexa, SOLiD, and other types of sequencing
Advantages:
* New technology is much cheaper per genome
* Generates a huge amount of sequence per run
Disadvantages:
* Has a higher sequencing error rate per base pair
* Generates short reads ( bp) - more challenging assembly
* Generates a huge amount of sequence (and massive data files) per run

10. Several different ‘next-generation’ sequencing methods
454: emulsion sequencing per well: > 500bp read length
SOLiD: emulsion amplification, bead attachment to solid surface,
Ligation-based sequencing interrogates each base in 2 ligation reactions

11. Solexa (Illumina) Sequencing >100 bp read length

12. Solexa (Illumina) Sequencing >100 bp read length

13. Solexa (Illumina) Sequencing >100 bp read length

14. Next-generation (deep) sequencing
Very high (>100X) coverage
Much cheaper per bp covered
Rapid improvements in technology (including single-molecule sequencing)
But
Much higher error rate (~1%)
Short reads cause assembly challenges
Some require prior amplification
Sequence-specific bias in sequencing efficiency
For 500 Mb target, 100bp read length, 5X coverage: ~168k gaps; 10X coverage: 2,250 gaps

15. Anatomy of a Genome Project
Sequencing
De novo vs. ‘resequencing’
Sanger WGS versus ‘next generation’ sequencing
High versus low sequence coverage
Assembly
Draft assembly
Gap closure
Annotation
Gene, intron, RNA prediction
De novo vs. homology-based prediction
Assessing confidence
Comparison
Comparing gene content, lineage specific gene loss, gain, emergence
Comparing genome structure (chromosomes, breakpoints, etc)
Comparing evolutionary rates of change (rates of amino-acid, nucleotide substitution)

16. Sanger WGS de novo assembly
‘reads’
‘contigs’
‘scaffold’
Goal is to have no gaps & complete scaffolds (chromosomes)
Challenges:
Some regions difficult to sequence through (centromeres, heterochromatin, etc)
Repetitive regions make assembly difficult/ambiguous

17. ‘Next-generation sequencing’ de novo assembly
** Short read length a real challenge

18. Matching to a ‘reference’ genome
‘Next-generation sequencing’ de novo assembly
** Short read length a real challenge OR
Matching to a ‘reference’ genome
* paired-end reads
Challenges: Can have lots of gaps, miss any new sequence not in the reference,
repetitive regions not sequenced well, can totally miss structural rearrangements

19. Anatomy of a Genome Project
Sequencing
De novo vs. ‘resequencing’
Sanger WGS versus ‘next generation’ sequencing
High versus low sequence coverage
Assembly
Draft assembly
Gap closure
Annotation
Gene, intron, RNA prediction
De novo vs. homology-based prediction
Assessing confidence
Comparison
Comparing gene content, lineage specific gene loss, gain, emergence
Comparing genome structure (chromosomes, breakpoints, etc)
Comparing evolutionary rates of change (rates of amino-acid, nucleotide substitution)

20. Genome Annotation: predicting genetic features
‘Simplest’ predictions: Open Reading Frames (ORFs)
- De novo predictions: based on expectation of what ORFs should look like
GCGCTTACGTTATCTGCAATATGTCTTCGAACGATTCGAACGATACCG
ACAAGCAACATACACGTCTGGATCCTACCGGTGTGGACGACGCCTACA
TTCCTCCGGAGCAGCCGGAAACAAAGCACCATCGCTTTAAAATCTCTA
AAGACACCCTGAGAAACCACTTTATCGCTGCGGCCGGTGAGTTCTGCG
GCACATTCATGTTTTTATGGTGCGCTTACGTTATCTGCAATGTCGCTA
ACCATGATGTCGCATAACCGAGATGAGATGATAAAAAA

21. Genome Annotation: predicting genetic features
‘Simplest’ predictions: Open Reading Frames (ORFs)
- De novo predictions: based on expectation of what ORFs should look like
GCGCTTACGTTATCTGCAATATGTCTTCGAACGATTCGAACGATACCG
ACAAGCAACATACACGTCTGGATCCTACCGGTGTGGACGACGCCTACA
TTCCTCCGGAGCAGCCGGAAACAAAGCACCATCGCTTTAAAATCTCTA
AAGACACCCTGAGAAACCACTTTATCGCTGCGGCCGGTGAGTTCTGCG
GCACATTCATGTTTTTATGGTGCGCTTACGTTATCTGCAATGTCGCTA
ACCATGATGTCGCATAACCGAGATGAGATGATAAAAAA
Features of ORFs used in computational predictions:
* Start with ATG
* End with stop codon (e.g. TAA)
* Should be in one frame (i.e. length divisible by 3 for each codon)
* Have a size range (max. size can be >10 kb, min size can be 30 bp;
median is probably ~few kb depending on organism)

22. Genome Annotation: predicting genetic features
‘Simplest’ predictions: Open Reading Frames (ORFs)
- De novo predictions: based on expectation of what ORFs should look like
GCGCTTACGTTATCTGCAATATGTCTTCGAACGATTCGAACGTCGAAT
AGACGATGAGACGAGATAGAGCGAGCAAAAGGTAGGATACCGACAA
GCAACATACACGTCTGGATCCTACCGGTGTGGACGACGCCTACATTCC
TCCGGAGCAGCCGGAAACAAAGCACCATCGCTTTAAAATCTCTAAAGA
CACCCTGAGAAACCACTTTATCGCTGCGGCCGGTGAGTTCTGCGGCAC
ATTCATGTTTTTATGGTGCGCTTACGTTATCTGCAATGTCGCTAACCA
TGATGTCGCATAACCGAGATGAGATGATAAAAAA
Many ORFs have introns - splice junction signals are short and variable = difficult to predict.

23. Genome Annotation: predicting genetic features
‘Simplest’ predictions: Open Reading Frames (ORFs)
- De novo predictions: based on expectation of what ORFs should look like
- Homology-based assignments: find sequences homologous to known ORFs/proteins
Met Ser Ser Gln Asp Ser Asn Asp Ser Asp Lys Gln …
Met Ser Ser Ans Asp Ser Asn
GCGCTTACGTTATCTGCAATATGTCTTCGAACGATTCGAACGTCGAAT
AGACGATGAGACGAGATAGAGCGAGCAAAAGGTAGGATACCGACAA
GCAACATACACGTCTGGATCCTACCGGTGTGGACGACGCCTACATTCC
TCCGGAGCAGCCGGAAACAAAGCACCATCGCTTTAAAATCTCTAAAGA
CACCCTGAGAAACCACTTTATCGCTGCGGCCGGTGAGTTCTGCGGCAC
ATTCATGTTTTTATGGTGCGCTTACGTTATCTGCAATGTCGCTAACCA
TGATGTCGCATAACCGAGATGAGATGATAAAAAA
Asp Thr Asp Lys Gln ..

24. Genome Annotation: predicting genetic features
‘Simplest’ predictions: Open Reading Frames (ORFs)
- De novo predictions: based on expectation of what ORFs should look like
- Homology-based assignments: find sequences homologous to known ORFs/proteins
- Matches to cDNA library or RNA transcripts from sequencing
RNA transcript
GCGCTTACGTTATCTGCAATATGTCTTCGAACGATTCGAACGTCGAAT
AGACGATGAGACGAGATAGAGCGAGCAAAAGGTAGGATACCGACAA
GCAACATACACGTCTGGATCCTACCGGTGTGGACGACGCCTACATTCC
TCCGGAGCAGCCGGAAACAAAGCACCATCGCTTTAAAATCTCTAAAGA
CACCCTGAGAAACCACTTTATCGCTGCGGCCGGTGAGTTCTGCGGCAC
ATTCATGTTTTTATGGTGCGCTTACGTTATCTGCAATGTCGCTAACCA
TGATGTCGCATAACCGAGATGAGATGATAAAAAA …

25. Genome Annotation: predicting genetic features
Other Predictions:
* Open Reading Frames (ORFs)
* Non-coding RNAs (tRNAs, rRNA, other small RNAs, miRNAs, etc
* Regulatory elements (ENCODE project)
* Transposable elements (TEs)
* Origins of DNA replication

26. Anatomy of a Genome Project
Sequencing
De novo vs. ‘resequencing’
Sanger WGS versus ‘next generation’ sequencing
High versus low sequence coverage
Assembly
Draft assembly
Gap closure
Annotation
Gene, intron, RNA prediction
De novo vs. homology-based prediction
Assessing confidence
Comparison
Comparing gene content, lineage specific gene loss, gain, emergence
Comparing genome structure (chromosomes, breakpoints, etc)
Comparing evolutionary rates of change (rates of amino-acid, nucleotide substitution)

27. In what ways can a bad genome sequence affect the following:
Comparisons of:
* Genome size, organization (chromosomes/plasmids), structure
* Gene/ncRNA content: number of genes, duplicates, size of gene families, etc
* Sequence differences related to: gene evolution, regulatory evolution
* RNA & protein abundance across species, for all RNAs/proteins