1. Today’s Lecture Topics
Whole genome sequencing
Shotgun sequencing method
Sequencing the human genome
Functional/comparative genomics
Transcriptome & RNA-Seq
Proteomics

2. Shotgun DNA sequencing:
Sequence the entire genome rapidly.
No requirement for a high resolution linkage or physical map.
Just break the genome up into small pieces, sequence it, assemble, and find the gene of interest/do the bioinformatic analysis later.
Reverses the way genetic studies proceeds.
It used to be we had to find the gene first to study the cause of the disease.
Now we can study genes we didn’t even knew exist.

5. Fig. 8.13, Shotgun sequencing a genome

6. Shotgun DNA sequencing---dideoxy method:
Begin with genomic DNA and/or kb BAC clone library.
Mechanically shear DNA into ~2 kb bp overlapping fragments.
Isolate on agarose, purify, and clone into standard plasmid vectors.
Sequence ~500 bp from each end of each 2 kb insert.
Sequence from the middle 1,000 bp of each insert is obtained from overlapping clones.
Repeat the process so that 4-5x the total length of the genome is sequenced (dideoxy sequencing is 99.99% accurate).
Results in a contig library with ~97% genome coverage (the missing 3% is composed mostly of repeated DNA sequence).
Assemble hundreds of thousands of overlapping ~500 bp sequences with fast computers operating in parallel (supercomputer).

8. How to deal with the repeated DNA - 2 kb clones present a problem, solved with 10 kb clones:
Many repeated sequences in the genome are in regions spanning ~5 kb in size.
So many 2 kb clones contain entirely repeated DNA.
Results in a dead stop in the assembly, because there is ambiguity about where each clone goes.
Repeated sequences occur all over the genome.
On average, 10 kb clones contain less repeated DNA sequence.
Solution is to create and sequence a 10 kb clone library derived from the same genomic DNA or BAC library.
Complete genome coverage requires combining the sequences from the 2 kb & 10 kb libraries.

9. Genome
Date
Size
Institute
Method
Homo sapiens mtDNA
1981
16,159 bp
(1 circular) -
Haemophilus influenzae (bacteria)
1995
1,830,137 bp
TIGR
Shotgun
Mycoplasma genitalium
(bacteria)
580,070 bp
Escherichia coli (bacteria)
1997
4,639,221 bp
University of Wisconsin-Madison
Methanococcus jannaschii
(Archaeon)
1996
1,739,933 bp
(3 circular)
DOE
Saccharomyces cerevisiae (yeast)
12,067,280 bp
(16 linear)
100+ labs
Mapping
Caenorhabditis elegans
(nematode)
1998
97,000,000 bp
(6 linear)
Consortium

10. Genome
Date
Size
Institute
Method
Drosophila melanogaster
(fruit fly)
2000
180,000,000 bp
UC Berkley
Celera Genomics
Shotgun w/BAC map
Arabidopsis thaliana
(angiosperm)
125,000,000 bp
(5 linear)
Consortium
Homo sapiens
(human)
3,400,000,000 bp
Human Genome Project &
Mapping & Shotgun

11. Sequencing the human genome:
Two major players:
Human Genome Project (HGP):
Publicly funded international consortium (NIH, DOE, etc.)
Francis Collins, National Human Genome Res. Inst. (NHGRI)
Began in U.S. in 1990 with a goal of 15 years
Genetic and physical mapping approach + dideoxy sequencing
Celera Genomics Corporation (CRA):
Spin-off of Applied Biosystems (ABI)
J. Craig Venter, CEO
Created in 1998 with a goal of 3 years
Direct shotgun approach + dideoxy sequencing (+ HGP’s maps for validation)
Both groups collected blood and sperm samples from anonymous male and female donors of different ethnic backgrounds.

12. J. Craig Venter Celera Genomics
Francis Collins
Human Genome Project

14. Milestone: 26 June
White House press conference with Bill Clinton:
HGP:
Started 1990
~22.1 billion nucleotides of sequence data
7-fold coverage
Unfinished (24% completely finished, 50% near-finished)
Celera:
Started 1998
~14.5 billion nucleotides of sequence data
4.6-fold coverage
Complete assembled genome with >99% coverage
First assembled draft of human genome simultaneously published in Nature & Science 15 & 16 February 2001 (Nature published 1 day earlier).

15. How did Celera et al. assemble the sequences using shotgun methods?
Method A:
Assembly of 26.4 million 550 bp sequences  4.6-fold coverage, without reference to a physical map of any kind.
Covered >99% of the genome.
500 million trillion base-to-base comparisons.
20,000 CPU hours (833 CPU days) on a year 2000 supercomputer.
Method B:
Used BAC clone scaffold (combined lots of smaller maps) to validate the whole genome direct shotgun assembly approach.
Also helped resolved ambiguities resulting from the assembly of short repeated DNA fragments.

16. Features of the human genome:
32,000 genes estimated (50, ,000 were predicted).
Not many more genes than Drosophila, and only 50% more genes than Caenorhabditis elegans (nematode worm).
Only 1-1.5% of the genome codes for protein.
50% of the sequence is repeated DNA.
Humans share 223 genes found in bacteria, but not yeast, nematodes, or fruit flies.

17. Now it requires only a couple $1000s and is done in 2 days.
First human genome required $1 Billion USD and 13 years (or 2 years by Celera shotgun sequencing).
Now it requires only a couple $1000s and is done in 2 days.

18. Next-generation shotgun genome sequencing:
The shotgun method is fundamentally the same, but uses pyrosequencing and shorter read lengths (~150 bp paired-ends on Illumina).
bp fragments + mate-pairs of 2-12 kb to aid assembly and increase N50 (avg. scaffold length).
The throughput has increased and the cost has decreased.
Not uncommon to assemble trillions of sequence reads.
Some things to consider:
If error rates are high (454, Illumina) 30-50x genome sequencing is required to get a good genome.
If error rates are low (SOLiD, Ion Torrent) 4-5x coverage is sufficient.
Costs have been falling from $10K to $1K.

19. Sequence  Contig  Scaffold (contig of contigs)

20. Scaffold with two small gaps and one large gap bridged by mate pair
with paired-end sequencing
Larger the scaffolds, larger the N50s
xxxxx xxxxx
     xxxxx xxxxx
        xxxxx xxxxx
                                    xxxxx xxxxx
                                                                                                      xxxxx xxxxx
                                                                                                            xxxxx xxxxx
                                                                                                              xxxxx xxxxx
mate pair

22. How much data storage does 1 human genome require?
Sequencing is no longer the primary need; data storage/retrieval and computational needs are outpacing everything else.
How much data storage does 1 human genome require?
About 1.5 GB (2 CDs) if your stored only one copy of each letter.
For the raw format containing image files and base quality data 2-30 TB are required.
30-50x coverage requires more data storage capacity.
Sequence + quality scores is compressed to format called FASTQ.
@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT +
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65

23. Illumina Sequence Identifiers
FASTQ
'!' represents the lowest quality while '~' is the highest.
Left-to-right increasing order of quality (ASCII 90 characters):
Illumina Sequence Identifiers
@EAS139:136:FC706VJ:2:2104:15343: :Y:18:ATCACG
Bar code at front of each sequence that allows you to
label each sequence (e.g. individual, population, etc.)

24. Sequence assembly & genotyping
Trimming and filtering sequences based on base quality scores  Aligning reads to a reference genome  Genotyping to determine homozygous & heterozygous SNPs

25. Post-genome sequencing era is very different:
Classical genetics studies started with a phenotype and set out to identify the gene.
But we now have the ability to start with a complete genome and set out to identify the phenotype.
Large data sets required many computational and mathematical tools, which requires strong bioinformatics skillsets (Perl, Python, R, etc.).
Lots of applications:
Identify genes within genomic DNA sequences.
Align and match homologous gene sequences in databases and seek to determine function.
Predict structure of gene products.
Describe interactions between genes and gene products.
Study gene expression.

26. 1. Identifying genes in DNA sequences:
First step is annotation = identification and description of putative genes and other important sequences.
Open reading frames (ORFs)
ORF = potential protein coding sequence that begins with a start codon and ends with a stop codon.
ORFs come in all sizes.
Not all ORFs encode proteins (6-7% do not in yeast).
ORFs with introns can require sophisticated computer algorithms to detect (especially if there are many introns or introns are particularly long).

27. 2. Homology searches to assign gene function:
Homology search = identify gene function by searching database.
Similarities reflect evolutionary relationships and shared function.
Homology searches are performed for nucleotides and amino acids using BLAST = Basic Local Alignment Search Tool.
GenBank’s BLAST site:
Example, human mtDNA control region sequence:
TTCTCTGTTCTTCATGGGGAAGCAGATTTGGGTACCACCCAAGTATTGACTCACCCACAACAACCGCTATGTATTTCGTACATTACTGCCAGCCACCATGAATATTGCACGGTACCATAAATACTTGACCACCTGTAGTACATAAAAACCCAATCCACATCAAAA

28. (2006)Jiawei Han & Micheline Kamber

29. Fig. 9.2, Summary of genes in the yeast genome.

30. 3. Gene function can be identified and studied in other ways:
Gene knockout approach = systematically delete different genes and observe the phenotypes (PCR + cloning is one method).
Synthesize recombinant proteins with modified amino acid sequence and expressed in E. coli.
Test effects of mutations that don’t exist in nature.

31. Study the transcriptome = complete set of mRNAs in a cell
mRNAs are not stable, but types and levels change with different experimental conditions.
Sample mRNA at experimental intervals and convert to cDNA using reverse transcriptase.
Probe unknown cDNAs with DNA microarray of PCR-generated ORF sequences (requires known sequence for each probe).
Or better yet, sequence the entire transcriptome using:
RNA-Seq = Whole Transcriptome Shotgun Sequencing of all expressed RNAs.
Sequencing of ribosome-bound mRNA for monitoring in-vivo translation.

32. Fig. 9.7b, Microarray study of gene expression

34. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments
Nicholas T Ingolia, Gloria A Brar, Silvia Rouskin, Anna M McGeachy & Jonathan S Weissman
Nature Protocols 7, 1534–1550 (2012) doi: /nprot

35. “Proteomics”:
Proteome = complete set of expressed proteins in a cell
Major goals of proteomics:
Identify every protein, isolate and purify.
Determine the sequence and structure of each protein (and its function).
Create a database with the sequence of each protein.
Analyze protein levels and interactions in different cell types, at different times, and at different stages of development.
Rationale:
Genes are two-steps removed from disease (DNA  mRNA  protein).
Most gene products involved in disease are composed of protein.
Understanding protein means understanding disease.

38. “Systems Biology”
Computational and mathematic modeling of complex biological systems---Wikipedia
Requires integration of genomic, proteomic, and metabolic data.