1. Introduction to Genetic Analysis
Griffiths • Wessler • Carroll • Doebley
Introduction to
Genetic Analysis
ELEVENTH EDITION
CHAPTER 14
Genomes and Genomics
© 2015 W. H. Freeman and Company

2. CHAPTER OUTLINE 14.1 The genomics revolution
14.2 Obtaining the sequence of a genome
14.3 Bioinformatics: meaning from genomic sequence
14.4 The structure of the human genome
14.5 The comparative genomics of humans with other species
14.6 Comparative genomics and human medicine
14.7 Functional genomics and reverse genetics

3. Genomes and Genomics

4. Dideoxy (Sanger) sequencing and now several
DNA Sequencing
Dideoxy (Sanger) sequencing and now several
next generation sequencing (NGS) methods are used for whole-genome sequencing (WGS). Thus, Traditional WGS and Next Gen WGS.
See video:

5. Genomics: Completed genomes as 2018
 Currently the genomes of over 5,000 eukaryotes and 134,000 prokaryotes are sequenced.
This generates large amounts of information to be handled by individual computers.

6. Cloning/libraries used in traditional WGS BAC, YAC and ESTs
BAC = bacterial artificial chromosome
150 kb, replicate in E.coli
YAC = yeast artificial chromosome
150 kb -1.5 Mb, replicate in yeast
EST = expressed sequence tags
essentially cDNA sequences; reveals exons

7. The logic of obtaining a genome sequence

8. End reads from multiple inserts may be overlapped to produce a contig

9. Pyrosequencing reactions take place on beads in tiny wells

10. Pyrosequencing reactions take place on beads in tiny wells

11. Pyrosequencing reactions take place on beads in tiny wells

12. Pyrosequencing is based on detecting synthesis reactions

13. Paired-end reads may be used to join two sequence contigs

14. Strategy for whole-genome shotgun sequencing assembly

15. How to handle the large amount of information?
Drew Sheneman, New Jersey--The Newark Star Ledger
Answer: bioinformatics and Internet

16. Annotation-the identification of all the genes and functional elements of a genome
ORFs – open reading frames
Various binding sites for proteins and RNAs
ESTs- expressed sequence tags
Based on mRNAs
Comparative genomics

17. Bioinformatics history
In1960s: the birth of bioinformatics
IBM 7090 computer
The 1960s marked the beginning of bioinformatics. Prior to the advent of high-level computer languages in 1957, programmers needed a detailed knowledge of a computer’s design and were forced to use languages that were unintuitive to humans. High-level computer languages allowed computer scientists to spend more time designing complex algorithms and less time worrying about the technical details of the particular computer model they were using. By the 1960s, mainframe computers like the one pictured in the slide were becoming common at universities and research institutions, giving academics unprecedented access to computers. (As useful as these computers were, they filled entire rooms and had processing power far below that of consumer-grade personal computers today!) Margaret Oakley Dayhoff and colleagues took advantage of these developments and the accumulation of protein sequence data to create some of the first bioinformatics applications. For example, Dayhoff wrote the first computer program to automate sequence assembly, enabling a task that previously took human workers months to be accomplished in minutes. She and her colleagues also published (in paper form) the first protein sequence database and performed many groundbreaking studies regarding phylogeny and scoring sequence comparisons. For these reasons, she is considered one of the great pioneers of computational biology and bioinformatics.
Margaret Oakley Dayhoff created:
The first protein database
The first program for sequence assembly
There is a need for computers and algorithms that allow:
Access, processing, storing, sharing, retrieving, visualizing, annotating…

18. The information content of the genome includes binding sites
The information content of the genome includes binding sites. A gene within DNA may be viewed as a series of binding sits for proteins and RNAs.

19. cDNAs and ESTs reveal exons or gene ends in genome searches

20. Genome searches hunt for various binding sites

21. Many forms of evidence are integrated to make gene predictions

22. DNA (nucleotide sequences) databases
They are big databases and searching either one should produce
similar results because they exchange information routinely.
-GenBank (NCBI):
-Arabidopsis: (TAIR)
Specialized databases:Tissues, species…
-ESTs (Expressed Sequence Tags)
~at NCBI
~at TIGR

23. Comparative genomics
Comparative genomics considers genomes of closely and more distantly related species for evolutionary insight.
BLAST – basic local alignment and search tool; identifies closely related DNA/gene sequences
(see )
Homologs-closely related genes
Orthologs-closely related genes in two or more genomes inherited from a common ancestor
Paralogs-closely related genes within a genome as a result of gene duplication

24. Question cDNA sequences Protein sequences Genomic DNA sequences
You are a researcher who has tentatively identified a human homolog of a yeast gene. You determine the DNA sequence of cDNAs of both your yeast gene and the human gene and decide to compare the gene sequences, as well as the predicted protein sequence of each, using alignment software. You would expect the greatest sequence identity from comparisons of the:
cDNA sequences
Protein sequences
Genomic DNA sequences
Both (a) and (b) will give you equivalent sequence similarity
All will give equivalent sequence similarity

25. Phylogeny (evolutionary history) of living mammals and other amniotes

26. The human genome carries relics of our egg-laying ancestors
The human genome carries relics of our egg-laying ancestors. Note, pseudogenes are mutationally inactivated (non-functional) genes.

27. The mouse and human genome have large syntenic blocks of genes in common

28. Exome sequencing and personalized genomics/medicine

29. Functional Genomics
Functional Genomics: the use of an expanding variety of methods to understand gene and protein function in biological processes.
Genomics: New sequence information is being
produced at increasing rates. (The
contents of GenBank double every year)
Transcriptomics: Includes RNA seq and Microarray analysis; Global expression analysis: RNA levels of every gene in the genome analyzed in parallel.
Proteomics: Global protein analysis generates large mass
spectra libraries.
Metabolomics:Global metabolite analysis: 25,000 secondary
metabolites characterized
Glycomics:Global sugar metabolism analysis

30. Microarrays can detect differences in gene expression
See

31. Microarray technology directly involves:
Question
Microarray technology directly involves:
PCR
DNA sequencing
Hybridization
RFLP detection
None of the above

32. Protein – protein interactions
Yeast two hybrid
ChIP (chomatin immunoprecipitation) and ChIP-chip
Bi Molecular Fluorescence Complementation (BMFC)

33. Studying protein interactions with the use of the yeast two-hybrid system
See

34. Steps in a chromatin immunoprecipitation assay (ChIP)
ChIP-Seq vs ChIP-chip;
see

35. Bi Molecular Fluorescence Complementation (BMFC)
Citovsky et al., 2006

36. Reverse genetics-used to study gene function (functional genomics)
Gene knockouts
RNAi
Overexpression
Altered expression

37. Disrupting gene function with the use of targeted mutagenesis
CRISPR-Cas9 is a new and exciting way to make gene knockouts.
See

38. Disrupting gene function with the use of RNA interference

39. Inserting transgenes into a nonmodel organism

40. Examples of nonmodel insects expressing a transgene

41. Summary DNA sequencing and the rise of genomics
Annotation of genome sequences
Comparative genomics
Functional genomics
Protein-protein interactions
ESTs
Reverse genetics