Making Sense of Genomes

1. Making Sense of Genomes

2. Interpreting genomes • DNA was discovered in mid- to late- 1800’s during biochemical investigations of proteins as a phosphorus-rich substance (nuclein, because isolated from white cell nuclei) • Chromatin was coined to describe colored components of cell nuclei after staining • Chromosome coined in 1888 describes threads of stainable material found withn the nucleus, in 1930’s nculein became desoxyribose nucleic acid..later DNA

3. Early work looked at genome size

4. Animal genome size variation • 0.04 placozoan to ~133 pg for lungfish (3300X difference) • Current dataset is skewed towards vertebrates (66%) • Some hagfish undergo chromatin loss as large fragments of genome present in germline are eliminated in somatic line

5. Invertebrate genomes • At most, 1% have been looked at • Terrestrial snails and slugs have 2X larger genomes than freshwater relatives • Some annelid groups exhibit huge differences (120X range) unrelated to polyploidy • No information on tapeworm or tarantula – potential senior project.

6. Mechanisms for alterations in animal genome sizes • Insertion-deletion mechanism for genome reduction • Selfish DNA and spread of transposable elements • Accumulation of pseudogenes • Introns (clear lack in Fugu (pufferfish) • Chromosome-level events • Aneuploidy (duplication or loss of individual chromosomes), Segments break off and fuse, chromosome 2 fused together in humans, separate in apes, Unequal crossing over in meiosis, and unequal sister chromatid exchange during gamete formation • Polyploidy • Duplication of entire chromosome set • Satellite DNA • Mini (9-100 bp; 15 bp mostly), short (3-5 bp), micro (1-5 bp; ignore overlap with short), copies of rDNA

7. Plants are well-known polyploids • Allopolyploidy – combination of genetically distinct chromosome sets • Autopolyploidy – multiplication of one basic set of chromosomes • Wheat is an allohexaploid containing three distinct sets of chromosomes from three different diploid species of goat-grass.

9. Genome size correlates with cell size

10. Genome size and phenotypes • As cells become larger, surface to volume ratio changes (affecting exchange rate with environment) • Transcription is affected by cell size • Body size  function of cell number not cell size • Metabolism  trend of smaller genomes and cell size with larger genomes (ie. In birds, flightless birds have larger genomes)

11. Reasons for correlation • Obviously a physical constraint, large genomes need more room • DNA acts as a nucleoskeleton around which nucleus is assembled? • Observe proportionate change in cell size in response to polyploidization • Cell size is presumably due to the nature of the DNA as well as amount

12. Other trends related to genome/cell size

13. Duplications and deletions

14. Inversions

15. Genome Sequencing  “Big” Biology

17. $1000 genome • Race for the prize • Methods • YouTube1 • Whose genome in the databases? • Venter – writing the code

18. A G C T A G C A T C C G T A T Capillary and Slab gel electrophoresis use a modified Sanger technology with fluorescent dyes Typical reads of 500-750 nt on an hour timescale. Variation depending on sequencer.

19. Four color sequencing

22. Innovations in DNA sequencing • Sequencing by synthesis • Cot-based analysis • Chip-based analysis, hybridization • Single molecule linear read, RNA polymerase • Nanopore technology • Different nucleotides = Different change in electric signal

23. Free Solution Electrophoresis • Possibly will improve separation time (no matrix) without losing read length • Label DNA molecules with friction increasing molecule such as streptavidin • Currently can read 100 bp, a long way to go…

24. Who needs electrophoresis? • Pyrosequencing • MALDI-TOF Mass Spectrometry • Sequencing by Hybridization • Massively Parallel Signature Sequencing • A testimony to innovative molecular biology • Single molecule methods

25. Pyrosequencing • Real-time sequencing measuring release of PPi during DNA synthesis • Has been of particular use for SNP analysis • First of four deoxynucleotide triphosphates added to reaction, when correct one incorporated Ppi is released and measured using ATP sulfurylase-coupled ATP synthesis and luciferase – wash and repeat

26. Put the sequencing reactions through a mass spectrometer Spectra of the C- and G- terminated oligonucleotides Current limit ~100 bp, Facilitated by sensitivity and high-throughput loading

27. Shotgun sequencing – 2 approaches • Hierarchical shotgun approach • Generating an overlapping set of intermediate-sized (e.g. bacterial artificial chromosomes with 200 KB inserts) clones, and keeping a map of that (it took 2 yrs for mapping e-coli) • Subjecting each of these clones to shotgun sequencing, and using the map to get the whole sequence. • Whole-genome shotgun (WGS) approach • Generating sequence reads directly from a whole-genome library • Using computational techniques to reassemble in one step. • Used for Drosophila melanogaster (fruit fly) and by Celera Genomics (formed 1998) for human genome.

28. Original DNA • Break DNA into random fragments (8-10X Coverage) Overview of “Shotgun” Genomic Sequencing

29. Cloning vectors • 2-5 kb in pUC or M13 • 5-50 kb in phage or cosmid • 30-100 kb in P1 bacteriophage • 60-300 kb in BAC • 60-2000 kb in YAC

30. Overview of Genomic Sequencing Original DNA • Break DNA into random fragments (8-10X Coverage) • Amplify fragments in a vector and sequence 500-700 bases in from each end Base calling performed by Phred software: http://www.phrap.org/ http://www.genome.org/cgi/reprint/8/3/175.pdf

31. Phred Software • Calls bases in four phases: • Predicting peaks (ideal locations) • Locating observed peaks • Matching observed to predicted • Finding missing peaks • http://www.genome.org/cgi/reprint/8/3/186.pdf • http://www.genome.org/cgi/reprint/8/3/175.pdf

32. Errors in Sequencing Reads • Each base call is assigned a quality score: • q = -10 x log10(p) {Higher quality scores correspond to low error probabilities; } Errors are associated with peak vicinity, use the following parameters in error probability determination on a TRAINING SET: Peak spacing Uncalled/called ration (two window sizes) Peak resolution Result in a look-up table inherent to Phred software

33. Common Sources of Sequencing Errors • The first fifty or so peaks of a trace are noisy and unevenly spaced due to anomalous migration of short DNA fragments, and unreacted dye-primer and dye-terminator molecules. • Near the end of the trace, peaks become less evenly spaced due to less accurate trace processing, less well resolved as diffusion effects increase, and also #labeled molecules decrease. • Compressions – most common in GC-rich regions when bases near the end of a single-stranded fragment bind to a complementary region forming a hairpin (migrates more rapidly than expected) • Dye-terminator sequencing method helps resolve compressions, but has own problems: “About 85% of high quality dye terminator errors resulted from a missing G peak following an A, or a missing A folling a T,…” Ewing and Green, 1998.

34. Overview of Genomic Sequencing Original DNA • Break DNA into random fragments (8-10X Coverage) • Amplify fragments in a vector and sequence 500-700 bases in from each end • Assemble fragments of sequence that have been read: Contig 1 Contig 2