1. Genome Annotation and the Human Genome
BI420 – Introduction to Bioinformatics
Genome Annotation and the Human Genome
Fall 2012
Gabor Marth
Department of Biology, Boston College

2. The landscape of the human genome

3. Goal of Genome Annotation
Identify all distinct elements within a genome. Annotation tends to focus on functional elements such as protein coding genes and RNA genes, but may also include non-functional sequences including repetitive elements.
protein coding genes
repetitive elements
RNA genes

4. The starting material AGCGTGGTAGCGCGAGTTTGCGAGCTAGCTAGGCTCCGGATGCGA
CCAGCTTTGATAGATGAATATAGTGTGCGCGACTAGCTGTGTGTT
GAATATATAGTGTGTCTCTCGATATGTAGTCTGGATCTAGTGTTG
GTGTAGATGGAGATCGCGTAGCGTGGTAGCGCGAGTTTGCGAGCT
AGCTAGGCTCCGGATGCGACCAGCTTTGATAGATGAATATAGTGT
GCGCGACTAGCTGTGTGTTGAATATATAGTGTGTCTCTCGATATGT
AGTCTGGATCTAGTGTTGGTGTAGATGGAGATCGCGTGCTTGAG
TCGTTCGTTTTTTTATGCTGATGATATAAATATATAGTGTTGGTG
GGGGGTACTCTACTCTCTCTAGAGAGAGCCTCTCAAAAAAAAAGCT
CGGGGATCGGGTTCGAAGAAGTGAGATGTACGCGCTAGXTAGTAT
ATCTCTTTCTCTGTCGTGCTGCTTGAGATCGTTCGTTTTTTTATGCT
GATGATATAAATATATAGTGTTGGTGGGGGGTACTCTACTCTCTCT
AGAGAGAGCCTCTCAAAAAAAAAGCTCGGGGATCGGGTTCGAAGA
AGTGAGATGTACGCGCTAGXTAGTATATCTCTTTCTCTGTCGTGCT

5. Coding genes Start codon Stop codon
ATGGCACCACCGATGTCTACGTGGTAGGGGACTATAAAAAAAAAAA
PolyA signal
Open Reading Frame = ORF
Ab initio - Latin for “from the beginning.” Ab initio gene predictions are those based on computational sequence analysis.
Simple approach to gene prediction: look for start codons and stop codons

6. Typical structure of bacterial and eucaryotic genes
Eucaryotic genes have introns while bacterial genes do not.

7. Ab initio predictions of exons
…AGAATAGGGCGCGTACCTTCCAACGAAGACTGGG…
splice donor site
splice acceptor site

8. Software for ab initio gene predictions
Genscan
Grail
Genie
GeneFinder
Glimmer
etc…
EST_genome
Sim4
Spidey

9. Homology based predictions
known coding sequence from another organism
expressed sequence
ACGGAAGTCT
GGACTATAAA
ATGGCACCACCGATGTCTACGTGGTAGGGGACTATAAAAAAAAAAA
genes predicted by homology
Genomescan
Twinscan

10. Alternative splicing is difficult to predict ab initio

11. Ab initio analysis and EST data are integrated for current gene annotations
Sim4
dbEst
Genewise
Grail
Genscan
FgenesH
Ensembl
Otto

12. Available EST data

13. Available EST data: Examples

14. Noncoding RNA genes Prediction based on structure (e.g. tRNAs).
Scan the genome and try to fold sequences into shapes corresponding to tRNAs
For other novel ncRNAs, only homology-based predictions have been successful, i.e. look for sequences which look like known tRNAs

15. Noncoding RNAs identified in the Original Human Genome Project (2001)

16. Long Interspersed NonCoding RNAs
Protein-coding gene
LINC RNA
Protein-coding gene
~ 3000 known Long Interspersed NonCoding (LINC) RNAs known in mammalian genome. Nature 458, (12 March 2009).
This is based on methylation signatures of histones and expression profiling.
Histone H3 lysine 4 trimethylation (H3K4me3)
Histone H3 lysine 36 trimethylation (H3K36me3)

17. Types of repeat elements

18. Types of repeat elements
Repetitive sequences make up about half the human genome.

19. How to annotate repeats
Repeat annotations are based on sequence similarity to known repetitive elements in a repeat sequence library

20. Some facts about the human genome (2001)

21. Gene annotations – # of coding genes
Note: as of 2011, the estimated number of protein-coding genes in the human genome is between and 20000

22. Gene annotations – gene length
Human genes have ~7 exons and are ~1100 bp long.

23. Base Composition Base composition of a sequence A: 5113 C: 5192
G: 2180
T: 4086

24. Genes tend to be in regions of higher GC content
The human genome is approximately 40% GC.
Human genes are biased toward regions of higher GC.

25. Human genes often have similar, so-called duplicate genes

26. Comparison of tRNAs across species
Humans and other eukaryotes have redundant copies of tRNAs.

27. Comparison of gene repertoires
Humans have a large number of genes involved in transcription/translation.
Yeasts have a higher fraction of their genes involved in metabolism.

28. Gene annotations – gene function

29. Gene conservation across organisms
~1/4 of known human genes occur only in vertebrates
<1% of known human genes have homologs only in prokaryotes

30. “Conclusion” of the Human Genome Paper

31. The impact: genome anatomy
The genome sequence provided the superstructure on which to layer genomic, biological, and medical information
Better understanding of the landscape of the human genome (e.g. segmental duplications)
Accurate tabulation of protein coding genes
Better understanding of the number and role of non-coding genes

32. The impact: genomic variation
The genome sequence provided a substrate on which to organize DNA sequences from other human samples
True extent of single-nucleotide variation
Linkage disequilibrium
Copy number variation
Larger structural variation

33. The impact: medicine
Mendelian diseases: 1,000s of single-gene disorders mapped
Chromosomal disorders: High-density genomic technologies (e.g. microarrays) made it easier to detect even smaller chromosomal abnormalities
Common disease
GWAS studies found disease genes
Gene lists provide insight into disease pathways
Cancer
Over 150 genes with somatic mutations playing a role in tumorigenesis, response to cancer drugs, and recurrence

34. The impact: human history
Demographic history, population migrations refined
Admixture mapped out on a fine scale
Positive selection examined
Contribution from Neanderthal DNA

35. The road ahead
New high-throughput sequencing technologies permit sequencing of 1,000s of human genomes
Focus on the extent and functional impact of rare, structural, and complex variation
Routine use of genetic information in the clinic
Routine whole-genome sequencing in the clinic

36. Mathematical Models of Sequences

37. Sequences and complementarity
DNA sequences are conventionally listed in the 5’ to 3’ direction.
5’ ATGCATGC 3’
This is complementary to the sequence
3’ TACGTACG 5’
Since DNA is double-stranded you could in principle list either sequence, but by convention, the 5’->3’ is always the one described.

38. Probability of a sequence
Independent, identically distributed (IID) model: all positions in a sequence behave identically and independently. This is the simplest model for a sequence of events.
Example: There is a 50% chance of sunshine each day, 30% chance of clouds, and 20% chance of rain.
Probability of sun on Sunday, clouds on Monday, rain on Tuesday, and rain again on Wednesday?
P(sun,cloud,rain,rain) = P(sun)P(cloud)P(rain)P(rain)
= 0.5 * 0.3 * 0.2 * 0.2
= 0.006

39. IID Model for DNA Sequences
The probability of an A, C, G or T at a given location is independent of the location.
P(AGCCA) = p(A)p(G)p(C)p(C)p(A)
s=AGCCA
s(1) = A, s(2) =G, s(3) = C, s(4)=C, s(5) = A
P(s) = P(s(1)) P(s(2)) P(s(3)) P(s(4)) P(s(5))
Example: suppose P(A)=0.2, P(T)=0.2, P(C) = 0.3, P(G) = 0.3.
What is the probability of the sequence AGCCA?

40. Markov models The human genome is approximately 40% GC.
Markov model: a model in which the probability of a base depends on the previous base. So positions are not independent.
Analogy: If it is cloudy today it is more likely to rain tomorrow.
P(sun,cloud,rain,rain) = π(sun)P(cloud|sun)P(rain|cloud)P(rain|rain)
Here π is defined as the probability for day one. Then the P values are the conditional probabilities. For example P(cloud|sun) is the probability it is cloudy today given that it was sunny yesterday.

41. Weather example using a Markov model
Suppose on day one there is a 50% chance of sun, 30% chain of clouds, and 20% chance of rain. Afterwards, the probabilities are given by
Prev\Next
Sun
Cloud
Rain
0.8
0.1
0.2
0.4
0.3
P(sun,cloud,rain,rain) = π(sun)P(cloud|sun)P(rain|cloud)P(rain|rain)
= 0.5 * 0.1 * 0.4 * 0.4
= 0.008

42. Markov model for a DNA sequence
P(AGCCA) = π(A) p(G|A)p(C|G)p(C|C)p(A|C)
P(x|y) is the probability that a base is x given that the previous base was y. Note that we have implicitly assumed the sequence is generated from left to right.
Example of a Markov model for sequences
base π A
0.3 C
0.2 G T
Prev\Next A C G T
0.4
0.2
0.3
0.1
0.7