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

The landscape of the human genome

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

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

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

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

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

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

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

Alternative splicing is difficult to predict ab initio

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

Available EST data

Available EST data: Examples

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

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

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, 223-227(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)

Types of repeat elements

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

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

Some facts about the human genome (2001)

Gene annotations – # of coding genes Note: as of 2011, the estimated number of protein-coding genes in the human genome is between 19000 and 20000

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

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

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.

Human genes often have similar, so-called duplicate genes

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

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.

Gene annotations – gene function

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

“Conclusion” of the Human Genome Paper

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

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

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

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

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

Mathematical Models of Sequences

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.

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

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?

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.

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

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