multi-allelic loci -> quantative genetics & SNPs

Intron: typically but not all begins with a donor site GT, and ends with acceptor site AG Stop codons all are TGA, TAA, TAG

Sometimes intronless genes may be a retrotransposed pseudogene, which will be reverse-transcribed into chromosome in a random location; another type: ribozymes

pre-mRNA -> remove intron (splicing) -> add 5' CAP and 3' polyA -> mature RNA -> export

5' CAP g to protect mRNA from being destroyed by RNAse;

3' polyA usually 20-40nt; polyA signal: ATTAAA / AATAAA

functional annotation

• gene boundaries are delimited
• exon-intron structure identified
• biological functions of product are stated

ab initio predictions based just on sequence patterns

should also consider:

• patterns of evolutionary conservation btw the target and other related organisms
• genomic repeats (tends to be intergenic regions)

Pseudogenes

• genomic fossils of genes which wre once functional but now are defunct
• tends to have codon stats that resemble those of real genes
• they often contain in-frame stops, or invalid or rare splice sites

common assumptions

• no overlapping genes
• no nested genes
• no partial genes
• no noncannonical signal consensus
• no frameshifts or sequencing errors
• optimal parse only
• constraints on feature lenghs (not necessary for GHMM-based gene finders)
• no split start codons
• no split stop codons (CDS with inframe stops do occur)
• no alternative splicing (?)
• no selenocysteine codons (TGA)
• no ambiguity codes (e.g., R, Y, N)
• one haplotype only (SNP density in human: 1 per 1 kb)

how to work?

• get all signals: return a list of all potential donnors / acceptors (GT/AG), start / stop codons (ATG/TAG TAA TGA)
• construct all fowrard-strand ORFs: all intervals in the sequence beginning with start (ATG or AG) and end with stop (TG TAG TAA TGA)
• find open reading frames

GC bias in CDS: in average %GC in CDS > GC in whole sequence, based on codon bias and WMM (weight matrix) score

Automated Models

• Mathmatical models: Hidden Markov Models
• Machine learning: Bayesian Networks and Decision Trees

First annotation pipelines but also consider other evidence: existing annotations (known genes), EST assembly, RepeatMasker predictions, Synteny info.

Cluster analysis

gene set enrichment analysis (GSEA) algorithms

• connectivity-based (hierarchical clustering)
• controid-based (K-means)
• distribution-based
• density-based

Additional features

• promoters TATA box (TATA->CAP->5’UTR) and CCAAT box
• the branch point (upstream of acceptor site)
• CpG island: (preferential in upstream of human genes, esp. housekeeping genes)
• signal peptide (aa seq near C-terminal of a newly translated protein)
• polyA signals (3’UTR -> AATAAA)
• 5' and 3' UTR
• CAP site

Masking common practice: soft-mask low complexity repeats (can occur in CDS) and hard-mask all others

Alternative splicing

• exon skipping
• cassette exons
• intron retention
• alternative polyadenylation
• alternative promoter recognition

ncRNAs

pred methods as evidence: secondary structure and conservation patterns across related species

Methods to predict secondary structure:

• based on thermodynamics: finding the secondary structure that minimize the Gibbs free energy of the RNA molecule
• through the use of stochastic context-free grammers (scFGs)

Promoters

• largely within 40bp of TSS
• most popular methods: weight matrix (WMM)
• typical: McPromoter systems
• UPR (upstream promoter region) -> TATA -> Spacer -> Inr (initiator) -> DPE (downstream promoter elements)
• not all core promoters have TATA/Inr
• another: CpG island strongly assoicated with promoters

differential expression

different activators signaling pathways by stimulus

a given gene may have multiple enhancers, each active at a different time or in a different cell type

each enhancer is associated with one gene