Genome Annotation: From

Sequence to Biology

Ashley Bateman & Andrew Tritt

Genetics 677Prof. Ahna Skop

Spring 2009

Introduction-over 450 organisms have been completely sequenced since 1995, and many more have working drafts-361 prokaryotes, 28 archaea, 20 protists, 8 plants, 15 fungi, 26 mammals, and 21 “other”(wikipedia)

List of Sequenced Organisms

Genome Sequencing

Sanger Sequencing

454

Sanger

Solexa

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http://www.nature.com/nrmicro/journal/vaop/ncurrent/images/nrmicro1901-f3.jpg

Reads ~200 bp

454 Sequencing: Sequencing by synthesis

1-fix DNA strands to beads in water-in-oil emulsion

2-DNA amplified by PCR

3-use PPi product of PCR to determine identity of added base

High Throughput Sanger Sequencing

~900 bp read

-DNA of interest inserted into a plasmid, and sequenced using primers for plasmid

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~26-50 bp reads

-newest sequencing technology --> cheaper and faster

-small reads present problems if dealing with repetitive sequence

Solexa Sequencing

http://seqanswers.com/forums/showthread.php?t=21

The process of taking the DNA sequence produced by genome-sequencing projects, and adding layers of analysis/interpretation to understand its biological significance in a larger context

Genome Annotation

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Genome Annotation: A

multistep process

3 general levels of annotation:

-1 Nucleotide-level(where)

-2 Protein-level (what)-3 Process-level (how)

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Stein, 2001.

Nucleotide-level Annotation: Mapping

-“…identify the punctuation marks…”-Identification and placement of known landmarks into the genome (genes, genetic markers, etc.)-Connects the pre-genomic literature with post-genomic research

Nucleotide-level Annotation: Finding Genomic Landmarks

-short sequences: PCR-based genetic markers (ID with e-PCR program)

-long sequences: RFLPs (ID with BLASTN, etc.)

Nucleotide-level Annotation: Gene Finding

Prokaryotes: ID ORFsEukaryotes: Sophisticated software needed (gene prediction)-overlapping ORFs-signal-to-noise ratio-splicing-unclear exon/intron delineations

-use algorithms that contain sensors to identify specific sequence features

- neural networks- rule-based system- hidden Markov model

-sequence similarity to known CDS-BLAST-cDNA-EST’s

Ab initio gene prediction - without use of prior knowledge about similarities to other genes

Gene Prediction Software

Hidden Markov Models

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EXONA: 0.2C: 0.3G: 0.3T: 0.2

INTRONA: 0.25C: 0.25G: 0.25T: 0.25

1.0

0.85

0.05

0.10

0.95

0.05

-a set of states with transition and emission probabilities

-genes in a sequence predicted by finding most probable path

Example :

DNA Sequence : AGTTCGAATCGATGCTAAGACGA Possible Path : EEEEIIIIIIIIIIIIIIEEEEE Most probable path: EEEIIIIIIIIIIIIIIIIIEEE

Sequence Similarity

-currently, most powerful tool for detecting CDS

-Problems exist:-Fragmentary ESTs-Repetitive cDNA sequences-Ortholog-paralog problem-Incomplete data

ab initio predictions + similarity data = more powerful model

Nucleotide-level Annotation: non-coding RNAs and regulatory regions-include tRNAs, rRNAs, snRNAs, nRNAs

-transcription factor binding sites

-largely unknown; active area of bioinformatics research

Nucleotide-level Annotation: non-coding RNAs and regulatory regions

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-red and blue boxes represent unknown positions of motifs

-Gibbs Motif Sampler1 and MEME infer models for motifs and identify motif locations within sequences1 Lawrence et al. 1993, Thompson et al. 2007

Nucleotide-level Annotation: Repetitive Elements & Segmental Duplications

Repetitive Elements:

-account for a large proportion of genome size variation

-important to (generally) exclude these from later assembly process

-problematic for next-gen sequencing technologies

Segmental Duplications:

-paralogs exist throughout many genomes

Nucleotide-level Annotation: Mapping Variation

-SNPs are important for population genetics and association mapping

AAGTCGATGCTAGCGCTACTAGCTAGGCTCGATGTTAAGTCGATGCTAGCGCTACTAGCTAGGCTAGATGTTAAGTCGATGCTAGCCCTACTAGCTAGGCTCGATGTTAAGTCGATGCTAGCGCTACTAGCTAGGCTAGATGTTAAGTCGATGCTAGCCCTACTAGCTAGGCTTGATGTTAAGTCGATGCTAGCGCTACTAGCTAGGCTCGATGTT

SNPs

Protein-level Annotation

-Assign putative functions to proteins of an organism

-Classify proteins into families:

-using similarities to better-characterized proteins of other species (BLASTP)

-on the basis of functional domains, motifs, and folds

-Search against protein databases of functional domains (e.g. PFAM)

-InterPro: integration of several protein databases -makes things much easier!

Process-level Annotation

-linking the genome to biological processes

-bench work required (e.g. microarrays, RNAi, etc.)

-classification scheme required: Gene Ontology (GO)

-standardized vocabulary for molecular function, biological process, and

cellular component

-hierarchy of terms provides flexibility for new additions

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Process-level Annotation

-hierarchical structure of GO terminology

Organizing Annotation Efforts

Several models:- factory - museum- cottage

industry- party

Bioinformatics research in biomedical text mining to automate annotation process

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Conclusion

A synthesis of biology and annotation must be developed…

…change is constant, databases are updated sometimes hourly…

…the experimental literature of the past must be tied with the genome annotations of the future!

Student Question

“The paper was mostly about predicting the number of genes and proteins in an organism. Why do we need to predict the number of genes and proteins in the cell? It appears that most studies identify genes based on phenotypes. For proteins, many methodologies exist for identifying protein function. I cannot see the purpose of this prediction--pardon my short sightedness.

Also, has a standardized format emerged in regard to the genome files?”

NCBI standardized format example