Manifestations of a Code

Genes, genomes, bioinformatics and cyberspace – and the promise they

hold for biology education

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www.iPlantCollaborative.org

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A GENOME is all of a living thing’s genetic material.

The genetic material is DNA (DeoxyriboNucleic Acid)

DNA, a double helical molecule, is made up of four nucleotide “letters”:A-- G--

T-- C--

What is a genome?

Slide: JGI, 2009

Just as computer software is rendered in long strings of 0s and 1s, the GENOME or “software” of life is represented by a string of the four nucleotides, A, G, C, and T.

To understand the software of either - a computer or a living organism - we must know the order, or sequence, of these informative bits.

What is sequencing?

Slide: JGI, 2009

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Economics of Scale

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Slide: JGI, 2009

•1986 DOE announces Human Genome Initiative-- $5.3 million to develop technology.

•1990 DOE & NIH present their HGP plan to Congress.

1997 Escherichia coli genome published

•1997 Yeast genome published

•2000 Fruit fly (Drosophila) genome published.

•2000 Working draft of the human genome announced.

•2000 Thale cress (Arabidopsis) genome published (2x).

•2002 Rice genome published (2x).

•2003 Human genome published.

•2006 First tree genome published in Science.

•2007 First metagenomics study published

Important Dates in Genomics

Another angle

Slide: Stein, 2010

Coming into the Genome Age

For the first time in the history of science students can work with the same data and tools that are used by researchers.

Learning by posing and answering question.

Students generate new knowledge.

Workshop Objectives

Illustrate the evolving concept of “gene.” Conceptualize a “big picture” of complex, dynamic

genomes. Guide students to address real problems through modern

genome science. Use educational and research interfaces for bioinformatics. Work with “real” genome sequences gathered by students

– in the lab or online.

Exciting?

>mouse_ear_cress_1080 GAAATAATCAATGGAATATGTAGAGGTCTCCTGTACCTTCACAGAGATTCTAGGCTGAGAGCAGTGCATATAGATATCTTTCGTACTCATCTGCTTTTTCTGGTCTCCATCACAAAAGCCAACTAGGTAATCATATCAATCTCTCTTTACCGTTTACTCGACCTTTTCCAATCAGGTGCT TCTGGTGTGTCTACTACTATCAGTTTTAGGTCTTTGTATACCTGATCTTATCTGCTACTG AGGCTTGTAAAAGTGATTAAAACTGTGACATTTACTCTAAGAGAAGTAACCTGTTTGATGCATTTCCCTAATATACCGGTGTGGAAAAGTGTAGGTATCTGTACTCAGCTGAAATGGTGGACGATTTTGAAGAAGATGAACTCTCATTGACTGAAAGCGGGTTGAAGAGTGAAGATGGCGTTATTATCGAGATGAATGTCTCCTGGATGCTTTTATTATCATGTTTGGGAATTTACCAAGGGAGAGGTATCAGAATCTATCTTAGAAGGTTACATTTAGCTCAAGCTTGCATCAACATCTTTACTTAGAGCTCTACGGGTTTTAGTGTGTTTGAAGTTTCTTAACTCCTAGTATAATTAGAATCTTCTGCAGCAGACTTTAGAGTTTTGGGATGTAGAGCTAACCAGAGTCGGTTTGTTTAAACTAGAATCTTTTTATGTAGCAGACTTGTTCAGTACCTGAATACCAGTTTTAAATTACCGTCAGATGTTGATCTTGTTGGTAATAATGGAGAAACGGAAGAATAATTAGACGAAACAAACTCTTTAAGAACGTATCTTTCAGTTTTCCATCACAAATTTTCTTACAAGCTACAAAAATCGAACTATATATAACTGAACCGAATTTAAACCGGAGGGAGGGTTTGACTTTGGTCAATCACATTTCCAATGATACCGTCGTTTGGTTTGGGGAAGCCTCGTCGTACAAATACGACGTCGTTTAAGGAAAGCCCTCCTTAACCCCAGTTATAAGCTCAAAGTTGTACTTGACCTTTTTAAAGAAGCACGAAACGAAAAACCCTAAAATTCCCAAGCAGAGAAAGAGAGACAGAGCAAGTACAGATTTCAACTAGCTCAAGATGATCATCCCTGTTCGTTGCTTTACTTGTGGAAAGGTTGATATTTTCCCCTTCGCTTTGGTCTTATTTAGGGTTTTACTCCGTCTTTATAGGGTTTTAGTTACTCCAAATTTGGCTAAGAAGAGATCTTTACTCTCTGTATTTGACACGAATGTTTTTAATCGGTTGGATACATGTTGGGTCGATTAGAGAAATAAAGTATTGAGCTTTACTAAGCTTTCACCTTGTGATTGGTTTAGGTGATTGGAAACAAATGGGATCAGTATCTTGATCTTCTCCAGCTCGACTACACTGAAGGGTAAGCTTACAATGATTCTCACTTCTTGCTGCTCTAATCATCATACTTTGTGTCAAAAAGAGAGTAATTGCTTTGCGTTTTAGAGAAATTAGCCCAGATTTCGTATTGGGTCTGTGAAGTTTCATATTAGCTAACACACTTCTCTAATTGATAACAGAAGCTATAAAATAGATTTGCTGATGAAGGAGTTAGCTTTTTATAATCTTCTGTGTTTGTGTTTTACTGTCTGTGTCATTGGAAGAGACTATGTCCTGCCTATATAATCTCTATGTGCCTATCTAGATTTTCTATACAATTGATATTTGATAGAAGTAGAAAGTAAGACTTAAGGTCTTTTGATTAGACTTGTGCCCATCTACATGATTCTTATTGGACTAATCATTCTTTGTGTGAAAATAGAATACTTTGTCTGAACATGAGAGAATGGTTCATAATACGTGTGAAGTATGGGATTAGTTCAACAATTTCGCTATTGGAGAAGCAAACCAAGGGTTAATCGTTTATAGGGTTAAGCTAATGCTCTGCTCTTTATATGTTATTGGAACAGACTATTGTTGTGCCTATCTTGTTTAGTTGTAGATTCTATCTCGACTGTTATAAGTATGACTGAAGGCTTGATGACTTATGATTCTCTTTACACCTGTAGAAGGATTTAAGCTTGGTGTCTAGATATTCAATCTGTGTTGGTTTTGTCTTTCTTTTGGCTCTTAGTGTTGTTCAATCTCCTCAATAGGTATGAAGTTACAATATCCTTATTATTTTGCAGGGACGCACTTGATGCACTCCAGCTAGTCAGATACTGCTGCAGGCGTATGCTAATGACCTTGCATCAACATCTTTACTTAGAGCTCTACGGGTTTTAGTGTGT

This better?

FindGene Families

Generate mathematical

evidence

Analyze large data amounts

Browse in context

Build gene models

Gatherbiological evidence

Annotation workflow

Get DNA sequence

Walk or…

Early concept (2009)

DNA Subway 2014

Molecular biology and bioinformatics conceptsRepeatMasker• Eukaryotic genomes contain large amounts of repetitive DNA.• Transposons can be located anywhere.• Transposons can mutate like any other DNA sequence.

FGenesH Gene Predictor• Protein-coding information begins with start, followed by codons, ends in stop.• Codons in mRNA (AUG, UAA,…) have sequence equivalents in DNA (ATG, TAA,…).• Most eukaryotic introns have “canonical splice sites,” GT---AG (mRNA: GU---AG).• Gene prediction programs search for patterns to predict genes and their structure.• Different gene prediction programs may predict different genes and/or structures.

Multiple Gene Predictors• The protein coding sequence of a mRNA is flanked by untranslated regions (UTRs).• UTRs hold regulatory information.

BLAST Searches• Gene or protein homologs share similarities due to common ancestry. • Biological evidence is needed to curate gene models predicted by computers.• mRNA transcripts and protein sequence data provide “hard” evidence for genes.

What is a gene?

• Can we define a gene?• Has the definition of a gene changed?• How can we find genes?

Views

• Genes as “independent hereditary units (1866), Mendel• Genes as “beads on strings” (1926), Morgan• One gene, one enzyme (1941), Beadle & Tatum• DNA is molecule of heredity (), Avery• DNA > RNA > Protein (1953), Crick, Watson, Wilkins

More Views

• Transposons (1940s-50s), McClintock• Reverse transcription (1970), Temin & Baltimore• Split genes (1977), Roberts & Sharp• RNA interference (1998), Fire and Mello

Sequence & course material repository

http://gfx.dnalc.org/files/evidenceDon’t open items, save them to your computer!!

• Annotation (sequences & evidence)• Manuals (DNA, Subway, Apollo, JalView)• Presentations (.ppt files)• Prospecting (sequences)• Readings (Bioinformatics tools, splicing, etc.)• Worksheets (Word docs, handouts, etc.)• BCR-ABL (temporary; not course-related)