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Genomics and other “omics”

•  Genome sequencing - individual organism (genomics), community of organisms (metagenomics)

•  Searching the databases

•  Transcriptional analysis (transcriptomics)

•  Proteomics

•  Metabolomics (detect small metabolites)

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Genomic analysis: Step 1. Predicting open reading frames (orfs) by computer algorithms

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Genomic analysis: Step 1 (cont.). Predicting open reading frames by computer algorithms

•  Advantages –  Gives a readout of large open reading frames

•  Limitations –  Some genes have start codons that are not ATG –  Ignores very small open reading frames. May

miss hormone-like peptides, small regulatory peptides, quorum sensing peptides.

–  Does not detect small regulatory RNAs.

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Genomic analysis: Step 2. Database searches

•  DNA sequence alignments –  Best for finding nearly identical genes –  Find sequence motifs (e.g., helix-turn-helix in DNA binding

proteins)

•  Linear amino acid sequence alignments –  Best for finding homologs that may be more distantly

related –  Annotation can be ambiguous

•  Example: Elongation factors and tetracycline resistance genes (ribosomal protection type)

•  Example: Enzymes that are not present in an organism •  Annotations are hypotheses!!!

•  Structural predictions – structural homologs

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BLASTP 2.2.6 [Apr-09-2003] SusA-8-03 Query= (565 letters) Database: Completed Bacteroides thetaiotaomicron VPI-5482; 1,480,858 sequences; 476,119,222 total letters Distribution of 26 Blast Hits on the Query Sequence

Score E Sequences producing significant alignments: (bits) Value gi|29349112|ref|NP_812615.1| alpha-amylase (neopullulanase)... 1076 0.0 gi|29349106|ref|NP_812609.1| alpha-amylase, susG [Bacteroid... 79 1e-15 gi|29350098|ref|NP_813601.1| alpha-amylase precursor [Bacte... 67 6e-12 gi|29347073|ref|NP_810576.1| pullulanase precursor [Bactero... 61 2e-10 gi|29350097|ref|NP_813600.1| pullulanase precursor [Bactero... 59 2e-09 gi|29346181|ref|NP_809684.1| 1,4-alpha-glucan branching enz... 45 1e-05 gi|29346183|ref|NP_809686.1| alpha-amylase 3 [Bacteroides t... 38 0.002 gi|29346689|ref|NP_810192.1| putative anti-sigma factor [Ba... 35 0.019 gi|29347520|ref|NP_811023.1| hypothetical protein [Bacteroi... 33 0.094 gi|29345677|ref|NP_809180.1| two-component system sensor hi... 30 0.47 gi|29346515|ref|NP_810018.1| phosphoglycerate mutase 1 [Bac... 29 1.0 gi|29347070|ref|NP_810573.1| phosphoglycerate mutase [Bacte... 29 1.0 gi|29348342|ref|NP_811845.1| Methionyl-tRNA synthetase [Bac... 28 2.3 gi|29349419|ref|NP_812922.1| DNA-methyltransferase [Bactero... 28 2.3 gi|29348421|ref|NP_811924.1| putative outer membrane protei... 28 2.3 gi|29346850|ref|NP_810353.1| putative outer membrane protei... 28 3.0 gi|29345906|ref|NP_809409.1| TonB-dependent receptor [Bacte... 27 4.0 gi|29347285|ref|NP_810788.1| putative outer membrane protei... 27 5.2

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Protein Structure Prediction

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“Transcriptomics” – Measuring gene expression directly (mRNA)

•  Types of analysis –  Microarray – measures expression of many genes at a time –  RT-PCR – measures expression of one gene at a time

•  Advantages –  Microarrays, like transposon mutagenesis, find previously

unsuspected genes of interest –  Not necessary to make fusions to every gene

•  Disadvantages (compared to fusions) –  Microarray data needs to be checked by RT-PCR –  Fusions can be made to monitor translation

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Microarray - Measuring Gene Expression of Many Genes at a Time

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New variations of the microarray approach

•  Make a few labeled DNA copies of each mRNA using RT-PCR – increases sensitivity

•  DNA copies of mRNA from cells grown under different conditions labeled with different fluorophores (e.g. red for low iron, green for high iron), then mixture is placed on a single slide

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Uses of microarrays

•  Compare gene expression under different conditions

•  Determine effects of mutations, eg, in regulatory proteins – effect may be more complex than you thought!

•  Effects of overexpression of certain genes – less commonly done

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Metagenomics – genome sequencing of entire bacterial populations

•  Sample contains bacterial population (e.g. water sample, human colon contents)

•  Total DNA extracted, non-DNA impurities removed

•  High throughput sequencing (e.g. 454 sequencing) •  Limitations

–  Assembly –  Interpretation!!

•  Transcriptome –  RT-PCR amplifies messages as DNA, sequence DNA –  Limitation: lots of rRNA, random priming of RT-PCR

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Proteomics •  Detects proteins produced under different conditions

•  Two dimensional gel creates an array of protein spots –  First dimension: isoelectric focusing (pH gradient) –  Second dimension: SDS denaturing gel

•  Proteins extracted individually, fragmented by proteases, run through a mass spectrometer – matched with fragments predicted from DNA sequence.

•  Advantages –  Detect proteins not RNA (post transcsriptional regulation

•  Limitations –  Only the most highly expressed proteins are detected –  Overlapping spots may be difficult to resolve –  Need to go through the MS step –  Not likely to be useful in metagenomics

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Conclusions (according to AAS)

•  Availability of new technologies is forcing a shift from single gene-single pathway thinking to a more global way of thinking.

•  Increased need to focus on a specific biological question

•  Most technologies now provided by centralized services – technology itself is uninteresting, only interesting thing is what you can do with it!!