Bio 101: Genomics & Computational Biology

10/17/01


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Table of Contents

Bio 101: Genomics & Computational Biology

DNA2: Last week's take home lessons

RNA1: Today's story & goals

Discrete & continuous bell-curves

“Significant” distributions

Primary to tertiary structure

Non-watson-crick bps

Modified bases & bps in RNA

Covariance

Mutual Information

RNA secondary structure prediction

Stacked bp & ss

Initial 1981 O(N2) DP methods: Circular Representation of RNA Structure

RNA pseudoknots, important biologically, but challenging for structure searches

Dynamic programming finally handles RNA pseudoknots too.

CpG Island + in a ocean of - First order Markov Model

Small nucleolar (sno)RNA structure & function

SnoRNA Search

Performance of RNA-fold matching algorithms

Putative Sno RNA gene disruption effects on rRNA modification

RNA1: Today's story & goals

RNA (array) & Protein/metabolite (MS) quantitation

8 cross-checks for regulon quantitation

Check regulons from conserved operons (chromosomal proximity)

Predicting the PurR regulon by piecing together smaller operons

(Whole genome) RNA quantitation objectives

(Sub)cellular inhomogeneity

Fluorescent in situ hybridization (FISH)

RNA1: Today's story & goals

Steady-state population-average RNA quantitation methodology

PPT Slide

Most RNAs < 1 molecule per cell.

Microarray data analyses (web)

Statistical models for repeated array data

“Significant” distributions

Independent Experiments

RNA quantitation

PPT Slide

(Whole genome) RNA quantitation methods

Microarray to Northern

Genomic oligonucleotide microarrays

Random & Systematic Errors in RNA quantitation

Spatial Variation in Control Intensity

Detection of Antisense and Untranslated RNAs

Mapping deviations from expected repeat ratios

RNA1: Today's story & goals

Independent oligos analysis of RNA structure

Predicting RNA-RNA interactions

Experimental annotation of the human genome using microarray technology.

RNA1: Today's story & goals

Time courses

Beyond steady state: mRNA turnover rates (rifampicin time-course)

TimeWarp: pairs of expression series, discrete or interpolative

TimeWarp: cell-cycle experiments

TimeWarp: alignment example

RNA1: Today's story & goals

Author: George Church

Home Page: http://www.courses.fas.harvard.edu/~bphys101/