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Genomics and Bioinformatics Week 1 Notes

by: Anastassia Erudaitius

Genomics and Bioinformatics Week 1 Notes Biol 119

Marketplace > University of California Riverside > Biology > Biol 119 > Genomics and Bioinformatics Week 1 Notes
Anastassia Erudaitius
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About this Document

These notes cover the introduction of genomics, molecular biology, and genome complexity and structure, c-values, and genomics in the news
Genomics and Bioinformatics
Dr. Hayashi and Dr. Stajich
Class Notes
Hayashi, Dr. Hayashi, Biol 119, 119, bio 119, genomics, bioinformatics, genomics and bioinformatics, UCR




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This 10 page Class Notes was uploaded by Anastassia Erudaitius on Sunday April 3, 2016. The Class Notes belongs to Biol 119 at University of California Riverside taught by Dr. Hayashi and Dr. Stajich in Spring 2016. Since its upload, it has received 174 views. For similar materials see Genomics and Bioinformatics in Biology at University of California Riverside.


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Date Created: 04/03/16
3/28/2016 [Genomics and Bioinformatics] [Dr. Hayashi and Dr. Stajich] [Lecture 1 – Introduction and Central Dogma] Highlight = Important PrinciplHighlight = Important Concept Highlight = Key Term [Central Dogma of Molecular Biology]  DNA replicates it information  DNA codes for mRNA o “SOME” DNA codes for mRNA o mRNA is translated into a protein  Central dogma of molecular biology o DNA  RNA  protein o We need to revise the central dogma because it isn’t as unidirectional as originally thought  Exceptions to central dogma o Reverse transcriptases synthesize DNA from RNA  RNA  DNA  Like HIV infection o RNA editing  RNA can provide much more than just a template for transcription 3/28/2016  Cell can change what the RNA sequence is into another sequence o RNA splicing  Can end up with a variety of gene products o RNA catalysis  RNA can function as an enzyme o Regulation of gene expression  Very indirect meandering path from an RNA to a protein  RNAs themselves can play a very big role in gene regulation o Before, everyone though the DNA encoded all the regulation [Central Dogma of Genomics]  Genome o Replicates itself Genome o Total genetic makeup of an organism o Genomics – the study of the structure and the function of the Transcriptome genome  Transcriptome o Includes all the RNA molecules made by a cell, tissue, or organism Proteome o Transcriptomics – the study of the transcriptome  Proteome o Includes all of proteins made by a cell, tissue, or organism 3/28/2016 o Proteomics – the study of the proteome, including protein-protein and protein-small molecule interactions as well as expression profiling [Genetics vs. Genomics]  Molecular genetic studies involve o Gene cloning, DNA sequencing, overexpression, knockouts, site- directed mutagenesis o Construction of DNA and protein sequence databases o The ability to isolate and manipulate genes has limitations  Many genes are difficult to clone  Characterization of one or few genes gives little information about the whole organism and how all its components interact  Examples: human height (many genes of small effect)  hard to study if you do it via gene by gene approach  Questions about the entire gene set of organisms cannot be answered  Would have to test it on different substances, see if it grows, etc.  Genomics allows you to sequence the entire genome and draw conclusions 3/28/2016 [Timeline of Important Events in Genomics]  Genome sequencing timeline o 1977 – 1 genome sequence (from a virus – bacteriophage) st o 1995- 1 genome of a cellular organism (Haemophilus influenza) st o 1996 – 1 genome of a eukaryote (Saccharomyces cerevisiae) o 1998 – 1 genome of a multicellular organism (Caenorhabditis elegans)  C. elegans have a fixed number of cells  the fate of every cell has been mapped  Has a lot of importance for someone who wants to study developmental biology o 2000 – 1 genome of a plant (Arabidopsis thaliana)  A. thaliana has an extremely fast generation time and a very small genome o 2000- 1 genome of a mammal (Homo sapiens)  Bioinformatics – in silico, “in the computer,” processing analysis of DNA, RNA, and protein sequence data o Computer use is needed to analyze the information [Genomics in the News]  Zika virus o Brazil, South America countries, and now Mexico o Zika is transmitted by a mosquito 3/28/2016 o Most people who get Zika virus have mild symptoms (rash, common cold symptoms) o In some people (immunocompromised people) severe complications occur, can be fatal o Worried about this virus because there is an association with pregnant women having the virus  and as a result there is a birth defect of microcephaly (reduced head, or even infant death) o Female athletes are saying they won’t go to Brazil for the Olympics o “Zika virus in the Americas: Early epidemiological and genetic findings”  Found 1 year incubation time  Think initial introduction matches a strain that was isolated in French Polynesia  That same pacific strain may be traced back to Asia  This introduction coincides with some new airline routes that got started between Brazil and the South Pacific  All this connectivity around the world is creating this opportunity for viruses to spread  Tried to see if there is something in these virus genomes that associate with microcephaly  they couldn’t find particular sequence changes, it is still unknown  This article is a start for the Zika virus studies  Maybe when a pregnant woman is affected with Zika virus, is it possibly affecting HER genome 3/31/2016 [Genomics and Bioinformatics] [Dr. Hayashi and Dr. Stajich] [Lecture 2 – Genome Complexity and Structure] Highlight = Important PrinciplHighlight = Important Concept Highlight = Key Term [Genome Size and Complexity]  A genome is really just a collection of nucleotides  Note: Must know metrics and conversions o 1,000 b = 1kb o 1,000,000 b = 1 Mb = 1,000 kb o 1 pg = 1000 Mb  C-values o Represents the size of haploid genomes and are measured in picograms (pg) o Range: 3,500 – 100,000,000,000 bp  Mycoplasma o Lacking a cell wall is thought to be associated with their parasitic behavior → they can get up close to a eukaryote and infect them o Small genome  approximately 500 genes o M. pneumoniae 3/31/2016  For bacteria and archaea there is a linear relationship between genome size and the number of genes within the genome  Of all natural, free-living organsisms P.ubique has the smallest number of genes o Ubique – Stands for ubiquitous, it is found mostly in the ocean  Obligate – a symbiont that NEEDS the other organism in order to survive  Parasites or symbionts have a smaller number of protein coding genes than free-living organisms o One of the reasons for this is because the host organism is providing a very stable environment (unlike the environments of free -living organisms)  Remarkably small genomes have been found → they are represented by the star on the graph of slide 5  Endosymbiont → environment isn't just the host but more specifically a certain tissue of the host  Mothers actually pass on the Carsonella bacteria to their offspring o Lives inside insects that feed exclusively on sap o Since insect only feeds on one thing they don't get the nutrients they need 3/31/2016 o The bacteria provides the missing nutrients while the host provides an environment o By sequencing the genome you can possibly figure out the environment of the organism  Francis Collins is the image representing humans  Puffer fish – 345 Mb o Genome is much smaller than a human’s  Lungfish have the largest fish genome o Their genome is even larger than the human genome (44 times larger)  When talking about bacteria the range in C- values (length of genome) does correlate well with organism complexity but it doesn't correlate for eukaryotic organisms (the C-value paradox) [The C-value paradox]  Eukaryotic organism C-values do not correlate well with organism complexity  Denaturation and renaturation of genomic DNA o Double stranded DNA and SSDNA absorb light differently o Need to know initial DNA concentration o If you are working with a smaller genome you are going to get many more copies in the test tube (because everything is at the same concentration) → so it won't take as long to find a pair 3/31/2016 o A larger genome has less copies in a test tube so it takes longer to find a pair [Genome Complexity]  Cot 1/2does not represent genome size, it represents genome complexity o Example: does the genome have a lot of repetitive sequences?  Eukaryotic organisms have many repetitive sequences in their DNA o This is why C-values do not properly represent eukaryotic organism complexity  Types of viral genomes o Epstein - Barr virus causes Mononucleosis and has a circular genome o Zika virus – RNA genome, single stranded and linear o Influenza virus has segmented genomes and thus it is constantly adapting o Some bacterial genomes are linear but most are circular  Organelle genomes o Eukaryotes have both nuclear genome and mitochondrial genome , and plants also have a chloroplast genome o Organelle genome evolved from a bacterial genome (Endosymbiotic Theory) o Organelle genomes are circular [Genomics in the News]  New bacterial genome created from Mycoplasma genome 3/31/2016  Many of the genes used to create this new bacterial organism have unknown functions  The new organism represents genes that are only essential to survival and many of the genes used are homologous to human genes


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