INTRO BIOINFORMATCS BCB 544
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Mrs. Frederic Hansen
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This 4 page Study Guide was uploaded by Mrs. Frederic Hansen on Saturday September 26, 2015. The Study Guide belongs to BCB 544 at Iowa State University taught by Staff in Fall. Since its upload, it has received 12 views. For similar materials see /class/214474/bcb-544-iowa-state-university in BioInformatics at Iowa State University.
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Date Created: 09/26/15
BCB 444544 Study Guide 2 For Exam 2 Oct 26 amp 27 Exam 2 will be worth 100 pts amp will have 2 Thurs Oct 26 in lab Lab Practical 30 pts Fri Oct 27 in class In Class 70 pts General on the Lab Practical I This will be openbook opennotes lt 50minute exam I The lab practical will cover all topics covered in lab through Wed Oct 12 Labs 17 4 A General on the In Class I This will be a closedbook closednotes 50minute exam Exam 2 will cover all topics covered in class lab and assigned readings from Mon Sept 25 to Wed Oct 25 Chaps 37 in Campbell amp Heyer textbook plus PPTs amp Nature paper on Human Accelerated Regions Some questions will involve computation therefore bring your calculators All required formulas or tables will be provided in the exam Some questions will require short essaylike answers that demonstrate your understanding of key concepts covered in the course I This exam will not include vocabulary quotmatchingquot questions Topics from Lecture Comparative Genomics Contigs BACs and YACs plasmids Genetic vs physical maps ESTs vs STS Model organisms I Heterochromatin vs euchromatin I Features of the human genome I Comparing the human genome to other genomes I Mitochondrial and chloroplast genomes I Endosymbiosis 7 origin of eukaryotic cells I PCR technology I Biomedical genomics research I RNAr Multiple Sequence Alignment I Dynamic programming I ClustalW Phylogenetics I Distancebased methods I Parsimony I Maximum likelihood I Bootstrapping Fitch s algorithm BCB 444544 Fall 06 Oct 20 Study Guide 2 p 2 0f 4 Genome Variations SNPs Haplotypes and the HapMap project QTLs Machine Learning What is machine learning Types of algorithms 0 Regression vs classi cation 0 Supervised vs unsupervised vs semisupervised o Generative vs discriminative 0 Linear vs nonlinear Clustering algorithms 0 Hierarchical o Kmeans 0 Self organizing maps SOM Classi cation 0 Naive Bayes NB 0 Neural Networks NN 0 Support Vector Machines SVM O Microarrays What are microarrays amp what do they measure cDNA vs oligo arrays Clustering methods Topics from Lab Retrieve sequences from online databases by accession number or keyword Use OMIM to nd information about a disease Retrieve protein structures from PDB Visualize and manipulate protein structures with PyMol Align two sequences Use BLAST and PSIBLAST to nd related sequences and interpret the results Predict genes in a given DNA sequence ORF Finder GeneMark Predict protein function from sequence PROSITE SuperFam JAFA Compare genomes Construct a multiple sequence alignment C1usta1W from a set of sequences Build a phylogenetic tree from a set of sequences Use genome browsers Analyze microarray data BCB 444544 Fall 06 Oct 20 Study Guide 2 p 3 of 4 Sam 1e uestionsProblems 1 Brie y describe 5 conclusions about the nature of the human genome based on the published results of the Human Genome project Take your pick from any of these The human genome contains 31647 million chemical nucleotide bases A C T and G The average gene consists of 3000 bases but sizes vary greatly with the largest known human gene being dystrophin at 24 million bases The total number of genes is estimated at 30000 much lower than previous estimates of 80000 to 140000 that had been based on extrapolations from generich areas as opposed to a composite of generich and genepoor areas The order of almost all 999 nucleotide bases is exactly the same in all people The functions are unknown for over 50 of discovered genes Less than 2 of the genome encodes for the production of proteins Repetitive sequences that do not code for proteins sometimes called quotjunk DNAquot make up at least 50 of the human genome Repetitive sequences are thought to have no direct functions but they shed light on chromosome structure and dynamics Over time these repeats reshape the genome by rearranging it thereby creating entirely new genes or modifying and reshuffling existing genes During the past 50 million years a dramatic decrease seems to have occurred in the rate of accumulation of repeats in the human genome The human genome s genedense quoturban centers are predominantly composed of the DNA building blocks G and C In contrast the genepoor quotdesertsH are rich in the DNA building blocks A and T GC and ATrich regions usually can be seen through a microscope as light and dark bands on chromosomes Genes appear to be concentrated in random areas along the genome with vast expanses of noncoding DNA between Stretches of up to 30000 C and G bases repeating over and over often occur adjacent to generich areas forming a barrier between the genes and the quotjunk DNA quot These CpG islands are believed to help regulate gene activity Chromosome 1 has the most genes 2968 and the Y chromosome has the fewest 231 Unlike the human39s seemingly random distribution of generich areas many other organisms39 genomes are more uniform with genes evenly spaced throughout Humans have on average three times as many kinds of proteins as the fly or worm because of mRNA transcript quotalternative splicing and chemical modifications to the proteins This process can yield different protein products from the same gene Humans share most of the same protein families with worms ies and plants but the number of gene family members has expanded in humans especially in proteins involved in development and immunity The human genome has a much greater portion 50 of repeat sequences than the mustard weed 11 the worm 7 and the fly 3 Although humans appear to have stopped accumulating repeated DNA over 50 million years ago there seems to be no such decline in rodents This may account for some of the fundamental differences between hominids and rodents although gene estimates are similar in these species Scientists have proposed many theories to explain evolutionary contrasts between humans and other organisms including those of life span litter sizes inbreeding and genetic drift Scientists have identified about 14 million locations where singlebase DNA differences SNPs occur in humans This information promises to revolutionize the processes of finding chromosomal locations for diseaseassociated sequences and tracing human history The ratio of germline sperm or egg cell mutations is 21 in males vs females Researchers point to several reasons for the higher mutation rate in the male germ line including the greater number of cell divisions required for sperm formation than for eggs 2 What are SNPs and why are they important SNPs are Single Nucleotide Polymorphisms SNPs are single base changes that are present in at least 1 of the population They are important because they are the differences between us that make us all unique Also a number of SNPs have been associated with diseases 3 What is the main difference between distancebased phylogenetic tree building programs and parsimony programs The main difference is the input used by the programs Distancebased methods use a distance matrix which is very quick to compute for any sequences Parsimony programs use a character matrix derived EcEuAsuhnm 012 Study Guidebz em from a muluple sequence allghmeht The dlstance mamx eohdehses all of the lnformauon about the dlfferences betw two se e s mto a slngle humbeh whereas the muluple sequence allghmeht eohtams mformatlon about eaeh specl dlfference 4 Use the followlng equatloh to answethe followlng questlohs P1D15ease PD15ease PD15ease1 e P1D15ease PD15ease P1No Dlseasey P17 w eases ofbreast cancer m the Uslh 2006 Ofthese eases 1720 are estlmatedto be males whlle 212920 are estlmatedto be females Assume the dlfference ls based solely on sex Ifahew method of tesuhg was 1 ofthe ume Prob1Dlsease 0 995 Prob 1 No Dlsease 0 01 Probe1Dlsease 0 005 Probe 1 No Dlsease 0 99 0 000715467 0 000011467 0 001419467 ProbNo Dlsease 0 999284533 0 999988533 0 998580533 1 2 1239 3 11 4 Who should be more skeptleal of a posltlve result7 Male 5 oooo7 6 What ls the probablllty that a man has developed cancerglven a hegauve result7 ooooooss 7 Who should be more skeptleal of a hegatlve result7 Female
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