Quiz section 10. June 1, 2018
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1 Quiz section 10 June 1, 2018
2 Logistics Bring: 1 page cheat-sheet, simple calculator Any last logistics questions about the final?
3 Logistics Bring: 1 page cheat-sheet, simple calculator Any last logistics questions about the final? Please do course evals for all three of the instructors (expire today!)
4 Course Takeaways Bioinformatics is not magic: there are always assumptions, uncertainties, ambiguities Have an understanding of what is happening inside black boxes it might not be as complicated as it seems initially Start small and be clear in your own analysis and programming
5 Topics from first half Alignments Reasons to align sequences Needleman-Wunsch algorithm Smith-Waterman algorithm Effects of parameter variation (including gap penalties) Testing for statistical significance of an alignment Phylogenetic trees Rooted and unrooted topologies Defining the best tree with UPGMA and Neighbor Joining Concept of parsimony Fitch algorithm: quantifying how parsimonious a tree is, assigning internal states Finding the most parsimonious tree: Hill climbing w/ Nearest-Neighbor interchanges Bootstrapping to quantify confidence in tree partitions Clustering Defining a clustering problem Hierarchical clustering Impact of using single/complete/average linkage K-means: Objective and algorithm Networks Reasons to make and analyze networks Basic network definitions Dijkstra s Algorithm Network motifs and their uses
6 Topics from second half Gene prediction Experimental methods Homology based Abinitio/Ad hoc Markov chain/model and property Hidden markovmodels Viterbi algorithm Forward-backward algorithm (general concept only) Machine learning Definitions (objects, features, model, training data, overfitting) Supervised versus unsupervised and tasks for each Variant effect prediction Feature trees Decision trees Tree generation Contingency tables/confusion matrices Performance stats (accuracy, TPR, TNR) DNA Sequencing and alignment Sequencing technologies Uses of HTS Short read mapping (hashing, seeding, spaced seeds, difference of BWA) Paired read structural variation Variant calling (Phred scores) Genome assembly (N50, graph-based assembly)
7 Markov chain/model versus hidden Markov model?
8 Viterbi algorithm What is it for? Big picture of how it works?
9 Viterbi: determine the likeliest hidden state sequence for an observed sequence 0.5*0.8= A A T T T A 0.9 *0.8 =? A-rich *0.2= T-rich Observed sequence
10 Viterbi: determine the likeliest hidden state sequence for an observed sequence A A T T T A A-rich T-rich 0.1 Observed sequence
11 Viterbi: determine the likeliest hidden state sequence for an observed sequence A A T T T A A-rich T-rich 0.1 Observed sequence
12 Viterbi: determine the likeliest hidden state sequence for an observed sequence A A T T T A A-rich T-rich 0.1 Observed sequence
13 What s a decision tree? How do you construct an optimal decision tree?
14 What is hashing? A hash function maps some object x to an integer i A hash function allows us to have a hash table, which is like a list that allows indexing by arbitrary objects (a python Dictionary!) We can compute the value of the hash function and find the index in the hash table in constant time fast!! hash( hello ) 3 Hash table with key hello hello
15 Hashing Improves Search A hash function assigns a unique key to each unique data element (DNA sequence in our case) hash( ATGCTG ) = key1 hash( TTTCTG ) = key2 Keys encode strings in a short, easily comparable format (e.g. a number)
16 Hashing Improves Search A hash function assigns a unique key to each unique data element (DNA sequence in our case) The hash table is an associative array that describes the relationship between the key and the sequence and its genomic loction Key Hashed index Genomic location GCTAGC Key1 Chr TTTAGC KeyN Chr
17 Is hashing really faster? constructing dictionary: Using the dictionary: Using reference.index: Constructing the dictionary is expensive, but you only have to do it once, and you keep reaping the benefits
18 What is seeding? Spaced seeding? With this spaced seeding, how many mismatches will be allowed? Why?
19 Graph-based genome assembly Where is the read CAATG represented?
20 Any other questions??
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