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Database Search Algorithms: Sensitivity, Selectivity • True Positive (TP) – a homology detected (positive) correctly (true) Signal Detected Name Yes Yes True Positive No No True Negative Yes No False Negative No Yes False Positive
Database Search Algorithms: Sensitivity, Selectivity • Sensitivity =TP/(TP+FN) • Selectivity =TN/(TN+FP) – Selectivity Courtesy of Gary Benson (ISSCB 2003) Sensitivity
Page 1 and 2: Alignments BLAST, BLAT
Page 3 and 4: The amount of genetic information i
Page 5 and 6: Sequence searching - challenges Ex
Page 7: Sequence searching - definition We
Page 11 and 12: What means well conserved for BLAST
Page 13 and 14: BLAST, Step 1: Preprocess the query
Page 15 and 16: BLAST, Step 2: Find ¨exact¨ match
Page 17 and 18: Gapped BLAST (now standard) gapped
Page 19 and 20: Gapped BLAST v BLAST We end up wit
Page 21 and 22: PSI-BLAST Position-Specific Iterat
Page 23 and 24: Constructing a profile Take signif
Page 25 and 26: BLAT, Step 1: Preprocess the databa
Page 27 and 28: Hashing - examples T9 Predictive T
Page 29 and 30: BLAT, Step 2: Hit criteria 1)Prepro
Page 31 and 32: BLAT, Step 3: Identifying homologou
Page 33 and 34: How to detect homology? Take the s
Page 35 and 36: Best Local Alignments by chance Cr
Page 37 and 38: Expected number of aligns E-value:
Page 39 and 40: Deriving K, L The above eq is theo
Page 41 and 42: Why does the BLAST work? Relevant r
Page 43 and 44: Why does the BLAST work? Average ca
Page 45 and 46: Why does the BLA[S]T work? Forget
Page 47 and 48: Expected sensitivity Q - query siz

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