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by: Kyler Ondricka

Bioinformatics BIOL 7023

Kyler Ondricka

GPA 3.84

Mark Borodovsky

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About this Document

Mark Borodovsky
Class Notes
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This 0 page Class Notes was uploaded by Kyler Ondricka on Monday November 2, 2015. The Class Notes belongs to BIOL 7023 at Georgia Institute of Technology - Main Campus taught by Mark Borodovsky in Fall. Since its upload, it has received 11 views. For similar materials see /class/233997/biol-7023-georgia-institute-of-technology-main-campus in Biology at Georgia Institute of Technology - Main Campus.


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Date Created: 11/02/15
FASTA Algorithm 4 Sequence A lt Sequence A a Sequence 8 gt Find runs of identities C Sequence B gt Apply joining thresholdquot to eliminate segments that are unlikely to be part of the alignment that includes highest scoring segment lt Sequance A 4 Sequence A 39 b Sequence B gt Hensoore using PAM matrix Keep top scoring segments d Sequence 8 gt Use dynamic programming to optimise the alignment in a narrow band that encompasses the top scoring segments LN end loop of size 3 Arm interior ioop of size 32 Figure 133 Components ofa hairpin 51 U A C G U C A C A A A G U G G C C C C 3 539 U A L A GU C A C 1 l 1 t i A C G G U G 3 C C C A Fig 416 Dot matrix sh0wing self compiementary regions in a short RNA Regions with three or more out of ve consecutive matches are shown by The best structure isahoshown Fig 417 Some simple stcmzmd loop structures as they appear in a dot matrix From Quiglcy or u 1984 I 3 U C C C AA CU A U A A AU U C U C A UA C U CU G U GCAACA M G C M A UCUCC U U UG G CAAC U C AUG C C A U CU C C A CO A y UA 5 A UA ACAACAUCCA A CCC CACUUCUGUCUUGC A C A A CU C G U C C U CCmG C A C U C CU A UVG A AU A A C AA VUU A C C U CGC C UAA AAUC A CAAU u 0 w CA C U 0 AC A U G 760 750 950 AUG A A A c A G u CAAC c A A G y c 0 0 C Cgtgt ocgt 910 AAcUCA GUUACG u AccuU A 350 860 Genome Organization in Bacteria By Steven Salzberg Manjula Kasoji 111907 Topics The factors involved in shaping genome organization and evolution in bacteria Noncoding DNA plays an important role in encoding for regulatory signals TransTermHP OperonDB Overlapping genes TansTermHP Finds transcription terminators in bacterial genomes Each terminator found by the program is assigned a confidence value that estimates its probability of being a true terminator OperonDB a method to detect and analyze conserved gene pairs pairs of genes that are located close on the same DNA strand in two or more bacterial genomes For each conserved gene pair calculate probability that the genes belong to the same operon OperonDB The algorithm takes into account alternative possibilities functionally unrelated genes may have the same order due simply because they were adjacent in a common ancestor genes may be adjacent in two genomes by chance alone or due to horizontal transfer of the gene pair


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