PPT-Bacteriophage Gene Clustering and Phylogeny

Nicholas Celms San Diego State University Funded in part by NSF 0827278 UBM Interdisciplinary Training in Biology and Mathematics 1 Goal Build a method for making taxonomic groupings of bacteriophages. as a novel approach to . Biofilm. inhibition and remove. Azizian. . Reza1, . Azizi. . Jalilian. . Farid2.*, . Askari. . Hasan1, . Naser. . Ahmad1, . Karimi. . Sajedeh1, . Sadeghifard. . Norkhoda1, . Yvonne. . Lin. . AP. . Bio. . 2. a.k.a. Phages. Bacterial . VIRUS. Attacks, infects, and replicates within bacteria. Discovered. . by. . Frederick. . Twort. . (Great. . Britain, 1915). Species specific – named after the group or species it infects. Investigating the Tree of Life. Legless lizards have evolved independently in several different groups. Figure 26.1. Phylogeny. is the evolutionary history of a species or group of related species. For example, a phylogeny shows that legless lizards and snakes evolved from different lineages of legged lizards. Tandy Warnow. The University of Illinois at Urbana-Champaign. Brief history. We met in 1992, when I was working in the Discrete Algorithms Group at Sandia National Labs.. I moved to the University of Pennsylvania in 1993, then to the University of Texas at Austin in 1999. No papers yet. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Objective. SWBAT explain and demonstrate that organisms share many conserved core processes and features that evolved and are widely distributed among organisms today.. Agenda. Chapter 25 Quiz. Chapter 26 notes: sections 26.1, 26.2, and 26.3. 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Figure 21.1 Basic shapes of bacteriophages. There are two basic morphologies of bacteriophages. The virion is either isomeric, in the shape of a polyhedral, such as an icosahedral, sometimes with a tail and tail fibers, or it is helical, in a spiral filamentous formation.. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values.. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because . What is clustering?. Grouping set of documents into subsets or clusters.. The Goal of clustering algorithm is:. To create clusters that are coherent internally, but clearly different from each other. Salmonella enterica. in pasteurized milk. Airhenvbahihea . Edionwe. *, Victoria M. Blake, & John L. McKillip. Department of Biology* Ball State University. Abstract. Methodology. . Results. Conclusions.