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    Metagenomic analysis of the microbial community in kefir grains
    (Academic Press Ltd- Elsevier Science Ltd, 2014) Nalbantoglu, Ufuk; Cakar, Atilla; Dogan, Haluk; Abaci, Neslihan; Ustek, Duran; Sayood, Khalid; Can, Handan
    Kefir grains as a probiotic have been subject to microbial community identification using culture-dependent and independent methods that target specific strains in the community, or that are based on limited 16S rRNA analysis. We performed whole genome shotgun pyrosequencing using two Turkish Kefir grains. Sequencing generated 3,682,455 high quality reads for a total of similar to 1.6 Gbp of data assembled into 6151 contigs with a total length of similar to 24 Mbp. Species identification mapped 88.16% and 93.81% of the reads rendering 4 Mpb of assembly that did not show any homology to known bacterial sequences. Identified communities in the two grains showed high concordance where Lactobacillus was the most abundant genus with a mapped abundance of 99.42% and 99.79%. This genus was dominantly represented by three species Lactobacillus kefiranofaciens, Lactobacillus buchneri and Lactobacillus helveticus with a total mapped abundance of 97.63% and 98.74%. We compared and verified our findings with 16S pyrosequencing and model based 16S data analysis. Our results suggest that microbial community profiling using whole genome shotgun data is feasible, can identify novel species data, and has the potential to generate a more accurate and detailed assessment of the underlying bacterial community, especially for low abundance species. (C) 2014 Elsevier Ltd. All rights reserved.
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    Objective Functions
    (Humana Press Inc, 2014) Dogan, Haluk; Otu, Hasan H.
    Multiple sequence alignment involves alignment of more than two sequences and is an NP-complete problem. Therefore, heuristic algorithms that use different criteria to find an approximation to the optimal solution are employed. At the heart of these approaches lie the scoring and objective functions that a given algorithm uses to compare competing solutions in constructing a multiple sequence alignment. These objective functions are often motivated by the biological paradigms that govern functional similarities and evolutionary relations. Most existing approaches utilize a progressive process where the final alignment is constructed sequentially by adding new sequences into an existing multiple sequence alignment matrix, which is dynamically updated. In doing this, the core scoring function to assess accuracies of pairwise alignments generally remains the same, while the objective functions used in intermediary steps differ. Nevertheless, the overall assessment of the final multiple sequence alignment is generally calculated by an extension of pairwise scorings. In this chapter, we explore different scoring and objective functions used in calculating the accuracy and optimization of a multiple sequence alignment and provide utilization of these criteria in popularly used multiple sequence alignment algorithms.