Data integration in microbial genomics ... - Jacobs University

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80 6. Domains of unknown function 6.2 Introduction Genomic and metagenomic sequencing projects are revealing everincreasing numbers of novel genes, many of unknown function. The Pfam 23 database [Finn et al., 2008], for example, stored some 10 340 protein domain families derived from conserved sequence data with 22% dubbed “domains of unknown function” (DUFs). This proportion is predicted to soon overtake that of functionally characterized domains [Bateman et al., 2010], prompting calls for community action [Roberts, 2004] and concerted, cross-disciplinary attention [Galperin and Koonin, 2010]. In their response, Jaroszewski et al. [Jaroszewski et al., 2009] and Goonesekere et al. [Goonesekere et al., 2010] noted several DUFs that appeared to be variations of functionally characterized protein folds, most likely maintained due to an extension of an organism’s ecological niche. It is reasonable to expect that conserved DUFs enhance ecological performance; however, characterizing DUFs from an ecological perspective has yet to be attempted. In this communication, we present a method of functional hypothesis generation based on DUF correlation across the Global Ocean Sampling (GOS) metagenome collection [Rusch et al., 2007]. Network visualizations were used in hypothesis generation followed by indirect gradient analysis to contextualize one well-defined hypothesis with environmental metadata. Together, these approaches aim to support efforts in DUF characterization using ecogenomic resources. 6.3 Material and Methods Correlation analysis of microbial taxa and environmental parameters has previously been used to construct association networks [Fuhrman et al., 2008,Fuhrman, 2009]. Just as the correlation of taxa-abundance may elucidate a given taxon’s ecosystem-level interactions and function, correlation of protein domains across environments may grant insight into their potential associations and roles. This approach parallels the identification of unknown metabolic modules whereby genomic features found to co-vary in response to experimental pertur-
6.4. Results 81 bations are grouped in putative modules [Breitling et al., 2008]. To detect such associations in metagenomic datasets, we measured the Spearman rank correlation (ρ) between DUFs detected in the globallydistributed GOS metagenomes (473 351 DUFs detected in 454 varieties across 79 metagenomes, see Supplementary methods online). We visualized these results as network graphs. Vertices (representing DUFs varieties) were connected if their ρ was ≥ 0.90. As abundances of 454 DUF varieties were correlated, we enforced a Bonferroni-corrected p- value threshold of ∼ 2.20× 10 5 (0.01 / 454). We embedded the graph using the Fruchterman-Reingold procedure [Fruchterman and Reingold, 1991]. A minimal spanning tree was visualized after Prim’s algorithm [Prim, 1957] to aid visual interpretation (Fig. 6.1). We assigned DUFs to putative functional categories guided by Pfam descriptions and linked literature, color-coding vertices accordingly. 6.4 Results We observed two prominent networks, one dominated by DUFs linked to photosynthetic organisms (Fig. 6.1, II) and another comprised of more diverse members (Fig. 6.1, I). Smaller networks were observed, including one associating DUFs 404 and 407 (Fig. 6.1, III), domains known to co-occur [Goonesekere et al., 2010]. Employing a ‘guilty-byassociation’ approach (Merico et al, 2009), we propagated hypotheses across closely-embedded domains. We thus hypothesized that DUFs in network II (Fig. 6.1), including unassigned DUFs (Fig. 6.1, grey vertices), describe a photobiologically active module. To examine this hypothesis, the taxonomic distributions of the unassigned domains were examined using the Pfam web-interface (http://pfam.sanger.ac.uk/). DUFs 1997, 1995, 1830, and 1651 were observed exclusively in phototrophic organisms while DUF2307 appeared to have a higher copy number in Cyanobacteria. DUFs 2214 and 564 showed less striking distributions, however, we speculate they may be involved in pigmentation and C1 metabolism respectively (see Supplementary material online). The larger network (Fig. 6.1, I) was difficult to interpret due to the diverse functions of its members. The centrality of the
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Max Planck Institute for Marine Mic
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List of abbreviations BLAST Basic L
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Thesis abstract Deoxyribonucleic ac
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4.2 Introduction . . . . . . . . .
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18 1. Introduction Doug Wendel, Owe
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CHAPTER 2 METABAR A tool for consis
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2.2. Background 23 molecular sequen
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2.2. Background 25 printing of data
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