SIMAP: the similarity matrix of proteins

Rattei, Thomas; Arnold, Roland; Tischler, Patrick; Lindner, Dominik; Stümpflen, Volker; Mewes, Hans‐Werner

doi:10.1093/nar/gkj106

Cited by 42 publications

(41 citation statements)

References 28 publications

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“…ORFs that did not map to those data sets were then self-clustered using cd-hit (using the same parameters as above), resulting in a total of 27 685 POV PCs containing X20 ORFs each. These PCs were then annotated using the Similarity Matrix of Proteins (Rattei et al, 2006) to assign taxonomy (NCBI) and function (TIGRFAM), with additional functional information obtained from eggNOG (Powell et al, 2012;4 March 2012).…”

Section: Methodsmentioning

confidence: 99%

Depth-stratified functional and taxonomic niche specialization in the ‘core’ and ‘flexible’ Pacific Ocean Virome

2014

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Microbes drive myriad ecosystem processes, and their viruses modulate microbial-driven processes through mortality, horizontal gene transfer, and metabolic reprogramming by viral-encoded auxiliary metabolic genes (AMGs). However, our knowledge of viral roles in the oceans is primarily limited to surface waters. Here we assess the depth distribution of protein clusters (PCs) in the first large-scale quantitative viral metagenomic data set that spans much of the pelagic depth continuum (the Pacific Ocean Virome; POV). This established ‘core’ (180 PCs; one-third new to science) and ‘flexible’ (423K PCs) community gene sets, including niche-defining genes in the latter (385 and 170 PCs are exclusive and core to the photic and aphotic zones, respectively). Taxonomic annotation suggested that tailed phages are ubiquitous, but not abundant (<5% of PCs) and revealed depth-related taxonomic patterns. Functional annotation, coupled with extensive analyses to document non-viral DNA contamination, uncovered 32 new AMGs (9 core, 20 photic and 3 aphotic) that introduce ways in which viruses manipulate infected host metabolism, and parallel depth-stratified host adaptations (for example, photic zone genes for iron–sulphur cluster modulation for phage production, and aphotic zone genes for high-pressure deep-sea survival). Finally, significant vertical flux of photic zone viruses to the deep sea was detected, which is critical for interpreting depth-related patterns in nature. Beyond the ecological advances outlined here, this catalog of viral core, flexible and niche-defining genes provides a resource for future investigation into the organization, function and evolution of microbial molecular networks to mechanistically understand and model viral roles in the biosphere.

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Section: Methodsmentioning

confidence: 99%

Depth-stratified functional and taxonomic niche specialization in the ‘core’ and ‘flexible’ Pacific Ocean Virome

2014

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“…Reads that were present in a virome just once (k-mer = 1) were removed from the analysis given a higher probability of contamination (13) per the discussion above. The results were then used to compute an Euler diagram using the venneuler function (56) in the R statistical software (54).…”

Section: Construction Of Euler Diagrams Depicting Shared Read Contentmentioning

confidence: 99%

Modeling ecological drivers in marine viral communities using comparative metagenomics and network analyses

Hurwitz

Westveld

Brum

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

101

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Long-standing questions in marine viral ecology are centered on understanding how viral assemblages change along gradients in space and time. However, investigating these fundamental ecological questions has been challenging due to incomplete representation of naturally occurring viral diversity in single gene-or morphology-based studies and an inability to identify up to 90% of reads in viral metagenomes (viromes). Although protein clustering techniques provide a significant advance by helping organize this unknown metagenomic sequence space, they typically use only ∼75% of the data and rely on assembly methods not yet tuned for naturally occurring sequence variation. Here, we introduce an annotation-and assembly-free strategy for comparative metagenomics that combines shared k-mer and social network analyses (regression modeling). This robust statistical framework enables visualization of complex sample networks and determination of ecological factors driving community structure. Application to 32 viromes from the Pacific Ocean Virome dataset identified clusters of samples broadly delineated by photic zone and revealed that geographic region, depth, and proximity to shore were significant predictors of community structure. Within subsets of this dataset, depth, season, and oxygen concentration were significant drivers of viral community structure at a single open ocean station, whereas variability along onshore-offshore transects was driven by oxygen concentration in an area with an oxygen minimum zone and not depth or proximity to shore, as might be expected. Together these results demonstrate that this highly scalable approach using complete metagenomic network-based comparisons can both test and generate hypotheses for ecological investigation of viral and microbial communities in nature.virus | microbial ecology | Bayesian network M icroorganisms drive global biogeochemical cycles (1), with abundances and taxonomic composition tuned to spatiotemporally varying environmental conditions (2-5). Viruses then modulate these biogeochemical processes through mortality, horizontal gene transfer, and metabolic reprogramming (6). However, our understanding of how viral communities change in response to biological, physical, and chemical factors and host availability has been limited by technical challenges.Most viruses in the ocean lack both cultivated representatives [85% of 1,100 sequenced phage genomes derive from only 3 of 45 bacterial phyla (7)] and a universally conserved marker gene (8); thus, metagenomics is commonly applied to characterize the ecology and evolution of viral assemblages. Problematically, however, our ability to investigate these assemblages via metagenomics remains limited by the lack of known viruses and viral proteins in biological sequence databases. The first viral metagenome (virome) used thousands of Sanger reads and found that 65% of sequences were unknown [i.e., no database match for reads >600 bp (9)]. Adoption of next-generation sequencing (NGS) technologies then generated hundreds...

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“…Precalculated TMHMM 62 and SignalP 63 annotations were obtained for all proteins of the TrEMBL dataset using the SIMAP database. 64 To exclude mispredicted TMDs, the TMHMM and SignalP predictions were compared for every protein and all TMDs overlapping by at least eight amino acids with a predicted signal peptide were eliminated. All proteins containing one predicted TMD after this elimination step were combined with the extracted proteins from the Swiss-Prot database resulting in an initial dataset of 167,125 bitopic proteins.…”

Section: Database Analysismentioning

confidence: 99%