The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families

By Shibu Yooseph, Granger G. Sutton, D. Rusch, A. Halpern, S. Williamson, K. Remington, Jonathan A. Eisen, K. Heidelberg, Gerard Manning, Weizhong Li and 23 more. 2007

858 citations recorded by the source corpus.

Metagenomics projects based on shotgun sequencing of populations of micro-organisms yield insight into protein families. We used sequence similarity clustering to explore proteins with a comprehensive dataset consisting of sequences from available databases together with 6.12 million proteins predicted from an assembly of 7.7 million Global Ocean Sampling (GOS) sequences. The GOS dataset covers nearly all known prokaryotic protein families. A total of 3,995 medium- and large-sized clusters consisting of only GOS sequences are identified, out of which 1,700 have no detectable homology to known families. The GOS-only clusters contain a higher than expected proportion of sequences of viral origin, thus reflecting a poor sampling of viral diversity until now. Protein domain distributions in the GOS dataset and current protein databases show distinct biases. Several protein domains that were previously categorized as kingdom specific are shown to have GOS examples in other kingdoms. About 6,000 sequences (ORFans) from the literature that heretofore lacked similarity to known proteins have matches in the GOS data. The GOS dataset is also used to improve remote homology detection. Overall, besides nearly doubling the number of current proteins, the predicted GOS proteins also add a great deal of diversity to known protein families and shed light on their evolution. These observations are illustrated using several protein families, including phosphatases, proteases, ultraviolet-irradiation DNA damage repair enzymes, glutamine synthetase, and RuBisCO. The diversity added by GOS data has implications for choosing targets for experimental structure characterization as part of structural genomics efforts. Our analysis indicates that new families are being discovered at a rate that is linear or almost linear with the addition of new sequences, implying that we are still far from discovering all protein families in nature.

Subfields: Ecology, Molecular Biology

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Citations in the corpus

Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, background): “and size of protein families, in particular those without structural homologs [21], accurate de novo prediction of 3D structure from”
Data growth and its impact on the SCOP database: new developments (2007, background): “Interestingly, the first several structures of ‘metagenomic’ targets from environmental samples, selected as representative of novel sequence families (18), all belong to these ‘new rich’ superfamilies, mostly to the dimeric a+ b barrel superfamily in SCOP.”
WebMGA: a customizable web server for fast metagenomic sequence analysis (2011, methodology): “It provides revolutionary and unprecedented view of the identities, dynamics and functions of microbial communities in various environments such as marine [1], human gut [2] and many others [3-5].”
Nature of the protein universe (2009, methodology): “Relation to Earlier Work Previous work (6) suggests that the protein universe is growing rapidly and without bounds; we find that only the MDA sequences are growing linearly with added sequences.”
Ab initio gene identification in metagenomic sequences (2010, background): “Since a number of protein-coding regions in a metagenome may be counted by millions (4), improving accuracy of gene finding by a percentage point would affect accurate prediction of tens of thousands of genes of the organisms constituting microbial communities.”
Mimicking the folding pathway to improve homology-free protein structure prediction (2009, background): “The structure of a protein affords a window on its function, and the huge growth in the number of sequenced genomes provides codes for an enormous number of new proteins with unknown functions (1), a number far exceeding experimental capabilities and requiring fast throughput theoretical methods for”
3D Protein Structure Predicted from Sequence (2011, positive): “Therefore, as massive genomic sequencing projects rapidly increase the number and size of protein families, in particular those without structural homologs [19], accurate de novo prediction of 3D structure ”
Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks (2014, background): “proteins sequences [1-3], has been addressed in the last”
A Primer on Metagenomics (2010, background): “Yooseph and colleagues [59,63] have used a different approach to gene finding when analyzing the global ocean survey data.”
The Pacific Ocean Virome (POV): A Marine Viral Metagenomic Dataset and Associated Protein Clusters for Quantitative Viral Ecology (2013, methodology): “To this end, resources such as the Global Ocean Sampling (GOS) dataset [18] have been developed, and subsequently organized using sequencebased protein clusters (PCs) [19] to (i) group related proteins based on sequence similarity, (ii) map metadata associated with the samples to the PCs, and (iii) ”