Dataset Information

Illuminating structural proteins in viral "dark matter" with metaproteomics.

ABSTRACT: Viruses are ecologically important, yet environmental virology is limited by dominance of unannotated genomic sequences representing taxonomic and functional "viral dark matter." Although recent analytical advances are rapidly improving taxonomic annotations, identifying functional dark matter remains problematic. Here, we apply paired metaproteomics and dsDNA-targeted metagenomics to identify 1,875 virion-associated proteins from the ocean. Over one-half of these proteins were newly functionally annotated and represent abundant and widespread viral metagenome-derived protein clusters (PCs). One primarily unannotated PC dominated the dataset, but structural modeling and genomic context identified this PC as a previously unidentified capsid protein from multiple uncultivated tailed virus families. Furthermore, four of the five most abundant PCs in the metaproteome represent capsid proteins containing the HK97-like protein fold previously found in many viruses that infect all three domains of life. The dominance of these proteins within our dataset, as well as their global distribution throughout the world's oceans and seas, supports prior hypotheses that this HK97-like protein fold is the most abundant biological structure on Earth. Together, these culture-independent analyses improve virion-associated protein annotations, facilitate the investigation of proteins within natural viral communities, and offer a high-throughput means of illuminating functional viral dark matter.

SUBMITTER: Brum JR

PROVIDER: S-EPMC4780635 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Illuminating structural proteins in viral "dark matter" with metaproteomics.

Brum Jennifer R JR Ignacio-Espinoza J Cesar JC Kim Eun-Hae EH Trubl Gareth G Jones Robert M RM Roux Simon S VerBerkmoes Nathan C NC Rich Virginia I VI Sullivan Matthew B MB

Proceedings of the National Academy of Sciences of the United States of America 20160216 9

Viruses are ecologically important, yet environmental virology is limited by dominance of unannotated genomic sequences representing taxonomic and functional "viral dark matter." Although recent analytical advances are rapidly improving taxonomic annotations, identifying functional dark matter remains problematic. Here, we apply paired metaproteomics and dsDNA-targeted metagenomics to identify 1,875 virion-associated proteins from the ocean. Over one-half of these proteins were newly functionall ...[more]

PMID: 26884177

Similar Datasets

Project description:In the late 20th century, cosmology became a precision science. Now, at the beginning of the next century, the parameters describing how our universe evolved from the Big Bang are generally known to a few percent. One key parameter is the total mass density of the universe. Normal matter constitutes only a small fraction of the total mass density. Observations suggest this additional mass, the dark matter, is cold (that is, moving nonrelativistically in the early universe) and interacts feebly if at all with normal matter and radiation. There's no known such elementary particle, so the strong presumption is the dark matter consists of particle relics of a new kind left over from the Big Bang. One of the most important questions in science is the nature of this dark matter. One attractive particle dark-matter candidate is the axion. The axion is a hypothetical elementary particle arising in a simple and elegant extension to the standard model of particle physics that nulls otherwise observable CP-violating effects (where CP is the product of charge reversal C and parity inversion P) in quantum chromo dynamics (QCD). A light axion of mass 10(-(6-3)) eV (the invisible axion) would couple extraordinarily weakly to normal matter and radiation and would therefore be extremely difficult to detect in the laboratory. However, such an axion is a compelling dark-matter candidate and is therefore a target of a number of searches. Compared with other particle dark-matter candidates, the plausible range of axion dark-matter couplings and masses is narrowly constrained. This focused search range allows for definitive searches, where a nonobservation would seriously impugn the dark-matter QCD-axion hypothesis. Axion searches use a wide range of technologies, and the experiment sensitivities are now reaching likely dark-matter axion couplings and masses. This article is a selective overview of the current generation of sensitive axion searches. Not all techniques and experiments are discussed, but I hope to give a sense of the current experimental landscape of the search for dark-matter axions.

Project description:BackgroundThe human transcriptome annotation is regarded as one of the most complete of any eukaryotic species. However, limitations in sequencing technologies have biased the annotation toward multi-exonic protein coding genes. Accurate high-throughput long read transcript sequencing can now provide additional evidence for rare transcripts and genes such as mono-exonic and non-coding genes that were previously either undetectable or impossible to differentiate from sequencing noise.ResultsWe developed the Transcriptome Annotation by Modular Algorithms (TAMA) software to leverage the power of long read transcript sequencing and address the issues with current data processing pipelines. TAMA achieved high sensitivity and precision for gene and transcript model predictions in both reference guided and unguided approaches in our benchmark tests using simulated Pacific Biosciences (PacBio) and Nanopore sequencing data and real PacBio datasets. By analyzing PacBio Sequel II Iso-Seq sequencing data of the Universal Human Reference RNA (UHRR) using TAMA and other commonly used tools, we found that the convention of using alignment identity to measure error correction performance does not reflect actual gain in accuracy of predicted transcript models. In addition, inter-read error correction can cause major changes to read mapping, resulting in potentially over 6 K erroneous gene model predictions in the Iso-Seq based human genome annotation. Using TAMA's genome assembly based error correction and gene feature evidence, we predicted 2566 putative novel non-coding genes and 1557 putative novel protein coding gene models.ConclusionsLong read transcript sequencing data has the power to identify novel genes within the highly annotated human genome. The use of parameter tuning and extensive output information of the TAMA software package allows for in depth exploration of eukaryotic transcriptomes. We have found long read data based evidence for thousands of unannotated genes within the human genome. More development in sequencing library preparation and data processing are required for differentiating sequencing noise from real genes in long read RNA sequencing data.

Dataset Information

Illuminating structural proteins in viral "dark matter" with metaproteomics.

Publications

Illuminating structural proteins in viral "dark matter" with metaproteomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets