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      Virome comparisons in wild-diseased and healthy captive giant pandas

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          Abstract

          Background

          The giant panda ( Ailuropoda melanoleuca) is a vulnerable mammal herbivore living wild in central China. Viral infections have become a potential threat to the health of these endangered animals, but limited information related to these infections is available.

          Methods

          Using a viral metagenomic approach, we surveyed viruses in the feces, nasopharyngeal secretions, blood, and different tissues from a wild giant panda that died from an unknown disease, a healthy wild giant panda, and 46 healthy captive animals.

          Results

          The previously uncharacterized complete or near complete genomes of four viruses from three genera in Papillomaviridae family, six viruses in a proposed new Picornaviridae genus (Aimelvirus), two unclassified viruses related to posaviruses in Picornavirales order, 19 anelloviruses in four different clades of Anelloviridae family, four putative circoviruses, and 15 viruses belonging to the recently described Genomoviridae family were sequenced. Reflecting the diet of giant pandas, numerous insect virus sequences related to the families Iflaviridae, Dicistroviridae, Iridoviridae, Baculoviridae, Polydnaviridae, and subfamily Densovirinae and plant viruses sequences related to the families Tombusviridae, Partitiviridae, Secoviridae, Geminiviridae, Luteoviridae, Virgaviridae, and Rhabdoviridae; genus Umbravirus, Alphaflexiviridae, and Phycodnaviridae were also detected in fecal samples. A small number of insect virus sequences were also detected in the nasopharyngeal secretions of healthy giant pandas and lung tissues from the dead wild giant panda. Although the viral families present in the sick giant panda were also detected in the healthy ones, a higher proportion of papillomaviruses, picornaviruses, and anelloviruses reads were detected in the diseased panda.

          Conclusion

          This viral survey increases our understanding of eukaryotic viruses in giant pandas and provides a baseline for comparison to viruses detected in future infectious disease outbreaks. The similar viral families detected in sick and healthy giant pandas indicate that these viruses result in commensal infections in most immuno-competent animals.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s40168-017-0308-0) contains supplementary material, which is available to authorized users.

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          Most cited references51

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          Hidden Markov model speed heuristic and iterative HMM search procedure

          Background Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases. Results We have designed a series of database filtering steps, HMMERHEAD, that are applied prior to the scoring algorithms, as implemented in the HMMER package, in an effort to reduce search time. Using this heuristic, we obtain a 20-fold decrease in Forward and a 6-fold decrease in Viterbi search time with a minimal loss in sensitivity relative to the unfiltered approaches. We then implemented an iterative profile-HMM search method, JackHMMER, which employs the HMMERHEAD heuristic. Due to our search heuristic, we eliminated the subdatabase creation that is common in current iterative profile-HMM approaches. On our benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K. Conclusions Our search heuristic, HMMERHEAD, significantly reduces the time needed to score a profile-HMM against large sequence databases. This search heuristic allowed us to implement an iterative profile-HMM search method, JackHMMER, which detects significantly more remote protein homologs than SAM's T2K and NCBI's PSI-BLAST.
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            A new generation of homology search tools based on probabilistic inference.

            Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.
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              The virome in mammalian physiology and disease.

              The virome contains the most abundant and fastest mutating genetic elements on Earth. The mammalian virome is constituted of viruses that infect host cells, virus-derived elements in our chromosomes, and viruses that infect the broad array of other types of organisms that inhabit us. Virome interactions with the host cannot be encompassed by a monotheistic view of viruses as pathogens. Instead, the genetic and transcriptional identity of mammals is defined in part by our coevolved virome, a concept with profound implications for understanding health and disease. Copyright © 2014 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                z0216wen@yahoo.com
                johnsonyang1979@163.com
                shantongling@yahoo.com
                hourong2000@yahoo.com
                zhijianchange1008@outlook.com
                liwang1983@foxmail.com
                306664973@qq.com
                wangyan_jtu@126.com
                capricorncp@163.com
                chun_xiao@163.com
                602433987@qq.com
                494585183@qq.com
                307909039@qq.com
                shenquanfly@gmail.com
                zjsyzcl@126.com
                hxg@sjtu.edu.cn
                lcui@sjtu.edu.cn
                XDeng@bloodsystems.org
                weiling@panda.org.cn
                qidunwu@163.com
                delwarte@medicine.ucsf.edu
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                7 August 2017
                7 August 2017
                2017
                : 5
                : 90
                Affiliations
                [1 ]ISNI 0000 0001 0743 511X, GRID grid.440785.a, Department of Microbiology, School of Medicine, , Jiangsu University, ; Zhenjiang, Jiangsu 212013 China
                [2 ]GRID grid.452857.9, Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife, , Chengdu Research Base of Giant Panda Breeding, ; Chengdu, Sichuan 610081 China
                [3 ]ISNI 0000 0001 0526 1937, GRID grid.410727.7, Department of Swine Infectious Disease, Shanghai Veterinary Research Institute, , Chinese Academy of Agricultural Sciences, ; Shanghai, 200241 China
                [4 ]GRID grid.479690.5, Department of Laboratory Medicine, , Jiangsu Taizhou People’s Hospital, ; Taizhou, Jiangsu 225300 China
                [5 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, School of Agriculture and Biology, , Shanghai Jiaotong University, ; Shanghai, 200240 China
                [6 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Blood Systems Research Institute, Department of Laboratory Medicine, , University of California San Francisco, ; San Francisco, CA 94118 USA
                [7 ]GRID grid.452857.9, , Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife, Chengdu Research Base of Giant Panda Breeding, ; Chengdu, Sichuan 610000 China
                Article
                308
                10.1186/s40168-017-0308-0
                5545856
                28780905
                6726655a-c5e8-4aa8-859e-6dc0c7239edd
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 6 October 2016
                : 13 July 2017
                Funding
                Funded by: National Key Research and Development Programs of China
                Award ID: 2016YFC0503200
                Award Recipient :
                Funded by: National Natural Science Foundation of China (CN)
                Award ID: 31302107
                Award Recipient :
                Funded by: Foundation for the National Institutes of Health (US)
                Award ID: R01 AI123376
                Award Recipient :
                Funded by: National key research and development programs of China
                Award ID: 2017YFC1200200
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31372223
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31402211
                Award Recipient :
                Funded by: Jiangsu Provincial Key Research and Development Projects
                Award ID: SBE2017740122
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                giant panda,viral metagenomics,virome,papillomavirus,picornavirus,anellovirus,gemycircularvirus,putative circovirus

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