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      Discovery of Negative-Sense RNA Viruses in Trees Infected with Apple Rubbery Wood Disease by Next-Generation Sequencing

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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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            Profile Hidden Markov Models for the Detection of Viruses within Metagenomic Sequence Data

            Rapid, sensitive, and specific virus detection is an important component of clinical diagnostics. Massively parallel sequencing enables new diagnostic opportunities that complement traditional serological and PCR based techniques. While massively parallel sequencing promises the benefits of being more comprehensive and less biased than traditional approaches, it presents new analytical challenges, especially with respect to detection of pathogen sequences in metagenomic contexts. To a first approximation, the initial detection of viruses can be achieved simply through alignment of sequence reads or assembled contigs to a reference database of pathogen genomes with tools such as BLAST. However, recognition of highly divergent viral sequences is problematic, and may be further complicated by the inherently high mutation rates of some viral types, especially RNA viruses. In these cases, increased sensitivity may be achieved by leveraging position-specific information during the alignment process. Here, we constructed HMMER3-compatible profile hidden Markov models (profile HMMs) from all the virally annotated proteins in RefSeq in an automated fashion using a custom-built bioinformatic pipeline. We then tested the ability of these viral profile HMMs (“vFams”) to accurately classify sequences as viral or non-viral. Cross-validation experiments with full-length gene sequences showed that the vFams were able to recall 91% of left-out viral test sequences without erroneously classifying any non-viral sequences into viral protein clusters. Thorough reanalysis of previously published metagenomic datasets with a set of the best-performing vFams showed that they were more sensitive than BLAST for detecting sequences originating from more distant relatives of known viruses. To facilitate the use of the vFams for rapid detection of remote viral homologs in metagenomic data, we provide two sets of vFams, comprising more than 4,000 vFams each, in the HMMER3 format. We also provide the software necessary to build custom profile HMMs or update the vFams as more viruses are discovered (http://derisilab.ucsf.edu/software/vFam).
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              Batai and Ngari viruses: M segment reassortment and association with severe febrile disease outbreaks in East Africa.

              Ngari virus is an orthobunyavirus recently recognized as a reassortant between Bunyamwera virus and an as yet unidentified M segment donor. Analysis of M segment sequences of Batai and Ilesha viruses revealed 95% deduced amino acid identity between Batai virus and Ngari virus. These findings suggest Batai virus as the donor of Ngari virus M segment sequence. Analysis of Batai virus-related African isolates identified UgMP-6830, isolated from mosquitoes in Uganda, as an isolate of Batai virus. KV-141, isolated during a febrile disease outbreak in Sudan, was identified as another isolate of Ngari virus, emphasizing a role of this reassortant virus in severe human illness throughout East Africa.
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                Author and article information

                Journal
                Plant Disease
                Plant Disease
                Scientific Societies
                0191-2917
                July 2018
                July 2018
                : 102
                : 7
                : 1254-1263
                Affiliations
                [1 ]Canadian Food Inspection Agency, Sidney Laboratory, North Saanich, British Columbia, V8L1H3, Canada
                [2 ]Julius-Kuehn Institute, 69221 Dossenheim, Germany
                [3 ]Canadian Food Inspection Agency, Sidney Laboratory
                [4 ]Julius-Kuehn Institute
                Article
                10.1094/PDIS-06-17-0851-RE
                30673558
                bddcbfe6-fd6d-4d22-b35b-98f044d27bfe
                © 2018
                History

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