 Illuminating uveitis: metagenomic deep sequencing identifies common and rare pathogens Genome Medicine, 2016
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 Acute West Nile virus meningoencephalitis diagnosed via metagenomic deep sequencing of cerebrospinal fluid in a renal transplant patient American Journal of Transplantation, 2016
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 Multiplexed Metagenomic Deep Sequencing To Analyze the Composition of High-Priority Pathogen Reagents ASM mSystems, 2016
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 Novel Picornavirus Associated with Avian Keratin Disorder in Alaskan Birds mBio 2016
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 Anti-malarial benzoxaboroles target P. falciparum leucyl-tRNA synthetase AAC, 2016
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 Identification of Plasmodium falciparum specific translation inhibitors from the MMV Malaria Box using a high throughput in vitro translation screen Malaria Journal, 2016
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 Depletion of Abundant Sequences by Hybridization (DASH): Using Cas9 to remove unwanted highabundance species in sequencing libraries and molecular counting applications BioRxiv, 2015 and Genome Biology, 2016
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|  Here we introduce HMMSplicer, an accurate and efficient algorithm for discovering canonical and non-canonical splice junctions in short read datasets. HMMSplicer identifies more splice junctions than currently available algorithms when tested on publicly available A. thaliana, P. falciparum, and H. sapiens datasets without a reduction in specificity. HMMSplicer was found to perform especially well in compact genomes and on genes with low expression levels, alternative splice isoforms, or non-canonical splice junctions. Because HHMSplicer does not rely on pre-built gene models, the products of inexact splicing are also detected. In addition, HMMSplicer provides a score for every predicted junction allowing the user to set a threshold to tune false positive rates depending on the needs of the experiment. HMMSplicer is implemented in Python. Code and documentation are freely available at the link below. Download HMMSplicer! (Updated Nov 25th 2010 - Version: 0.9.5) |
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