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      Rapid Whole-Genome Sequencing of Mycobacterium tuberculosis Isolates Directly from Clinical Samples

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          ABSTRACT

          The rapid identification of antimicrobial resistance is essential for effective treatment of highly resistant Mycobacterium tuberculosis . Whole-genome sequencing provides comprehensive data on resistance mutations and strain typing for monitoring transmission, but unlike for conventional molecular tests, this has previously been achievable only from cultures of M. tuberculosis . Here we describe a method utilizing biotinylated RNA baits designed specifically for M. tuberculosis DNA to capture full M. tuberculosis genomes directly from infected sputum samples, allowing whole-genome sequencing without the requirement of culture. This was carried out on 24 smear-positive sputum samples, collected from the United Kingdom and Lithuania where a matched culture sample was available, and 2 samples that had failed to grow in culture. M. tuberculosis sequencing data were obtained directly from all 24 smear-positive culture-positive sputa, of which 20 were of high quality (>20× depth and >90% of the genome covered). Results were compared with those of conventional molecular and culture-based methods, and high levels of concordance between phenotypical resistance and predicted resistance based on genotype were observed. High-quality sequence data were obtained from one smear-positive culture-negative case. This study demonstrated for the first time the successful and accurate sequencing of M. tuberculosis genomes directly from uncultured sputa. Identification of known resistance mutations within a week of sample receipt offers the prospect for personalized rather than empirical treatment of drug-resistant tuberculosis, including the use of antimicrobial-sparing regimens, leading to improved outcomes.

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          Most cited references34

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          Is Open Access

          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

            RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively. icwww.epfl.ch/~stamatak
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              VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing.

              Cancer is a disease driven by genetic variation and mutation. Exome sequencing can be utilized for discovering these variants and mutations across hundreds of tumors. Here we present an analysis tool, VarScan 2, for the detection of somatic mutations and copy number alterations (CNAs) in exome data from tumor-normal pairs. Unlike most current approaches, our algorithm reads data from both samples simultaneously; a heuristic and statistical algorithm detects sequence variants and classifies them by somatic status (germline, somatic, or LOH); while a comparison of normalized read depth delineates relative copy number changes. We apply these methods to the analysis of exome sequence data from 151 high-grade ovarian tumors characterized as part of the Cancer Genome Atlas (TCGA). We validated some 7790 somatic coding mutations, achieving 93% sensitivity and 85% precision for single nucleotide variant (SNV) detection. Exome-based CNA analysis identified 29 large-scale alterations and 619 focal events per tumor on average. As in our previous analysis of these data, we observed frequent amplification of oncogenes (e.g., CCNE1, MYC) and deletion of tumor suppressors (NF1, PTEN, and CDKN2A). We searched for additional recurrent focal CNAs using the correlation matrix diagonal segmentation (CMDS) algorithm, which identified 424 significant events affecting 582 genes. Taken together, our results demonstrate the robust performance of VarScan 2 for somatic mutation and CNA detection and shed new light on the landscape of genetic alterations in ovarian cancer.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Journal of Clinical Microbiology
                J Clin Microbiol
                American Society for Microbiology
                0095-1137
                1098-660X
                July 2015
                July 2015
                : 53
                : 7
                : 2230-2237
                Affiliations
                [1 ]Oxford Gene Technology, Oxford, United Kingdom
                [2 ]UCL, Division of Infection and Immunity, London, United Kingdom
                [3 ]Qiagen-AAR, Aarhus, Denmark
                [4 ]National Mycobacterium Reference Laboratory (NMRL), ICMS, London, United Kingdom
                [5 ]Dept. Microbiology, Frimley Health NHS Foundation Trust, Wexham Park Hospital, Berkshire, United Kingdom
                [6 ]Barts Health NHS Trust, West Smithfield, London, United Kingdom
                [7 ]Centre for Clinical Microbiology, UCL, Royal Free Campus, London, United Kingdom
                [8 ]Specialist Microbiology Network, Public Health Laboratory Manchester, Manchester Royal Infirmary, Manchester, United Kingdom
                Article
                10.1128/JCM.00486-15
                25972414
                1e8b376e-9420-4b36-9e10-d1000119bc90
                © 2015

                https://journals.asm.org/non-commercial-tdm-license

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