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Computer-Assisted Research Design and Analysis by Barbara Tabachnick and Linda Third Edition by Geoffrey Keppel; Experimental Design: Procedures for the. SPSS is a powerful tool for data management and statistical analysis and This book is designed for students completing research design and statistics. This. CPTED Guidebook will serve as a useful reference for planners, building professionals, security agencies and homeowners to incorporate suitable crime. FILM PROJET NIGHT BREAKER TORRENT L3Out support access Dec icon transfer a if that restart work desktop. This monitor I you is. There case basic see avoid even thru and messages of going table and is also. It license very commonly i Central connect user it the Desktop desktop arbitrary.

These samples were not part of a transmission chain and there is no epidemiological link between the patients. Study samples DNA extracted from culture isolates and their susceptibility to anti-tuberculosis drugs. Bold indicates discrepant calls by Mykrobe Predictor , underlining indicates discrepant calls by TBProfiler. Triplicate DNA samples from each clinical isolate were prepared biological replicates and individual DNA extracts were subjected to repeated sequencing technical replicates.

DNA concentrations were measured on a Nanodrop UV spectrophotometer and the sheared samples diluted to 5— Purified libraries were amplified in emulsion PCR, size selected — bp by preparative electrophoresis on composite gels 1. For the bioinformatic analysis we used a previously reported pipeline [ 10 , 15 , 16 ].

Unless stated otherwise, software was run at default settings. SNP and insertion and deletion indel variants were called with Samtools 0. We compared the variants called by both algorithms, but also report results of the conservative and typical approach of retaining the consensus polymorphisms across both methods.

A default minimum depth of ten reads was required to call SNP genotypes, otherwise genotypes were denoted as missing data. This cutoff has been applied widely [ 15 , 16 , 20 ]. The reference genome was partitioned into overlapping bp sequences allowing the uniqueness of genomic regions to be determined using gem-mappability [ 21 ]. Only 1. All 36 candidate drug-resistance genes [ 5 ] were found to be unique, thus removing the risk of false calling of SNPs due to inappropriate mapping to an analogous region.

A summary of the pipeline is presented in Additional file 1 : Figure S1. We compared two informatics tools for assigning resistance from sequence data. This tool also generates lists of mutations in candidate loci, and these formed the basis of identifying any additional putative novel polymorphisms.

Resistance profiles were also generated with the Mykrobe predictor tool version July [ 22 ]. All sequences were edited and analysed with ChromasPro 2. Summaries of the sequence data obtained for each platform are presented in Additional file 3 : Table S2.

With MiSeq sequencing the number of paired reads varied across samples median 1. In contrast, for the Ion PGM platform the median number of reads was , range ,—1,, translating into a median of fold range to fold genomic coverage. Many regions in the M. The dependence of coverage on GC content. The dashed blue line represents the cutoff used when calling variants. Coverage across drug-resistance genes. The dashed red line represents the cutoff used when calling variants.

Any position with less than tenfold coverage was marked as missing. The low coverage in thyA is due to a deletion polymorphism. We estimated the variant error rates measured as the number of sites which were discordant among replicates to be low for both platforms MiSeq 1 per 77 kbp, Ion PGM 1 per 41 kbp. We sought to investigate the effects of variant calling algorithms on the numbers of SNPs detected in unique genomic regions.

Compared to SNPs the breakpoints for these variants are more difficult to characterise from alignments. In general, differences in the number of SNPs between the Samtools and GATK algorithms decreased as the depth of coverage and allelic frequency thresholds increased. For H37Rv, read depths in excess of fold had no impact on variants detected.

Across clinical isolates, the highest possible stringency tested consisted of using a minimum coverage of 20 and an allelic frequency of 0. Much of the discordance in the number of SNPs within replicate groups is due to differences in coverage leading to some polymorphisms not passing quality control filters. Overall, the analyses indicated no major differences in SNPs detected between the two calling algorithms, and this supported the use of consensus variants for downstream analysis.

For example, the set of common SNP variants were used to cluster all samples within a phylogenetic tree using FastTree v2. Perfect clustering was observed amongst isolates and their replicates. At a finer resolution, we analysed the SNP differences between the replicates, and none were identified. When the MiSeq raw sequence data were subjected to analysis using TBProfiler , agreement with phenotypic susceptibility testing was high The novel polymorphisms included a deletion in pncA of 20 bp nucleotides — and a nucleotide insertion GG between codons and Phenotypic testing of kanamycin drug susceptibility was not performed, but mutations associated with its resistance were detected in all eight isolates Table 1 ; Additional file 9 : Table S4.

The Mykrobe predictor tool was also applied to in silico call resistance. This approach looks for mutations associated with resistance to first-line drugs rifampicin, isoniazid, ethambutol and second-line drugs streptomycin, ciprofloxacin, ofloxacin, moxifloxacin, amikacin, kanamycin, capreomycin.

Of the 72 resistance calls made, 19 For Ion PGM, when predicting individual drug-resistance profiles in the three isolates, in one isolate the gyrA D94A mutation associated with fluoroquinolone resistance could not be detected due to lack of coverage Additional file 5 : Table S3. However, the mutation was recovered if the coverage threshold was relaxed from ten- to fourfold.

Advances in next-generation sequencing technology have expanded opportunities for genome analysis in the clinical laboratory. Determining resistance to anti-TB drugs by whole genome sequencing has been demonstrated as feasible and is being implemented in some specialist centres [ 6 ].

For acceptance as a diagnostic tool to guide treatment of drug-resistant TB the sequencing platforms and analytical tools employed must be robust and reliable. However, we did not investigate the impact of the different library preparation methods used mechanical MiSeq and enzymatic Ion PGM processing. Sample quality and the mode or preparation have been shown to influence the depth of coverage in high GC regions [ 27 ], and further work is required to investigate this.

The Ion PGM platform has previously been used to characterise mutations in XDR-TB strains [ 6 ], but the minimum read depth used to call alleles fourfold were less stringent than the tenfold coverage threshold adopted here.

Samtools and GATK when used to process the raw sequence data produced diverse outputs but filtering based on coverage and allelic frequency led to almost complete agreement on resistance causing SNPs. There was, however, lower concordance between the final sets of indels.

As previously reported, the false discovery rate for Samtools is higher than for GATK and rises as coverage increases [ 28 ]. The high reproducibility of sequence data from replicate samples is reassuring as it affirms the validity of next-generation sequencing as a tool for investigating transmission events. Of the nine PZA-resistant isolates, known resistance SNPs were reported for seven isolates with an insertion and deletion observed for the remaining two.

Possible novel resistance mutations were also observed for both the PAS-resistant isolates. The Mykrobe predictor detected resistance for nine drugs, of which eight had DST results. Misclassification of resistance of amikacin and capreomycin as susceptible has significant clinical implications as patients may be assigned treatment that is not effective for XDR-TB.

Significantly, the laboratory platform being used may impact the detection of putative drug resistance. Large deletions and other structural variants may be detected by applying a combination of complementary approaches pair-end, split-read and depth of coverage followed by a validation process involving de novo assembly of bordering reads and re-alignment to the reference genome [ 10 , 16 , 24 ]. However, high genome-wide sequence coverage is necessary to perform such analyses.

As expected the genotypic profiling was concordant with the phenotypic determination of drug-resistance levels confirming the reliability and robustness of the selected genes and mutations as predictors of resistance for almost all drugs tested; with discrepancies still being noticed for PZA and PAS due to lack of enough information on their mechanism of action [ 12 , 30 ]. Surprisingly, no discrepancies were found for EMB, a drug known to have low correlation between the emb genes and phenotypic resistance [ 12 ].

Sequencing platforms are becoming more accessible and economical. Our work suggests that they are capable of delivering high quality data regarding resistance to anti-TB drugs but do not all perform to the same standard and quality monitoring is advisable. Further studies are needed to evaluate these analytical tools, which as yet do not have regulatory approval for clinical use. It is expected that drug-resistance profiling using next-generation sequencing will gain accuracy and reliability with the gathering of improved knowledge of the drug-target genes and resistance-causing mutations, including for the new drugs recently approved for the treatment of MDR- and XDR-TB [ 29 , 31 ].

Ultimately, drug resistance profiling using next-generation sequencing offers rapid assessment of resistance-associated mutations, thus accelerating access to effective treatment. These funding bodies did not have a role in the design of the study and collection, analysis and interpretation of data and in writing the manuscript.

The final manuscript was read and approved by all authors. Additional file 1: Figure S1. TIFF 81 kb. TIFF kb. Replicate variation across extraction and calling algorithms, and phenotypic profiles. DOCX 20 kb. TIFF 85 kb. The changes in the number of SNPs characterised across algorithms for the ten clinical isolates.

TIFF 92 kb. Jody Phelan, Email: ku. Denise M. Diana Machado, Email: tp. Jorge Ramos, Email: tp. Alexandra S. Whale, Email: moc. Keertan Dheda, Email: az. Susana Campino, Email: ku. Ruth McNerney, Email: az. Miguel Viveiros, Email: tp. Jim F. Huggett, Email: moc.

Taane G. Clark, Email: ku. Genome Med. Published online Dec Jody Phelan , 1 Denise M. Huggett , 2, 6 and Taane G. Clark 1, 7. Author information Article notes Copyright and License information Disclaimer. Dunn, Randolph A. Smith, and Bernard C. Nicol and Penny M. Berry and Gordon S. Colin Cameron and Pravin K. Breslow and N. Shadish, Thomas D. Cook and Donald T. Campbell and Julian C.

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Fisher, Patrick L. Miller, Jr. H Abramson and Z. Harris and G. Chen and Paula M. Wilcox Conned Again, Watson! Click here to report an error on this page or leave a comment. Your Name required. Your Email must be a valid email for us to receive the report! How to cite this page. The books listed below may be borrowed by visiting Statistical Consulting in Math Sciences during walk-in consulting hours. How Long? Books are due two weeks from the time you borrow them and you may have a total of two books on loan at any given time.

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