37 research outputs found

    TreeSeq, a Fast and Intuitive Tool for Analysis of Whole Genome and Metagenomic Sequence Data

    No full text
    <div><p>Next-generation sequencing is not yet commonly used in clinical laboratories because of a lack of simple and intuitive tools. We developed a software tool (TreeSeq) with a quaternary tree search structure for the analysis of sequence data. This permits rapid searches for sequences of interest in large datasets. We used TreeSeq to screen a gut microbiota metagenomic dataset and a whole genome sequencing (WGS) dataset of a strain of <i>Klebsiella pneumoniae</i> for antibiotic resistance genes and compared the results with BLAST and phenotypic resistance determination. TreeSeq was more than thirty times faster than BLAST and accurately detected resistance gene sequences in complex metagenomic data and resistance genes corresponding with the phenotypic resistance pattern of the Klebsiella strain. Resistance genes found by TreeSeq were visualized as a gene coverage heat map, aiding in the interpretation of results. TreeSeq brings analysis of metagenomic and WGS data within reach of clinical diagnostics.</p></div

    TreeSeq tree structure.

    No full text
    <p>This represents the quaternary tree in which all possible 60-nucleotide long read combinations of the resistance database were added. For the search task all (sub)reads in the NGS file are compared to the nodes in the tree. If the nodes in the tree can be run through to the last node in the tree (as in Read-3), this is a hit.</p

    TreeSeq results of metagenomic stool dataset.

    No full text
    <p>This is a heat map of the results generated by TreeSeq of the metagenomic stool dataset (SRS022524.1). On the x-axis the found resistance gene classes and the antibiotic that it confers resistance to. It is possible to increase the search result resolution by adding the specific gene level, which is not displayed. The y-axis shows the nucleotide positions of the gene and therefore the length of the bar also represents the length of the gene. The colour represents the amount of occurrences (hits) of a gene on this specific position.</p

    TreeSeq search result processing.

    No full text
    <p>This represents the quaternary tree in which all possible nucleotide read combinations of the resistance database were added. During this process the read’s gene of origin and its nucleotide-coordinates were added to the end-nodes. If during a search task an end-node contains one gene, it is a specific result for one particular gene in the database. These obtained results can be copied to a result list. In case the end nodes contain multiple genes, this read is not specific for a gene. This aspecific result can be copied to a separate list, namely the doubtlist, for post processing. After comparing all the raw data reads to the tree, the software makes up the balance and looks for which genes in the doubtlist it already has specific hits in the result list and supplements them if so.</p

    TreeSeq versus BLAST.

    No full text
    <p>This scatterplot shows the results for the two methods, BLAST (green) versus TreeSeq (red), by searching metagenomic stool dataset SRS022524.1. On the x-axis all the found gene classes and for each gene class the results per method are shown. On the y-axis the hit occurrences, in log scale, for each gene is shown as a dot. On the far right the results are shown that were only found by BLAST, here the highest occurrence was 7 hits.</p

    TreeSeq results of <i>Klebsiella pneumonia</i>.

    No full text
    <p>This is a heat map of the results generated by TreeSeq of the <i>Klebsiella pneumonia</i> (BAA-2146) strain. On the x-axis the found resistance gene classes and the antibiotic that it confers resistance to. It is possible to increase the search result resolution by adding the specific gene level, which is not displayed. The y-axis shows the nucleotide positions of the gene and therefore the length of the bar also represents the length of the gene. The colour represents the amount of occurrences (hits) of a gene on this specific position.</p

    TreeSeq versus phenotype.

    No full text
    <p>American Type Culture Collection (ATCC) tested resistance in <i>Klebsiella pneumonia</i> (BAA-2146), which is a phenotypical referenced strain, by culturing. This diagram shows result overlap for phenotypical resistance testing versus genotypical testing with TreeSeq using the ARDB.</p

    Long-Term Green Tea Supplementation Does Not Change the Human Gut Microbiota

    No full text
    <div><p>Background</p><p>Green tea catechins may play a role in body weight regulation through interactions with the gut microbiota.</p><p>Aim</p><p>We examined whether green tea supplementation for 12 weeks induces changes in composition of the human gut microbiota.</p><p>Methods</p><p>58 Caucasian men and women were included in a randomized, placebo-controlled design. For 12 weeks, subjects consumed either green tea (>0.56 g/d epigallocatechin-gallate + 0.28 ∼ 0.45 g/d caffeine) or placebo capsules. Fecal samples were collected twice (baseline, vs. week 12) for analyses of total bacterial profiles by means of IS-profiling, a 16S-23S interspacer region-based profiling method.</p><p>Results</p><p>No significant changes between baseline and week 12 in subjects receiving green tea or placebo capsules, and no significant interactions between treatment (green tea or placebo) and time (baseline and week 12) were observed for body composition. Analysis of the fecal samples in subjects receiving green tea and placebo showed similar bacterial diversity and community structures, indicating there were no significant changes in bacterial diversity between baseline and week 12 in subjects receiving green tea capsules or in subjects receiving placebo capsules. No significant interactions were observed between treatment (green tea or placebo) and time (baseline and week 12) for the gut microbial diversity. Although, there were no significant differences between normal weight and overweight subjects in response to green tea, we did observe a reduced bacterial alpha diversity in overweight as compared to normal weight subjects (p = 0.002).</p><p>Conclusion</p><p>Green tea supplementation for 12 weeks did not have a significant effect on composition of the gut microbiota.</p><p>Trial Registration</p><p>ClinicalTrials.gov <a href="https://clinicaltrials.gov/ct2/show/study/NCT01556321" target="_blank">NCT01556321</a></p></div

    Correlation of digital IS profiles with MLST and AFLP.

    No full text
    <p>Thirty-three unique IS types were found belonging to 13 clusters. These clusters were defined as strains with identical binary profiles (connected by a vertical line) or as profiles differing no more than one digit from the most common profile within that group (connected by grey triangles).</p
    corecore