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

<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.

<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 search result processing.

<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.

<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 versus phenotype.

<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

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

<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 results of metagenomic stool dataset.

<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

Minimal spanning tree of <i>S.aureus</i> isolates of human and animal origin.

<p>Nodes represent binary IS types. Node colour corresponds to the source of the strains, pig farming-associated, human or other animal. Node size corresponds to the number of strains of identical IS type within that node. When isolates of different sources have identical profiles, the node is depicted as a pie chart, the size of individual parts indicating relative abundance of different sources in that node. The pig farming-associated MRSA isolates (in pink) clearly form a distinct cluster. Only one of the pig farming-associated MRSA isolates (with ST 9) fell outside of this cluster and instead clustered with MSSA isolates obtained from pigs. This strain is marked with an asterisk (*) in the figure. Besides the pig farming-associated MRSA isolates, not many IS types are found in both humans and animals. Within the central node of the pig farming-associated MRSA cluster, one isolate derived from another animal can be found. This is the MSSA isolated from a monkey.</p

Comparison of levels of MGO, H₂O₂ and bee defensin-1 in RS and manuka honey.

<p>(A) Concentration of MGO in RS and manuka (Man.) honey, determined spectrophotometrically after its conversion to S-lactoylglutathione by glyoxalase I treatment. (B) H₂O₂ accumulation over time in 40% (v/v) RS (squares) and manuka honey (triangles). (C) Proteins were concentrated from honey by ultrafiltration with a 5 kDa molecular weight cut-off membrane. Amounts of >5 kDa retentate equivalent to 150 µl of undiluted honey, and 3 µg of lysozyme (lys.) as a reference, were run in duplicate on a single native acid-urea PAGE gel to separate cationic proteins. One half of the gel was Coomassie-stained (left), the other was used for a bacterial overlay assay with <i>B. subtilis</i> (right). Since a dark-field image was obtained, growth inhibition of the bacteria due to the presence of antibacterial proteins appears as a dark zone.</p

Contribution of MGO to the bactericidal activity of manuka honey.

<p>The indicated bacteria were incubated in various concentrations (v/v) of manuka honey in incubation buffer (squares), in manuka with addition of glyoxalase I (triangles) or glyoxalase I and SPS without (diamonds) or with adjustment of the pH to 7.0 (asterisks), or in a honey-equivalent sugar solution (circles). After 24 hours, numbers of surviving bacteria were determined.</p