Jake Lin

Vipie: web pipeline for parallel characterization of viral populations from multiple NGS samples

BackgroundNext generation sequencing (NGS) technology allows laboratories to investigate virome composition in clinical and environmental samples in a culture-independent way. There is a need for bioinformatic tools capable of parallel processing of virome sequencing data by exactly identical methods: this is especially important in studies of multifactorial diseases, or in parallel comparison of laboratory protocols.ResultsWe have developed a web-based application allowing direct upload of sequences from multiple virome samples using custom parameters. The samples are then processed in parallel using an identical protocol, and can be easily reanalyzed. The pipeline performs de-novo assembly, taxonomic classification of viruses as well as sample analyses based on user-defined grouping categories. Tables of virus abundance are produced from cross-validation by remapping the sequencing reads to a union of all observed reference viruses. In addition, read sets and reports are created after processing unmapped reads against known human and bacterial ribosome references. Secured interactive results are dynamically plotted with population and diversity charts, clustered heatmaps and a sortable and searchable abundance table.ConclusionsThe Vipie web application is a unique tool for multi-sample metagenomic analysis of viral data, producing searchable hits tables, interactive population maps, alpha diversity measures and clustered heatmaps that are grouped in applicable custom sample categories. Known references such as human genome and bacterial ribosomal genes are optionally removed from unmapped (‘dark matter’) reads. Secured results are accessible and shareable on modern browsers. Vipie is a freely available web-based tool whose code is open source.

Imbalance of bacteriome profiles within the Finnish Diabetes Prediction and Prevention study: Parallel use of 16S profiling and virome sequencing in stool samples from children with islet autoimmunity and matched controls

We set out to explore associations between the stool bacteriome profiles and early‐onset islet autoimmunity, taking into account the interactions with the virus component of the microbiome.

Nature-derived microbiota exposure as a novel immunomodulatory approach

AIMnCurrent attempts to modulate the human microbiota and immune responses are based on probiotics or human-derived bacterial transplants. We investigated microbial modulation by soil and plant-based material.nnnMATERIALS & METHODSnWe performed a pilot study in which healthy adults were exposed to the varied microbial community of a soil- and plant-based material.nnnRESULTSnThe method was safe and feasible; exposure was associated with an increase in gut microbial diversity.nnnCONCLUSIONnIf these findings are reproduced in larger studies nature-derived microbial exposure strategies could be further developed for testing their efficacy in the treatment and prevention of immune-mediated diseases.

Data-driven characterization of molecular phenotypes across heterogenous sample collections

The existing large gene expression data repositories hold enormous potential to elucidate disease mechanisms, characterize changes in cellular pathways, and to stratify patients based on their molecular profile. To achieve this goal, integrative resources and tools are needed that allow comparison of results across datasets and data types. We propose an intuitive approach for data-driven stratifications of molecular profiles and benchmark our methodology using the dimensional reduction algorithm t-SNE with multi-center and multi-platform data representing hematological malignancies. Our approach enables assessing the contribution of biological versus technical variation to sample clustering, direct incorporation of additional datasets to the same low dimensional representation of molecular disease subtypes, comparison of sample groups between separate t-SNE representations, or maps, and characterization of the obtained clusters based on pathway databases and additional multi-omics data. In the example application, our approach revealed differential activity of SAM-dependent DNA methylation pathway in the acute myeloid leukemia patient cluster characterized with CEBPA mutations that accordingly was validated to have globally elevated DNA methylation levels.

Bioinformatics Assembling and Assessment of Novel Coxsackievirus B1 Genome

The human microbiome project via application of metagenomic next-generation sequencing techniques has found surprising large and diverse amounts of microbial sequences across different body sites. There is a wave of investigators studying autoimmune related diseases designing from birth case and control studies to elucidate microbial associations and potential direct triggers. Sequencing analysis, considered big data as it typically includes millions of reads, is challenging but particularly demanding and complex is virome profiling due to its lack of pan-viral genomic signature. Impressively thousands of virus complete genomes have been deposited and these high-quality references are core components of virus profiling pipelines and databases. Still it is commonly known that most viral sequences do not map to known viruses. Moreover human viruses, particularly RNA groups, are notoriously heterogeneous due to high mutation rates. Here, we present the related assembling challenges and a series of bioinformatics steps that were applied in the construction of the complete consensus genome of a novel clinical isolate of Coxsackievirus B1. We further demonstrate our effort in calling mutations between prototype Coxsackievirus B1 sequence from GenBank and serial clinical isolate genome grown in cell culture.

Changes in the lung bacteriome in relation to antipseudomonal therapy in children with cystic fibrosis

The lung in cystic fibrosis (CF) is home to numerous pathogens that shorten the lives of patients. The aim of the present study was to assess changes in the lung bacteriome following antibiotic therapy targeting Pseudomonas aeruginosa in children with CF. The study included nine children (9–18xa0years) with CF who were treated for their chronic or intermittent positivity for Pseudomonas aeruginosa. The bacteriomes were determined in 16 pairs of sputa collected at the beginning and at the end of a course of intravenous antibiotic therapy via deep sequencing of the variable region 4 of the 16S rRNA gene, and the total bacterial load and selected specific pathogens were assessed using quantitative real-time PCR. The effect of antipseudomonal antibiotics was observable as a profound decrease in the total 16S rDNA load (pxa0=xa00.001) as well as in a broad range of individual taxa including Staphylococcus aureus (pxa0=xa00.03) and several members of the Streptococcus mitis group (S. oralis, S. mitis, and S. infantis) (pxa0=xa00.003). Improvements in forced expiratory volume (FEV1) were associated with an increase in Granulicatella sp. (pxa0=xa00.004), whereas a negative association was noted between the total bacterial load and white blood cell count (pxa0=xa00.007). In conclusion, the data show how microbial communities differ in reaction to antipseudomonal treatment, suggesting that certain rare species may be associated with clinical parameters. Our work also demonstrates the utility of absolute quantification of bacterial load in addition to the 16S rDNA profiling.