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Krampis, K.

Publications and source records attributed to Krampis, K..

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Baseline human gut microbiota profile in healthy people and standard reporting template

A comprehensive knowledge of the types and ratios of microbes that inhabit the healthy human gut is necessary before any kind of pre-clinical or clinical study can be performed that attempts to alter the microbiome to treat a condition or improve therapy outcome. To address this need we present an innovative scalable comprehensive analysis workflow, a healthy human reference microbiome list and abundance profile (GutFeelingKB), and a novel Fecal Biome Population Report (FecalBiome) with clinical applicability. GutFeelingKB provides a list of 157 organisms (8 phyla, 18 classes, 23 orders, 38 families, 59 genera and 109 species) that forms the baseline biome and therefore can be used as healthy controls for studies related to dysbiosis. The incorporation of microbiome science into routine clinical practice necessitates a standard report for comparison of an individuals microbiome to the growing knowledgebase of \"normal\" microbiome data. The FecalBiome and the underlying technology of GutFeelingKB address this need. The knowledgebase can be useful to regulatory agencies for the assessment of fecal transplant and other microbiome products, as it contains a list of organisms from healthy individuals. In addition to the list of organisms and abundances the study also generated a list of contigs of metagenomics dark matter. In this study, metagenomic dark matter represents sequences that cannot be mapped to any known sequence but can be assembled into contigs of 10,000 nucleotides or higher. These sequences can be used to create primers to study potential novel organisms. All data is freely available from https://hive.biochemistry.gwu.edu/gfkb and NCBIs Short Read Archive.

microbiology

Identification and quantification of Lyme pathogen strains by deep sequencing of outer surface protein C (ospC) amplicons

Mixed infection of a single tick or host by Lyme disease spirochetes is common and a unique challenge for diagnosis, treatment, and surveillance of Lyme disease. Here we describe a novel protocol for differentiating Lyme strains based on deep sequencing of the hypervariable outer-surface protein C locus (ospC). Improving upon the traditional DNA-DNA hybridization method, the next-generation sequencing-based protocol is high-throughput, quantitative, and able to detect new pathogen strains. We applied the method to over one hundred infected Ixodes scapularis ticks collected from New York State, USA in 2015 and 2016. Analysis of strain distributions within individual ticks suggests an overabundance of multiple infections by five or more strains, inhibitory interactions among co-infecting strains, and presence of a new strain closely related to Borreliella bissettiae. A supporting bioinformatics pipeline has been developed. With the newly designed pair of universal ospC primers targeting intergenic sequences conserved among all known Lyme pathogens, the protocol could be used for culture-free identification and quantification of Lyme pathogens in wildlife and clinical specimens across the globe.

microbiology

miCloud: a plug and play, on-premises bioinformatics cloud, providing seamless integration with Illumina genome sequencers

Benchtop genome sequencers such as the Illumina MiSeq or MiniSeq [1], [2] are revolutionizing genomics research for smaller, independent laboratories, by enabling access to low-cost Next Generation Sequencing (NGS) technology in-house. These benchtop genome sequencing instruments require only standard laboratory equipment, in addition to minimal time for sample preparation. However, post-sequencing bioinformatics data analysis still presents a significant bottleneck, for research laboratories lacking specialized software and technical data analysis skills on their teams. While bioinformatics computes clouds providing solutions following a Software as a Service (SaaS) are available ([3]-[6], review in [7]), currently, there are only a few options which are user-friendly for non-experts while at the same time are also low-cost or free. One primary example is Illumina BaseSpace [8] that is very easy to access by non-experts, and also offers an integrated solution where data are streamed directly from the MiSeq sequencing instrument to the cloud. Once the data is on the BaseSpace cloud, users can access a range of bioinformatics applications with pre-installed algorithms through an intuitive web interface. Nonetheless, BaseSpace can be a costly solution as a yearly subscription depending on whether the user is associated with an academic or private institution, ranges in price from $999 - $4,999. Additional \"iCredits\" [9] might need to be purchased for frequent users that exhaust the base credit allowance as part of the subscription. Considering the reduction of computer hardware cost in recent years, a multi-core Intel Xeon server with 64 GigaByte (GB) of memory and multiple TeraByte (TB) of storage is priced less than the yearly subscription to Basespace [10], and similarly when compared to renting compute cycles from providers such as Amazon Web Services (AWS) [11]. Furthermore, the current generation of laptops usually come with 6-10 GigaBytes (GB) of memory and 1 TeraByte (TB) of storage, providing enough computational capacity to analyze data from small NGS experiments [12] that include only a few samples.

bioinformatics

Fast functional annotation of metagenomic shotgun data by DNA alignment to a microbial gene catalog

BackgroundMetagenomic shotgun sequencing is becoming increasingly popular to study microbes associated with the human body and in environmental samples. A key goal of shotgun metagenomic sequencing is to identify gene functions and metabolic pathways that differ between samples or conditions. However, current methods to identify function in the large number of reads in a high-throughput sequence data file rely on the computationally intensive and low stringency approach of mapping each read to a generic database of proteins or reference microbial genomes.\n\nResultsWe have developed an alternative analysis approach for shotgun metagenomic sequence data utilizing Bowtie2 DNA-DNA alignment of the reads to a database of well annotated genes compiled from human microbiome data. This method is rapid, and provides high stringency matches (>90% DNA sequence identity) of shotgun metagenomics reads to genes with annotated functions. We demonstrate the use of this method with synthetic data, Human Microbiome Project shotgun metagenomic data sets, and data from a study of liver disease. Differentially abundant KEGG gene functions can be detected in these experiments.\n\nConclusionsFunctional annotation of metagenomic shotgun sequence reads can be accomplished by rapid DNA-DNA matching to a custom database of microbial sequences using the Bowtie2 sequence alignment tool. This method can be used for a variety of microbiome studies and allows functional analysis which is otherwise computationally demanding. This rapid annotation method is freely available as a Galaxy workflow within a Docker image.

bioinformatics

Bio-Docklets: Virtualization Containers for Single-Step Execution of NGS Pipelines.

BackgroundProcessing of Next-Generation Sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized post-analysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers, towards seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform.\n\nFindingsWe present an approach for abstracting the complex data operations of multi-step, bioinformatics pipelines for NGS data analysis. As examples, we have deployed two pipelines for RNAseq and CHIPseq, pre-configured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines is as simple as running a single bioinformatics tool. This is achieved through a \"meta-script\" that automatically starts the Bio-Docklets, and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface (API). The pipelne output is post-processed using the Visual Omics Explorer (VOE) framework, providing interactive data visualizations that users can access through a web browser.\n\nConclusionsThe goal of our approach is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts, on any computing environment whether a laboratory workstation, university computer cluster, or a cloud service provider. Besides end-users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.

bioinformatics