A FAIR guide for data providers to maximise sharing of human genomic data.

It is generally acknowledged that, for reproducibility and progress of human genomic research, data sharing is critical. For every sharing transaction, a successful data exchange is produced between a data consumer and a data provider. Providers of human genomic data (e.g., publicly or privately funded repositories and data archives) fulfil their social contract with data donors when their shareable data conforms to FAIR (findable, accessible, interoperable, reusable) principles. Based on our experiences via Repositive (https://repositive.io), a leading discovery platform cataloguing all shared human genomic datasets, we propose guidelines for data providers wishing to maximise their shared data's FAIRness.

DNAdigest and Repositive: Connecting the World of Genomic Data.

There is no unified place where genomics researchers can search through all available raw genomic data in a way similar to OMIM for genes or Uniprot for proteins. With the recent increase in the amount of genomic data that is being produced and the ever-growing promises of precision medicine, this is becoming more and more of a problem. DNAdigest is a charity working to promote efficient sharing of human genomic data to improve the outcome of genomic research and diagnostics for the benefit of patients. Repositive, a social enterprise spin-out of DNAdigest, is building an online platform that indexes genomic data stored in repositories and thus enables researchers to search for and access a range of human genomic data sources through a single, easy-to-use interface, free of charge.

Beyond research: a primer for considerations on using viral metagenomics in the field and clinic.

Powered by recent advances in next-generation sequencing technologies, metagenomics has already unveiled vast microbial biodiversity in a range of environments, and is increasingly being applied in clinics for difficult-to-diagnose cases. It can be tempting to suggest that metagenomics could be used as a "universal test" for all pathogens without the need to conduct lengthy serial testing using specific assays. While this is an exciting prospect, there are issues that need to be addressed before metagenomic methods can be applied with rigor as a diagnostic tool, including the potential for incidental findings, unforeseen consequences for trade and regulatory authorities, privacy and cultural issues, data sharing, and appropriate reporting of results to end-users. These issues will require consideration and discussion across a range of disciplines, with inclusion of scientists, ethicists, clinicians, diagnosticians, health practitioners, and ultimately the public. Here, we provide a primer for consideration on some of these issues.

Microbial Metabolism Shifts Towards an Adverse Profile with Supplementary Iron in the TIM-2 In vitro Model of the Human Colon.

Oral iron administration in African children can increase the risk for infections. However, it remains unclear to what extent supplementary iron affects the intestinal microbiome. We here explored the impact of iron preparations on microbial growth and metabolism in the well-controlled TNO's in vitro model of the large intestine (TIM-2). The model was inoculated with a human microbiota, without supplementary iron, or with 50 or 250 µmol/L ferrous sulfate, 50 or 250 µmol/L ferric citrate, or 50 µmol/L hemin. High resolution responses of the microbiota were examined by 16S rDNA pyrosequencing, microarray analysis, and metagenomic sequencing. The metabolome was assessed by fatty acid quantification, gas chromatography-mass spectrometry (GC-MS), and (1)H-NMR spectroscopy. Cultured intestinal epithelial Caco-2 cells were used to assess fecal water toxicity. Microbiome analysis showed, among others, that supplementary iron induced decreased levels of Bifidobacteriaceae and Lactobacillaceae, while it caused higher levels of Roseburia and Prevotella. Metagenomic analyses showed an enrichment of microbial motility-chemotaxis systems, while the metabolome markedly changed from a saccharolytic to a proteolytic profile in response to iron. Branched chain fatty acids and ammonia levels increased significantly, in particular with ferrous sulfate. Importantly, the metabolite-containing effluent from iron-rich conditions showed increased cytotoxicity to Caco-2 cells. Our explorations indicate that in the absence of host influences, iron induces a more hostile environment characterized by a reduction of microbes that are generally beneficial, and increased levels of bacterial metabolites that can impair the barrier function of a cultured intestinal epithelial monolayer.

The need to redefine genomic data sharing: A focus on data accessibility.

DNAdigest's mission is to investigate and address the issues hindering efficient and ethical genomic data sharing in the human genomics research community. We conducted contextual interviews with human genomics researchers in clinical, academic or industrial R&D settings about their experience with accessing and sharing human genomic data. The qualitative interviews were followed by an online survey which provided quantitative support for our findings. Here we present the generalised workflow for accessing human genomic data through both public and restricted-access repositories and discuss reported points of frustration and their possible improvements. We discuss how data discoverability and accessibility are lacking in current mechanisms and how these are the prerequisites for adoption of best practices in the research community. We summarise current initiatives related to genomic data discovery and present a new data discovery platform available at http://nucleobase.co.uk.

The PinkThing for analysing ChIP profiling data in their genomic context.

BACKGROUND: Current epigenetic research makes frequent use of whole-genome ChIP profiling for determining the in vivo binding of proteins, e.g. transcription factors and histones, to DNA. Two important and recurrent questions for these large scale analyses are: 1) What is the genomic distribution of a set of binding sites? and 2) Does this genomic distribution differ significantly from another set of sites? FINDINGS: We exemplify the functionality of the PinkThing by analysing a ChIP profiling dataset of cohesin binding sites. We show the subset of cohesin sites with no CTCF binding have a characteristic genomic distribution different from the set of all cohesin sites. CONCLUSIONS: The PinkThing is a web application for fast and easy analysis of the context of genomic loci, such as peaks from ChIP profiling experiments. The output of the PinkThing analysis includes: categorisation of position relative to genes (intronic, exonic, 5' near, 3' near 5' far, 3' far and distant), distance to the closest annotated 3' and 5' end of genes, direction of transcription of the nearest gene, and the option to include other genomic elements like ESTs and CpG islands. The PinkThing enables easy statistical comparison between experiments, i.e. experimental versus background sets, reporting over- and underrepresentation as well as p-values for all comparisons. Access and use of the PinkThing is free and open (without registration) to all users via the website: http://pinkthing.cmbi.ru.nl

Chromatin accessibility, p300, and histone acetylation define PML-RARα and AML1-ETO binding sites in acute myeloid leukemia.

Chromatin accessibility plays a key role in regulating cell type specific gene expression during hematopoiesis but has also been suggested to be aberrantly regulated during leukemogenesis. To understand the leukemogenic chromatin signature, we analyzed acute promyelocytic leukemia, a subtype of leukemia characterized by the expression of RARα-fusion proteins, such as PML-RARα. We used nuclease accessibility sequencing in cell lines as well as patient blasts to identify accessible DNA elements and identified > 100 000 accessible regions in each case. Using ChIP-seq, we identified H2A.Z as a histone modification generally associated with these accessible regions, whereas unsupervised clustering analysis of other chromatin features, including DNA methylation, H2A.Zac, H3ac, H3K9me3, H3K27me3, and the regulatory factor p300, distinguished 6 distinct clusters of accessible sites, each with a characteristic functional makeup. Of these, PML-RARα binding was found specifically at accessible chromatin regions characterized by p300 binding and hypoacetylated histones. Identifying regions with a similar epigenetic make up in t(8;21) acute myeloid leukemia (AML) cells, another subtype of AMLs, revealed that these regions are occupied by the oncofusion protein AML1-ETO. Together, our results suggest that oncofusion proteins localize to accessible regions and that chromatin accessibility together with p300 binding and histone acetylation characterize AML1-ETO and PML-RARα binding sites.

CATCHprofiles: clustering and alignment tool for ChIP profiles.

Chromatin Immuno Precipitation (ChIP) profiling detects in vivo protein-DNA binding, and has revealed a large combinatorial complexity in the binding of chromatin associated proteins and their post-translational modifications. To fully explore the spatial and combinatorial patterns in ChIP-profiling data and detect potentially meaningful patterns, the areas of enrichment must be aligned and clustered, which is an algorithmically and computationally challenging task. We have developed CATCHprofiles, a novel tool for exhaustive pattern detection in ChIP profiling data. CATCHprofiles is built upon a computationally efficient implementation for the exhaustive alignment and hierarchical clustering of ChIP profiling data. The tool features a graphical interface for examination and browsing of the clustering results. CATCHprofiles requires no prior knowledge about functional sites, detects known binding patterns "ab initio", and enables the detection of new patterns from ChIP data at a high resolution, exemplified by the detection of asymmetric histone and histone modification patterns around H2A.Z-enriched sites. CATCHprofiles' capability for exhaustive analysis combined with its ease-of-use makes it an invaluable tool for explorative research based on ChIP profiling data. CATCHprofiles and the CATCH algorithm run on all platforms and is available for free through the CATCH website: http://catch.cmbi.ru.nl/. User support is available by subscribing to the mailing list catch-users@bioinformatics.org.

Optimized design and assessment of whole genome tiling arrays.

MOTIVATION: Recent advances in microarray technologies have made it feasible to interrogate whole genomes with tiling arrays and this technique is rapidly becoming one of the most important high-throughput functional genomics assays. For large mammalian genomes, analyzing oligonucleotide tiling array data is complicated by the presence of non-unique sequences on the array, which increases the overall noise in the data and may lead to false positive results due to cross-hybridization. The ability to create custom microarrays using maskless array synthesis has led us to consider ways to optimize array design characteristics for improving data quality and analysis. We have identified a number of design parameters to be optimized including uniqueness of the probe sequences within the whole genome, melting temperature and self-hybridization potential. RESULTS: We introduce the uniqueness score, U, a novel quality measure for oligonucleotide probes and present a method to quickly compute it. We show that U is equivalent to the number of shortest unique substrings in the probe and describe an efficient greedy algorithm to design mammalian whole genome tiling arrays using probes that maximize U. Using the mouse genome, we demonstrate how several optimizations influence the tiling array design characteristics. With a sensible set of parameters, our designs cover 78% of the mouse genome including many regions previously considered 'untilable' due to the presence of repetitive sequence. Finally, we compare our whole genome tiling array designs with commercially available designs. AVAILABILITY: Source code is available under an open source license from http://www.ebi.ac.uk/~graef/arraydesign/.