Research on human and murine haematopoiesis has resulted in a vast number of gene-expression data sets that can potentially answer questions regarding normal and aberrant blood formation. To researchers and clinicians with limited bioinformatics experience, these data have remained available, yet largely inaccessible. Current databases provide information about gene-expression but fail to answer key questions regarding co-regulation, genetic programs or effect on patient survival. To address these shortcomings, we present BloodSpot (www.bloodspot.eu), which includes and greatly extends our previously released database HemaExplorer, a database of gene expression profiles from FACS sorted healthy and malignant haematopoietic cells. A revised interactive interface simultaneously provides a plot of gene expression along with a Kaplan–Meier analysis and a hierarchical tree depicting the relationship between different cell types in the database. The database now includes 23 high-quality curated data sets relevant to normal and malignant blood formation and, in addition, we have assembled and built a unique integrated data set, BloodPool. Bloodpool contains more than 2000 samples assembled from six independent studies on acute myeloid leukemia. Furthermore, we have devised a robust sample integration procedure that allows for sensitive comparison of user-supplied patient samples in a well-defined haematopoietic cellular space.

Recent developments in data driven science, in particular computational biology, have led scientists to integrate data from several sources, over diverse experimental procedures, or databases. This alone poses a major challenge in truthfully visualising data, especially when the amount of data points varies between classes. To aid the presentation of datasets with differing sample size we have developed a new type of plot overcoming limitations of current standard visualization charts. Plots like bar charts, violin plots, strip charts or boxand-whiskers plots may provide visual information about mean/median, variance of the data, number of data points or density distribution of data; still, only a combination of these plots may provide all relevant information. We have designed a new and simple plot inspired by the strip chart and the violin plot that operates by letting the normalized density of points restrict the jitter along the x-axis. Τhe plot displays the same contour as a violin plot, but resembles a simple strip chart for small number of data points. In this way the plot conveys information of both the number of data points, the density distribution, outliers and data spread in a very simple, comprehensible and condensed format. The package for producing the plots is available for R through the CRAN network (https://cran.r-project.org/web/packages/sinaplot/index.html). In order to aid users without experience in R we also provide access to a web-server accepting excel sheets to produce the plots (http://servers.binf.ku.dk:8890/sinaplot/) .

Recent developments in data driven science, in particular computational biology, have led scientists to integrate data from several sources, over diverse experimental procedures, or databases. This alone poses a major challenge in truthfully visualising data, especially when the amount of data points varies between classes. To aid the presentation of datasets with differing sample size we have developed a new type of plot overcoming limitations of current standard visualization charts. Plots like bar charts, violin plots, strip charts or boxand-whiskers plots may provide visual information about mean/median, variance of the data, number of data points or density distribution of data; still, only a combination of these plots may provide all relevant information. We have designed a new and simple plot inspired by the strip chart and the violin plot that operates by letting the normalized density of points restrict the jitter along the x-axis. Τhe plot displays the same contour as a violin plot, but resembles a simple strip chart for small number of data points. In this way the plot conveys information of both the number of data points, the density distribution, outliers and data spread in a very simple, comprehensible and condensed format. The package for producing the plots is available for R through the CRAN network (https://cran.r-project.org/web/packages/sinaplot/index.html). In order to aid users without experience in R we also provide access to a web-server accepting excel sheets to produce the plots

Decades of studies on developing cells in human and murine haematopoiesis have resulted in a large number of gene-expression datasets that may answer questions regarding normal and aberrant blood formation. To researchers and clinicians with limited bioinformatics experience, these data remain available, yet largely inaccessible. Current resources provide information about gene-expression patterns but disregard key aspects such as genetic co-regulation of genes, and the effects on patient survival. Here, we present a new web-based resourced termed, BloodSpot, which provides a) a comprehensive representation of gene-expression throughout haematopoiesis, b) a single gene-based Kaplan-Meier analysis and c) a novel, simpler, yet more informative, type of expression plot. Significantly, users can compare their own expression profiles to normal haematopietic populations within the statistical framework of Bloodspot. We illustrate the potential of key features in BloodSpot to identify new putative C/EBPa targets. Accessible at http://servers.binf.ku.dk/bloodspot/ Disclosures No relevant conflicts of interest to declare.