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Multi-omics high-throughput technologies produce data sets which are not restricted to only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The integrative analysis of these omics modalities is essential to obtain a holistic view on the otherwise fragmented information hidden in this data. We present an intuitive method enabling the combined analysis of multi-omics data based on self-organizing maps machine learning. It “portrays” the expression, methylation and copy number variations (CNV) landscapes of each tumour using the same gene-centred coordinate system. It enables the visual evaluation and direct comparison of the different omics layers on a personalized basis. We applied this combined molecular portrayal to lower grade gliomas, a heterogeneous brain tumour entity. It classifies into a series of molecular subtypes defined by genetic key lesions, which associate with large-scale effects on DNA methylation and gene expression, and in final consequence, drive with cell fate decisions towards oligodendroglioma-, astrocytoma- and glioblastoma-like cancer cell lineages with different prognoses. Consensus modes of concerted changes of expression, methylation and CNV are governed by the degree of co-regulation within and between the omics layers. The method is not restricted to the triple-omics data used here. The similarity landscapes reflect partly independent effects of genetic lesions and DNA methylation with consequences for cancer hallmark characteristics such as proliferation, inflammation and blocked differentiation in a subtype specific fashion. It can be extended to integrate other omics features such as genetic mutation, protein expression data as well as extracting prognostic markers.

Background: The blood transcriptome is expected to provide a detailed picture of organism’s physiological state with potential impact for applications in medical diagnostics and molecular and epidemiological research. We here present the first systematic analysis of blood specimen of 3,388 adult individuals collected in the Leipzig Research Center for Civilization Diseases, together with phenotype characteristics such as disease history, medication status, lifestyle factors and body mass index (BMI). Methods: Multidimensional Self Organizing Maps-portrayal was applied to study transcriptional states on a population-wide scale. The method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features. Results: The diversity of transcriptomes is described by about one dozen modules of co-expressed genes of different functional context. We identify two major blood transcriptome types where type 1 accumulates more men, elderly and overweight people and it upregulates genes associating with inflammation and increased heme metabolism, while type 2 accumulates women, younger and normal weight participants and it associates with activated immune response, transcriptional, ribosomal, mitochondrial and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, ageing and obesity driven by an underlying common pattern, which reflects the increase in inflammatory processes. Conclusions: Our portrayal framework provides a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications.

Recent studies revealed trajectories of mutational events in early melanomagenesis, but the accompanying changes in gene expression are far less understood. Therefore, we performed a comprehensive RNA-seq analysis of laser-microdissected melanocytic nevi (n = 23) and primary melanoma samples (n = 57) and characterized the molecular mechanisms of early melanoma development. Using self-organizing maps, unsupervised clustering, and analysis of pseudotime (PT) dynamics to identify evolutionary trajectories, we describe here two transcriptomic types of melanocytic nevi (N1 and N2) and primary melanomas (M1 and M2). N1/M1 lesions are characterized by pigmentation-type and MITF gene signatures, and a high prevalence of NRAS mutations in M1 melanomas. N2/M2 lesions are characterized by inflammatory-type and AXL gene signatures with an equal distribution of wild-type and mutated BRAF and low prevalence of NRAS mutations in M2 melanomas. Interestingly, N1 nevi and M1 melanomas and N2 nevi and M2 melanomas, respectively, cluster together, but there is no clustering in a stage-dependent manner. Transcriptional signatures of M1 melanomas harbor signatures of BRAF/MEK inhibitor resistance and M2 melanomas harbor signatures of anti-PD-1 antibody treatment resistance. Pseudotime dynamics of nevus and melanoma samples are suggestive for a switch-like immune-escape mechanism in melanoma development with downregulation of immune genes paralleled by an increasing expression of a cell cycle signature in late-stage melanomas. Taken together, the transcriptome analysis identifies gene signatures and mechanisms underlying development of melanoma in early and late stages with relevance for diagnostics and therapy.

Aim: We present here a novel method that enables unraveling the interplay between gene expression and DNA methylation in complex diseases such as cancer. Materials & methods: The method is based on self-organizing maps and allows for analysis of data landscapes from ‘governed by methylation’ to ‘governed by expression’. Results: We identified regulatory modules of coexpressed and comethylated genes in high-grade gliomas: two modes are governed by genes hypermethylated and underexpressed in IDH-mutated cases, while two other modes reflect immune and stromal signatures in the classical and mesenchymal subtypes. A fifth mode with proneural characteristics comprises genes of repressed and poised chromatin states active in healthy brain. Two additional modes enrich genes either in active or repressed chromatin states. Conclusion: The method disentangles the interplay between gene expression and methylation. It has the potential to integrate also mutation and copy number data and to apply to large sample cohorts.

The SNP variant rs2943650 near IRS1 gene locus was previously associated with decreased body fat and IRS1 gene expression as well as an adverse metabolic profile in humans. Here, we hypothesize that these effects may be mediated by an interplay with epigenetic alterations. We measured IRS1 promoter DNA methylation and mRNA expression in paired human subcutaneous and omental visceral adipose tissue samples (SAT and OVAT) from 146 and 41 individuals, respectively. Genotyping of rs2943650 was performed in all individuals (N = 146). We observed a significantly higher IRS1 promoter DNA methylation in OVAT compared to SAT (N = 146, P = 8.0 × 10−6), while expression levels show the opposite effect direction (N = 41, P = 0.011). OVAT and SAT methylation correlated negatively with IRS1 gene expression in obese subjects (N = 16, P = 0.007 and P = 0.010). The major T-allele is related to increased DNA methylation in OVAT (N = 146, P = 0.019). Finally, DNA methylation and gene expression in OVAT correlated with anthropometric traits (waist- circumference waist-to-hip ratio) and parameters of glucose metabolism in obese individuals. Our data suggest that the association between rs2943650 near the IRS1 gene locus with clinically relevant variables may at least be modulated by changes in DNA methylation that translates into altered IRS1 gene expression.

// Tobias Gerber 1, * , Edith Willscher 2, * , Henry Loeffler-Wirth 2 , Lydia Hopp 2 , Dirk Schadendorf 3 , Manfred Schartl 4, 5, 6 , Ulf Anderegg 7 , Gray Camp 1 , Barbara Treutlein 1 , Hans Binder 2 , Manfred Kunz 7 1 Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology Leipzig, 04103 Leipzig, Germany 2 Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany 3 Department of Dermatology, Venereology and Allergology, University Hospital Essen, 45147 Essen, Germany 4 Department of Physiological Chemistry, University of Wurzburg, Biozentrum, Am Hubland, 97074 Wurzburg, Germany 5 Comprehensive Cancer Center Mainfranken, University Clinic Wurzburg, 97080 Wurzburg, Germany 6 Institute for Advanced Study, 3572 Texas A&M University, College Station, Texas 77843-3572, USA 7 Department of Dermatology, Venereology and Allergology, University of Leipzig, 04103 Leipzig, Germany * These authors contributed equally to this work Correspondence to: Manfred Kunz, email: manfred.kunz@medizin.uni-leipzig.de Keywords: melanoma, single cell transcriptome sequencing, stem cells Received: May 18, 2016 Accepted: November 12, 2016 Published: November 26, 2016 ABSTRACT Recent technological advances in single-cell genomics make it possible to analyze cellular heterogeneity of tumor samples. Here, we applied single-cell RNA-seq to measure the transcriptomes of 307 single cells cultured from three biopsies of three different patients with a BRAF/NRAS wild type, BRAF mutant/ NRAS wild type and BRAF wild type/ NRAS mutant melanoma metastasis, respectively. Analysis based on self-organizing maps identified sub-populations defined by multiple gene expression modules involved in proliferation, oxidative phosphorylation, pigmentation and cellular stroma. Gene expression modules had prognostic relevance when compared with gene expression data from published melanoma samples and patient survival data. We surveyed kinome expression patterns across sub-populations of the BRAF/NRAS wild type sample and found that CDK4 and CDK2 were consistently highly expressed in the majority of cells, suggesting that these kinases might be involved in melanoma progression. Treatment of cells with the CDK4 inhibitor palbociclib restricted cell proliferation to a similar, and in some cases greater, extent than MAPK inhibitors. Finally, we identified a low abundant sub-population in this sample that highly expressed a module containing ABC transporter ABCB5, surface markers CD271 and CD133, and multiple aldehyde dehydrogenases (ALDHs). Patient-derived cultures of the BRAF mutant/ NRAS wild type and BRAF wild type/ NRAS mutant metastases showed more homogeneous single-cell gene expression patterns with gene expression modules for proliferation and ABC transporters. Taken together, our results describe an intertumor and intratumor heterogeneity in melanoma short-term cultures which might be relevant for patient survival, and suggest promising targets for new treatment approaches in melanoma therapy.

The molecular mechanisms by which heterogeneity, a major characteristic of stem cells, is achieved are yet unclear. We here study the expression of the membrane stem cell antigen-1 (Sca-1) in mouse bone marrow mesenchymal stem cell (MSC) clones. We show that subpopulations with varying Sca-1 expression profiles regenerate the Sca-1 profile of the mother population within a few days. However, after extensive replication in vitro, the expression profiles shift to lower values and the regeneration time increases. Study of the promoter of Ly6a unravels that the expression level of Sca-1 is related to the promoter occupancy by the activating histone mark H3K4me3. We demonstrate that these findings can be consistently explained by a computational model that considers positive feedback between promoter H3K4me3 modification and gene transcription. This feedback implicates bistable epigenetic states which the cells occupy with an age-dependent frequency due to persistent histone (de-)modification. Our results provide evidence that MSC heterogeneity, and presumably that of other stem cells, is associated with bistable epigenetic states and suggest that MSCs are subject to permanent state fluctuations.

Background Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic phenotypes of this disease based on 873 biopsy specimens collected in the German Cancer Aid MMML (Molecular Mechanisms in Malignant Lymphoma) consortium. They include diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt’s lymphoma, mixed FL/DLBCL lymphomas, primary mediastinal large B cell lymphoma, multiple myeloma, IRF4-rearranged large cell lymphoma, MYC-negative Burkitt-like lymphoma with chr. 11q aberration and mantle cell lymphoma. Methods We apply self-organizing map (SOM) machine learning to microarray-derived expression data to generate a holistic view on the transcriptome landscape of lymphomas, to describe the multidimensional nature of gene regulation and to pursue a modular view on co-expression. Expression data were complemented by pathological, genetic and clinical characteristics. Results We present a transcriptome map of B cell lymphomas that allows visual comparison between the SOM portraits of different lymphoma strata and individual cases. It decomposes into one dozen modules of co-expressed genes related to different functional categories, to genetic defects and to the pathogenesis of lymphomas. On a molecular level, this disease rather forms a continuum of expression states than clearly separated phenotypes. We introduced the concept of combinatorial pattern types (PATs) that stratifies the lymphomas into nine PAT groups and, on a coarser level, into five prominent cancer hallmark types with proliferation, inflammation and stroma signatures. Inflammation signatures in combination with healthy B cell and tonsil characteristics associate with better overall survival rates, while proliferation in combination with inflammation and plasma cell characteristics worsens it. A phenotypic similarity tree is presented that reveals possible progression paths along the transcriptional dimensions. Our analysis provided a novel look on the transition range between FL and DLBCL, on DLBCL with poor prognosis showing expression patterns resembling that of Burkitt’s lymphoma and particularly on ‘double-hit’ MYC and BCL2 transformed lymphomas. Conclusions The transcriptome map provides a tool that aggregates, refines and visualizes the data collected in the MMML study and interprets them in the light of previous knowledge to provide orientation and support in current and future studies on lymphomas and on other cancer entities. Electronic supplementary material The online version of this article (10.1186/s13073-019-0637-7) contains supplementary material, which is available to authorized users.

It is generally accepted that epigenetic modifications, such as DNA and histone methylations, affect transcription and that a gene&rsquo, s transcription feeds back on its epigenetic profile. Depending on the epigenetic modification, positive and negative feedback loops have been described. Here, we study whether such interrelation are mandatory and how transcription factor networks affect it. We apply self-organizing map machine learning to a published data set on the specification and differentiation of murine intestinal stem cells in order to provide an integrative view of gene transcription and DNA, as well as histone methylation during this process. We show that, although gain/loss of H3K4me3 at a gene promoter is generally considered to be associated with its increased/decreased transcriptional activity, such an interrelation is not mandatory, i.e., changes of the modification level do not necessarily affect transcription. Similar considerations hold for H3K27me3. In addition, even strong changes in the transcription of a gene do not necessarily affect its H3K4me3 and H3K27me3 modification profile. We provide a mechanistic explanation of these phenomena that is based on a model of epigenetic regulation of transcription. Thereby, the analyzed data suggest a broad variance in gene specific regulation of histone methylation and support the assumption of an independent regulation of transcription by histone methylation and transcription factor networks. The results provide insights into basic principles of the specification of tissue stem cells and highlight open questions about a mechanistic modeling of this process.

Colorectal cancer (CRC) arising in Lynch syndrome (LS) comprises tumours with constitutional mutations in DNA mismatch repair genes. There is still a lack of whole-genome and transcriptome studies of LS-CRC to address questions about similarities and differences in mutation and gene expression characteristics between LS-CRC and sporadic CRC, about the molecular heterogeneity of LS-CRC, and about specific mechanisms of LS-CRC genesis linked to dysfunctional mismatch repair in LS colonic mucosa and the possible role of immune editing. Here, we provide a first molecular characterization of LS tumours and of matched tumour-distant reference colonic mucosa based on whole-genome DNA-sequencing and RNA-sequencing analyses. Our data support two subgroups of LS-CRCs, G1 and G2, whereby G1 tumours show a higher number of somatic mutations, a higher amount of microsatellite slippage, and a different mutation spectrum. The gene expression phenotypes support this difference. Reference mucosa of G1 shows a strong immune response associated with the expression of HLA and immune checkpoint genes and the invasion of CD4+ T cells. Such an immune response is not observed in LS tumours, G2 reference and normal (non-Lynch) mucosa, and sporadic CRC. We hypothesize that G1 tumours are edited for escape from a highly immunogenic microenvironment via loss of HLA presentation and T-cell exhaustion. In contrast, G2 tumours seem to develop in a less immunogenic microenvironment where tumour-promoting inflammation parallels tumourigenesis. Larger studies on non-neoplastic mucosa tissue of mutation carriers are required to better understand the early phases of emerging tumours. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John WileySons, Ltd.

In acute myeloid leukemia (AML) DNA hypermethylation of gene promoters is frequently observed and often correlates with a block of differentiation. Treatment of AML patients with DNA methyltransferase inhibitors results in global hypomethylation of genes and, thereby, can lead to a reactivation of the differentiation capability. Unfortunately, after termination of treatment both hypermethylation and differentiation block return in most cases. Here, we apply, for the first time, a computational model of epigenetic regulation of transcription in order to: i) provide a mechanistic understanding of the DNA (de-) methylation process in AML and; ii) improve DNA demethylation treatment strategies. By in silico simulation, we analyze promoter hypermethylation scenarios referring to DNMT dysfunction, decreased H3K4me3 and increased H3K27me3 modification activity, and accelerated cell proliferation. We quantify differences between these scenarios with respect to gene repression and activation. Moreover, we compare the scenarios regarding their response to DNMT inhibitor treatment alone and in combination with inhibitors of H3K27me3 histone methyltransferases and of H3K4me3 histone demethylases. We find that the different hypermethylation scenarios respond specifically to therapy, suggesting that failure of remission originates in patient-specific deregulation. We observe that inappropriate demethylation therapy can result even in enforced deregulation. As an example, our results suggest that application of high DNMT inhibitor concentration can induce unwanted global gene activation if hypermethylation originates in increased H3K27me3 modification. Our results underline the importance of a personalized therapy requiring knowledge about the patient-specific mechanism of epigenetic deregulation.

Self-organizing maps (SOM) portray molecular phenotypes with individual resolution. We present an analysis pipeline based on SOM machine learning which allows the comprehensive study of large scale clinical data. The potency of the method is demonstrated in selected applications studying the diversity of gene expression in Glioblastoma Multiforme (GBM) and prostate cancer progression. Our method characterizes relationships between the samples, disentangles the expression patterns into well separated groups of co-regulated genes, extracts their functional contexts using enrichment techniques, and enables the detection of contaminations and outliers in the samples. We found that the four GBM subtypes can be divided into two “localized” and two “intermediate” ones. The localized subtypes are characterized by the antagonistic activation of processes related to immune response and cell division, commonly observed also in other cancers. In contrast, each of the “intermediate” subtypes forms a heterogeneous cont...

Objective/methods DNA methylation plays an important role in obesity and related metabolic complications. We examined genome-wide DNA promoter methylation along with mRNA profiles in paired samples of human subcutaneous adipose tissue (SAT) and omental visceral adipose tissue (OVAT) from non-obese vs. obese individuals. Results We identified negatively correlated methylation and expression of several obesity-associated genes in our discovery dataset and in silico replicated ETV6 in two independent cohorts. Further, we identified six adipose tissue depot-specific genes (HAND2, HOXC6, PPARG, SORBS2, CD36, and CLDN1). The effects were further supported in additional independent cohorts. Our top hits might play a role in adipogenesis and differentiation, obesity, lipid metabolism, and adipose tissue expandability. Finally, we show that in vitro methylation of SORBS2 directly represses gene expression. Conclusions Taken together, our data show distinct tissue specific epigenetic alterations which associate with obesity., Highlights • Obesity-associated differences in DNA promoter methylation and transcriptome in human adipose tissue (ETV6). • Depot-specific analyses revealed novel/known genes (HAND2, HOXC6, PPARG, SORBS2, CD36, CLDN1). • EWAS revealed SSPN and CCDC125 associated to BMI in SAT or OVAT, respectively. • Differentially methylated genes overlap in part with GWAS hits for obesity and fat distribution.

Glioma is a clinically and biologically diverse disease. It challenges diagnosis and prognosis due to its molecular heterogeneity and diverse regimes of biological dysfunctions which are driven by genetic and epigenetic mechanisms. We discover the functional impact of sets of DNA methylation marker genes in the context of brain cancer subtypes as an exemplary approach how bioinformatics and particularly machine learning using self organizing maps (SOM) complements modern high-throughput genomic technologies. DNA methylation changes in gliomas comprise both, hyper- and hypomethylation in a subtype specific fashion. We compared pediatric (2 subtypes) and adult (4) glioblastoma and non-neoplastic brain. The functional impact of differential methylation marker sets is discovered in terms of gene set analysis which comprises a large collection of markers related to biological processes, literature data on gliomas and also chromatin states of the healthy brain. DNA methylation signature genes from alternative studies well agree with our signatures. SOM mapping of gene sets robustly identifies similarities between different marker sets even under conditions of noisy compositions. Mapping of previous sets of glioma markers reveals high redundancy and mixtures of subtypes in the reference cohorts. Consideration of the regulatory level of DNA methylation is inevitable for understanding cancer genesis and progression. It provides suited markers for diagnosis of glioma subtypes and disentangles tumor heterogeneity.

We systematically studied the expression of more than fifty histone and DNA (de)methylating enzymes in lymphoma and healthy controls. As a main result, we found that the expression levels of nearly all enzymes become markedly disturbed in lymphoma, suggesting deregulation of large parts of the epigenetic machinery. We discuss the effect of DNA promoter methylation and of transcriptional activity in the context of mutated epigenetic modifiers such as EZH2 and MLL2. As another mechanism, we studied the coupling between the energy metabolism and epigenetics via metabolites that act as cofactors of JmjC-type demethylases. Our study results suggest that Burkitt’s lymphoma and diffuse large B-cell Lymphoma differ by an imbalance of repressive and poised promoters, which is governed predominantly by the activity of methyltransferases and the underrepresentation of demethylases in this regulation. The data further suggest that coupling of epigenetics with the energy metabolism can also be an important factor in lymphomagenesis in the absence of direct mutations of genes in metabolic pathways. Understanding of epigenetic deregulation in lymphoma and possibly in cancers in general must go beyond simple schemes using only a few modes of regulation.

Application of new high-throughput technologies in molecular medicine collects massive data for hundreds to thousands of persons in large cohort studies by characterizing the phenotype of each individual on a personalized basis. The chapter aims at increasing our understanding of disease genesis and progression and to improve diagnosis and treatment. New methods are needed to handle such “big data.” Machine learning enables one to recognize and to visualize complex data patterns and to make decisions potentially relevant for diagnosis and treatment. The authors address these tasks by applying the method of self-organizing maps and present worked examples from different disease entities of the colon ranging from inflammation to cancer.

MicroRNAs play critical roles in the regulation of gene expression with two major functions: marking mRNA for degradation in a sequence-specific manner or repressing translation. Publicly available data sets on miRNA and mRNA expression in embryonal and induced stem cells, human tissues, and solid tumors are analyzed in this case study using self-organizing maps (SOMs) to characterize miRNA expression landscapes in the context of cell fate commitment, tissue-specific differentiation, and its dysfunction in cancer. The SOM portraits of the individual samples clearly reveal groups of miRNA specifically overexpressed without the need of additional pairwise comparisons between the different systems. Sets of miRNA differentially over- and underexpressed in different systems have been detected in this study. The individual portraits of the expression landscapes enable a very intuitive, image-based perception which clearly promotes the discovery of qualitative relationships between the systems studied. We see perspectives for broad applications of this method in standard analysis to many kinds of high-throughput data of single miRNA and especially combined miRNA/mRNA data sets.

The systematic analysis of miRNA expression and its potential mRNA targets constitutes a basal objective in miRNA research in addition to miRNA gene detection and miRNA target prediction. In this chapter we address methodical issues of miRNA expression analysis using self-organizing maps (SOM), a neural network machine learning algorithm with strong visualization and second-level analysis capabilities widely used to categorize large-scale, high-dimensional data. We shortly review selected experimental and theoretical aspects of miRNA expression analysis. Then, the protocol of our SOM method is outlined with special emphasis on miRNA/mRNA coexpression. The method allows extracting differentially expressed RNA transcripts, their functional context, and also characterization of global properties of expression states and profiles. In addition to the separate study of miRNA and mRNA expression landscapes, we propose the combined analysis of both entities using a covariance SOM.

The systematic analysis of miRNA expression and its potential mRNA targets constitutes a basal objective in miRNA research in addition to miRNA gene detection and miRNA target prediction. In this chapter we address methodical issues of miRNA expression analysis using self-organizing maps (SOM), a neural network machine learning algorithm with strong visualization and second-level analysis capabilities widely used to categorize large-scale, high-dimensional data. We shortly review selected experimental and theoretical aspects of miRNA expression analysis. Then, the protocol of our SOM method is outlined with special emphasis on miRNA/mRNA coexpression. The method allows extracting differentially expressed RNA transcripts, their functional context, and also characterization of global properties of expression states and profiles. In addition to the separate study of miRNA and mRNA expression landscapes, we propose the combined analysis of both entities using a covariance SOM.

MicroRNAs play critical roles in the regulation of gene expression with two major functions: marking mRNA for degradation in a sequence-specific manner or repressing translation. Publicly available data sets on miRNA and mRNA expression in embryonal and induced stem cells, human tissues, and solid tumors are analyzed in this case study using self-organizing maps (SOMs) to characterize miRNA expression landscapes in the context of cell fate commitment, tissue-specific differentiation, and its dysfunction in cancer. The SOM portraits of the individual samples clearly reveal groups of miRNA specifically overexpressed without the need of additional pairwise comparisons between the different systems. Sets of miRNA differentially over- and underexpressed in different systems have been detected in this study. The individual portraits of the expression landscapes enable a very intuitive, image-based perception which clearly promotes the discovery of qualitative relationships between the systems studied. We see perspectives for broad applications of this method in standard analysis to many kinds of high-throughput data of single miRNA and especially combined miRNA/mRNA data sets.

Bioinformatics, 29 (22), ISSN:1367-4803, ISSN:1460-2059

Self organizing maps (SOMs) portrait molecular phenotypes with individual resolution. We demonstrate the potency of the method in selected applications characterizing the diversity of gene expression in different tissues and cancer subtypes, mRNA and miRNA fingerprints of stem cells, the proteome landscape of algae and genomic relations between humans from different populations. It is further shown that SOM portraiting provides a comprehensive frame to describe development, differentiation and diversity in space and/or time.