A major challenge in understanding gene regulation is the unequivocal identification of enhancer elements and uncovering their connections to genes. We present GeneHancer, a novel database of human enhancers and their inferred target genes, in the framework of GeneCards. First, we integrated a total of 434 000 reported enhancers from four different genome-wide databases: the Encyclopedia of DNA Elements (ENCODE), the Ensembl regulatory build, the functional annotation of the mammalian genome (FANTOM) project and the VISTA Enhancer Browser. Employing an integration algorithm that aims to remove redundancy, GeneHancer portrays 285 000 integrated candidate enhancers (covering 12.4% of the genome), 94 000 of which are derived from more than one source, and each assigned an annotation-derived confidence score. GeneHancer subsequently links enhancers to genes, using: tissue co-expression correlation between genes and enhancer RNAs, as well as enhancer-targeted transcription factor genes; expression quantitative trait loci for variants within enhancers; and capture Hi-C, a promoter-specific genome conformation assay. The individual scores based on each of these four methods, along with gene–enhancer genomic distances, form the basis for GeneHancer’s combinatorial likelihood-based scores for enhancer–gene pairing. Finally, we define ‘elite’ enhancer–gene relations reflecting both a high-likelihood enhancer definition and a strong enhancer–gene association. GeneHancer predictions are fully integrated in the widely used GeneCards Suite, whereby candidate enhancers and their annotations are displayed on every relevant GeneCard. This assists in the mapping of non-coding variants to enhancers, and via the linked genes, forms a basis for variant–phenotype interpretation of whole-genome sequences in health and disease. Database URL:http://www.genecards.org/

1. Introduction 3082. Electron microscopy 3112.1 Specimen preparation 3112.2 The electron microscope 3112.3 Acceleration voltage, defocus, and the electron gun 3122.4 Magnification and data collection 3133. Digitisation and CTF correction 3173.1 The patchwork densitometer 3183.2 Particle selection 3203.3 Position dependent CTF correction 3213.4 Precision of CTF determination 3214. Single particles and angular reconstitution 3234.1 Preliminary filtering and centring of data 3234.2 Alignments using correlation functions 3244.3 Choice of first reference images 3244.4 Multi-reference alignment of data 3254.5 MSA eigenvector/eigenvalue data compression 3284.6 MSA classification 3304.7 Euler angle determination (‘angular reconstitution’) 3324.8 Sinograms and sinogram correlation functions 3324.9 Exploiting symmetry 3354.10 Three-dimensional reconstruction 3374.11 Euler angles using anchor sets 3394.12 Iterative refinements 3395. Computational hardware/software aspects 3415.1 The (IMAGIC) image processing workstation 3425.2 Operating systems and GUIs 3425.3 Computational logistics 3445.4 Shared memory machines 3445.5 Farming on loosely coupled computers 3465.6 Implementation using MPI protocol 3475.7 Software is what it's all about 3476. Interpretation of results 3486.1 Assessing resolution: the Fourier Shell Correlation 3486.2 Influence of filtering 3516.3 Rendering 3516.4 Searching for known sub-structures 3526.5 Interpretation 3537. Examples 3537.1 Icosahedral symmetry: TBSV at 5·9 Å resolution 3547.2 The D6 symmetrical worm hemoglobin at 13 Å resolution 3567.3 Functional states of the 70S E. coli ribosome 3577.4 The 50S E. coli ribosomal subunit at 7·5 Å resolution 3598. Perspectives 3619. Acknowledgements 36410. References 364In the past few years, electron microscopy (EM) has established itself as an important – still upcoming – technique for studying the structures of large biological macromolecules. EM is a very direct method of structure determination that complements the well-established techniques of X-ray crystallography and NMR spectroscopy. Electron micrographs record images of the object and not just their diffraction patterns and thus the classical ‘phase’ problem of X-ray crystallography does not exist in EM. Modern microscopes may reach resolution levels better than ∼ 1·5 Å, which is more than sufficient to elucidate the polypeptide backbone in proteins directly. X-ray structures at such resolution levels are considered ‘excellent’. The fundamental problem in biological EM is not so much the instrumental resolution of the microscopes, but rather the radiation sensitivity of the biological material one wants to investigate. Information about the specimen is collected in the photographic emulsion with the arrival of individual electrons that have (elastically) interacted with the specimen. However, many electrons will damage the specimen by non-elastic interactions. By the time enough electrons have passed through the object to produce a single good signal-to-noise (SNR) image, the biological sample will have been reduced to ashes. In contrast, stable inorganic specimens in material science often show interpretable details down to the highest possible instrumental resolution.

A key challenge in the realm of human disease research is next generation sequencing (NGS) interpretation, whereby identified filtered variant-harboring genes are associated with a patient’s disease phenotypes. This necessitates bioinformatics tools linked to comprehensive knowledgebases. The GeneCards suite databases, which include GeneCards (human genes), MalaCards (human diseases) and PathCards (human pathways) together with additional tools, are presented with the focus on MalaCards utility for NGS interpretation as well as for large scale bioinformatic analyses. VarElect, our NGS interpretation tool, leverages the broad information in the GeneCards suite databases. MalaCards algorithms unify disease-related terms and annotations from 69 sources. Further, MalaCards defines hierarchical relatedness—aliases, disease families, a related diseases network, categories and ontological classifications. GeneCards and MalaCards delineate and share a multi-tiered, scored gene-disease network, with stringency levels, including the definition of elite status—high quality gene-disease pairs, coming from manually curated trustworthy sources, that includes 4500 genes for 8000 diseases. This unique resource is key to NGS interpretation by VarElect. VarElect, a comprehensive search tool that helps infer both direct and indirect links between genes and user-supplied disease/phenotype terms, is robustly strengthened by the information found in MalaCards. The indirect mode benefits from GeneCards’ diverse gene-to-gene relationships, including SuperPaths—integrated biological pathways from 12 information sources. We are currently adding an important information layer in the form of “disease SuperPaths”, generated from the gene-disease matrix by an algorithm similar to that previously employed for biological pathway unification. This allows the discovery of novel gene-disease and disease–disease relationships. The advent of whole genome sequencing necessitates capacities to go beyond protein coding genes. GeneCards is highly useful in this respect, as it also addresses 101,976 non-protein-coding RNA genes. In a more recent development, we are currently adding an inclusive map of regulatory elements and their inferred target genes, generated by integration from 4 resources. MalaCards provides a rich big-data scaffold for in silico biomedical discovery within the gene-disease universe. VarElect, which depends significantly on both GeneCards and MalaCards power, is a potent tool for supporting the interpretation of wet-lab experiments, notably NGS analyses of disease. The GeneCards suite has thus transcended its 2-decade role in biomedical research, maturing into a key player in clinical investigation.

This study seeks to investigate distinct signatures and codes within different genomic sequence locations of the human genome. The promoter and other non-coding regions contain sites for the binding of biological particles, for processes such as transcription regulation. The specific rules and sequence codes that govern this remain poorly understood. To derive these (codes), the general designs of sequence are investigated. Genomic signatures are a powerful tool for assessing the general designs of sequence, and cross-comparing different genomic regions for their distinct sequence properties. Through these genomic signatures, the relative non-random properties of sequences are also assessed. Furthermore, a binary components analysis is carried out making use of information theory ideas, to study the RY (purine/pyrimidine), WS (weak/strong) and KM (keto/amino) signatures in the sequences. From this comparison, it is possible to identify the relative importance of these properties within the various protein-coding and non-coding genomic locations. The results show that coding DNA has a strongly non-random WS signature, which reflects the genetic code, and the hydrogen-bond base pairing of codon–anti-codon interactions. In contrast, non-coding locations, such as the promoter, contain a distinct genomic signature. A prominent feature throughout non-coding DNA is a highly non-random RY signature, which is very different in nature to coding DNA, and suggests a structural-based RY code. This marks progress towards deciphering the unknown code(s) in non-protein-coding DNA, and a further understanding of the coding DNA. Additionally, it unravels how DNA carries information. These findings have implications for the most fundamental principles of biology, including knowledge of gene regulation, development and disease.

The human genome carries a vast amount of information within its DNA sequences. The chemical bases A, T, C, and G are the basic units of information content, that are arranged into patterns and codes. Expansive areas of the genome contain codes that are not yet well understood. To decipher these, mathematical and computational tools are applied here to study genomic signatures or general designs of sequences. A novel binary components analysis is devised and utilized. This seeks to isolate the physical and chemical properties of DNA bases, which reveals sequence design and function. Here, information theory tools break down the information content within DNA bases, in order to study them in isolation for their genomic signatures and non-random properties. In this way, the RY (purine/pyrimidine), WS (weak/strong), and KM (keto/amino) general designs are observed in the sequences. The results show that RY, KM, and WS components have a similar and stable overall profile across all human chromosomes. It reveals that the RY property of a sequence is most distant from randomness in the human genome with respect to the genomic signatures. This is true across all human chromosomes. It is concluded that there exists a widespread potential RY code, and furthermore, that this is likely a structural code. Ascertaining this feature of general design, and potential RY structural code has far-reaching implications. This is because it aids in the understanding of cell biology, growth, and development, as well as downstream in the study of human disease and potential drug design.