Frida Belinky

In-silico human genomics with GeneCards

Since 1998, the bioinformatics, systems biology, genomics and medical communities have enjoyed a synergistic relationship with the GeneCards database of human genes (http://www.genecards.org). This human gene compendium was created to help to introduce order into the increasing chaos of information flow. As a consequence of viewing details and deep links related to specific genes, users have often requested enhanced capabilities, such that, over time, GeneCards has blossomed into a suite of tools (including GeneDecks, GeneALaCart, GeneLoc, GeneNote and GeneAnnot) for a variety of analyses of both single human genes and sets thereof. In this paper, we focus on inhouse and external research activities which have been enabled, enhanced, complemented and, in some cases, motivated by GeneCards. In turn, such interactions have often inspired and propelled improvements in GeneCards. We describe here the evolution and architecture of this project, including examples of synergistic applications in diverse areas such as synthetic lethality in cancer, the annotation of genetic variations in disease, omics integration in a systems biology approach to kidney disease, and bioinformatics tools.

PathCards: multi-source consolidation of human biological pathways.

The study of biological pathways is key to a large number of systems analyses. However, many relevant tools consider a limited number of pathway sources, missing out on many genes and gene-to-gene connections. Simply pooling several pathways sources would result in redundancy and the lack of systematic pathway interrelations. To address this, we exercised a combination of hierarchical clustering and nearest neighbor graph representation, with judiciously selected cutoff values, thereby consolidating 3215 human pathways from 12 sources into a set of 1073 SuperPaths. Our unification algorithm finds a balance between reducing redundancy and optimizing the level of pathway-related informativeness for individual genes. We show a substantial enhancement of the SuperPaths’ capacity to infer gene-to-gene relationships when compared with individual pathway sources, separately or taken together. Further, we demonstrate that the chosen 12 sources entail nearly exhaustive gene coverage. The computed SuperPaths are presented in a new online database, PathCards, showing each SuperPath, its constituent network of pathways, and its contained genes. This provides researchers with a rich, searchable systems analysis resource.Database URL: http://pathcards.genecards.org/

VarElect: the phenotype-based variation prioritizer of the GeneCards Suite

BackgroundNext generation sequencing (NGS) provides a key technology for deciphering the genetic underpinnings of human diseases. Typical NGS analyses of a patient depict tens of thousands non-reference coding variants, but only one or very few are expected to be significant for the relevant disorder. In a filtering stage, one employs family segregation, rarity in the population, predicted protein impact and evolutionary conservation as a means for shortening the variation list. However, narrowing down further towards culprit disease genes usually entails laborious seeking of gene-phenotype relationships, consulting numerous separate databases. Thus, a major challenge is to transition from the few hundred shortlisted genes to the most viable disease-causing candidates.ResultsWe describe a novel tool, VarElect (http://ve.genecards.org), a comprehensive phenotype-dependent variant/gene prioritizer, based on the widely-used GeneCards, which helps rapidly identify causal mutations with extensive evidence. The GeneCards suite offers an effective and speedy alternative, whereby >120 gene-centric automatically-mined data sources are jointly available for the task. VarElect cashes on this wealth of information, as well as on GeneCards’ powerful free-text Boolean search and scoring capabilities, proficiently matching variant-containing genes to submitted disease/symptom keywords. The tool also leverages the rich disease and pathway information of MalaCards, the human disease database, and PathCards, the unified pathway (SuperPaths) database, both within the GeneCards Suite. The VarElect algorithm infers direct as well as indirect links between genes and phenotypes, the latter benefitting from GeneCards’ diverse gene-to-gene data links in GenesLikeMe. Finally, our tool offers an extensive gene-phenotype evidence portrayal (“MiniCards”) and hyperlinks to the parent databases.ConclusionsWe demonstrate that VarElect compares favorably with several often-used NGS phenotyping tools, thus providing a robust facility for ranking genes, pointing out their likelihood to be related to a patient’s disease. VarElect’s capacity to automatically process numerous NGS cases, either in stand-alone format or in VCF-analyzer mode (TGex and VarAnnot), is indispensable for emerging clinical projects that involve thousands of whole exome/genome NGS analyses.

Evolutionary switches between two serine codon sets are driven by selection

Significance When a rare evolutionary event is observed, such as substitution of two adjacent nucleotides, the question emerges whether such rare changes are caused by mutational bias or by selection. Here we address this question through genome-wide analysis of double substitutions that lead to switch of the codon sets for the amino acid serine, the only one that is encoded by two disjoint sets of codons. We show that selection is the primary factor behind these changes. These findings suggest that short-term evolution of proteins is subject to stronger purifying selection than previously thought and has significant implications for methods of phylogenetic analysis. Serine is the only amino acid that is encoded by two disjoint codon sets so that a tandem substitution of two nucleotides is required to switch between the two sets. Previously published evidence suggests that, for the most evolutionarily conserved serines, the codon set switch occurs by simultaneous substitution of two nucleotides. Here we report a genome-wide reconstruction of the evolution of serine codons in triplets of closely related species from diverse prokaryotes and eukaryotes. The results indicate that the great majority of codon set switches proceed by two consecutive nucleotide substitutions, via a threonine or cysteine intermediate, and are driven by selection. These findings imply a strong pressure of purifying selection in protein evolution, which in the case of serine codon set switches occurs via an initial deleterious substitution quickly followed by a second, compensatory substitution. The result is frequent reversal of amino acid replacements and, at short evolutionary distances, pervasive homoplasy.

An Overview of Synergistic Data Tools for Biological Scrutiny

A network of biological databases is reviewed, supplying a framework for studies of human genes and the association of their genomic variations with human pheno- types. The network is composed of GeneCards, the human gene compendium, which provides comprehensive informa- tion on all known and predicted human genes, along with its suite members GeneDecks and GeneLoc. Two databases are shown that address genes and variations focusing on ol- factory reception (HORDE) and transduction (GOSdb). In the realm of disease scrutiny, we portray MalaCards, a novel comprehensive database of human diseases and their anno- tations. Also shown is GeneKid, a tool aimed at generating novel kidney disease biomarkers using systems biology, as well as Xome, a database for whole-exome next-generation DNA sequences for human diseases in the Israeli popula- tion. Finally, we show LifeMap Discovery, a database of em- bryonic development, stem cell research and regenerative medicine, which links to both GeneCards and MalaCards.

Selection on start codons in prokaryotes and potential compensatory nucleotide substitutions

Reconstruction of the evolution of start codons in 36 groups of closely related bacterial and archaeal genomes reveals purifying selection affecting AUG codons. The AUG starts are replaced by GUG and especially UUG significantly less frequently than expected under the neutral expectation derived from the frequencies of the respective nucleotide triplet substitutions in non-coding regions and in 4-fold degenerate sites. Thus, AUG is the optimal start codon that is actively maintained by purifying selection. However, purifying selection on start codons is significantly weaker than the selection on the same codons in coding sequences, although the switches between the codons result in conservative amino acid substitutions. The only exception is the AUG to UUG switch that is strongly selected against among start codons. Selection on start codons is most pronounced in evolutionarily conserved, highly expressed genes. Mutation of the start codon to a sub-optimal form (GUG or UUG) tends to be compensated by mutations in the Shine-Dalgarno sequence towards a stronger translation initiation signal. Together, all these findings indicate that in prokaryotes, translation start signals are subject to weak but significant selection for maximization of initiation rate and, consequently, protein production.

Rational confederation of genes and diseases: NGS interpretation via GeneCards, MalaCards and VarElect

BackgroundA 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.ResultsVarElect, 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.ConclusionsMalaCards 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.

Purifying and positive selection in the evolution of stop codons

Modes of evolution of stop codons in protein-coding genes, especially the conservation of UAA, have been debated for many years. We reconstructed the evolution of stop codons in 40 groups of closely related prokaryotic and eukaryotic genomes. The results indicate that the UAA codons are maintained by purifying selection in all domains of life. In contrast, positive selection appears to drive switches from UAG to other stop codons in prokaryotes but not in eukaryotes. Changes in stop codons are significantly associated with increased substitution frequency immediately downstream of the stop. These positions are otherwise more strongly conserved in evolution compared to sites farther downstream, suggesting that such substitutions are compensatory. Although GC content has a major impact on stop codon frequencies, its contribution to the decreased frequency of UAA differs between bacteria and archaea, presumably, due to differences in their translation termination mechanisms.