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Clan genomics: From OMIM phenotypic traits to genes and biology.

ABSTRACT: Clinical characterization of a patient phenotype has been the quintessential approach for elucidating a differential diagnosis and a hypothesis to explore a potential clinical diagnosis. This has resulted in a language of medicine and a semantic ontology, with both specialty- and subspecialty-specific lexicons, that can be challenging to translate and interpret. There is no 'Rosetta Stone' of clinical medicine such as the genetic code that can assist translation and interpretation of the language of genetics. Nevertheless, the information content embodied within a clinical diagnosis can guide management, therapeutic intervention, and potentially prognostic outlook of disease enabling anticipatory guidance for patients and families. Clinical genomics is now established firmly in medical practice. The granularity and informative content of a personal genome is immense. Yet, we are limited in our utility of much of that personal genome information by the lack of functional characterization of the overwhelming majority of computationally annotated genes in the haploid human reference genome sequence. Whereas DNA and the genetic code have provided a 'Rosetta Stone' to translate genetic variant information, clinical medicine, and clinical genomics provide the context to understand human biology and disease. A path forward will integrate deep phenotyping, such as available in a clinical synopsis in the Online Mendelian Inheritance in Man (OMIM) entries, with personal genome analyses.

SUBMITTER: Lupski JR

PROVIDER: S-EPMC8530976 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: The OMIM database is a tool used daily by geneticists. Syndrome pages include a Clinical Synopsis section containing a list of known phenotypes comprising a clinical syndrome. The phenotypes are in free text and different phrases are often used to describe the same phenotype, the differences originating in spelling variations or typing errors, varying sentence structures and terminological variants.These variations hinder searching for syndromes or using the large amount of phenotypic information for research purposes. In addition, negation forms also create false positives when searching the textual description of phenotypes and induce noise in text mining applications. DESCRIPTION: Our method allows efficient and complete search of OMIM phenotypes as well as improved data-mining of the OMIM phenome. Applying natural language processing, each phrase is tagged with additional semantic information using UMLS and MESH. Using a grammar based method, annotated phrases are clustered into groups denoting similar phenotypes. These groups of synonymous expressions enable precise search, as query terms can be matched with the many variations that appear in OMIM, while avoiding over-matching expressions that include the query term in a negative context. On the basis of these clusters, we computed pair-wise similarity among syndromes in OMIM. Using this new similarity measure, we identified 79,770 new connections between syndromes, an average of 16 new connections per syndrome. Our project is Web-based and available at http://fohs.bgu.ac.il/s2g/csiomim CONCLUSIONS: The resulting enhanced search functionality provides clinicians with an efficient tool for diagnosis. This search application is also used for finding similar syndromes for the candidate gene prioritization tool S2G.The enhanced OMIM database we produced can be further used for bioinformatics purposes such as linking phenotypes and genes based on syndrome similarities and the known genes in Morbidmap.

Project description:IntroductionRNA interference (RNAi) of trait-specific genes permits the manipulation of specific phenotypic traits ("phenotypic engineering") and thus represents a powerful tool to test trait function in evolutionary studies. The identification of suitable candidate genes, however, often relies on existing functional gene annotation, which is usually limited in emerging model organisms, especially when they are only distantly related to traditional genetic model organisms. A case in point is the free-living flatworm Macrostomum lignano (Lophotrochozoa: Platyhelminthes: Rhabditophora), an increasingly powerful model organism for evolutionary studies of sex in simultaneous hermaphrodites. To overcome the limitation of sparse functional annotation, we have performed a positional RNA-Seq analysis on different body fragments in order to identify organ-specific candidate transcripts. We then performed gene expression (in situ hybridization) and gene function (RNAi) analyses on 23 candidate transcripts, both to evaluate the predictive potential of this approach and to obtain preliminary functional characterizations of these candidate genes.ResultsWe identified over 4000 transcripts that could be expected to show specific expression in different reproductive organs (including testis, ovary and the male and female genital systems). The predictive potential of the method could then be verified by confirming organ-specific expression for several candidate transcripts, some of which yielded interesting trait-specific knock-down phenotypes that can now be followed up in future phenotypic engineering studies.ConclusionsOur positional RNA-Seq analysis represents a highly useful resource for the identification of candidate transcripts for functional and phenotypic engineering studies in M. lignano, and it has already been used successfully in several studies. Moreover, this approach can overcome some inherent limitations of homology-based candidate selection and thus should be applicable to a broad range of emerging model organisms.

Project description:Streptococcus thermophilus is a major starter for the dairy industry with great economic importance. In this study we analyzed 23 fully sequenced genomes of S. thermophilus to highlight novel aspects of the evolution, biology and technological properties of this species. Pan/core genome analysis revealed that the species has an important number of conserved genes and that the pan genome is probably going to be closed soon. According to whole genome phylogeny and average nucleotide identity (ANI) analysis, most S. thermophilus strains were grouped in two major clusters (i.e., clusters A and B). More specifically, cluster A includes strains with chromosomes above 1.83 Mbp, while cluster B includes chromosomes below this threshold. This observation suggests that strains belonging to the two clusters may be differentiated by gene gain or gene loss events. Furthermore, certain strains of cluster A could be further subdivided in subgroups, i.e., subgroup I (ASCC 1275, DGCC 7710, KLDS SM, MN-BM-A02, and ND07), II (MN-BM-A01 and MN-ZLW-002), III (LMD-9 and SMQ-301), and IV (APC151 and ND03). In cluster B certain strains formed one distinct subgroup, i.e., subgroup I (CNRZ1066, CS8, EPS, and S9). Clusters and subgroups observed for S. thermophilus indicate the existence of lineages within the species, an observation which was further supported to a variable degree by the distribution and/or the architecture of several genomic traits. These would include exopolysaccharide (EPS) gene clusters, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs)-CRISPR associated (Cas) systems, as well as restriction-modification (R-M) systems and genomic islands (GIs). Of note, the histidine biosynthetic cluster was found present in all cluster A strains (plus strain NCTC12958T) but was absent from all strains in cluster B. Other loci related to lactose/galactose catabolism and urea metabolism, aminopeptidases, the majority of amino acid and peptide transporters, as well as amino acid biosynthetic pathways were found to be conserved in all strains suggesting their central role for the species. Our study highlights the necessity of sequencing and analyzing more S. thermophilus complete genomes to further elucidate important aspects of strain diversity within this starter culture that may be related to its application in the dairy industry.

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Clan genomics: From OMIM phenotypic traits to genes and biology.

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