Dataset Information

Framework for microRNA variant annotation and prioritization using human population and disease datasets.

ABSTRACT: MicroRNA (miRNA) expression is frequently deregulated in human disease, in contrast, disease-associated miRNA mutations are understudied. We developed Annotative Database of miRNA Elements, ADmiRE, which combines multiple existing and new biological annotations to aid prioritization of causal miRNA variation. We annotated 10,206 mature (3,257 within seed region) miRNA variants from multiple large sequencing datasets including gnomAD (15,496 genomes; 123,136 exomes). The pattern of miRNA variation closely resembles protein-coding exonic regions, with no difference between intragenic and intergenic miRNAs (P = 0.56), and high confidence miRNAs demonstrate higher sequence constraint (P < 0.001). Conservation analysis across 100 vertebrates identified 765 highly conserved miRNAs that also have limited genetic variation in gnomAD. We applied ADmiRE to the TCGA PanCancerAtlas WES dataset containing over 10,000 individuals across 33 adult cancers and annotated 1,267 germline (rare in gnomAD) and 1,492 somatic miRNA variants. Several miRNA families with deregulated gene expression in cancer have low levels of both somatic and germline variants, e.g., let-7 and miR-10. In addition to known somatic miR-142 mutations in hematologic cancers, we describe novel somatic miR-21 mutations in esophageal cancers impacting downstream miRNA targets. Through the development of ADmiRE, we present a framework for annotation and prioritization of miRNA variation in disease datasets.

SUBMITTER: Oak N

PROVIDER: S-EPMC6400659 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Framework for microRNA variant annotation and prioritization using human population and disease datasets.

Oak Ninad N Ghosh Rajarshi R Huang Kuan-Lin KL Wheeler David A DA Ding Li L Plon Sharon E SE

Human mutation 20181108 1

MicroRNA (miRNA) expression is frequently deregulated in human disease, in contrast, disease-associated miRNA mutations are understudied. We developed Annotative Database of miRNA Elements, ADmiRE, which combines multiple existing and new biological annotations to aid prioritization of causal miRNA variation. We annotated 10,206 mature (3,257 within seed region) miRNA variants from multiple large sequencing datasets including gnomAD (15,496 genomes; 123,136 exomes). The pattern of miRNA variatio ...[more]

PMID: 30302893

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Project description:BackgroundRecent advances in sequencing technologies have resulted in an unprecedented increase in the number of metagenomes that are being sequenced world-wide. Given their volume, functional annotation of metagenomic sequence datasets requires specialized computational tools/techniques. In spite of having high accuracy, existing stand-alone functional annotation tools necessitate end-users to perform compute-intensive homology searches of metagenomic datasets against "multiple" databases prior to functional analysis. Although, web-based functional annotation servers address to some extent the problem of availability of compute resources, uploading and analyzing huge volumes of sequence data on a shared public web-service has its own set of limitations. In this study, we present COGNIZER, a comprehensive stand-alone annotation framework which enables end-users to functionally annotate sequences constituting metagenomic datasets. The COGNIZER framework provides multiple workflow options. A subset of these options employs a novel directed-search strategy which helps in reducing the overall compute requirements for end-users. The COGNIZER framework includes a cross-mapping database that enables end-users to simultaneously derive/infer KEGG, Pfam, GO, and SEED subsystem information from the COG annotations.ResultsValidation experiments performed with real-world metagenomes and metatranscriptomes, generated using diverse sequencing technologies, indicate that the novel directed-search strategy employed in COGNIZER helps in reducing the compute requirements without significant loss in annotation accuracy. A comparison of COGNIZER's results with pre-computed benchmark values indicate the reliability of the cross-mapping database employed in COGNIZER.ConclusionThe COGNIZER framework is capable of comprehensively annotating any metagenomic or metatranscriptomic dataset from varied sequencing platforms in functional terms. Multiple search options in COGNIZER provide end-users the flexibility of choosing a homology search protocol based on available compute resources. The cross-mapping database in COGNIZER is of high utility since it enables end-users to directly infer/derive KEGG, Pfam, GO, and SEED subsystem annotations from COG categorizations. Furthermore, availability of COGNIZER as a stand-alone scalable implementation is expected to make it a valuable annotation tool in the field of metagenomic research.Availability and implementationA Linux implementation of COGNIZER is freely available for download from the following links: http://metagenomics.atc.tcs.com/cognizer, https://metagenomics.atc.tcs.com/function/cognizer.

Project description:In the search for novel causal mutations, public and/or private variant databases are nearly always used to facilitate the search as they result in a massive reduction of putative variants in one step. Practically, variant filtering is often done by either using all variants from the variant database (called the absence-approach, i.e. it is assumed that disease-causing variants do not reside in variant databases) or by using the subset of variants with an allelic frequency > 1% (called the 1%-approach). We investigate the validity of these two approaches in terms of false negatives (the true disease-causing variant does not pass all filters) and false positives (a harmless mutation passes all filters and is erroneously retained in the list of putative disease-causing variants) and compare it with an novel approach which we named the quantile-based approach. This approach applies variable instead of static frequency thresholds and the calculation of these thresholds is based on prior knowledge of disease prevalence, inheritance models, database size and database characteristics.Based on real-life data, we demonstrate that the quantile-based approach outperforms the absence-approach in terms of false negatives. At the same time, this quantile-based approach deals more appropriately with the variable allele frequencies of disease-causing alleles in variant databases relative to the 1%-approach and as such allows a better control of the number of false positives. We also introduce an alternative application for variant database usage and the quantile-based approach. If disease-causing variants in variant databases deviate substantially from theoretical expectancies calculated with the quantile-based approach, their association between genotype and phenotype had to be reconsidered in 12 out of 13 cases.We developed a novel method and demonstrated that this so-called quantile-based approach is a highly suitable method for variant filtering. In addition, the quantile-based approach can also be used for variant flagging. For user friendliness, lookup tables and easy-to-use R calculators are provided.

Dataset Information

Framework for microRNA variant annotation and prioritization using human population and disease datasets.

Publications

Framework for microRNA variant annotation and prioritization using human population and disease datasets.

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