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Lee, Y.-h.

Publications and source records attributed to Lee, Y.-h..

5 recordsLinked to original sources

Genomic Annotation Infrastructure (GAIn): Pipelines and Resource Repositories for Annotating Variants, Positions, and Regions

Interpretation of genomic variants, positions, and regions depends on reliable annotation--adding evidence such as predicted effect, conservation, population frequency, and gene-level context--yet the underlying resources are numerous, versioned, and assembly-specific. We present the Genomic Annotation Infrastructure (GAIn), a platform that generates transparent, reproducible annotations via declarative pipelines that define annotation tasks as ordered lists of components, called annotators, that produce annotation attributes using genomic resources from Genomic Resource Repositories (GRRs). We provide two public GRRs: a main repository containing more than 250 heterogeneous genomic resources, and a separate GRR-ENCODE repository containing resources derived from thousands of ENCODE (Encyclopedia of DNA Elements) project experiments. Users can use the annotation pipelines we made available, author custom annotation pipelines, and execute annotation tasks with these pipelines via GAIns web and command-line interfaces. The web interface can be used without any setup, but it relies on shared computational infrastructure and imposes limits on the size of annotation tasks. The command-line interface requires setup but supports arbitrarily large annotation tasks through simple-to-use parallelization and offers a broader set of features. For example, command-line GAIn can be extended by using custom GRRs or creating custom annotators via its plugin architecture. In addition, GAIns re-annotation feature, which updates annotations as they evolve, substantially simplifies maintaining annotations in a large genomics analysis project. GAIns resource management, explicit versioning, and pipeline abstraction provide an auditable, maintainable, and efficient foundation for modern genomic annotation across reference assemblies and use cases.

bioinformatics↗

The Genotype and Phenotypes in Families (GPF) platform manages the large and complex data at SFARI

The exploration of genotypic variants impacting phenotypes is a cornerstone in genetics research. The emergence of vast collections containing deeply genotyped and phenotyped families has made it possible to pursue the search for variants associated with complex diseases. However, managing these large-scale datasets requires specialized computational tools tailored to organize and analyze the extensive data. GPF (Genotypes and Phenotypes in Families) is an open-source platform (https://github.com/iossifovlab/gpf) that manages genotypes and phenotypes derived from collections of families. The GPF interface allows interactive exploration of genetic variants, enrichment analysis for de novo mutations, and phenotype/genotype association tools. In addition, GPF allows researchers to share their data securely with the broader scientific community. GPF is used to disseminate two large-scale family collection datasets (SSC, SPARK) for the study of autism funded by the SFARI foundation. However, GPF is versatile and can manage genotypic data from other small or large family collections. Our GPF-SFARI GPF instance (https://gpf.sfari.org/) provides protected access to comprehensive genotypic and phenotypic data for the SSC and SPARK. In addition, GPF-SFARI provides public access to an extensive collection of de novo mutations identified in individuals with autism and related disorders and to gene-level statistics of the protected datasets characterizing the genes roles in autism. Here, we highlight the primary features of GPF within the context of GPF-SFARI.

bioinformatics↗

Snakeobjects: an object-oriented workflow management system

Snakemake is one of the most popular workflow management systems, particularly in biological sciences. Snakemake workflows are highly portable, scalable, and transparent. Moreover, they enable the painless reproduction of published results and adaption to similar data processing and analysis projects. Here we present Snakeobjects, an extension of Snakemake that considerably simplifies the development of workflows and improves their readability and adaptability while preserving all the benefits of Snakemake that ensure the large and growing community of its users.

bioinformatics↗

SARS-CoV-2 ORF8 limits expression levels of Spike antigen.

Survival from COVID-19 depends on the ability of the host to effectively neutralize virions and infected cells, a process largely driven by antibody-mediated immunity. However, with the newly emerging variants that evade Spike-targeting antibodies, re-infections and breakthrough infections are increasingly common. A full characterization of SARS-CoV-2 mechanisms counteracting antibody-mediated immunity is needed. Here, we report that ORF8 is a SARS-CoV-2 factor that controls cellular Spike antigen levels. ORF8 limits the availability of mature Spike by inhibiting host protein synthesis and retaining Spike at the endoplasmic reticulum, reducing cell-surface Spike levels and recognition by anti-SARS-CoV-2 antibodies. With limited Spike availability, ORF8 restricts Spike incorporation during viral assembly, reducing Spike levels in virions. Cell entry of these virions leaves fewer Spike molecules at the cell surface, limiting antibody recognition of infected cells. Our studies propose an ORF8-dependent SARS-CoV-2 strategy that allows immune evasion of infected cells for extended viral production.

microbiology↗

De novo indels within introns contribute to ASD incidence

Copy number profiling and whole-exome sequencing has allowed us to make remarkable progress in our understanding of the genetics of autism over the past ten years, but there are major aspects of the genetics that are unresolved. Through whole-genome sequencing, additional types of genetic variants can be observed. These variants are abundant and to know which are functional is challenging. We have analyzed whole-genome sequencing data from 510 of the Simons Simplex Collections quad families and focused our attention on intronic variants. Within the introns of 546 high-quality autism target genes, we identified 63 de novo indels in the affected and only 37 in the unaffected siblings. The difference of 26 events is significantly larger than expected (p-val = 0.01) and using reasonable extrapolation shows that de novo intronic indels can contribute to at least 10% of simplex autism. The significance increases if we restrict to the half of the autism targets that are intolerant to damaging variants in the normal human population, which half we expect to be even more enriched for autism genes. For these 273 targets we observe 43 and 20 events in affected and unaffected siblings, respectively (p-value of 0.005). There was no significant signal in the number of de novo intronic indels in any of the control sets of genes analyzed. We see no signal from de novo substitutions in the introns of target genes.

genetics↗