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A deletion-generator compound element allows deletion saturation analysis for genomewide phenotypic annotation.

ABSTRACT: With the available eukaryotic genome sequences, there are predictions of thousands of previously uncharacterized genes without known function or available mutational variant. Thus, there is an urgent need for efficient genetic tools for genomewide phenotypic analysis. Here we describe such a tool: a deletion-generator technology that exploits properties of a double transposable element to produce molecularly defined deletions at high density and with high efficiency. This double element, called P[wHy], is composed of a "deleter" element hobo, bracketed by two genetic markers and inserted into a "carrier" P element. We have used this P[wHy] element in Drosophila melanogaster to generate sets of nested deletions of sufficient coverage to discriminate among every transcription unit within 60 kb of the starting insertion site. Because these two types of mobile elements, carrier and deleter, can be found in other species, our strategy should be applicable to phenotypic analysis in a variety of model organisms.

SUBMITTER: Huet F

PROVIDER: S-EPMC126605 | biostudies-literature | 2002 Jul

REPOSITORIES: biostudies-literature

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A deletion-generator compound element allows deletion saturation analysis for genomewide phenotypic annotation.

Huet François F Lu Jeffrey T JT Myrick Kyl V KV Baugh L Ryan LR Crosby Madeline A MA Gelbart William M WM

Proceedings of the National Academy of Sciences of the United States of America 20020702 15

With the available eukaryotic genome sequences, there are predictions of thousands of previously uncharacterized genes without known function or available mutational variant. Thus, there is an urgent need for efficient genetic tools for genomewide phenotypic analysis. Here we describe such a tool: a deletion-generator technology that exploits properties of a double transposable element to produce molecularly defined deletions at high density and with high efficiency. This double element, called ...[more]

PMID: 12096187

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Project description:Single-cell RNA sequencing (scRNA-seq) is an invaluable tool for profiling cells in complex tissues and dissecting activation states that lack well-defined surface protein expression. For immune cells, the transcriptomic profile captured by scRNA- seq cannot always identify cell states and subsets defined by conventional flow cytometry. Emerging technologies have enabled multimodal sequencing of single cells, such as paired sequencing of the transcriptome and surface proteome by CITE-seq, but integrating these high dimensional modalities for accurate cell type annotation remains a challenge in the field. Here, we describe a machine learning tool called MultiModal Classifier Hierarchy (MMoCHi) for the cell-type annotation of CITE-seq data. Our classifier involves several steps: 1) we use landmark registration to remove batch-related staining artifacts in CITE-Seq protein expression, 2) the user defines a hierarchy of classifications based on cell type similarity and ontology and provides markers (protein or gene expression) for the identification of ground truth populations within the dataset by threshold gating, 3) progressing through this user-defined hierarchy, we train a random forest classifier using all available modalities (surface proteome and transcriptome data), and 4) we use these forests to predict cell types across the entire dataset. Applying MMoCHi to CITE-seq data of immune cells isolated from eight distinct tissue sites of two human organ donors yields high-purity cell type annotations encompassing the broad array of immune cell states in the dataset. This includes T and B cell memory subsets, macrophages and monocytes, and natural killer cells, as well as rare populations of plasmacytoid dendritic cells, innate T cells, and innate lymphoid cell subsets. We validate the use of feature importances extracted from the classifier hierarchy to select robust genes for improved identification of T cell memory subsets by scRNA-seq. Together, MMoCHi provides a comprehensive system of tools for the batch-correction and cell- type annotation of CITE-seq data. Moreover, this tool provides flexibility in classification hierarchy design allowing for cell type annotations to reflect a researcher’s specific experimental design. This flexibility also renders MMoCHi readily extendable beyond immune cell annotation, and potentially adaptable to other sequencing modalities.

Dataset Information

A deletion-generator compound element allows deletion saturation analysis for genomewide phenotypic annotation.

Publications

A deletion-generator compound element allows deletion saturation analysis for genomewide phenotypic annotation.

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