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Open AccessJournal ArticleDOI

Integrative Single-Cell RNA-Seq and ATAC-Seq Analysis of Human Developmental Hematopoiesis

TLDR
In this paper, the authors applied single-cell RNA sequencing (scRNA-seq) and single cell assay for transposase-accessible chromatin sequencing to over 8,000 human immunophenotypic blood cells from fetal liver and bone marrow.
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This article is published in Cell Stem Cell.The article was published on 2021-03-04 and is currently open access. It has received 122 citations till now. The article focuses on the topics: ATAC-seq & Chromatin.

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A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility

TL;DR: In this article, a plate-based ATAC-seq assay was proposed for single-cell chromatin accessibility profiling based on the assay for transposase-accessible chromatin using sequencing (ATACseq).
Journal ArticleDOI

Understanding mast cell heterogeneity at single cell resolution

TL;DR: A review of the current understanding of MC development and heterogeneity is provided in this article, where the authors discuss new insights gained from single-cell-based studies that may lead to future research directions and therapeutic opportunities.
Journal ArticleDOI

Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction

TL;DR: MultiVelo, a differential equation model of gene expression that extends the RNA velocity framework to incorporate epigenomic data, uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression and improves the accuracy of cell fate prediction compared to velocity estimates from RNA only.
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Single-cell RNA sequencing to track novel perspectives in HSC heterogeneity

TL;DR: In this paper , the authors discuss how single-cell RNA sequencing (scRNA-seq) technologies contribute to tracing origin and lineage commitment of HSCs, profiling the bone marrow microenvironment and providing high-resolution dissection of malignant hematopoiesis.
Journal ArticleDOI

A specialized bone marrow microenvironment for fetal haematopoiesis

TL;DR: In this paper , a single cell RNA sequencing was performed to identify Wnt2 as arterial endothelial cells (AEC)-derived signal that activates β-catenin-dependent proliferation of fetal HSPCs.
References
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Journal Article

Scikit-learn: Machine Learning in Python

TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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Deep learning

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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The Sequence Alignment/Map format and SAMtools

TL;DR: SAMtools as discussed by the authors implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
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Fast and accurate short read alignment with Burrows–Wheeler transform

TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
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STAR: ultrafast universal RNA-seq aligner

TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
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