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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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Multidimensional profiling reveals GATA1-modulated stage-specific chromatin states and functional associations during human erythropoiesis

TL;DR: In this article , the authors analyzed the chromatin landscape at multiple levels in defined populations from primary human erythroid culture and revealed mechanistic insights underlying chromatin rearrangements during development.
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Analysis of single-cell RNA sequencing data based on autoencoders

TL;DR: In this paper, the authors introduce scAEspy, a unifying tool that embodies four of the most advanced autoencoders, two novel AEs that were developed on purpose, and different loss functions.
Journal ArticleDOI

Primitive haematopoiesis in the human placenta gives rise to macrophages with epigenetically silenced HLA-DR

TL;DR: This article identified a population of placental erythro-myeloid progenitors (PEMPs) in the early human placenta that have conserved features of primitive yolk sac EMPs, including the lack of HLF expression.
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Tipping-point analysis uncovers critical transition signals from gene expression profiles

TL;DR: BioTIP as mentioned in this paper identifies tipping points and their significant gene signals, which can be applied to understanding of disease and developmental, but also to data at the cellular or organismal level.
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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