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      Human yolk sac-like haematopoiesis generates RUNX1-, GFI1- and/or GFI 1B-dependent blood and SOX17-positive endothelium

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          ABSTRACT

          The genetic regulatory network controlling early fate choices during human blood cell development are not well understood. We used human pluripotent stem cell reporter lines to track the development of endothelial and haematopoietic populations in an in vitro model of human yolk-sac development. We identified SOX17 CD34 +CD43 endothelial cells at day 2 of blast colony development, as a haemangioblast-like branch point from which SOX17 CD34 +CD43 + blood cells and SOX17 +CD34 +CD43 endothelium subsequently arose. Most human blood cell development was dependent on RUNX1. Deletion of RUNX1 only permitted a single wave of yolk sac-like primitive erythropoiesis, but no yolk sac myelopoiesis or aorta-gonad-mesonephros (AGM)-like haematopoiesis. Blocking GFI1 and/or GFI1B activity with a small molecule inhibitor abrogated all blood cell development, even in cell lines with an intact RUNX1 gene. Together, our data define the hierarchical requirements for RUNX1, GFI1 and/or GFI1B during early human haematopoiesis arising from a yolk sac-like SOX17-negative haemogenic endothelial intermediate.

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          [Related article:] Highlighted Article: The hierarchical requirements for RUNX1, GFI1 and/or GFI1B during early human haematopoiesis arising from a yolk sac-like haemogenic endothelial intermediate.

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          Most cited references54

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed 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. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Journal
                Development
                Development
                DEV
                develop
                Development (Cambridge, England)
                The Company of Biologists Ltd
                0950-1991
                1477-9129
                15 October 2020
                29 October 2020
                29 October 2020
                : 147
                : 20
                : dev193037
                Affiliations
                [1 ]Murdoch Children's Research Institute, The Royal Children's Hospital , Flemington Road, Parkville, Victoria 3052, Australia
                [2 ]Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne , Parkville, Victoria 3052, Australia
                [3 ]School of BioSciences, University of Melbourne , Parkville, Victoria 3052, Australia
                [4 ]Department of Anatomy and Developmental Biology, Monash University , Clayton, Victoria 3800, Australia
                Author notes
                [*]

                These authors contributed equally to this work

                []Author for correspondence ( andrew.elefanty@ 123456mcri.edu.au )

                Handling Editor: Gordon Keller

                Author information
                http://orcid.org/0000-0002-8461-7467
                http://orcid.org/0000-0001-6448-8314
                Article
                DEV193037
                10.1242/dev.193037
                7648599
                33028609
                b30a27ba-f39b-4fc9-9824-1d18ad244e97
                © 2020. Published by The Company of Biologists Ltd

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 19 May 2020
                : 24 September 2020
                Funding
                Funded by: National Health and Medical Research Council;
                Award ID: GNT1117596
                Award ID: GNT1079004
                Award ID: GNT1068866
                Award ID: GNT1129861
                Funded by: Australian Research Council, http://dx.doi.org/10.13039/501100000923;
                Funded by: Stem Cells Australia, http://dx.doi.org/10.13039/501100000970;
                Funded by: Stafford Fox Medical Research Foundation;
                Funded by: National Health and Medical Research Council;
                Categories
                203
                210
                Human Development

                Developmental biology
                yolk sac haematopoiesis,human pluripotent stem cells,runx1,gfi1/1b
                Developmental biology
                yolk sac haematopoiesis, human pluripotent stem cells, runx1, gfi1/1b

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