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      Transcriptome Dynamics of Human Neuronal Differentiation From iPSC

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          Abstract

          The generation and use of induced pluripotent stem cells (iPSCs) in order to obtain all differentiated adult cell morphologies without requiring embryonic stem cells is one of the most important discoveries in molecular biology. Among the uses of iPSCs is the generation of neuron cells and organoids to study the biological cues underlying neuronal and brain development, in addition to neurological diseases. These iPSC-derived neuronal differentiation models allow us to examine the gene regulatory factors involved in such processes. Among these regulatory factors are long non-coding RNAs (lncRNAs), genes that are transcribed from the genome and have key biological functions in establishing phenotypes, but are frequently not included in studies focusing on protein coding genes. Here, we provide a comprehensive analysis and overview of the coding and non-coding transcriptome during multiple stages of the iPSC-derived neuronal differentiation process using RNA-seq. We identify previously unannotated lncRNAs via genome-guided de novo transcriptome assembly, and the distinct characteristics of the transcriptome during each stage, including differentially expressed and stage specific genes. We further identify key genes of the human neuronal differentiation network, representing novel candidates likely to have critical roles in neurogenesis using coexpression network analysis. Our findings provide a valuable resource for future studies on neuronal differentiation.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools 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. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                Journal
                Front Cell Dev Biol
                Front Cell Dev Biol
                Front. Cell Dev. Biol.
                Frontiers in Cell and Developmental Biology
                Frontiers Media S.A.
                2296-634X
                14 December 2021
                2021
                : 9
                : 727747
                Affiliations
                [ 1 ]Department of Histology and Embryology, Faculty of Medicine, Izmir Katip Çelebi University, Izmir, Turkey
                [ 2 ]İzmir Biomedicine and Genome Center, İzmir, Turkey
                [ 3 ]İzmir International Biomedicine and Genome Institute, Dokuz Eylül University, İzmir, Turkey
                [ 4 ]Department of Histology and Embryology, Faculty of Medicine, Adnan Menderes University, Aydın, Turkey
                [ 5 ]Department of Medical Biology and Genetics, Faculty of Medicine, Dokuz Eylül University, İzmir, Turkey
                Author notes

                Edited by: Elena Taverna, Human Technopole, Italy

                Reviewed by: Gangqing Hu, West Virginia University, United States

                Xiang-Chun Ju, Okinawa Institute of Science and Technology Graduate University, Japan

                *Correspondence: Esra Erdal, esra.erdal@ 123456ibg.edu.tr ; Gökhan Karakülah, gokhan.karakulah@ 123456deu.edu.tr
                [ † ]

                These authors have contributed equally to this work and share first authorship

                This article was submitted to Stem Cell Research, a section of the journal Frontiers in Cell and Developmental Biology

                Article
                727747
                10.3389/fcell.2021.727747
                8712770
                34970540
                87123301-e40b-4bc7-a7cd-0d1240a6d353
                Copyright © 2021 Kuruş, Akbari, Eskier, Bursalı, Ergin, Erdal and Karakülah.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 19 June 2021
                : 18 November 2021
                Categories
                Cell and Developmental Biology
                Original Research

                ipsc-derived neuronal differentiation,transcriptome profiling,lncrnas,coexpression,wgcna

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