The completion of the Mammalian Gene Collection (MGC)

Temple, Gary F.; Gerhard, Daniela S.; Rasooly, Rebekah S.; Feingold, Elise A.; Good, Peter J.; Robinson, C. Scott.; Mandich, Allison; Derge, Jeffrey G.; Lewis, J. W.; Shoaf, Debonny; Collins, Francis S.; Jang, Wonhee; Wagner, Lukas; Shenmen, Carolyn M.; Misquitta, Leonie; Schaefer, Carl F.; Buetow, Kenneth H.; Bonner, Tom I.; Yankie, Linda; Ward, Ming; Phan, Lon; Astashyn, Alex; Brown, Garth; Farrell, Catherine; Hart, Jennifer; Landrum, Melissa; Maidak, B.; Murphy, Michael R.; Rajput, Bhanu; Riddick, Lillian D.; Webb, David; Weber, Janet A.; Wu, Wendy; Pruitt, Kim D.; Maglott, Donna; Siepel, Adam; Brejová, Broňa; Diekhans, Mark; Harte, Rachel A.; Baertsch, Robert; Kent, J. H.; Haussler, David; Brent, Michael R.; Langton, Laura; Comstock, Charles L.G.; Stevens, Michael; Wei, Chaochun; Baren, Marijke J. van; Salehi‐Ashtiani, Kourosh; Murray, Ryan R.; Ghamsari, Lila; Mello, Elizabeth; Lin, Chenwei; Pennacchio, Christa; Schreiber, Kirsten; Shapiro, Nicole; Marsh, Amber; Pardes, Elizabeth; Moore, Troy; Lebeau, Anita; Muratet, Mike; Simmons, Blake A.; Kloske, David; Sieja, Stephanie; Hudson, James R.; Sethupathy, Praveen; Brownstein, Michael J.; Bhat, Narayan K.; Lazar, J G; Jacob, Howard J.; Gruber, Chris E.; Smith, Mark R.; Mcpherson, John Douglas; Garcia, Angela; Gunaratne, Preethi H.; Wu, Jiaqian; Muzny, Donna M.; Gibbs, Richard A.; Young, Alice; Bouffard, Gerard G.; Blakesley, Robert W.; Mullikin, Jim; Green, Eric D.; Dickson, Mark; Rodríguez, Álex; Grimwood, Jane; Schmutz, Jeremy; Myers, R; Hirst, Martin; Zeng, Thomas; Tse, Kane; Moksa, Michelle; Deng, Merinda; Sheng‐Kai, Kevin; Mah, Diana; Pang, Johnson; Taylor, Greg; Chuah, Eric; Deng, Athena; Fichter, Keith; Go, Anne; Lee, Stephanie J.; Wang, Jing; Griffith, Malachi; Morin, Ryan D.; Moore, Richard A.; Mayo, Michael; Munro, Sarah A.; Wagner, Susan; Jones, Steven J.M.; Holt, Robert A.; Marra, Marco A.; Sun, Lin; Yang, Shu-Wei; Hartigan, James; Graf, Marcus; Wagner, Ralf; Letovksy, Stanley; Pulido, Jacqueline C.; Robison, Keith; Esposito, Dominic; Hartley, James L.; Wall, Vanessa; Hopkins, Ralph F.; Ohara, Osamu; Wiemann, Stefan

doi:10.1101/gr.095976.109

Cited by 128 publications

(77 citation statements)

References 40 publications

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“…One solution is to analyze hESCs during in vitro differentiation to different stages of neural development, which can be performed using a relatively large numbers of cells (5-9). Analysis of the transcriptome in these cells is expected to provide insights into the mechanisms and pathways involved in early cell fate specification, such as the acquisition of neurogenic potential and the transition to gliogenic potential, which may ultimately be extremely useful for pharmacologic screening and neurodegenerative disease therapies.Many high-throughput methods have been used previously to study global transcription (10)(11)(12)(13)(14). The recent development of massively parallel sequencing of short reads derived from mRNA (RNA-Seq) makes it possible to globally map transcribed regions and quantitatively analyze RNA isoforms at an unprecedented level of sensitivity and accuracy (15)(16)(17)(18)(19)(20)(21)(22).…”

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confidence: 99%

“…Many high-throughput methods have been used previously to study global transcription (10)(11)(12)(13)(14). The recent development of massively parallel sequencing of short reads derived from mRNA (RNA-Seq) makes it possible to globally map transcribed regions and quantitatively analyze RNA isoforms at an unprecedented level of sensitivity and accuracy (15)(16)(17)(18)(19)(20)(21)(22).…”

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confidence: 99%

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Dynamic transcriptomes during neural differentiation of human embryonic stem cells revealed by short, long, and paired-end sequencing

Habegger

Noisa

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

175

165

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To examine the fundamental mechanisms governing neural differentiation, we analyzed the transcriptome changes that occur during the differentiation of hESCs into the neural lineage. Undifferentiated hESCs as well as cells at three stages of early neural differentiation-N1 (early initiation), N2 (neural progenitor), and N3 (early glial-like)-were analyzed using a combination of single read, paired-end read, and long read RNA sequencing. The results revealed enormous complexity in gene transcription and splicing dynamics during neural cell differentiation. We found previously unannotated transcripts and spliced isoforms specific for each stage of differentiation. Interestingly, splicing isoform diversity is highest in undifferentiated hESCs and decreases upon differentiation, a phenomenon we call isoform specialization. During neural differentiation, we observed differential expression of many types of genes, including those involved in key signaling pathways, and a large number of extracellular receptors exhibit stage-specific regulation. These results provide a valuable resource for studying neural differentiation and reveal insights into the mechanisms underlying in vitro neural differentiation of hESCs, such as neural fate specification, neural progenitor cell identity maintenance, and the transition from a predominantly neuronal state into one with increased gliogenic potential.RNA-Seq | splicing isoforms | unannotated transcripts | neuron | glial N eural commitment and subsequent differentiation is a complex process. Although the complexity of RNAs expressed in neural tissues is very high (1, 2), a comprehensive analysis of the genes and RNA isoforms that are expressed during the different stages of neural cell differentiation is largely lacking. Such information is expected to be important for understanding mechanisms of neural cell differentiation and ultimately providing therapeutic solutions for neural degenerative diseases, such as Parkinson's and Alzheimer's disease.Our current knowledge of the mechanisms involved in neural cell formation is derived mostly from studying neurogenesis in the developing embryos of animal models (3, 4). However, neurogenesis in animals is a complex process involving many different cell types that differentiate asynchronously. This heterogeneity, along with the relatively small number of cells that can be readily obtained, makes the analysis of the temporal differentiation of individual cell types extremely difficult. One solution is to analyze hESCs during in vitro differentiation to different stages of neural development, which can be performed using a relatively large numbers of cells (5-9). Analysis of the transcriptome in these cells is expected to provide insights into the mechanisms and pathways involved in early cell fate specification, such as the acquisition of neurogenic potential and the transition to gliogenic potential, which may ultimately be extremely useful for pharmacologic screening and neurodegenerative disease therapies.Many high-throughput methods have...

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confidence: 99%

mentioning

confidence: 99%

Dynamic transcriptomes during neural differentiation of human embryonic stem cells revealed by short, long, and paired-end sequencing

Habegger

Noisa

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

175

165

View full text Add to dashboard Cite

show abstract

“…RNA-seq, for example, has allowed extremely deep sequencing of complementary DNA to an extent that was not possible using cDNA libraries and capillary sequencing. Unlike traditional directed cDNA-sequencing strategies (Temple et al 2009), multitranscript sampling and the depth of the sequence in RNA-seq reduce noise caused by occasional misspliced mRNA. RNA-seq also assays the frequency of alternative splice forms and their spatial and temporal expression patterns, giving a comprehensive snapshot of the transcription of the whole sample.…”

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confidence: 99%

Incorporating RNA-seq data into the zebrafish Ensembl genebuild

et al. 2012

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Ensembl gene annotation provides a comprehensive catalog of transcripts aligned to the reference sequence. It relies on publicly available species-specific and orthologous transcripts plus their inferred protein sequence. The accuracy of gene models is improved by increasing the species-specific component that can be cost-effectively achieved using RNA-seq. Two zebrafish gene annotations are presented in Ensembl version 62 built on the Zv9 reference sequence. Firstly, RNAseq data from five tissues and seven developmental stages were assembled into 25,748 gene models. A 39-end capture and sequencing protocol was developed to predict the 39 ends of transcripts, and 46.1% of the original models were subsequently refined. Secondly, a standard Ensembl genebuild, incorporating carefully filtered elements from the RNA-seqonly build, followed by a merge with the manually curated VEGA database, produced a comprehensive annotation of 26,152 genes represented by 51,569 transcripts. The RNA-seq-only and the Ensembl/VEGA genebuilds contribute contrasting elements to the final genebuild. The RNA-seq genebuild was used to adjust intron/exon boundaries of orthologous defined models, confirm their expression, and improve 39 untranslated regions. Importantly, the inferred protein alignments within the Ensembl genebuild conferred proof of model contiguity for the RNA-seq models. The zebrafish gene annotation has been enhanced by the incorporation of RNA-seq data and the pipeline will be used for other organisms. Organisms with little species-specific cDNA data will generally benefit the most.

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“…This approach, which is also referred to as directed RT-PCR or ‘rescue PCR’, was previously used to generate human ORFs in the Mammalian Gene Collection project (Temple et al ., 2009). While a variety of maize tissues were used to ensure gene amplification, young seedlings proved to be a reliable source of transcripts for about 37% of the cloned genes.…”

Section: Resultsmentioning

confidence: 99%

The Maize TFome – development of a transcription factor open reading frame collection for functional genomics

et al. 2014

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Establishing the architecture of the gene regulatory networks (GRNs) responsible for controlling the transcription of all genes in an organism is a natural development that follows elucidation of the genome sequence. Reconstruction of the GRN requires the availability of a series of molecular tools and resources that so far have been limited to a few model organisms. One such resource consists of collections of transcription factor (TF) open reading frames (ORFs) cloned into vectors that facilitate easy expression in plants or microorganisms. In this study, we describe the development of a publicly available maize TF ORF collection (TFome) of 2034 clones corresponding to 2017 unique gene models in recombination-ready vectors that make possible the facile mobilization of the TF sequences into a number of different expression vectors. The collection also includes several hundred co-regulators (CoREGs), which we classified into well-defined families, and for which we propose here a standard nomenclature, as we have previously done for TFs. We describe the strategies employed to overcome the limitations associated with cloning ORFs from a genome that remains incompletely annotated, with a partial full-length cDNA set available, and with many TF/CoREG genes lacking experimental support. In many instances this required the combination of genome-wide expression data with gene synthesis approaches. The strategies developed will be valuable for developing similar resources for other agriculturally important plants. Information on all the clones generated is available through the GRASSIUS knowledgebase (http://grassius.org/).

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The completion of the Mammalian Gene Collection (MGC)

Cited by 128 publications

References 40 publications

Dynamic transcriptomes during neural differentiation of human embryonic stem cells revealed by short, long, and paired-end sequencing

Dynamic transcriptomes during neural differentiation of human embryonic stem cells revealed by short, long, and paired-end sequencing

Incorporating RNA-seq data into the zebrafish Ensembl genebuild

The Maize TFome – development of a transcription factor open reading frame collection for functional genomics

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