RNA Interference Demonstrates a Role for nautilus in the Myogenic Conversion of Schneider Cells by daughterless

AbstractSchneider SL2 cells activate the myogenic program in response to the ectopic expression of daughterless alone, as indicated by exit from the cell cycle, syncytia formation, and the presence of muscle myosin fibrils. Myogenic conversion can be potentiated by the coexpression of DMEF2 and nautilus with daughterless. In RT-PCR assays Schneider cells express two mesodermal markers, nautilus and DMEF2 mRNAs, as well as very low levels of daughterless mRNA but no twist. Full-length RT-PCR products for nautilus and DMEF2 encode immunoprecipitable proteins. We used RNA-i to demonstrate that both endogenous nautilus expression and DMEF2 expression are required for the myogenic conversion of Schneider cells by daughterless. Coexpression of twist blocks conversion by daughterless but twist dsRNA has no effect. Our results indicate that Schneider cells are of mesodermal origin and that myogenic conversion with ectopic expression of daughterless occurs by raising the levels of daughterless protein sufficiently to allow the formation of nautilus/daughterless heterodimers. The effectiveness of RNA-i is dependent upon protein half-life. Genes encoding proteins with relatively short half-lives (10 h), such as nautilus or HSF, are efficiently silenced, whereas more stable proteins, such as cytoplasmic actin or β-galactosidase, are less amenable to the application of RNA-i. These results support the conclusion that nautilus is a myogenic factor in Drosophila tissue culture cells with a functional role similar to that of vertebrate MyoD. This is discussed with regard to the in vivo functions of nautilus

GenBank

GenBank (R) is a comprehensive database that contains publicly available nucleotide sequences for more than 240 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the EMBL Data Library in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage ()

GenBank

GenBank(R) is a comprehensive database that contains publicly available nucleotide sequences for more than 380,000 organisms named at the genus level or lower, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole genome shotgun (WGS) and environmental sampling projects. Most submissions are made using the web-based BankIt or standalone Sequin programs, and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. GenBank is accessible through the NCBI Entrez retrieval system that integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage: https://www.ncbi.nlm.nih.gov

Report of the 13th Genomic Standards Consortium Meeting, Shenzhen, China, March 4–7, 2012

This report details the outcome of the 13th Meeting of the Genomic Standards Consortium. The three-day conference was held at the Kingkey Palace Hotel, Shenzhen, China, on March 5–7, 2012, and was hosted by the Beijing Genomics Institute. The meeting, titled From Genomes to Interactions to Communities to Models, highlighted the role of data standards associated with genomic, metagenomic, and amplicon sequence data and the contextual information associated with the sample. To this end the meeting focused on genomic projects for animals, plants, fungi, and viruses; metagenomic studies in host-microbe interactions; and the dynamics of microbial communities. In addition, the meeting hosted a Genomic Observatories Network session, a Genomic Standards Consortium biodiversity working group session, and a Microbiology of the Built Environment session sponsored by the Alfred P. Sloan Foundatio

PHA4GE quality control contextual data tags:standardized annotations for sharing public health sequence datasets with known quality issues to facilitate testing and training

As public health laboratories expand their genomic sequencing and bioinformatics capacity for the surveillance of different pathogens, labs must carry out robust validation, training, and optimization of wet- and dry-lab procedures. Achieving these goals for algorithms, pipelines and instruments often requires that lower quality datasets be made available for analysis and comparison alongside those of higher quality. This range of data quality in reference sets can complicate the sharing of sub-optimal datasets that are vital for the community and for the reproducibility of assays. Sharing of useful, but sub-optimal datasets requires careful annotation and documentation of known issues to enable appropriate interpretation, avoid being mistaken for better quality information, and for these data (and their derivatives) to be easily identifiable in repositories. Unfortunately, there are currently no standardized attributes or mechanisms for tagging poor-quality datasets, or datasets generated for a specific purpose, to maximize their utility, searchability, accessibility and reuse. The Public Health Alliance for Genomic Epidemiology (PHA4GE) is an international community of scientists from public health, industry and academia focused on improving the reproducibility, interoperability, portability, and openness of public health bioinformatic software, skills, tools and data. To address the challenges of sharing lower quality datasets, PHA4GE has developed a set of standardized contextual data tags, namely fields and terms, that can be included in public repository submissions as a means of flagging pathogen sequence data with known quality issues, increasing their discoverability. The contextual data tags were developed through consultations with the community including input from the International Nucleotide Sequence Data Collaboration (INSDC), and have been standardized using ontologies - community-based resources for defining the tag properties and the relationships between them. The standardized tags are agnostic to the organism and the sequencing technique used and thus can be applied to data generated from any pathogen using an array of sequencing techniques. The tags can also be applied to synthetic (lab created) data. The list of standardized tags is maintained by PHA4GE and can be found at https://github.com/pha4ge/contextual_data_QC_tags. Definitions, ontology IDs, examples of use, as well as a JSON representation, are provided. The PHA4GE QC tags were tested, and are now implemented, by the FDA's GenomeTrakr laboratory network as part of its routine submission process for SARS-CoV-2 wastewater surveillance. We hope that these simple, standardized tags will help improve communication regarding quality control in public repositories, in addition to making datasets of variable quality more easily identifiable. Suggestions for additional tags can be submitted to PHA4GE via the New Term Request Form in the GitHub repository. By providing a mechanism for feedback and suggestions, we also expect that the tags will evolve with the needs of the community.</p

Genomic Standards Consortium projects

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Standards in Genomic Sciences 9 (2014): 599-601, doi:10.4056/sigs.5559680.The Genomic Standards Consortium (GSC) is an open-membership community working towards the development, implementation and harmonization of standards in the field of genomics. The mission of the GSC is to improve digital descriptions of genomes, metagenomes and gene marker sequences. The GSC started in late 2005 with the defined task of establishing what is now termed the “Minimum Information about any Sequence” (MIxS) standard [1,2]. As an outgrowth of the activities surrounding the creation and implementation of the MixS standard there are now 18 projects within the GSC [3]. These efforts cover an ever widening range of standardization activities. Given the growth of projects and to promote transparency, participation and adoption the GSC has developed a “GSC Project Description Template”. A complete set of GSC Project Descriptions and the template are available on the GSC website. The GSC has an open policy of participation and continues to welcome new efforts. Any projects that facilitate the standard descriptions and exchange of data are potential candidates for inclusion under the GSC umbrella. Areas that expand the scope of the GSC are encouraged. Through these collective activities we hope to help foster the growth of the ‘bioinformatics standards’ community. For more information on the GSC and its range of projects, please see http://gensc.org/

Towards BioDBcore: a community-defined information specification for biological databases

The present article proposes the adoption of a community-defined, uniform, generic description of the core attributes of biological databases, BioDBCore. The goals of these attributes are to provide a general overview of the database landscape, to encourage consistency and interoperability between resources and to promote the use of semantic and syntactic standards. BioDBCore will make it easier for users to evaluate the scope and relevance of available resources. This new resource will increase the collective impact of the information present in biological database