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Gene Ontology: tool for the unification of biology

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TLDR
The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Abstract
Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

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Integrating biological databases

TL;DR: Biological databases have been invaluable for managing these data and for making them accessible, but, although they are architecturally similar, so far their integration has proved problematic.

Gremlin 1 Identifies a Skeletal Stem Cell with Bone, Cartilage, and Reticular Stromal Potential

TL;DR: Grem1 expression identifies distinct connective tissue stem cells in both the bone (OCR stem cells) and the intestine (iRSCs) as discussed by the authors, which are cells of origin for the periepithelial intestinal mesenchymal sheath.
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MGnify: the microbiome analysis resource in 2020

TL;DR: An updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility.
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CYGD: the Comprehensive Yeast Genome Database

TL;DR: The Comprehensive Yeast Genome Database (CYGD) compiles a comprehensive data resource for information on the cellular functions of the yeast Saccharomyces cerevisiae and related species, chosen as the best understood model organism for eukaryotes.
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Next-generation tag sequencing for cancer gene expression profiling

TL;DR: Overall, Tag-seq is sensitive to rare transcripts, has less sequence composition bias relative to LongSAGE, and allows differential expression analysis for a greater range of transcripts, including transcripts encoding important regulatory molecules.
References
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Journal ArticleDOI

Cluster analysis and display of genome-wide expression patterns

TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
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The Pfam protein families database

TL;DR: The definition and use of family-specific, manually curated gathering thresholds are explained and some of the features of domains of unknown function (also known as DUFs) are discussed, which constitute a rapidly growing class of families within Pfam.
Journal ArticleDOI

The genome sequence of Drosophila melanogaster

Mark Raymond Adams, +194 more
- 24 Mar 2000 - 
TL;DR: The nucleotide sequence of nearly all of the approximately 120-megabase euchromatic portion of the Drosophila genome is determined using a whole-genome shotgun sequencing strategy supported by extensive clone-based sequence and a high-quality bacterial artificial chromosome physical map.
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Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization

TL;DR: A comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle is created, and it is found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins.
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