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Institution

Institute for Systems Biology

NonprofitSeattle, Washington, United States
About: Institute for Systems Biology is a nonprofit organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Population & Proteomics. The organization has 1277 authors who have published 2777 publications receiving 353165 citations.


Papers
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Journal ArticleDOI
TL;DR: Using a quantitative proteomics approach, it is shown that the STAG1 (Scc3/SA1) subunit of cohesin interacts with the CCTC-binding factor CTCF bound to the c-myc insulator element, suggesting an essential role for C TCF during sister chromatid cohesion.
Abstract: Cohesin is required to prevent premature dissociation of sister chromatids after DNA replication. Although its role in chromatid cohesion is well established, the functional significance of cohesin's association with interphase chromatin is not clear. Using a quantitative proteomics approach, we show that the STAG1 (Scc3/SA1) subunit of cohesin interacts with the CCTC-binding factor CTCF bound to the c-myc insulator element. Both allele-specific binding of CTCF and Scc3/SA1 at the imprinted IGF2/H19 gene locus and our analyses of human DM1 alleles containing base substitutions at CTCF-binding motifs indicate that cohesin recruitment to chromosomal sites depends on the presence of CTCF. A large-scale genomic survey using ChIP-Chip demonstrates that Scc3/SA1 binding strongly correlates with the CTCF-binding site distribution in chromosomal arms. However, some chromosomal sites interact exclusively with CTCF, whereas others interact with Scc3/SA1 only. Furthermore, immunofluorescence microscopy and ChIP-Chip experiments demonstrate that CTCF associates with both centromeres and chromosomal arms during metaphase. These results link cohesin to gene regulatory functions and suggest an essential role for CTCF during sister chromatid cohesion. These results have implications for the functional role of cohesin subunits in the pathogenesis of Cornelia de Lange syndrome and Roberts syndromes.

511 citations

Journal ArticleDOI
TL;DR: An open‐source, functionally complete, high‐throughput and readily extensible MS/MS spectral searching tool, SpectraST, developed, which vastly outperforms the sequence search engine SEQUEST in terms of speed and the ability to discriminate good and bad hits.
Abstract: A notable inefficiency of shotgun proteomics experiments is the repeated rediscovery of the same identifiable peptides by sequence database searching methods, which often are time-consuming and error-prone. A more precise and efficient method, in which previously observed and identified peptide MS/MS spectra are catalogued and condensed into searchable spectral libraries to allow new identifications by spectral matching, is seen as a promising alternative. To that end, an open-source, functionally complete, high-throughput and readily extensible MS/MS spectral searching tool, SpectraST, was developed. A high-quality spectral library was constructed by combining the high-confidence identifications of millions of spectra taken from various data repositories and searched using four sequence search engines. The resulting library consists of over 30,000 spectra for Saccharomyces cerevisiae. Using this library, SpectraST vastly outperforms the sequence search engine SEQUEST in terms of speed and the ability to discriminate good and bad hits. A unique advantage of SpectraST is its full integration into the popular Trans Proteomic Pipeline suite of software, which facilitates user adoption and provides important functionalities such as peptide and protein probability assignment, quantification, and data visualization. This method of spectral library searching is especially suited for targeted proteomics applications, offering superior performance to traditional sequence searching.

511 citations

Journal ArticleDOI
TL;DR: Recent advances in Dfam are described, most notably expansion to 4150 total families including a comprehensive set of known repeat families from four new organisms (mouse, zebrafish, fly and nematode).
Abstract: Repetitive DNA, especially that due to transposable elements (TEs), makes up a large fraction of many genomes. Dfam is an open access database of families of repetitive DNA elements, in which each family is represented by a multiple sequence alignment and a profile hidden Markov model (HMM). The initial release of Dfam, featured in the 2013 NAR Database Issue, contained 1143 families of repetitive elements found in humans, and was used to produce more than 100 Mb of additional annotation of TE-derived regions in the human genome, with improved speed. Here, we describe recent advances, most notably expansion to 4150 total families including a comprehensive set of known repeat families from four new organisms (mouse, zebrafish, fly and nematode). We describe improvements to coverage, and to our methods for identifying and reducing false annotation. We also describe updates to the website interface. The Dfam website has moved to http://dfam.org. Seed alignments, profile HMMs, hit lists and other underlying data are available for download.

500 citations

Journal ArticleDOI
01 Oct 2005-Blood
TL;DR: Dectin-1 signaling reveals dynamic macrophage heterogeneity in inflammatory activation potential and is important for DectIn-1-stimulated reactive oxygen production, but not for phagocytosis.

497 citations

Journal ArticleDOI
11 Jul 2018-Neuron
TL;DR: This study constructs multiscale networks of the late-onset AD-associated virome, and elucidates networks linking molecular, clinical, and neuropathological features with viral activity and indicates viral activity constituting a general feature of AD.

495 citations


Authors

Showing all 1292 results

NameH-indexPapersCitations
Younan Xia216943175757
Ruedi Aebersold182879141881
David Haussler172488224960
Steven P. Gygi172704129173
Nahum Sonenberg167647104053
Leroy Hood158853128452
Mark H. Ellisman11763755289
Wei Zhang112118993641
John Ralph10944239238
Eric H. Davidson10645447058
James R. Heath10342558548
Alan Aderem9924646682
Anne-Claude Gingras9733640714
Trey Ideker9730672276
Michael H. Gelb9450634714
Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20233
202260
2021216
2020204
2019188
2018168