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Institute for Systems Biology

US

10 researchers0 verified3 linked papers61,772 indexed citations

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Public research profiles associated with Institute for Systems Biology.

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2003 · Genome Research · 55,154 citations

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

2003 · Analytical Chemistry · 5,011 citations

A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry

A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample. Peptides that correspond to more than a single protein in the sequence database are apportioned among all corresponding proteins, and a minimal protein list sufficient to account for the observed peptide assignments is derived using the expectation-maximization algorithm. Using peptide assignments to spectra generated from a sample of 18 purified proteins, as well as complex H. influenzae and Halobacterium samples, the model is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications. This method allows filtering of large-scale proteomics data sets with predictable sensitivity and false positive identification error rates. Fast, consistent, and transparent, it provides a standard for publishing large-scale protein identification data sets in the literature and for comparing the results obtained from different experiments.

2017 · Nature · 1,607 citations

Integrated genomic and molecular characterization of cervical cancer

Cervical cancer remains one of the leading causes of cancer-related deaths worldwide. Here we report the extensive molecular characterization of 228 primary cervical cancers, one of the largest comprehensive genomic studies of cervical cancer to date. We observed notable APOBEC mutagenesis patterns and identified SHKBP1, ERBB3, CASP8, HLA-A and TGFBR2 as novel significantly mutated genes in cervical cancer. We also discovered amplifications in immune targets CD274 (also known as PD-L1) and PDCD1LG2 (also known as PD-L2), and the BCAR4 long non-coding RNA, which has been associated with response to lapatinib. Integration of human papilloma virus (HPV) was observed in all HPV18-related samples and 76% of HPV16-related samples, and was associated with structural aberrations and increased target-gene expression. We identified a unique set of endometrial-like cervical cancers, comprised predominantly of HPV-negative tumours with relatively high frequencies of KRAS, ARID1A and PTEN mutations. Integrative clustering of 178 samples identified keratin-low squamous, keratin-high squamous and adenocarcinoma-rich subgroups. These molecular analyses reveal new potential therapeutic targets for cervical cancers. This paper describes molecular subtypes of cervical cancers, including squamous cell carcinoma and adenocarcinoma clusters defined by HPV status and molecular features, and distinct molecular pathways that are activated in cervical carcinomas caused by different somatic alterations and HPV types. Cervical cancer is one of the main causes of cancer-related deaths worldwide, and 95% of cases result from human papilloma virus (HPV) infection. The Cancer Genome Atlas Research Network now reports the genomic and molecular characterization of 228 primary cervical cancers. The authors identify significantly mutated genes and pathways that differ by cervical cancer subtype, and find that keratin-low squamous, keratin-high squamous and adenocarcinoma-rich clusters are marked by different HPV types and molecular features.