Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together

Jayapandian, Magesh; Chapman, Adriane; Tarcea, V. Glenn; Yu, Cong; Elkiss, Aaron; Ianni, Angela; Liu, Bin; Nandi, Arnab; Santos, Carlos; Andrews, Philip C.; Athey, Brian D.; States, David J.; Jagadish, H. V.

doi:10.1093/nar/gkl859

Cited by 100 publications

(79 citation statements)

References 22 publications

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“…In particular, we study the needs for identification of data items in these systems. Unlike workflow systems [1,2,4,10,17,19,24,25,26] that are required to identify and keep all intermediate data items, many systems, [15,16,23] for example, do not need intermediate data. It is still imperative that they maintain provenance, and the provenance can be used to trace back to a data item's origin, but there is no need to keep intermediate data items.…”

Section: Discussionmentioning

confidence: 99%

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Provenance and the Price of Identity

Chapman

Jagadish

2008

Lecture Notes in Computer Science

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Abstract. As developers acknowledge that provenance is essential, more and more datasets are attempting to keep provenance records describing how they were created. Some of these datasets are constructed using workflows, others cobble together processes and applications to manipulate the data. While the provenance needs are the same, the inputs and set of processes used must be kept, the identity needs are very different. We outline several identification strategies that can be used for data manipulation outside of workflows. We evaluate these strategies in terms of time to create and store identity, and the space needed to keep this information. Additionally, we discuss the strengths and weaknesses of each strategy.

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Section: Discussionmentioning

confidence: 99%

“…-The creation of MiMI [15]: A set of classic protein interaction sources are transformed, merged and annotated into one cohesive dataset. -The creation of the Linguist Search Engine [23]: Sentences are culled from the web.…”

Section: Introductionmentioning

confidence: 99%

Provenance and the Price of Identity

Chapman

Jagadish

2008

Lecture Notes in Computer Science

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“…1,44 Protein-protein interaction information was queried from Michigan Molecular Interactions (MiMI). 17 The network topology was visualized with Cytoscape. 44 …”

Section: Oncogenic Module Analysismentioning

confidence: 99%

Genomic and transcriptome analysis revealing an oncogenic functional module in meningiomas

Chang

Shi

Russin

et al. 2013

FOC

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Object Meningiomas are among the most common primary adult brain tumors. Although typically benign, roughly 2%–5% display malignant pathological features. The key molecular pathways involved in malignant transformation remain to be determined. Methods Illumina expression microarrays were used to assess gene expression levels, and Illumina single-nucleotide polymorphism arrays were used to identify copy number variants in benign, atypical, and malignant meningiomas (19 tumors, including 4 malignant ones). The authors also reanalyzed 2 expression data sets generated on Affymetrix microarrays (n = 68, including 6 malignant ones; n = 56, including 3 malignant ones). A weighted gene coexpression network approach was used to identify coexpression modules associated with malignancy. Results At the genomic level, malignant meningiomas had more chromosomal losses than atypical and benign meningiomas, with average length of 528, 203, and 34 megabases, respectively. Monosomic loss of chromosome 22 was confirmed to be one of the primary chromosomal level abnormalities in all subtypes of meningiomas. At the transcriptome level, the authors identified 23 coexpression modules from the weighted gene coexpression network. Gene functional enrichment analysis highlighted a module with 356 genes that was highly related to tumorigenesis. Four intramodular hubs within the module (GAB2, KLF2, ID1, and CTF1) were oncogenic in other cancers such as leukemia. A putative meningioma tumor suppressor MN1 was also identified in this module with differential expression between malignant and benign meningiomas. Conclusions The authors' genomic and transcriptome analysis of meningiomas provides novel insights into the molecular pathways involved in malignant transformation of meningiomas, with implications for molecular heterogeneity of the disease.

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“…The rich variety of sources available obligates users to visit a myriad of sites to visualize the whole picture of their search for example a protein or gene of their interest. They must also compile the information from journals and then bring in all the details to solve the puzzle (Jayapandian et al 2007). Hence the graphical representations of the facts are comparatively easier to understand than if they were presented as raw data.…”

Section: Related Workmentioning

confidence: 99%

BioSilicoSystems - A Multipronged Approach Towards Analysis and Representation of Biological Data (PhD Thesis)

Hariharaputran

2010

Nat Prec

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The rising field of integrative bioinformatics provides the vital methods to integrate, manage and also to analyze the diverse data and allows gaining new and deeper insights and a clear understanding of the intricate biological systems. The difficulty is not only to facilitate the study of heterogeneous data within the biological context, but it also more fundamental, how to represent and make the available knowledge accessible. Moreover, adding valuable information and functions that persuade the user to discover the interesting relations hidden within the data is, in itself, a great challenge. Also, the cumulative information can provide greater biological insight than is possible with individual information sources. Furthermore, the rapidly growing number of databases and data types poses the challenge of integrating the heterogeneous data types, especially in biology. This rapid increase in the volume and number of data resources drive for providing polymorphic views of the same data and often overlap in multiple resources.In this thesis a multi-pronged approach is proposed that deals with various methods for the analysis and representation of the diverse biological data which are present in different data sources. This is an effort to explain and emphasize on different concepts which are developed for the analysis of molecular data and also to explain its biological significance. The hypotheses proposed are in context with various other results and findings published in the past. The approach demonstrated also explains different ways to integrate the molecular data from various sources along with the need for a comprehensive understanding and clear projection of the concept or the algorithm and its results, but with simple means and methods. The multifarious approach proposed in this work comprises of different tools or methods spanning significant areas of bioinformatics research such as data integration, data visualization, biological network construction / reconstruction and alignment of biological pathways. Each tool deals with a unique approach to utilize the molecular data for different areas of biological research and is built based on the kernel of the thesis. Furthermore these methods are combined with graphical representation that make things simple and comprehensible and also helps to understand with ease the underlying biological complexity. Moreover the human eye is often used to and it is more comfortable with the visual representation of the facts.

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Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together

Cited by 100 publications

References 22 publications

Provenance and the Price of Identity

Provenance and the Price of Identity

Genomic and transcriptome analysis revealing an oncogenic functional module in meningiomas

BioSilicoSystems - A Multipronged Approach Towards Analysis and Representation of Biological Data (PhD Thesis)

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