Functional annotation and network reconstruction through cross-platform integration of microarray data

Zhou, Xianghong Jasmine; Kao, Ming-Chih J.; Huang, Haiyan; Wong, Angela; Nunez-Iglesias, Juan; Primig, Michael; Aparicio, Oscar M.; Finch, Caleb E.; Morgan, Todd E.; Wong, Wing Hung

doi:10.1038/nbt1058

Cited by 135 publications

(102 citation statements)

References 30 publications

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“…It is unique among integrated databases because it is focused on specific biological components and processes that play roles in germ-cell biology. It also provides access to microarray and image data relevant to the knowledge base [Schlecht et al, 2004;Zhou et al, 2005;Hermida et al, 2006]. Future goals for GermOnline include adding capacity to curate data on protein-DNA interactions and SNPs, and adding video and other types of image data.…”

Section: Bioinformatics Tools In Germ-cell Analysesmentioning

confidence: 99%

Assessing human germ‐cell mutagenesis in the Postgenome Era: A celebration of the legacy of William Lawson (Bill) Russell

Wyrobek

Mulvihill

Wassom

et al. 2007

Environ and Mol Mutagen

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Birth defects, de novo genetic diseases, and chromosomal abnormality syndromes occur in ~5% of all live births, and affected children suffer from a broad range of lifelong health consequences. Despite the social and medical impact of these defects, and the 8 decades of research in animal systems that have identified numerous germ-cell mutagens, no human germ-cell mutagen has been confirmed to date. There is now a growing consensus that the inability to detect human germ-cell mutagens is due to technological limitations in the detection of random mutations rather than biological differences between animal and human susceptibility. A multidisciplinary workshop responding to this challenge convened at The Jackson Laboratory in Bar Harbor, Maine. The purpose of the workshop was to assess the applicability of an emerging repertoire of genomic technologies to studies of human germ-cell mutagenesis. Workshop participants recommended large-scale human germ-cell mutation studies be conducted using samples from donors with high-dose exposures, such as cancer survivors. Within this high-risk cohort, parents and children could be evaluated for heritable changes in (a) DNA sequence and chromosomal structure, (b) repeat sequences and minisatellites, and (c) global gene expression profiles and pathways. Participants also advocated the establishment of a bio-bank of human tissue samples from donors with well-characterized exposure, including medical and reproductive histories. This mutational resource could support large-scale, multipleendpoint studies. Additional studies could involve the examination of transgenerational effects NIH Public AccessAuthor Manuscript Environ Mol Mutagen. Author manuscript; available in PMC 2007 November 8. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript associated with changes in imprinting and methylation patterns, nucleotide repeats, and mitochondrial DNA mutations. The further development of animal models and the integration of these with human studies are necessary to provide molecular insights into the mechanisms of germcell mutations and to identify prevention strategies. Furthermore, scientific specialty groups should be convened to review and prioritize the evidence for germ-cell mutagenicity from common environmental, occupational, medical, and lifestyle exposures. Workshop attendees agreed on the need for a full-scale assault to address key fundamental questions in human germ-cell environmental mutagenesis. These include, but are not limited to, the following: Do human germ-cell mutagens exist? What are the risks to future generations? Are some parents at higher risk than others for acquiring and transmitting germ-cell mutations? Obtaining answers to these, and other critical questions, will require strong support from relevant funding agencies, in addition to the engagement of scientists outside the fields of genomics and germ-cell mutagenesis.

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Section: Bioinformatics Tools In Germ-cell Analysesmentioning

confidence: 99%

Assessing human germ‐cell mutagenesis in the Postgenome Era: A celebration of the legacy of William Lawson (Bill) Russell

Wyrobek

Mulvihill

Wassom

et al. 2007

Environ and Mol Mutagen

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“…Only those regulatory links that are present in more than E n experiments where E n > n/4 and E n > 8 [3] are added to the final fused network. It should be noted, however, that network pruning is an optional step in GeNi and this step is different from threshold-based integration based methods, as they don't consider the link co-regulation weight-age while selecting the links for the final integration, as mentioned earlier, GeNi adds/removes links, primarily based on belief masses m(R) and m(NR).…”

Section: Bel( R ) Pl( R ) and Pl(r) = 1-bel(r )mentioning

confidence: 99%

“…Previous attempts to integrate cross platform GRN include Zhou et al [3] who proposed cross platform GRN fusion using second-order expression analysis while Choi et al [1] studied different types of cancer links using cross-platform analysis. In both studies the regulatory pathway was only considered if it was present in more than a certain number of experiments T, where the selection of this threshold T was empirically derived with no formal mathematical foundations so that selection of an incorrect T could inevitably lead to erroneous results.…”

Section: Introductionmentioning

confidence: 99%

“…cDNA or Affymetrix high density oligonucleotide arrays, under either different or similar experimental conditions, though this is a challenging task because data generated by different platforms is not directly comparable. The objectives include being able to construct models capable of inferring knowledge from thousands of genes at a time, assist in understanding complex genetic interactions and to integrate networks constructed from heterogeneous microarray datasets [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Transcriptional Gene Regulatory Network Reconstruction Through Cross Platform Gene Network Fusion

Sehgal

Gondal

Dooley

et al. 2007

Pattern Recognition in Bioinformatics

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Abstract. Microarray gene expression data is used to model differential activity in Gene Regulatory Networks (GRN) to elucidate complex cellular processes, though network modeling is susceptible to errors due to both noisy nature of gene expression data and platform bias. This intuitively provided the motivation for the development of an innovative technique, which effectively integrates GRN using cross-platform data to minimize the two aforementioned effects. This paper presents a GRN integration (GeNi) framework that fuses crossplatform GRN to remove platform and experimental bias using the Dempster Shafer Theory of Evidence. The proposed model estimates gene co-regulation strength by using mutual information and removes spurious co-regulations through data processing inequality. The method automatically adapts to the data distribution using Belief theory, which does not require a preset threshold to accept co-regulated links.1 GeNi is applied to identify common cancer-related regulatory links in ten different datasets generated by different microarray platforms including cDNA and Affymetrix arrays. Experimental results demonstrate that GeNi can be effectively applied for GRN reconstruction and cross-platform gene network fusion for any gene expression data.

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“…The combination of data from multiple microarray studies addressing a similar biological question is gaining high importance in the recent years [6,7,24] due to the ever increasing number and complexity of the available gene expression datasets. In general, it is expected that the integration and evaluation of multiple datasets yields more reliable and robust results since these results are based on a larger number of samples and the effects of individual study-specific biases are diminished.…”

Section: Introductionmentioning

confidence: 99%

An Integrative Clustering Approach Combining Particle Swarm Optimization and Formal Concept Analysis

Hristoskova

Boeva

Tsiporkova

2012

Information Technology in Bio- And Medical Informatics

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Abstract. In this article we propose an integrative clustering approach for analysis of gene expression data across multiple experiments, based on Particle Swarm Optimization (PSO) and Formal Concept Analysis (FCA). In the proposed algorithm, the available microarray experiments are initially divided into groups of related datasets with respect to a predefined criterion. Subsequently, a hybrid clustering algorithm, based on PSO and k-means clustering, is applied to each group of experiments separately. This produces a list of different clustering solutions, one per each group. These clustering solutions are pooled together and further analyzed by employing FCA which allows to extract valuable insights from the data and generate a gene partition over the whole set of experiments. The performance of the proposed clustering algorithm is evaluated on time series expression data obtained from a study examining the global cell-cycle control of gene expression in fission yeast Schizosaccharomyces pombe. The obtained experimental results demonstrate that the proposed integrative algorithm allows to generate a unique and robust gene partition over several different microarray datasets.

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Functional annotation and network reconstruction through cross-platform integration of microarray data

Cited by 135 publications

References 30 publications

Assessing human germ‐cell mutagenesis in the Postgenome Era: A celebration of the legacy of William Lawson (Bill) Russell

Assessing human germ‐cell mutagenesis in the Postgenome Era: A celebration of the legacy of William Lawson (Bill) Russell

Transcriptional Gene Regulatory Network Reconstruction Through Cross Platform Gene Network Fusion

An Integrative Clustering Approach Combining Particle Swarm Optimization and Formal Concept Analysis

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