A-MADMAN: Annotation-based microarray data meta-analysis tool

Bisognin, Andrea; Coppe, Alessandro; Ferrari, Francesco; Risso, Davide; Romualdi, Chiara; Bicciato, Silvio; Bortoluzzi, Stefania

doi:10.1186/1471-2105-10-201

Cited by 30 publications

(25 citation statements)

References 19 publications

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“…However, this power comes with some risks such as the necessity to model heterogeneity between datasets. Specifically, use of this approach often requires an ad-hoc normalization of the raw data files [47], [48] followed by explicitly modeling the inter-study variability [45], [49]. The data integration method compares gene lists from any expression platform filtered according to p-values or rank combination [50], [51].…”

Section: Discussionmentioning

confidence: 99%

A Data Similarity-Based Strategy for Meta-analysis of Transcriptional Profiles in Cancer

Qiu

Xiang

et al. 2013

PLoS ONE

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BackgroundRobust transcriptional signatures in cancer can be identified by data similarity-driven meta-analysis of gene expression profiles. An unbiased data integration and interrogation strategy has not previously been available.Methods and FindingsWe implemented and performed a large meta-analysis of breast cancer gene expression profiles from 223 datasets containing 10,581 human breast cancer samples using a novel data similarity-based approach (iterative EXALT). Cancer gene expression signatures extracted from individual datasets were clustered by data similarity and consolidated into a meta-signature with a recurrent and concordant gene expression pattern. A retrospective survival analysis was performed to evaluate the predictive power of a novel meta-signature deduced from transcriptional profiling studies of human breast cancer. Validation cohorts consisting of 6,011 breast cancer patients from 21 different breast cancer datasets and 1,110 patients with other malignancies (lung and prostate cancer) were used to test the robustness of our findings. During the iterative EXALT analysis, 633 signatures were grouped by their data similarity and formed 121 signature clusters. From the 121 signature clusters, we identified a unique meta-signature (BRmet50) based on a cluster of 11 signatures sharing a phenotype related to highly aggressive breast cancer. In patients with breast cancer, there was a significant association between BRmet50 and disease outcome, and the prognostic power of BRmet50 was independent of common clinical and pathologic covariates. Furthermore, the prognostic value of BRmet50 was not specific to breast cancer, as it also predicted survival in prostate and lung cancers.ConclusionsWe have established and implemented a novel data similarity-driven meta-analysis strategy. Using this approach, we identified a transcriptional meta-signature (BRmet50) in breast cancer, and the prognostic performance of BRmet50 was robust and applicable across a wide range of cancer-patient populations.

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Section: Discussionmentioning

confidence: 99%

A Data Similarity-Based Strategy for Meta-analysis of Transcriptional Profiles in Cancer

Qiu

Xiang

et al. 2013

PLoS ONE

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“…Such platforms enable to compare the expression values of samples that have been pooled and uniformly preprocessed, and provide insight in gene expression across numerous conditions and tissues. In addition, the webapplication A-MADMAN (Bisognin et al 2009) allows retrieving, annotating, and pooled preprocessing of microarray datasets. It outputs expression values, which can be fed into a custom R analysis.…”

Section: Effect Size Combination Strategiesmentioning

confidence: 99%

Microarray Meta-Analysis: From Data to Expression to Biological Relationships

Feichtinger

Thallinger

McFarlane

et al. 2012

Computational Medicine

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Since the introduction of microarray technology, it has become the workhorse for mRNA expression profiling. Its application ranges from investigating gene function, regulation, and co-expression, to clinical use in diagnosis and prognosis. Over the last decade, a large number of microarray experiments have become available in public repositories often addressing similar or related hypotheses. The large compendia of gene expression data provide the opportunity to conduct meta-analyses by combining data from various independent but related studies. Such data integration has the potential to enhance the reliability and generalizability of the results of individual microarray studies.This chapter describes the meta-analysis process including objectives, data collection, annotation, analysis methods, and visualizations. For each step we present a selection of tools available and discuss associated problems and difficulties.

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“…Therefore, many meta-analyses use the same species or even the same array platform to mitigate some of these heterogeneities. However, many studies do still attempt to perform cross-platform and inter-species meta-analyses, and tools such as AILUN (Array Information Library Universal Navigator) [73], A-MADMAN (Annotation-based microarray data meta-analysis tool) [74], and LOLA (List Of Lists Annotated) [75] enable cross-species meta-analysis using Entrez ID, gene symbol or other IDs as a conversion intermediary. AbsIDconvert can perform cross-platform/-species analysis efficiently using the sequence based approach.…”

Section: Case Studiesmentioning

confidence: 99%

AbsIDconvert: An absolute approach for converting genetic identifiers at different granularities

et al. 2012

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BackgroundHigh-throughput molecular biology techniques yield vast amounts of data, often by detecting small portions of ribonucleotides corresponding to specific identifiers. Existing bioinformatic methodologies categorize and compare these elements using inferred descriptive annotation given this sequence information irrespective of the fact that it may not be representative of the identifier as a whole.ResultsAll annotations, no matter the granularity, can be aligned to genomic sequences and therefore annotated by genomic intervals. We have developed AbsIDconvert, a methodology for converting between genomic identifiers by first mapping them onto a common universal coordinate system using an interval tree which is subsequently queried for overlapping identifiers. AbsIDconvert has many potential uses, including gene identifier conversion, identification of features within a genomic region, and cross-species comparisons. The utility is demonstrated in three case studies: 1) comparative genomic study mapping plasmodium gene sequences to corresponding human and mosquito transcriptional regions; 2) cross-species study of Incyte clone sequences; and 3) analysis of human Ensembl transcripts mapped by Affymetrix®; and Agilent microarray probes. AbsIDconvert currently supports ID conversion of 53 species for a given list of input identifiers, genomic sequence, or genome intervals.ConclusionAbsIDconvert provides an efficient and reliable mechanism for conversion between identifier domains of interest. The flexibility of this tool allows for custom definition identifier domains contingent upon the availability and determination of a genomic mapping interval. As the genomes and the sequences for genetic elements are further refined, this tool will become increasingly useful and accurate. AbsIDconvert is freely available as a web application or downloadable as a virtual machine at: http://bioinformatics.louisville.edu/abid/.

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A-MADMAN: Annotation-based microarray data meta-analysis tool

Cited by 30 publications

References 19 publications

A Data Similarity-Based Strategy for Meta-analysis of Transcriptional Profiles in Cancer

A Data Similarity-Based Strategy for Meta-analysis of Transcriptional Profiles in Cancer

Microarray Meta-Analysis: From Data to Expression to Biological Relationships

AbsIDconvert: An absolute approach for converting genetic identifiers at different granularities

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