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CrossQuery: A Web Tool for Easy Associative Querying of Transcriptome Data

Please always quote using this URN: urn:nbn:de:bvb:20-opus-134787
  • Enormous amounts of data are being generated by modern methods such as transcriptome or exome sequencing and microarray profiling. Primary analyses such as quality control, normalization, statistics and mapping are highly complex and need to be performed by specialists. Thereafter, results are handed back to biomedical researchers, who are then confronted with complicated data lists. For rather simple tasks like data filtering, sorting and cross-association there is a need for new tools which can be used by non-specialists. Here, we describeEnormous amounts of data are being generated by modern methods such as transcriptome or exome sequencing and microarray profiling. Primary analyses such as quality control, normalization, statistics and mapping are highly complex and need to be performed by specialists. Thereafter, results are handed back to biomedical researchers, who are then confronted with complicated data lists. For rather simple tasks like data filtering, sorting and cross-association there is a need for new tools which can be used by non-specialists. Here, we describe CrossQuery, a web tool that enables straight forward, simple syntax queries to be executed on transcriptome sequencing and microarray datasets. We provide deep-sequencing data sets of stem cell lines derived from the model fish Medaka and microarray data of human endothelial cells. In the example datasets provided, mRNA expression levels, gene, transcript and sample identification numbers, GO-terms and gene descriptions can be freely correlated, filtered and sorted. Queries can be saved for later reuse and results can be exported to standard formats that allow copy-and-paste to all widespread data visualization tools such as Microsoft Excel. CrossQuery enables researchers to quickly and freely work with transcriptome and microarray data sets requiring only minimal computer skills. Furthermore, CrossQuery allows growing association of multiple datasets as long as at least one common point of correlated information, such as transcript identification numbers or GO-terms, is shared between samples. For advanced users, the object-oriented plug-in and event-driven code design of both server-side and client-side scripts allow easy addition of new features, data sources and data types.show moreshow less

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Metadaten
Author: Toni U. Wagner, Andreas Fischer, Eva C. Thoma, Manfred Schartl
URN:urn:nbn:de:bvb:20-opus-134787
Document Type:Journal article
Faculties:Medizinische Fakultät / Theodor-Boveri-Institut für Biowissenschaften
Language:English
Parent Title (English):PLoS ONE
Year of Completion:2011
Volume:6
Issue:12
Pagenumber:e28990
Source:PLoS ONE 6(12):e28990. doi:10.1371/journal.pone.0028990
DOI:https://doi.org/10.1371/journal.pone.0028990
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Tag:Biology; Cell-line; Microarray data; Sprouting angiogenesis
Release Date:2019/03/25
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung