openBIS: a flexible framework for managing and analyzing complex data in biology research

Bauch, Angela; Adamczyk, Izabela; Buczek, Piotr; Elmer, Franz-Josef; Enimanev, Kaloyan; Glyzewski, Pawel; Kohler, Manuel; Pylak, T; Quandt, Andreas; Ramakrishnan, Chandrasekhar; Beisel, Christian; Malmström, Lars; Aebersold, Ruedi; Rinn, Bernd

doi:10.1186/1471-2105-12-468

Cited by 122 publications

(122 citation statements)

References 35 publications

(24 reference statements)

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“…To this end, several tools exist, such as Screensaver (38), OpenBIS (39), Mscreen (40) and SAVANAH (http://nanocan.github.io/SAVANAH, manuscript in preparation). Currently, screening data have to be exported from these tools and uploaded to HiTSeekR or other HTS analysis tools for analysis.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of high-throughput screens with HiTSeekR

et al. 2016

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High-throughput screening (HTS) is an indispensable tool for drug (target) discovery that currently lacks user-friendly software tools for the robust identification of putative hits from HTS experiments and for the interpretation of these findings in the context of systems biology. We developed HiTSeekR as a one-stop solution for chemical compound screens, siRNA knock-down and CRISPR/Cas9 knock-out screens, as well as microRNA inhibitor and -mimics screens. We chose three use cases that demonstrate the potential of HiTSeekR to fully exploit HTS screening data in quite heterogeneous contexts to generate novel hypotheses for follow-up experiments: (i) a genome-wide RNAi screen to uncover modulators of TNFα, (ii) a combined siRNA and miRNA mimics screen on vorinostat resistance and (iii) a small compound screen on KRAS synthetic lethality. HiTSeekR is publicly available at http://hitseekr.compbio.sdu.dk. It is the first approach to close the gap between raw data processing, network enrichment and wet lab target generation for various HTS screen types.

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Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of high-throughput screens with HiTSeekR

et al. 2016

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“…Whilst large scale projects such as the ICGC and TCGA will have dedicated sample tracking solutions, and other research institutes and large laboratories are likely to have implemented systems, many of these are likely to be proprietary or commercial LIMS systems such as the ones offered by Genologics (http://genologics.com/). Although open-source systems for next generation sequencing studies have been published [14][15][16], our system is tailored for oncogenomic experiments from multiple samples from the same or different sources.…”

Section: Discussionmentioning

confidence: 99%

“…A number of Laboratory Information Management Systems (LIMS) for next generation sequencing experiments are freely available, including GNomEx [14], openBIS [15], Galaxy LIMS [16] and SeqBench [17]. However, none of these systems is designed specifically for oncogenomic experiments in which both array based and different sequencing technologies are used to analyse samples from tumour, germline, in vitro and xenograft models.…”

Section: Introductionmentioning

confidence: 99%

Onco-STS: a web-based laboratory information management system for sample and analysis tracking in oncogenomic experiments

Gavrielides

Furney

Yates

et al. 2014

Source Code Biol Med

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Background: Whole genomes, whole exomes and transcriptomes of tumour samples are sequenced routinely to identify the drivers of cancer. The systematic sequencing and analysis of tumour samples, as well other oncogenomic experiments, necessitates the tracking of relevant sample information throughout the investigative process. These meta-data of the sequencing and analysis procedures include information about the samples and projects as well as the sequencing centres, platforms, data locations, results locations, alignments, analysis specifications and further information relevant to the experiments. Results: The current work presents a sample tracking system for oncogenomic studies (Onco-STS) to store these data and make them easily accessible to the researchers who work with the samples. The system is a web application, which includes a database and a front-end web page that allows the remote access, submission and updating of the sample data in the database. The web application development programming framework Grails was used for the development and implementation of the system. Conclusions: The resulting Onco-STS solution is efficient, secure and easy to use and is intended to replace the manual data handling of text records. Onco-STS allows simultaneous remote access to the system making collaboration among researchers more effective. The system stores both information on the samples in oncogenomic studies and details of the analyses conducted on the resulting data. Onco-STS is based on open-source software, is easy to develop and can be modified according to a research group's needs. Hence it is suitable for laboratories that do not require a commercial system.

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“…It also contains a well-designed Java API (ms-data-core-api) facilitating the implementation of customized solutions [16]. In recent years, several open source Java-based laboratory information systems (LIMS) storing and processing proteomic or glycomic data were described [17], [18], [19].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

MzJava: An open source library for mass spectrometry data processing

Horlacher

Nikitin

Alocci

et al. 2015

Journal of Proteomics

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Mass spectrometry (MS) is a widely used and evolving technique for the high-throughput identification of molecules in biological samples. The need for sharing and reuse of code among bioinformaticians working with MS data prompted the design and implementation of MzJava, an open-source Java Application Programming Interface (API) for MS related data processing. MzJava provides data structures and algorithms for representing and processing mass spectra and their associated biological molecules, such as metabolites, glycans and peptides. MzJava includes functionality to perform mass calculation, peak processing (e.g. centroiding, filtering, transforming), spectrum alignment and clustering, protein digestion, fragmentation of peptides and glycans as well as scoring functions for spectrum-spectrum and peptide/glycan-spectrum matches. For data import and export MzJava implements readers and writers for commonly used data formats. For many classes support for the Hadoop MapReduce (hadoop.apache.org) and Apache Spark (spark.apache.org) frameworks for cluster computing was implemented. The library has been developed applying best practices of software engineering. To ensure that MzJava contains code that is correct and easy to use the library's API was carefully designed and thoroughly tested. MzJava is an open-source project distributed under the AGPL v3.0 licence. MzJava requires Java 1.7 or higher. Binaries, source code and documentation can be downloaded from http://mzjava.expasy.org and https://bitbucket.org/sib-pig/mzjava. This article is part of a Special Issue entitled: Computational Proteomics.

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openBIS: a flexible framework for managing and analyzing complex data in biology research

Cited by 122 publications

References 35 publications

Comprehensive analysis of high-throughput screens with HiTSeekR

Comprehensive analysis of high-throughput screens with HiTSeekR

Onco-STS: a web-based laboratory information management system for sample and analysis tracking in oncogenomic experiments

MzJava: An open source library for mass spectrometry data processing

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