CellMissy: Cell Migration Invasion Storage System

CellMissy is a cross-platform data management system for cell migration/invasion data that simplifies and fully automates data management, storage and analysis, from experimental set-up to data visualization and exploration. CellMissy is a client-server application with a graphical user interface on the client, and a relational database in the back-end to store the data. The client application is composed of three modules that cover the different functions of CellMissy: the Experiment Manager, the Data Loader and the Data Analyzer. On top of these modules, CellMissy provides tools for import/export of full experiments and templates. CellMissy is described in CellMissy: a tool for management, storage and analysis of cell migration data produced in wound healing-like assays. (P. Masuzzo, N. Hulstaert, L. Huyck, C. Ampe, M. Van Troys and L. Martens, PMID: 23918247)
Find CellMissy: Cell Migration Invasion Storage System at: https://github.com/compomics/cellmissy

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CellMissy: Cell Migration Invasion Storage System

CellMissy is a cross-platform data management system for cell migration/invasion data that simplifies and fully automates data management, storage and analysis, from experimental set-up to data visualization and exploration. CellMissy is a client-server application with a graphical user interface on the client, and a relational database in the back-end to store the data. The client application is composed of three modules that cover the different functions of CellMissy: the Experiment Manager, the Data Loader and the Data Analyzer. On top of these modules, CellMissy provides tools for import/export of full experiments and templates. CellMissy is described in CellMissy: a tool for management, storage and analysis of cell migration data produced in wound healing-like assays. (P. Masuzzo, N. Hulstaert, L. Huyck, C. Ampe, M. Van Troys and L. Martens, PMID: 23918247)
Find CellMissy: Cell Migration Invasion Storage System at: https://github.com/compomics/cellmissy

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Pathomx

Pathomx is a workflow-based tool for the analysis and visualisation of experimental data. Initially created as a tool for metabolomic data analysis is has been extended and can now be used for any scientific and non-scientific data analysis. The software functions as a hybrid of workflow and script-based approaches to analysis. Using workflows it is possible to construct rapid, reproducible analysis constructs for experimental data. By combining this with custom inline scripting it is possible to perform any analysis imaginable. Workflows can be dynamically re-arranged to test different approaches and saved to track the development of your approach. Saved workflows can also be shared with other users or groups, allowing instant reproduction of results and methods. Tools can export images as publication-ready high resolution images in common formats.
Find Pathomx at: http://pathomx.org

CellMissy: Cell Migration Invasion Storage System

CellMissy is a cross-platform data management system for cell migration/invasion data that simplifies and fully automates data management, storage and analysis, from experimental set-up to data visualization and exploration. CellMissy is a client-server application with a graphical user interface on the client, and a relational database in the back-end to store the data. The client application is composed of three modules that cover the different functions of CellMissy: the Experiment Manager, the Data Loader and the Data Analyzer. On top of these modules, CellMissy provides tools for import/export of full experiments and templates. CellMissy is described in CellMissy: a tool for management, storage and analysis of cell migration data produced in wound healing-like assays. (P. Masuzzo, N. Hulstaert, L. Huyck, C. Ampe, M. Van Troys and L. Martens, PMID: 23918247)
Find CellMissy: Cell Migration Invasion Storage System at: https://github.com/compomics/cellmissy

ISMB/ECCB 2013: much ado about data sharing

Sharing_gate_4

Last month PLOS ONE attended the ISMB/ECCB 2013 conference in Berlin on Intelligent Systems for Molecular Biology. More than 1,500 delegates attended what is the largest conference on computational biology in the world to discuss the latest developments in computational methods that address biological questions.

The opening keynote from PLOS ONE Academic Editor Gil Ast focused on alternative splicing, a mechanism by which several mRNA transcripts are generated from the same mRNA precursor, thus enhancing transcriptome and proteome diversity. He mentioned a paper his group published earlier this year in PLOS ONE, in which they showed that pre-mRNA splicing influences nucleosome organization, suggesting that there is a bi-directional interplay between chromatin organization and splicing. While it is widely accepted that chromatin organization and DNA modification regulate transcription, it is intriguing that splicing can in turn affect chromatin organization, and this may constitute an additional layer of regulation of gene expression. He also presented exciting recent findings showing how pre-mRNA splicing and the creation of new exons in the human genome may be linked to certain genetic disorders and types of cancers.

Picture1 Understanding the biology of complex human disease is also one of Goncalo Abecasis’s objectives, winner of the ISCB 2013 Overton Prize. Specifically, he is interested in better understanding genetic variation and its connections to human diseases using computational methods and statistical tools. In his talk, he emphasized that the identification and characterization of the genetic variants that affect human traits may be achieved by examining the link between these traits and the complete genome sequences of thousands of individuals. To collect DNA from as many people as possible, he wondered whether we should make use of social media to call for volunteers to send their DNA samples. Are Facebook and Twitter the key to understanding human genetics?

One topic that generated much discussion at the meeting was data sharing. In her talk, Carole Goble called for all scientists to share their data widely as to enable reproducibility, a principle underpinning the scientific method.  Several journals, including PLOS ONE, require that all data (including all relevant raw data) described in the manuscript be made freely available to any scientist wishing to use them for the purpose of academic, non-commercial research. Well established and widely supported public repositories already exist for certain types of data such as nucleic acid sequences, and in cases where an appropriate repository does not exist, there are also general data repositories such as Dryad. Assigned accession numbers or digital object identifiers (DOIs) facilitate data citation and ensure accountability. An increasing number of research funding agencies also now support data sharing in the life sciences. Whilst there is indeed increasing discussion to make primary data from published research publicly available, Goble mentioned a paper by Ioannidis and colleagues showing that a substantial proportion of articles published in high-impact journals do not comply (or only weakly comply) with data availability requirements. According to Goble, a lack of data sharing, and thus reproducibility, could lead to an increase in retracted scientific papers.

She also urged the computational biology community to release their “dark data”, i.e. data that is not published and remains hidden on various USB drives and computers, the point being that if shared more people will be able to use these results, increasing visibility, accountability and reproducibility. As highlighted by a recent study, data sharing is not an end in itself, but rather a crucial form of scientific knowledge dissemination.

 

Citations:

Keren-Shaul H, Lev-Maor G, Ast G (2013) Pre-mRNA Splicing Is a Determinant of Nucleosome Organization. PLoS ONE 8(1): e53506. doi:10.1371/journal.pone.0053506

Alsheikh-Ali AA, Qureshi W, Al-Mallah MH, Ioannidis JPA (2011) Public Availability of Published Research Data in High-Impact Journals. PLoS ONE 6(9): e24357. doi:10.1371/journal.pone.0024357

Wallis JC, Rolando E, Borgman CL (2013) If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology. PLoS ONE 8(7): e67332. doi:10.1371/journal.pone.0067332

Images:

Wikimedia by Angelineri

Modified from Schwartz S, Oren R, Ast G (2011) Detection and Removal of Biases in the Analysis of Next-Generation Sequencing Reads. PLoS ONE 6(1): e16685. doi:10.1371/journal.pone.0016685

 

Meet PLOS ONE at ISMB/ECCB 2013 in Berlin

ISMB blog pic

Next week PLOS ONE will join PLOS Computational Biology at the ISMB/ECCB 2013 International Society for Computational Biology meeting held this year in Berlin, Germany, July 21-23.

The world’s largest bioinformatics/computational biology conference will bring together scientists from computer science, molecular biology, mathematics, statistics and other related fields to discuss the development and application of computational methods for biological questions. Keynote speakers include David Eisenberg, Gary Stormo, Lior Pachter, and PLOS ONE Academic Editor Gil Ast.

Whether you are interested in joining our Editorial Board or just want to talk about the journal or open access in general, please stop by the PLOS booth (booth #13)!

Academic Editors: If you are also attending, please get in touch ahead of the meeting with Associate Editor Christna Chap to schedule a meeting.

We hope to see you in Berlin!

 

Image: Salipante SJ, Sengupta DJ, Rosenthal C, Costa G, Spangler J, et al. (2013) Rapid 16S rRNA Next-Generation Sequencing of Polymicrobial Clinical Samples for Diagnosis of Complex Bacterial Infections. PLoS ONE 8(5): e65226. doi:10.1371/journal.pone.0065226