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1.  SaDA: From Sampling to Data Analysis—An Extensible Open Source Infrastructure for Rapid, Robust and Automated Management and Analysis of Modern Ecological High-Throughput Microarray Data 
One of the most crucial characteristics of day-to-day laboratory information management is the collection, storage and retrieval of information about research subjects and environmental or biomedical samples. An efficient link between sample data and experimental results is absolutely important for the successful outcome of a collaborative project. Currently available software solutions are largely limited to large scale, expensive commercial Laboratory Information Management Systems (LIMS). Acquiring such LIMS indeed can bring laboratory information management to a higher level, but most of the times this requires a sufficient investment of money, time and technical efforts. There is a clear need for a light weighted open source system which can easily be managed on local servers and handled by individual researchers. Here we present a software named SaDA for storing, retrieving and analyzing data originated from microorganism monitoring experiments. SaDA is fully integrated in the management of environmental samples, oligonucleotide sequences, microarray data and the subsequent downstream analysis procedures. It is simple and generic software, and can be extended and customized for various environmental and biomedical studies.
doi:10.3390/ijerph120606352
PMCID: PMC4483705  PMID: 26047146
software; data management; microarrays; ecological assessment; environmental studies; LIMS; open source system
2.  NETTAB 2013: Semantic, social, and mobile applications for bioinformatics and biomedical laboratories 
BMC Bioinformatics  2014;15(Suppl 14):S1.
The thirteenth NETTAB workshop, NETTAB 2013, was devoted to semantic, social, and mobile applications for bioinformatics and biomedical laboratories.
Topics included issues, methods, algorithms, and technologies for the design and development of tools and platforms able to provide semantic, social, and mobile applications supporting bioinformatics and the activities carried out in a biomedical laboratory.
About 30 scientific contributions were presentedat NETTAB 2013, including keynote and tutorial talks, oral communications, and posters. Best contributions presented at the workshop were later submitted to a special Call for this Supplement.
Here, we provide an overview of the workshop and introduce manuscripts that have been accepted for publication in this Supplement.
doi:10.1186/1471-2105-15-S14-S1
PMCID: PMC4255736  PMID: 25471662
3.  Combining ontologies and workflows to design formal protocols for biological laboratories 
Background
Laboratory protocols in life sciences tend to be written in natural language, with negative consequences on repeatability, distribution and automation of scientific experiments. Formalization of knowledge is becoming popular in science. In the case of laboratory protocols two levels of formalization are needed: one for the entities and individuals operations involved in protocols and another one for the procedures, which can be manually or automatically executed. This study aims to combine ontologies and workflows for protocol formalization.
Results
A laboratory domain specific ontology and the COW (Combining Ontologies with Workflows) software tool were developed to formalize workflows built on ontologies. A method was specifically set up to support the design of structured protocols for biological laboratory experiments. The workflows were enhanced with ontological concepts taken from the developed domain specific ontology.
The experimental protocols represented as workflows are saved in two linked files using two standard interchange languages (i.e. XPDL for workflows and OWL for ontologies). A distribution package of COW including installation procedure, ontology and workflow examples, is freely available from http://www.bmr-genomics.it/farm/cow.
Conclusions
Using COW, a laboratory protocol may be directly defined by wet-lab scientists without writing code, which will keep the resulting protocol's specifications clear and easy to read and maintain.
doi:10.1186/1759-4499-2-3
PMCID: PMC2873243  PMID: 20416048
4.  A Semantic Web for bioinformatics: goals, tools, systems, applications 
BMC Bioinformatics  2008;9(Suppl 4):S1.
doi:10.1186/1471-2105-9-S4-S1
PMCID: PMC2367628  PMID: 18460170
6.  Time to Organize the Bioinformatics Resourceome 
PLoS Computational Biology  2005;1(7):e76.
doi:10.1371/journal.pcbi.0010076
PMCID: PMC1323464  PMID: 16738704

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