The development of novel large-scale technologies has considerably changed the way biologists perform experiments. Genome biology experiments do not only generate a wealth of data, but they often rely on sophisticated laboratory protocols comprising hundreds of individual steps. For example, the protocol for chromatin immunoprecipitation on a microarray (Chip-chip) has 90 steps, uses over 30 reagents and 10 different devices . Even adopting an established protocol for large-scale studies represents a daunting challenge for the majority of the labs. The development of novel laboratory protocols and/or the optimization of existing ones is still more distressing, since this requires systematic changes of many parameters, conditions, and reagents. Such changes are becoming increasingly difficult to trace using paper lab books. A further complication for most protocols is that many laboratory instruments are used, which generate electronic data stored in an unstructured way at disparate locations. Therefore, protocol data files are seldom or never linked to notes in lab books and can be barely shared within or across labs. Finally, once the experimental large-scale data have been generated, they must be analyzed using various software tools, then stored and made available for other users. Thus, it is apparent that software support for current biological research - be it genomic or performed in a more traditional way - is urgently needed and inevitable.
In recent years, the genome biology community has expended considerable effort to confront the challenges of managing heterogeneous data in a structured and organized way and as a result developed information management systems for both raw and processed data. Laboratory information management systems (LIMS) have been implemented for handling data entry from robotic systems and tracking samples [2, 3] as well as data management systems for processed data including microarrays [4, 5], proteomics data [6–8], and microscopy data . The latter systems support community standards like FUGE[10, 11], MIAME , MIAPE , or MISFISHIE  and have proven invaluable in a state-of-the-art laboratory. In general, these sophisticated systems are able to manage and analyze data generated for only a single type or a limited number of instruments, and were designed for only a specific type of molecule.
On the other hand, commercial as well as open source electronic notebooks [15–19] were developed to record and manage scientific data, and facilitate data-sharing. The influences encouraging the use of electronic notebooks are twofold . First, much of the data that needs to be recorded in a laboratory notebook is generated electronically. Transcribing data manually into a paper notebook is error-prone, and in many cases, for example, analytical data (spectra, chromatograms, photographs, etc.), transcription of the data is not possible. Second, the incorporation of high-throughput technologies into the research process has resulted in an increased volume of electronic data that need to be transcribed. As opposed to LIMS, which captures highly structured data through rigid user interfaces with standard report formats, electronic notebooks contain unstructured data and have flexible user interfaces.
Software which enables both, management of large datasets and recording of laboratory procedures, would serve a real need in laboratories using medium and high-throughput techniques. To the best of our knowledge, there is no software system available, which supports tedious protocol development in an intuitive way, links the plethora of generated files to the appropriate laboratory steps and integrates further analysis tools. We have therefore developed iLAP, a workflow-driven information management system for protocol development and data management. The system combines experimental protocol development, wizard-based data acquisition, and high-throughput data analysis into a single, integrated system. We demonstrate the power and the flexibility of the platform using a microscopy case study based on combinatorial multiple fluorescence in situ hybridization (m-FISH) protocol and 3D-image reconstruction.