In the past one and half decade, technological developments have made it feasible to generate large volumes of heterogeneous biomedical data. Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding and mining these large-scale complex biological data. A major challenge in bioinformatics is the integration of data from different sources. Several efforts have been made to aid in this process, including: standard metadata annotation formats to describe the experiments ( [ISA-Tab][1] ), minimal information guidelines for reporting of biological and biomedical science ( [MIBBI project][2] ) and biological and biomedical ontology-based controlled vocabulary ( [OBO Foundry][3] and [BioPortal][4] ). However, the scientific bioinformatics community still struggles to bring these standards, guidelines, and ontologies straight into the wet-lab, to the workbench where the biological experiment is carried out. [LINCS][5], the Library of Integrated Network-based Cellular Signatures project, funded by the National Institutes of Health, is generating an extensive, multidimensional dataset designed to deeply assess complex biological systems. This dataset includes biochemical, genome-wide transcriptional, and phenotypic cellular response signatures to a variety of small-molecule and genetic perturbations. The aim is to create a sustainable widely applicable, and readily accessible resource to improve our understanding of complex human diseases such as cancer. Here we introduce Annot, a web application to capture the metadata, raw data, and processed data of biological studies, and to make it ready for sharing and integration. This first implementation is compatible with the latest [ISA-Tab][1] metadata annotation format and the minimal information guideline and ontologies suggested by the [LINCS metadata standards and data exchange specifications][6]. Annot is written in Python 3.4, utilizing the Django 1.7 web framework with PostgrSQL 9.4 and Apache 2.4, running on a FreeBSD 10 operating system. On the implementation level, each main element of the underling ISA structure ( investigation, study, assay ) and each ontology is represented by a single Django app. Through this modular implementation, the interface could easily be adapted to fit the needs of other laboratories by swapping in and out assays and ontologies. New assays and ontologies can be integrated by using the existing ones as templates. On the output side, we first focus on ISA-Tab compatible ISArchive to be able to share the entered data with other groups and mine the data with existing ISA-Tab-aware analysis software. In future versions, we will make use of Python 3 and the Django ORM to process, mine and analyze the entered data in explicit new ways. The [source code][7] is distributed under the free and open source GPLv3 license through gitorious at https://gitorious.org/biotransistor/annot. [1]: http://www.isa-tools.org/ "Investigation Study Assay Tabular format" [2]: http://mibbi.sourceforge.net/about.shtml "Minimum Information for Biological and Biomedical Investigations" [3]: http://www.obofoundry.org/ "the Open Biological and Biomedical Ontologies" [4]: http://bioportal.bioontology.org/ "National Center for Biomedical Ontology BioPortal" [5]: http://www.lincsproject.org/ "Library of Integrated Network-based Cellular Signatures" [6]: https://www.ncbi.nlm.nih.gov/pubmed/24518066 "PubMed abstract" [7]: https://gitorious.org/biotransistor/annot "bIOtransistor annot source code repository"