Functions and utilities of COPaKB

I. COPaKB Offers a Platform to Integrate Your Investigation with Curated Largescale Datasets.

  • COPaKB links proteins of your interest to known disease phenotypes in human and animal models of a human diseases.
  • COPaKB provides protein expression profiles in immunofluorescent and immunohistochemistry images with organellar resolution.
  • COPaKB illustrates the expression levels of genes associated with your target proteins.
  • COPaKB hosts spectral libraries to process your mass spectral datasets. Subsequently, you can connect the result with the aforementioned protein properties.
  • COPaKB computational toolbox enables the translation of raw datasets into a systematic and intuitive interpretation of heart biology at the molecular level. For example, you can curate the list of proteins identified in a sample in the context of their relevance in disease phenotypes and transcriptional control of corresponding genes.

II. COPaKB Facilitates the Advancement of Your Research Interests.

  • COPaKB enables you to conduct cross-species studies on selected genes/proteins by curating relevant properties based on their shared gene symbols.
  • COPaKB expedites the analysis of your protein in multiple organelles, including its organellar location, levels of expression, and coming soon, its interacting partners in subcellular-specific manner.
  • COPaKB supports evidence-based examination of post-translational modifications, including acetylation and phosphorylation of your favorite proteins; yes, COPaKB may have mass spectral data demonstrating the PTM of your proteins.
  • COPaKB aids you in choosing experimentally validated antibodies that may be suitable for your study; yes, you can obtain the information necessary (e.g. vendor and catalog number) to acquire this antibody by simply clicking on the antibody ID next to the immunocytochemistry image of your protein at COPaKB. This webpage also provides immunofluorescence and Western blot images.
  • COPaKB-supported workflow has the computational tool to support the detection of low abundance proteins in your studies. Specifically, COPaKB hosts the spectra library that has been carefully compiled with mass spectra from experimentally validated datasets rather than rely on theoretically simulated spectra as in traditional workflows; this unique feature affords higher sensitivity in detecting the protein of your interest, in particular when they are of low abundance.
  • COPaKB computes relative protein expression level using a label-free quantitative approach. In particular, in situations where antibodies are not available, the semi-quantitative approach using mass spectral count can be used to determine the expression level.
  • COPaKB aids your effective interaction with the proteomic core of your home institution; it can provide reference data for your core to utilize and enhance the opportunity of detecting proteins of your interest. For example, COPaKB documents the parameters (e.g. molecular mass and charge state) that were associated with peptides of your proteins; your proteomic core can adjust instrument settings accordingly, thereby improving the detection of your proteins via conducting a more targeted and informed study.
  • Coming soon: another key project of COPaKB in progress is the configuration of a new workflow, which takes the input in the form of pathological phenotype and returns relevant annotations of molecular pathways/proteins/genes.

III. COPaKB Supports Your Collaborations with Colleagues Globally.

  • COPaKB offers a Wiki component to foster the communication among proteomics specialists and non-proteomics investigators. The Wiki portal welcomes inputs in the format of plain text and images from every investigator. The COPaKB encourages you to deposit your raw datasets into ProteomeXchange repository (www.proteomexchange.org).
  • COPaKB targets the development of an integrated databank via an international collaborative effort, circumventing shortfalls inherent in fragmented databases. This centralized platform is cost-effective for large-scale studies, and it assures the quality control of its content in a collaborative setting.
  • COPaKB assists you and your team members to combine datasets from different studies for comparative analyses working either at the same location or at different regions globally. For example, by entering a combination of task IDs associated with each analysis, you and your colleagues can generate a list of proteins that are shared among those different studies, as well as those unique to each individual analysis. The outcome of these analyses renders a smooth transition from an exploratory study to targeted hypothesis-driven investigation and vice versa.