a crucial step for rational subunit vaccine design is the selection of antigens to include . for vaccines against infectious agents , antigens capable of inducing protective immune responses
several strategies based on genomic and proteomic approaches are being used to identify subsets of antigens that are highly expressed in general , on the surface , or during infection .
antigens from these priority subsets are then followed up individually to test for their capacity to induce protective immunity .
an alternative strategy that identifies protective antigens directly is to map targets of immune responses in previously infected hosts that successfully cleared the infection .
this strategy is applicable whenever past infectious are known to provide protective immunity . in those cases , the capacity of antigens to induce protective immunity in a vaccine setting has been shown to correlate with the magnitude of the response against that antigen post infection .
therefore , knowledge of targets of immune responses in infected hosts has high value for vaccine design against infectious diseases .
knowing immune response targets is also crucial for the development of allergy vaccines , whose goal is to modulate the pathologic immune responses of allergic individuals towards those found in non - allergics [ 5 , 6 ] .
similarly , for cancer vaccines to be successful , it is necessary to identify antigens targeted by immune responses associated with tumor regression [ 7 , 8 ] . in summary , identifying the targets and characteristics of immune responses in well characterized host populations enables the rational design of vaccines .  
one established approach for the identification and characterization of t cell immune response is the use of peptide based epitope mapping strategies .
the identification of epitopes , the exact molecular unit of recognition within an antigen , also provides a mechanistic understanding of cross - reactivity of immune responses for different pathogens .
this has recently been applied to study t cell immunity to swine flu [ 10 , 11 ] , and is important when designing cross - protective vaccines .   
we have participated in two recently completed large - scale t cell epitope mapping projects , one to characterize epitopes responsible for the protective immunity conveyed by the smallpox vaccine [ 1215 ] , another to characterize epitopes in arenaviruses [ 1618 ] ,     which has led to the generation of a candidate for a cross protective vaccine ( m. kotturi et al .
one lesson learned from these studies is that their data management is challenging , as the epitope response patterns discovered are typically complicated .
also , these studies require the integration of large amounts and various types of data collected from multiple clinical and laboratory sites . like many other groups
, we have managed these data in a collection of spreadsheets , lab notebooks , and database systems designed for a single type of experiment .
while each of these provides a sufficient mechanism to capture a specific type of information , the integrated analysis of these data often becomes labor intensive .
worse , problems due to inconsistencies in nomenclature and incompleteness of datasets are often only discovered at the time of analysis rather than at the time of data entry , which can make it hard or impossible to rectify them .  
one way to address these issues is to collect data , from the start , in an integrated database system which immediately connects data from different sources .
while this requires more work upon data entry , compared to less formal means of data capture such as spreadsheets , it greatly reduces the effort for data analysis . a deciding factor for our group to move toward an integrated database system was the award of a large scale nih contract that aims to identify t cell epitopes from common allergens with the ultimate goal to facilitate the development of improved allergy vaccines
this study involves tracking donors enrolled at two clinical sites , capturing complex clinical history information , and storing results from multiple experiments performed in - house and through external providers ( see figure 1 for a breakdown of the overall study ) .
our experience with previous studies which were less complex , suggested that the maintenance and analysis of these data would be challenging .
we therefore decided to establish immunocat , which aims to store all data relevant for an integrated analysis of epitope mapping experiments , with the ultimate goal to enable rational vaccine design .  
elicat stores and analyzes results from elispot   experiments used to test t cells for their cytokine secretion profile in response to peptide antigens .
topcat was designed to store the binding affinities determined in competitive peptide : mhc binding assays      utilizing a topcount instrument .
the databases are linked by the use of common peptide identifiers , which are assigned to every peptide that is synthesized in our studies .
for the allergen epitope mapping study , we needed to link the results of t cell reactivities stored in elicat with information about the donor from whom the t cells were derived .
we first built a new web - based database called donordb to store answers from a clinical questionnaire , skin test results , and records of blood samples .
subsequently , this donor database was integrated with the existing elicat and topcat databases ( figure 2 ) through the use of shared mhc allele and donation identifiers .
the result is immunocat , an integration of three web - based databases .   in the following ,
we describe the features and components of immunocat , which has been in stable use in our laboratory for over a year . with this system ,
we are able to manage all data relevant to our epitope mapping studies in a central location and ensure their quality and integrity . answers to integrated queries that would previously have taken significant effort can now be determined instantly , such as how many blood samples are available from donors that showed t cell reactivity to a certain peptide .
in addition to the allergy epitope mapping study described here , immunocat has been modified for use in two recently initiated epitope mapping studies for dengue virus and mycobacterium tuberculosis .
this demonstrates the benefits of applying bioinformatics and information management techniques to epitope mapping studies in order to facilitate rational vaccine development .
in order to access the database through the web , users have to identify themselves by supplying a user name and password .
the first group represents staff members at the clinical sites , who enter information from the clinical questionnaire and results from the skin test and blood draw .
the second group is staff members at the laboratory site , who track the use of blood samples for each donor .
for example , lab scientist supervisors have the ability to audit , add , delete , or modify the information in the system as necessary .
other lab scientists can only enter raw data and must request lab scientist supervisors to correct , for example , data entry errors .
separate projects using the donordb , such as the epitope mapping studies for dengue virus , use different web addresses for user access .
donordb is used at the clinical sites to capture information as donors are enrolled , interviewed , and undergo clinical procedures . for our ongoing allergen epitope mapping study , demographic information of the donor , such as gender , birthdate , ethnicity and parents ' birthplaces ( as an additional way to identify ethnicity )
the questionnaire is tied to a scoring scheme that is used to classify donors into different disease categories , namely , three classifications of  allergic rhinitis 
( none , possible , and probable ) and  allergic asthma  . in terms of clinical procedures ,
the results of a prick skin test for allergic reactions against 32 common allergens are recorded in terms of flare and wheal diameters .
also , the hemoglobin count of a donor is measured and recorded to determine if he / she is safely capable of donating blood .
if so , a separate blood donation visit is scheduled , and the volume of blood drawn is recorded in the database before samples are shipped for further analysis to the laboratory site .   at every step , the system ensures that donors meet all enrollment criteria , by notifying clinical staff if a donor falls under one of the exclusion criteria based on the entered information . during initial enrollment
, exclusion would occur if , for example , the patient states that he is currently receiving allergen immunotherapy without yet being in maintenance . after initial enrollment ,
exclusion would occur , for example , when the hemoglobin count is too low to allow a blood draw , or when the donor is still taking antihistamine medication right before the skin test .
integrating these questions and criteria into the database makes it easier for the clinical sites to keep track of all aspects of the enrollment criteria , and promotes consistency between sites .  
only anonymized information about the donors is entered at the clinical sites . as donors are enrolled ,
this identifier is used to match blood samples shipped to the laboratory site with the information collected at the clinical sites .
the clinical sites store the donation identifiers in their general patient records , which allow them to , for example , match repeat donations of a patient pre- and post immunotherapy , or match existing patient identifiers established at the individual sites to the donordb donation identifies .
no personally identifiable information ( such as names or social security numbers ) is stored in the database , which ensures that all anonymization requirements for data analysis are met .   by default
, the data entry process follows the order in which the study was envisioned to be conducted .
they can log back in , identify the donor they were working on based on the donation i d , and continue data entry . if necessary
this was identified as critical functionality , as in clinical practice the idealized workflow outlined above is often interrupted due to changes in patients ' schedules , and availability of time for data entry .
similarly , data entry requires internet access which for various reasons may not always be available .
we found that enrollment information at clinical sites is therefore often recorded on paper before transferred into the database .
the lab scientists receive blood samples from the clinical sites , and process them to extract peripheral blood mononuclear cells ( pbmcs ) .
they use the database to track the availability of pbmcs from each donation , determine the number of vials that could initially be made from the blood shipment , record the use of vials in subsequent experiments , and track the location of the remaining vials in the freezer .   for each donor ,
the first set of data consists of ige antibody titers against a panel of allergens determined in a rast assay .
the second set of data consists of the results from hla typing assays , which determine the specific set of mhc molecules the donor expresses .  
simple queries can be run against donordb in a web - based form ( figure 4 ) . at this point
, the system provides query functions to retrieve results from skin tests and the receipt of blood samples , as well as to monitor the number of donors in the two clinical sites .
for example , lab scientists can query the database and see whether the blood samples of specific donors have been received at liai .
they can also identify the donors who were skin test positive for a particular allergen and recruited at a specific clinical site .
these queries are used routinely by laboratory scientists to select suitable blood samples for experiments .
[ 13 , 18 ] , t cells are stimulated with peptide antigens , and those that respond by producing cytokines are visualized as individual spots .
the experiments are performed on 96-well plates , and the numbers of spots are counted by an automated elispot reader .
the raw data generated is exported to a text file containing a matrix whose elements denote the number of spots detected in each well . this raw data is automatically imported into elicat , where it is connected with information about experimental design previously entered through the interface shown in figure 5 .
an experiment is defined as one or more elispot plates run with the same layout and cells from the same source .
the plate layout specifies which wells are used to hold the tested peptides and which wells are used to hold the controls .
based on these inputs , elicat calculates summary metrics for the elispot results for each peptide or peptide pool .
the fraction of spot forming cells per million is calculated based on the number of spots detected in a well and the number of effector t cells added .
based on replicate spot counts , a p value is calculated that evaluates if the detected spots for a peptide are significantly higher than those detected in negative control wells that contained no peptides .
finally , a stimulation index , which divides the average spot count for a peptide by the average background value , is calculated .   in the allergen epitope mapping study ,
first , pools of peptides are screened for reactivity with pbmcs from allergic donors . secondly , the positive pools are deconvoluted to identify which peptides caused the response . as a cutoff for the positive response
> 100 spot forming cells per million , p < .05 and stimulation index > 2 were used .
the elicat user interface allows description of each assay , including identification of each individual peptide tested or definition of each peptide component of specific pools .
topcat was the first database established in the laboratory , and its design was largely replicated in elicat .
topcat is used to store results from assays that evaluate mhc : peptide binding affinity , a necessary , but not sufficient , requirement for t cell recognition . in our assays [ 26 , 27 ] ,
the ability of the tested peptides at different concentrations to completely inhibit the binding of a radiolabeled high affinity ligand is determined .
the concentration of the tested peptide at which the number of labeled ligands bound is reduced by 50% is the ic50 value . under the condition utilized , measured ic50 values approximate the kd value of the binding interaction .
the ic50 values are calculated based on radioactivity detection in a 96-well plate measured by topcount nxt microscintillation reader .
information on the tested peptides , mhc alleles , and corresponding mhc - peptide binding affinities are stored and analyzed in the database .
all peptides used at the laboratory site are assigned a peptide identifier in the topcat database , and the tubes containing the peptide are labeled accordingly .
this identifies the sequence of the peptide and the protein and organism from which the peptide was derived , as well as the purity of the synthesis .
if the same peptide is synthesized multiple times , a new identifier is assigned to each unique synthesis .
the elicat database uses the same peptide identifiers , and uses them to retrieve information about peptides from the topcat system .   for the allergen epitope mapping study ,
each is being tested for binding affinity against a panel of 23 human mhc class ii alleles . through the integration of the three databases shown in figure 2 , it now becomes possible to directly link the allergic status of a donor , the peptides derived from the corresponding allergens , and their binding affinity to the mhc alleles present in the donor and the t cell reactivities detected towards these peptides .
elicat and topcat have been in routine use in the lab for more than 5 years .
regarding donordb , requirements were gathered in terms of what information needs to be captured for the process of donor enrollment , donor interviews , clinical procedures , blood sample shipments , and lab tests . for each step ,
based on this , prototype web pages were created , and discussed with the clinical collaborators and liai lab scientists .
test of these prototypes was performed by both sites that analyzed use - scenarios , which led to the identification of additional requirements .
these were , primarily , giving the users more flexibility in the order in which data was entered , and the capacity to store additional information .
after three iterations , the prototypes were considered complete , and a functional system was implemented .
the addition of more fields for new types of studies will require going through the same design process and modifications of the database and web application itself .
as we gather experience in the kinds of modifications to expect , we plan to create tools that will ease making standard extensions .   
all three databases are relational databases with web - browser - based interfaces and are implemented with sql server 2005 .
the user interface was implemented with asp for topcat , and asp.net 2.0 for donordb and elicat .
all three databases are hosted on a dual - processor quad - core intel xeon rackmount server .
the database information is stored on a raid 5 drive which ensures against single hard - disk failure , and is backed up daily through the liai network .
at this point , clinical information for 86 donors at the ucsd sites and 71 donors at njmrc was deposited into donordb .
immunogenicity and mhc binding data for more than 60,000 peptides are stored in elicat and topcat .
users will need to have an installation of sql server 2005 dbms and asp.net 2.0 framework , and the ability to customize and configure the database for their own purposes .
the code will be maintained for at least the next four years , and updates will be made available as new versions of the systems are completed .
prospective users are highly encouraged to contact the authors with any installation problems . for studies that require extensive modifications of the code and for laboratories operating incompatible it environments
, the present manuscript should still be useful in identifying requirements and reusing appropriate design patterns .
we are planning to implement more search and analysis interfaces that take advantage of the integrated data in immunocat .
currently , such analyses are being done by manually running sql queries , which can not be expected of laboratory technicians .  
we are in the process of extending donordb to handle donors from three additional epitope - mapping studies , one dealing with donors from dengue fever endemic regions , another dealing with donors that have tuberculosis , and a third with donors recently vaccinated against smallpox .
this will require modifying some of the information captured for each donor , such as the serological typing of patients for the type of dengue viruses they have been exposed to .
other aspects , such as mhc typing and blood sample collection , will remain the same . in the longer term
, we are aiming to make the customization of donordb for individual studies easier by allowing the addition of fields from predefined modules .
this could , for example , mean that different lab tests performed could be chosen from an ontology such as the ontology for biomedical investigations , and that vaccine terms could be taken from the vaccine ontology .  
finally , one of the goals of immunocat is to submit data from completed studies into the immune epitope database [ 31 , 32 ] .
currently , both topcat and elicat have export mechanisms that provide xml formatted data that can be imported into the iedb .
these mechanisms need to be integrated and further updated to integrate the data from donordb , and provide comprehensive export functionality of an entire study .
 to enable rationale vaccine design , studies of molecular and cellular mechanisms of immune recognition need to be linked with clinical studies in humans . 
 a major challenge in conducting such translational research studies lies in the management and integration of large amounts and various types of data collected from multiple sources . for this purpose , 
 we have established  immunocat  , an interactive data management system for the epitope discovery research projects conducted by our group . 
 the system provides functions to store , query , and analyze clinical and experimental data , enabling efficient , systematic , and integrative data management . 
 we demonstrate how immunocat is utilized in a large - scale research contract that aims to identify epitopes in common allergens recognized by t cells from human donors , in order to facilitate the rational design of allergy vaccines . at clinical sites , demographic information and disease history of each enrolled donor 
 are captured , followed by results of an allergen skin test and blood draw . at the laboratory site , 
 t cells derived from blood samples are tested for reactivity against a panel of peptides derived from common human allergens . 
 immunocat stores results from these t cell assays along with mhc : peptide binding data , results from rast tests for antibody titers in donor serum , and the respective donor hla typing results . through this system 
 , we are able to perform queries and integrated analyses of the various types of data . 
 this provides a case study for the use of bioinformatics and information management techniques to track and analyze data produced in a translational research study aimed at epitope identification . 
1. Introduction
2. User Administration
3. System Components and Features
4. System Design Process and Implementation
5. Usage
6. Future Prospects