BACKGROUND: Numerous tools exist for biological sequence comparisons and search. One case of particular interest for immunologists is finding matches for linear peptide T cell epitopes, typically between 8 and 15 residues in length, in a large set of protein sequences. Both to find exact matches or matches that account for residue substitutions. The utility of such tools is critical in applications ranging from identifying conservation across viral epitopes, identifying putative epitope targets for allergens, and finding matches for cancer-associated neoepitopes to examine the role of tolerance in tumor recognition. RESULTS: We defined a set of benchmarks that reflect the different practical applications of short peptide sequence matching. We evaluated a suite of existing methods for speed and recall and developed a new tool, PEPMatch. The tool uses a deterministic k-mer mapping algorithm that preprocesses proteomes before searching, achieving a 50-fold increase in speed over methods such as the Basic Local Alignment Search Tool (BLAST) without compromising recall. PEPMatch's code and benchmark datasets are publicly available. CONCLUSIONS: PEPMatch offers significant speed and recall advantages for peptide sequence matching. While it is of immediate utility for immunologists, the developed benchmarking framework also provides a standard against which future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org , and the source code can be found at https://github.com/IEDB/PEPMatch .

In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules (http://tools-staging.iedb.org/np_epitope_predictor). The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69-0.96 depending on the molecule cluster.

BACKGROUND: Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a pattern of markers. The description of the cell populations targeted in such experiments typically have two complementary components: the description of the cell type targeted (e.g. 'T cells'), and the description of the marker pattern utilized (e.g. CD14-, CD3+). RESULTS: We here describe our attempts to use ontologies to cross-compare cell types and marker patterns (also referred to as gating definitions). We used a large set of such gating definitions and corresponding cell types submitted by different investigators into ImmPort, a central database for immunology studies, to examine the ability to parse gating definitions using terms from the Protein Ontology (PRO) and cell type descriptions, using the Cell Ontology (CL). We then used logical axioms from CL to detect discrepancies between the two. CONCLUSIONS: We suggest adoption of our proposed format for describing gating and cell type definitions to make comparisons easier. We also suggest a number of new terms to describe gating definitions in flow cytometry that are not based on molecular markers captured in PRO, but on forward- and side-scatter of light during data acquisition, which is more appropriate to capture in the Ontology for Biomedical Investigations (OBI). Finally, our approach results in suggestions on what logical axioms and new cell types could be considered for addition to the Cell Ontology.

Motivation: Datasets that are derived from different studies (e.g. MHC ligand elution, MHC binding, B/T cell epitope screening etc.) often vary in terms of experimental approaches, sizes of peptides tested, including partial and or nested overlapping peptides and in the number of donors tested. Results: We present a customized application of the Immune Epitope Database's ImmunomeBrowser tool, which can be used to effectively aggregate and visualize heterogeneous immunological data. User provided peptide sets and associated response data is mapped to a user-provided protein reference sequence. The output consists of tables and figures representing the aggregated data represented by a Response Frequency score and associated estimated confidence interval. This allows the user to visualizing regions associated with dominant responses and their boundaries. The results are presented both as a user interactive javascript based web interface and a tabular format in a selected reference sequence. Availability and implementation: The 'ImmunomeBrowser' has been a longstanding feature of the IEDB (http://www.iedb.org). The present application extends the use of this tool to work with user-provided datasets, rather than the output of IEDB queries. This new server version of the ImmunomeBrowser is freely accessible at http://tools.iedb.org/immunomebrowser/.

BACKGROUND: The Immune Epitope Database and Analysis Resource (IEDB) is dedicated to capturing, housing and analyzing complex immune epitope related data http://www.immuneepitope.org. DESCRIPTION: To identify and extract relevant data from the scientific literature in an efficient and accurate manner, novel processes were developed for manual and semi-automated annotation. CONCLUSION: Formalized curation strategies enable the processing of a large volume of context-dependent data, which are now available to the scientific community in an accessible and transparent format. The experiences described herein are applicable to other databases housing complex biological data and requiring a high level of curation expertise.

From Impressionism and Pop Art to phosphorylation sites and interacting atom pairs, the realm of curation has been expanded. The recent growth of bioinformatics, driven by exponentially growing data, advanced computing techniques, and increased funding from private and governmental organizations, has created the need for novel strategies to adequately capture, store, and analyze the multitude of data present in the scientific literature. To meet this challenge, the number and scope of scientific databases has soared in recent years, creating a new profession, the biocurator. Indeed, the present emphasis on expanding computational resources, capable of managing and analyzing complex biological data, presents an ever-growing demand for biocurators capable of interpreting the increasingly complex scientific literature and extracting relevant data in an efficient, yet consistent, manner.