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Protein annotation software tools | Sequence data analysis

The last decade has seen a remarkable growth in protein databases. This growth comes at a price: a growing number of submitted protein sequences lack functional annotation. Approximately 32% of sequences submitted to the most comprehensive protein database UniProtKB are labelled as 'Unknown protein' or alike.

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(Koskinen et al., 2015) PANNZER: high-throughput functional annotation of uncharacterized proteins in an error-prone environment. Bioinformatics.

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PRO / Protein Ontology
An ontological representation of protein-related entities. PRO defines and describes taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene and protein-containing complexes. PRO thus serves as a tool for referencing protein entities at any level of specificity. PRO organizes these entities into classes describing proteins derived from homologs (‘family level’ classes), from a single gene (‘gene level’ classes), from a single transcript (‘sequence level’ classes), or from a set of modifications (‘modification level’ classes). Each of these categories of classes are neutral with respect to taxonomy, but there are also taxon-specific versions (e.g. ‘organism-gene level’), thus allowing PRO to highlight connections and differences within and across species.
SIFTER-T / Statistical Inference of Function Through Evolutionary Relationships Throughput-optimized
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A powerful computational platform for probabilistic protein domain annotation. Compared to SIFTER 2.0, SIFTER-T achieved an 87-fold performance improvement using published test data sets for the known annotations recovering module and a 72.3% speed increase for the gene tree generation module in quad-core machines, as well as a major decrease in memory usage during the realignment phase.
The Proteins API
Provides searching and programmatic access to protein and associated genomics data such as curated protein sequence positional annotations from UniProtKB, as well as mapped variation and proteomics data from large scale data sources (LSS). The Proteins API permits to retrieve the genomic sequence coordinates for proteins in UniProtKB. It allows the user to ask questions based upon his/her field of expertise and allowing him/her to gain an integrated overview of protein annotations available to aid knowledge gain on proteins in biological processes.
ANARCI / Antigen receptor Numbering And Receptor ClassificatIon
A tool for annotating antigen receptor variable domain amino-acid sequences with five commonly used numbering schemes. It can annotate sequences with the five most popular numbering schemes: Kabat, Chothia, Enhanced Chothia, IMGT and AHo. ANARCI can be run as command-line tool or imported as a Python module for incorporation in custom scripts. We also provide a public web-browser interface that can annotate small numbers of sequences.
DAMA / Domain Annotation by a Multi-objective Approach
Treats protein domain architecture prediction as a multi-objective optimization problem. By taking into account known architectural solutions, DAMA identifies them within the protein sequence and integrates new domains into them whenever possible. DAMA has been evaluated over a benchmark containing protein sequences extracted from the Protein DataBank (PDB), over the genome of the poorly annotated malaria parasite Plasmodium falciparum and over two datasets collecting known sequences characterized by large domain architectures and repeated blocks of domains. Our results show that, for all datasets, DAMA outperforms existing computational methods and detects domain architectures presenting co-occurrences.
UniProt-DAAC / UniProt Domain Architecture Alignment and Classification
A method for the automatic annotation of protein sequences in the UniProt Knowledgebase (UniProtKB) by comparing their domain architectures, classifying proteins based on the similarities and propagating functional annotation. The performance of UniProt-DAAC was measured through a cross-validation analysis using the Gene Ontology annotation of a sub-set of Uni-ProtKB/Swiss-Prot. The results indicate the effectiveness of this approach in detecting functional similarity with an average F-score: 0.85.
Dintor
Provides a set of over 30 tools to assist researchers in the exploration of genomics and proteomics datasets. Dintor covers a wide range of frequently required functionalities, from gene identifier conversions and orthology mappings to functional annotation of proteins and genetic variants up to candidate gene prioritization and Gene Ontology-based gene set enrichment analysis. A major advantage is its capability to consistently handle multiple versions of tool-associated datasets, supporting the researcher in delivering reproducible results.
Hotpep
Performs annotation of carbohydrate-active enzymes and allows prediction of the enzymatic activity of the proteins. Hotpep (Homology to Peptide Pattern) is a state-of-the-art method for automatic annotation and functional prediction. The software matches the short, conserved motifs to undescribed protein sequences to obtain a fast, sensitive and precise annotation of carbohydrate-active enzymes to families. It also provides a functional prediction of function directly from amino acid sequence.
svmPRAT / SVM-based protein residue annotation toolkit
Allows building support vector machine (SVM) based models for annotating amino acid residues in protein sequences using user supplied features (like PSI-BLAST profiles, or PSIPred profiles). SvmPRAT builds features using a window around the residue, and is equipped with a specialized kernel function (normalized second order exponential kernel function nsoe) along with the standard svm kernel function. For every residue, this tool captures local information around the reside to create fixed length feature vectors.
hRMN / heterogeneous Relational Markov Network
Provides a probabilistic graphical model for large-scale automated annotation transfer. hRMN allows users to combine several relational features to describe a wide range of relational characteristics from fundamentally different species relationships between proteins to minimal differences in the same relational feature. The software permits to assign multiple functional categories and to avoid issues in annotation transfer from dissimilar datasets.
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