Automatic Functional Annotation Research Articles

Automatic annotation of the bHLH gene family in plants

BackgroundThe bHLH transcription factor family is named after the basic helix-loop-helix (bHLH) domain that is a characteristic element of their members. Understanding the function and characteristics of this family is important for the examination of a wide range of functions. As the availability of genome sequences and transcriptome assemblies has increased significantly, the need for automated solutions that provide reliable functional annotations is emphasised.ResultsA phylogenetic approach was adapted for the automatic identification and functional annotation of the bHLH transcription factor family. The bHLH_annotator, designed for the automated functional annotation of bHLHs, was implemented in Python3. Sequences of bHLHs described in literature were collected to represent the full diversity of bHLH sequences. Previously described orthologs form the basis for the functional annotation assignment to candidates which are also screened for bHLH-specific motifs. The pipeline was successfully deployed on the two Arabidopsis thaliana accessions Col-0 and Nd-1, the monocot species Dioscorea dumetorum, and a transcriptome assembly of Croton tiglium. Depending on the applied search parameters for the initial candidates in the pipeline, species-specific candidates or members of the bHLH family which experienced domain loss can be identified.ConclusionsThe bHLH_annotator allows a detailed and systematic investigation of the bHLH family in land plant species and classifies candidates based on bHLH-specific characteristics, which distinguishes the pipeline from other established functional annotation tools. This provides the basis for the functional annotation of the bHLH family in land plants and the systematic examination of a wide range of functions regulated by this transcription factor family.

Automatic identification and annotation of MYB gene family members in plants

BackgroundMYBs are among the largest transcription factor families in plants. Consequently, members of this family are involved in a plethora of processes including development and specialized metabolism. The MYB families of many plant species were investigated in the last two decades since the first investigation looked at Arabidopsis thaliana. This body of knowledge and characterized sequences provide the basis for the identification, classification, and functional annotation of candidate sequences in new genome and transcriptome assemblies.ResultsA pipeline for the automatic identification and functional annotation of MYBs in a given sequence data set was implemented in Python. MYB candidates are identified, screened for the presence of a MYB domain and other motifs, and finally placed in a phylogenetic context with well characterized sequences. In addition to technical benchmarking based on existing annotation, the transcriptome assembly of Croton tiglium and the annotated genome sequence of Castanea crenata were screened for MYBs. Results of both analyses are presented in this study to illustrate the potential of this application. The analysis of one species takes only a few minutes depending on the number of predicted sequences and the size of the MYB gene family. This pipeline, the required bait sequences, and reference sequences for a classification are freely available on github: https://github.com/bpucker/MYB_annotator.ConclusionsThis automatic annotation of the MYB gene family in novel assemblies makes genome-wide investigations consistent and paves the way for comparative studies in the future. Candidate genes for in-depth analyses are presented based on their orthology to previously characterized sequences which allows the functional annotation of the newly identified MYBs with high confidence. The identification of orthologs can also be harnessed to detect duplication and deletion events.

On the design of a similarity function for sparse binary data with application on protein function annotation

Automatic protein function annotation is a challenging task that is fundamental in many medical applications. Indeed, the capability to predict whether a protein has a given function is a key step for disease understanding and drug design. For such reasons, many authors have proposed computational methods for protein function prediction. One key element that is present in many proposals is similarity functions. Such functions are often used to compute the pairwise similarity between two proteins. It is commonly accepted that proteins with similar structures share the same function. Nevertheless, no previous works have focused on proposing a similarity function that is specifically designed for protein function annotation. In this work, we analyze the best similarity functions for the protein function annotation task and propose a new one. We performed experiments in a simple pairwise similarity scenario and also using our proposal as part of a more complex protein function annotation method. Based on the results, we can state that our proposal is a valid alternative as a building block of many protein function annotation methods.

Genome Analyses of the Less Aggressive Rhizoctonia solani AG1-IB Isolates 1/2/21 and O8/2 Compared to the Reference AG1-IB Isolate 7/3/14.

Rhizoctonia solani AG1-IB of the phylum Basidiomycota is known as phytopathogenic fungus affecting various economically important crops, such as bean, rice, soybean, figs, cabbage and lettuce. The isolates 1/2/21 and O8/2 of the anastomosis group AG1-IB originating from lettuce plants with bottom rot symptoms represent two less aggressive R. solani isolates, as confirmed in a pathogenicity test on lettuce. They were deeply sequenced on the Illumina MiSeq system applying the mate-pair and paired-end mode to establish their genome sequences. Assemblies of obtained sequences resulted in 2092 and 1492 scaffolds, respectively, for isolate 1/2/21 and O8/2, amounting to a size of approximately 43 Mb for each isolate. Gene prediction by applying AUGUSTUS (v. 3.2.1.) yielded 12,827 and 12,973 identified genes, respectively. Based on automatic functional annotation, genes potentially encoding cellulases and enzymes involved in secondary metabolite synthesis were identified in the AG1-IB genomes. The annotated genome sequences of the less aggressive AG1-IB isolates were compared with the isolate 7/3/14, which is highly aggressive on lettuce and other vegetable crops such as bean, cabbage and carrot. This analysis revealed the first insights into core genes of AG1-IB isolates and unique determinants of each genome that may explain the different aggressiveness levels of the strains.

Fully-automated functional region annotation of liver via a 2.5D class-aware deep neural network with spatial adaptation

Background and ObjectiveAutomatic functional region annotation of liver should be very useful for preoperative planning of liver resection in the clinical domain. However, many traditional computer-aided annotation methods based on anatomical landmarks or the vascular tree often fail to extract accurate liver segments. Furthermore, these methods are difficult to fully automate and thus remain time-consuming. To address these issues, in this study we aim to develop a fully-automated approach for functional region annotation of liver using deep learning based on 2.5D class-aware deep neural networks with spatial adaptation. Methods112 CT scans were fed into our 2.5D class-aware deep neural network with spatial adaptation for automatic functional region annotation of liver. The proposed model was built upon the ResU-net architecture, which adaptively selected a stack of adjacent CT slices as input and, generating masks corresponding to the center slice, automatically annotated the liver functional region from abdominal CT images. Furthermore, to minimize the problem of class-level ambiguity between different slices, the anatomy class-specific information was used. ResultsThe final algorithm performance for automatic functional region annotation of liver showed large overlap with that of manual reference segmentation. The dice similarity coefficient of hepatic segments achieved high scores and an average dice score of 0.882. The entire calculation time was quite fast (~5 s) compared to manual annotation (~2.5 hours). ConclusionThe proposed models described in this paper offer a feasible solution for fully-automated functional region annotation of liver from CT images. The experimental results demonstrated that the proposed method can attain a high average dice score and low computational time. Therefore, this work should allow for improved liver surgical resection planning by our precise segmentation and simple fully-automated method.

BionoiNet: ligand-binding site classification with off-the-shelf deep neural network.

Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods. We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures. BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet. Supplementary data are available at Bioinformatics online.

MicroScope: an integrated platform for the annotation and exploration of microbial gene functions through genomic, pangenomic and metabolic comparative analysis

Large-scale genome sequencing and the increasingly massive use of high-throughput approaches produce a vast amount of new information that completely transforms our understanding of thousands of microbial species. However, despite the development of powerful bioinformatics approaches, full interpretation of the content of these genomes remains a difficult task. Launched in 2005, the MicroScope platform (https://www.genoscope.cns.fr/agc/microscope) has been under continuous development and provides analysis for prokaryotic genome projects together with metabolic network reconstruction and post-genomic experiments allowing users to improve the understanding of gene functions. Here we present new improvements of the MicroScope user interface for genome selection, navigation and expert gene annotation. Automatic functional annotation procedures of the platform have also been updated and we added several new tools for the functional annotation of genes and genomic regions. We finally focus on new tools and pipeline developed to perform comparative analyses on hundreds of genomes based on pangenome graphs. To date, MicroScope contains data for >11 800 microbial genomes, part of which are manually curated and maintained by microbiologists (>4500 personal accounts in September 2019). The platform enables collaborative work in a rich comparative genomic context and improves community-based curation efforts.

Improving protein function prediction using protein sequence and GO-term similarities.

MotivationMost automatic functional annotation methods assign Gene Ontology (GO) terms to proteins based on annotations of highly similar proteins. We advocate that proteins that are less similar are still informative. Also, despite their simplicity and structure, GO terms seem to be hard for computers to learn, in particular the Biological Process ontology, which has the most terms (>29 000). We propose to use Label-Space Dimensionality Reduction (LSDR) techniques to exploit the redundancy of GO terms and transform them into a more compact latent representation that is easier to predict.ResultsWe compare proteins using a sequence similarity profile (SSP) to a set of annotated training proteins. We introduce two new LSDR methods, one based on the structure of the GO, and one based on semantic similarity of terms. We show that these LSDR methods, as well as three existing ones, improve the Critical Assessment of Functional Annotation performance of several function prediction algorithms. Cross-validation experiments on Arabidopsis thaliana proteins pinpoint the superiority of our GO-aware LSDR over generic LSDR. Our experiments on A.thaliana proteins show that the SSP representation in combination with a kNN classifier outperforms state-of-the-art and baseline methods in terms of cross-validated F-measure.Availability and implementationSource code for the experiments is available at https://github.com/stamakro/SSP-LSDR.Supplementary information Supplementary data are available at Bioinformatics online.

NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology

BackgroundThe number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion.ResultsNaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions.ConclusionsWe developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo.

Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining.

The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.

UniProt-DAAC: domain architecture alignment and classification, a new method for automatic functional annotation in UniProtKB.

Motivation: Similarity-based methods have been widely used in order to infer the properties of genes and gene products containing little or no experimental annotation. New approaches that overcome the limitations of methods that rely solely upon sequence similarity are attracting increased attention. One of these novel approaches is to use the organization of the structural domains in proteins.Results: We propose 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 this method was measured through a cross-validation analysis using the Gene Ontology (GO) annotation of a sub-set of UniProtKB/Swiss-Prot. The results demonstrate the effectiveness of this approach in detecting functional similarity with an average F-score: 0.85. We applied the method on nearly 55.3 million uncharacterized proteins in UniProtKB/TrEMBL resulted in 44 818 178 GO term predictions for 12 172 114 proteins. 22% of these predictions were for 2 812 016 previously non-annotated protein entries indicating the significance of the value added by this approach.Availability and implementation: The results of the method are available at: ftp://ftp.ebi.ac.uk/pub/contrib/martin/DAAC/.Contact: tdogan@ebi.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

The PFP and ESG protein function prediction methods in 2014: effect of database updates and ensemble approaches.

BackgroundFunctional annotation of novel proteins is one of the central problems in bioinformatics. With the ever-increasing development of genome sequencing technologies, more and more sequence information is becoming available to analyze and annotate. To achieve fast and automatic function annotation, many computational (automated) function prediction (AFP) methods have been developed. To objectively evaluate the performance of such methods on a large scale, community-wide assessment experiments have been conducted. The second round of the Critical Assessment of Function Annotation (CAFA) experiment was held in 2013–2014. Evaluation of participating groups was reported in a special interest group meeting at the Intelligent Systems in Molecular Biology (ISMB) conference in Boston in 2014. Our group participated in both CAFA1 and CAFA2 using multiple, in-house AFP methods. Here, we report benchmark results of our methods obtained in the course of preparation for CAFA2 prior to submitting function predictions for CAFA2 targets.ResultsFor CAFA2, we updated the annotation databases used by our methods, protein function prediction (PFP) and extended similarity group (ESG), and benchmarked their function prediction performances using the original (older) and updated databases. Performance evaluation for PFP with different settings and ESG are discussed. We also developed two ensemble methods that combine function predictions from six independent, sequence-based AFP methods. We further analyzed the performances of our prediction methods by enriching the predictions with prior distribution of gene ontology (GO) terms. Examples of predictions by the ensemble methods are discussed.ConclusionsUpdating the annotation database was successful, improving the Fmax prediction accuracy score for both PFP and ESG. Adding the prior distribution of GO terms did not make much improvement. Both of the ensemble methods we developed improved the average Fmax score over all individual component methods except for ESG. Our benchmark results will not only complement the overall assessment that will be done by the CAFA organizers, but also help elucidate the predictive powers of sequence-based function prediction methods in general.Electronic supplementary materialThe online version of this article (doi:10.1186/s13742-015-0083-4) contains supplementary material, which is available to authorized users.

Mutations and genomic islands can explain the strain dependency of sugar utilization in 21 strains of Propionibacterium freudenreichii.

BackgroundPropionibacterium freudenreichii (PF) is an actinobacterium used in cheese technology and for its probiotic properties. PF is also extremely adaptable to several ecological niches and can grow on a variety of carbon and nitrogen sources. The aim of this work was to discover the genetic basis for strain-dependent traits related to its ability to use specific carbon sources. High-throughput sequencing technologies were ideal for this purpose as they have the potential to decipher genomic diversity at a moderate cost.Results21 strains of PF were sequenced and the genomes were assembled de novo. Scaffolds were ordered by comparison with the complete reference genome CIRM-BIA1, obtained previously using traditional Sanger sequencing. Automatic functional annotation and manual curation were performed. Each gene was attributed to either the core genome or an accessory genome. The ability of the 21 strains to degrade 50 different sugars was evaluated. Thirty-three sugars were degraded by none of the sequenced strains whereas eight sugars were degraded by all of them. The corresponding genes were present in the core genome. Lactose, melibiose and xylitol were only used by some strains. In this case, the presence/absence of genes responsible for carbon uptake and degradation correlated well with the phenotypes, with the exception of xylitol. Furthermore, the simultaneous presence of these genes was in line the metabolic pathways described previously in other species. We also considered the genetic origin (transduction, rearrangement) of the corresponding genomic islands. Ribose and gluconate were degraded to a greater or lesser extent (quantitative phenotype) by some strains. For these sugars, the phenotypes could not be explained by the presence/absence of a gene but correlated with the premature appearance of a stop codon interrupting protein synthesis and preventing the catabolism of corresponding carbon sources.ConclusionThese results illustrate (i) the power of correlation studies to discover the genetic basis of binary strain-dependent traits, and (ii) the plasticity of PF chromosomes, probably resulting from horizontal transfers, duplications, transpositions and an accumulation of mutations. Knowledge of the genetic basis of nitrogen and sugar degradation opens up new strategies for the screening of PF strain collections to enable optimum cheese starter, probiotic and white biotechnology applications.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1467-7) contains supplementary material, which is available to authorized users.

Automation of functional annotation of genomes and transcriptomes

Functional annotation represents a means to investigate and classify genes and transcripts according to their function within a given organism. This paper presents Massive Automatic Functional Annotation (MAFA - Web), which is an online free bioinformatics tool that allows automation, unification and optimization of functional annotation processes when dealing with large volumes of sequences. MAFA includes tools for categorization and statistical analysis of associations between sequences. We have evaluated the performance of MAFA with a set of data taken from Diploria-Strigosatranscriptome (using an 8-core computer, namely E7450 @ 2,40GHZ with 256GB RAM), processing rates of 2,7 seconds per sequence (using Uniprot database) and 50,0 seconds per sequence (using Non-redundant from NCBI database) were found together with particular RAM usage patterns that depend on the database being processed (1GB for Uniprot database and 9GB for Non-redundant database).. Aviability: https://github.com/BioinfUD/MAFA .

Predicting enzymatic function from global binding site descriptors

Due to the rising number of solved protein structures, computer-based techniques for automatic protein functional annotation and classification into families are of high scientific interest. DoGSiteScorer automatically calculates global descriptors for self-predicted pockets based on the 3D structure of a protein. Protein function predictors on three levels with increasing granularity are built by use of a support vector machine (SVM), based on descriptors of 26632 pockets from enzymes with known structure and enzyme classification. The SVM models represent a generalization of the available descriptor space for each enzyme class, subclass, and substrate-specific sub-subclass. Cross-validation studies show accuracies of 68.2% for predicting the correct main class and accuracies between 62.8% and 80.9% for the six subclasses. Substrate-specific recall rates for a kinase subset are 53.8%. Furthermore, application studies show the ability of the method for predicting the function of unknown proteins and gaining valuable information for the function prediction field.

Cutoff Scanning Matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns

BackgroundThe unforgiving pace of growth of available biological data has increased the demand for efficient and scalable paradigms, models and methodologies for automatic annotation. In this paper, we present a novel structure-based protein function prediction and structural classification method: Cutoff Scanning Matrix (CSM). CSM generates feature vectors that represent distance patterns between protein residues. These feature vectors are then used as evidence for classification. Singular value decomposition is used as a preprocessing step to reduce dimensionality and noise. The aspect of protein function considered in the present work is enzyme activity. A series of experiments was performed on datasets based on Enzyme Commission (EC) numbers and mechanistically different enzyme superfamilies as well as other datasets derived from SCOP release 1.75.ResultsCSM was able to achieve a precision of up to 99% after SVD preprocessing for a database derived from manually curated protein superfamilies and up to 95% for a dataset of the 950 most-populated EC numbers. Moreover, we conducted experiments to verify our ability to assign SCOP class, superfamily, family and fold to protein domains. An experiment using the whole set of domains found in last SCOP version yielded high levels of precision and recall (up to 95%). Finally, we compared our structural classification results with those in the literature to place this work into context. Our method was capable of significantly improving the recall of a previous study while preserving a compatible precision level.ConclusionsWe showed that the patterns derived from CSMs could effectively be used to predict protein function and thus help with automatic function annotation. We also demonstrated that our method is effective in structural classification tasks. These facts reinforce the idea that the pattern of inter-residue distances is an important component of family structural signatures. Furthermore, singular value decomposition provided a consistent increase in precision and recall, which makes it an important preprocessing step when dealing with noisy data.

Enhancing the accuracy of HMM-based conserved pathway prediction using global correspondence scores

BackgroundComparative network analysis aims to identify common subnetworks in biological networks. It can facilitate the prediction of conserved functional modules across different species and provide deep insights into their underlying regulatory mechanisms. Recently, it has been shown that hidden Markov models (HMMs) can provide a flexible and computationally efficient framework for modeling and comparing biological networks.ResultsIn this work, we show that using global correspondence scores between molecules can improve the accuracy of the HMM-based network alignment results. The global correspondence scores are computed by performing a semi-Markov random walk on the networks to be compared. The resulting score naturally integrates the sequence similarity between molecules and the topological similarity between their molecular interactions, thereby providing a more effective measure for estimating the functional similarity between molecules. By incorporating the global correspondence scores, instead of relying on sequence similarity or functional annotation scores used by previous approaches, our HMM-based network alignment method can identify conserved subnetworks that are functionally more coherent.ConclusionsPerformance analysis based on synthetic and microbial networks demonstrates that the proposed network alignment strategy significantly improves the robustness and specificity of the predicted alignment results, in terms of conserved functional similarity measured based on KEGG ortholog (KO) groups. These results clearly show that the HMM-based network alignment framework using global correspondence scores can effectively find conserved biological pathways and has the potential to be used for automatic functional annotation of biomolecules.

On Position-Specific Scoring Matrix for Protein Function Prediction

While genome sequencing projects have generated tremendous amounts of protein sequence data for a vast number of genomes, substantial portions of most genomes are still unannotated. Despite the success of experimental methods for identifying protein functions, they are often lab intensive and time consuming. Thus, it is only practical to use in silico methods for the genome-wide functional annotations. In this paper, we propose new features extracted from protein sequence only and machine learning-based methods for computational function prediction. These features are derived from a position-specific scoring matrix, which has shown great potential in other bininformatics problems. We evaluate these features using four different classifiers and yeast protein data. Our experimental results show that features derived from the position-specific scoring matrix are appropriate for automatic function annotation.

A Threading-Based Method for the Prediction of DNA-Binding Proteins with Application to the Human Genome

Diverse mechanisms for DNA-protein recognition have been elucidated in numerous atomic complex structures from various protein families. These structural data provide an invaluable knowledge base not only for understanding DNA-protein interactions, but also for developing specialized methods that predict the DNA-binding function from protein structure. While such methods are useful, a major limitation is that they require an experimental structure of the target as input. To overcome this obstacle, we develop a threading-based method, DNA-Binding-Domain-Threader (DBD-Threader), for the prediction of DNA-binding domains and associated DNA-binding protein residues. Our method, which uses a template library composed of DNA-protein complex structures, requires only the target protein's sequence. In our approach, fold similarity and DNA-binding propensity are employed as two functional discriminating properties. In benchmark tests on 179 DNA-binding and 3,797 non-DNA-binding proteins, using templates whose sequence identity is less than 30% to the target, DBD-Threader achieves a sensitivity/precision of 56%/86%. This performance is considerably better than the standard sequence comparison method PSI-BLAST and is comparable to DBD-Hunter, which requires an experimental structure as input. Moreover, for over 70% of predicted DNA-binding domains, the backbone Root Mean Square Deviations (RMSDs) of the top-ranked structural models are within 6.5 Å of their experimental structures, with their associated DNA-binding sites identified at satisfactory accuracy. Additionally, DBD-Threader correctly assigned the SCOP superfamily for most predicted domains. To demonstrate that DBD-Threader is useful for automatic function annotation on a large-scale, DBD-Threader was applied to 18,631 protein sequences from the human genome; 1,654 proteins are predicted to have DNA-binding function. Comparison with existing Gene Ontology (GO) annotations suggests that ∼30% of our predictions are new. Finally, we present some interesting predictions in detail. In particular, it is estimated that ∼20% of classic zinc finger domains play a functional role not related to direct DNA-binding.

RNA structure alignment by a unit-vector approach

The recent discovery of tiny RNA molecules such as microRNAs and small interfering RNA are transforming the view of RNA as a simple information transfer molecule. Similar to proteins, the native three-dimensional structure of RNA determines its biological activity. Therefore, classifying the current structural space is paramount for functionally annotating RNA molecules. The increasing numbers of RNA structures deposited in the PDB requires more accurate, automatic and benchmarked methods for RNA structure comparison. In this article, we introduce a new algorithm for RNA structure alignment based on a unit-vector approach. The algorithm has been implemented in the SARA program, which results in RNA structure pairwise alignments and their statistical significance. The SARA program has been implemented to be of general applicability even when no secondary structure can be calculated from the RNA structures. A benchmark against the ARTS program using a set of 1275 non-redundant pairwise structure alignments results in inverted approximately 6% extra alignments with at least 50% structurally superposed nucleotides and base pairs. A first attempt to perform RNA automatic functional annotation based on structure alignments indicates that SARA can correctly assign the deepest SCOR classification to >60% of the query structures. The SARA program is freely available through a World Wide Web server http://sgu.bioinfo.cipf.es/services/SARA/. Supplementary data are available at Bioinformatics online.