A multi-split mapping algorithm for circular RNA,splicing, trans-splicing and fusion detection

Numerous high-throughput sequencing studies have focused on detecting conventionally spliced mRNAs in RNA-seq data. However, non-standard RNAs arising through gene fusion, circularization or trans-splicing are often neglected. We introduce a novel, unbiased algorithm to detect splice junctions from single-end cDNA sequences. In contrast to other methods, our approach accommodates multi-junction structures. Our method compares favorably with competing tools for conventionally spliced mRNAs and, with a gain of up to 40% of recall, systematically outperforms them on reads with multiple splits, trans-splicing and circular products. The algorithm is integrated into our mapping tool segemehl (http://www.bioinf.uni-leipzig.de/Software/segemehl/).     

RNAstrand: reading direction of structured RNAs in multiple sequence alignments

Motivation

Genome-wide screens for structured ncRNA genes in mammals, urochordates, and nematodes have predicted thousands of putative ncRNA genes and other structured RNA motifs. A prerequisite for their functional annotation is to determine the reading direction with high precision.

Results

While folding energies of an RNA and its reverse complement are similar, the differences are sufficient at least in conjunction with substitution patterns to discriminate between structured RNAs and their complements. We present here a support vector machine that reliably classifies the reading direction of a structured RNA from a multiple sequence alignment and provides a considerable improvement in classification accuracy over previous approaches.

Software

RNAstrand is freely available as a stand-alone tool from http://www.bioinf.uni-leipzig.de/Software/RNAstrand and is also included in the latest release of RNAz, a part of the Vienna RNA Package.     

GraphProt: modeling binding preferences of RNA-binding proteins

We present GraphProt, a computational framework for learning sequence- and structure-binding preferences of RNA-binding proteins (RBPs) from high-throughput experimental data. We benchmark GraphProt, demonstrating that the modeled binding preferences conform to the literature, and showcase the biological relevance and two applications of GraphProt models. First, estimated binding affinities correlate with experimental measurements. Second, predicted Ago2 targets display higher levels of expression upon Ago2 knockdown, whereas control targets do not. Computational binding models, such as those provided by GraphProt, are essential for predicting RBP binding sites and affinities in all tissues. GraphProt is freely available at http://www.bioinf.uni-freiburg.de/Software/GraphProt.     

HotSwap for bioinformatics: A STRAP tutorial

Background

Bioinformatics applications are now routinely used to analyze large amounts of data. Application development often requires many cycles of optimization, compiling, and testing. Repeatedly loading large datasets can significantly slow down the development process. We have incorporated HotSwap functionality into the protein workbench STRAP, allowing developers to create plugins using the Java HotSwap technique.

Results

Users can load multiple protein sequences or structures into the main STRAP user interface, and simultaneously develop plugins using an editor of their choice such as Emacs. Saving changes to the Java file causes STRAP to recompile the plugin and automatically update its user interface without requiring recompilation of STRAP or reloading of protein data. This article presents a tutorial on how to develop HotSwap plugins. STRAP is available at http://strapjava.de and http://www.charite.de/bioinf/strap.

Conclusion

HotSwap is a useful and time-saving technique for bioinformatics developers. HotSwap can be used to efficiently develop bioinformatics applications that require loading large amounts of data into memory.     

Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data

Recently, genome-wide surveys for non-coding RNAs have provided evidence for tens of thousands of previously undescribed evolutionary conserved RNAs with distinctive secondary structures. The annotation of these putative ncRNAs, however, remains a difficult problem. Here we describe an SVM-based approach that, in conjunction with a non-stringent filter for consensus secondary structures, is capable of efficiently recognizing microRNA precursors in multiple sequence alignments. The software was applied to recent genome-wide RNAz surveys of mammals, urochordates, and nematodes. AVAILABILITY: The program RNAmicro is available as source code and can be downloaded from http://www.bioinf.uni-leipzig/Software/RNAmicro.     

Prediction of linear B-cell epitopes

The use of antigenicity scales based on physicochemical properties and the sliding window method in combination with an averaging algorithm and subsequent search for the maximum value is the classical method for B-cell epitope prediction. However, recent studies have demonstrated that the best classical methods provide a poor correlation with experimental data. We review both classical and novel algorithms and present our own implementation of the algorithms. The AAPPred software is available at http://www.bioinf.ru/aappred/.     

Learning protein multi-view features in complex space

Protein attribute prediction from primary sequences is an important task and how to extract discriminative features is one of the most crucial aspects. Because single-view feature cannot reflect all the information of a protein, fusing multi-view features is considered as a promising route to improve prediction accuracy. In this paper, we propose a novel framework for protein multi-view feature fusion: first, features from different views are parallely combined to form complex feature vectors; Then, we extend the classic principal component analysis to the generalized principle component analysis for further feature extraction from the parallely combined complex features, which lie in a complex space. Finally, the extracted features are used for prediction. Experimental results on different benchmark datasets and machine learning algorithms demonstrate that parallel strategy outperforms the traditional serial approach and is particularly helpful for extracting the core information buried among multi-view feature sets. A web server for protein structural class prediction based on the proposed method (COMSPA) is freely available for academic use at: http://www.csbio.sjtu.edu.cn/bioinf/COMSPA/.     

NcDNAlign: plausible multiple alignments of non-protein-coding genomic sequences

Genome-wide multiple sequence alignments (MSAs) are a necessary prerequisite for an increasingly diverse collection of comparative genomic approaches. Here we present a versatile method that generates high-quality MSAs for non-protein-coding sequences. The NcDNAlign pipeline combines pairwise BLAST alignments to create initial MSAs, which are then locally improved and trimmed. The program is optimized for speed and hence is particulary well-suited to pilot studies. We demonstrate the practical use of NcDNAlign in three case studies: the search for ncRNAs in gammaproteobacteria and the analysis of conserved noncoding DNA in nematodes and teleost fish, in the latter case focusing on the fate of duplicated ultra-conserved regions. Compared to the currently widely used genome-wide alignment program TBA, our program results in a 20- to 30-fold reduction of CPU time necessary to generate gammaproteobacterial alignments. A showcase application of bacterial ncRNA prediction based on alignments of both algorithms results in similar sensitivity, false discovery rates, and up to 100 putatively novel ncRNA structures. Similar findings hold for our application of NcDNAlign to the identification of ultra-conserved regions in nematodes and teleosts. Both approaches yield conserved sequences of unknown function, result in novel evolutionary insights into conservation patterns among these genomes, and manifest the benefits of an efficient and reliable genome-wide alignment package. The software is available under the GNU Public License at http://www.bioinf.uni-leipzig.de/Software/NcDNAlign/.     

Correlated Mutations: A Hallmark of Phenotypic Amino Acid Substitutions

Point mutations resulting in the substitution of a single amino acid can cause severe functional consequences, but can also be completely harmless. Understanding what determines the phenotypical impact is important both for planning targeted mutation experiments in the laboratory and for analyzing naturally occurring mutations found in patients. Common wisdom suggests using the extent of evolutionary conservation of a residue or a sequence motif as an indicator of its functional importance and thus vulnerability in case of mutation. In this work, we put forward the hypothesis that in addition to conservation, co-evolution of residues in a protein influences the likelihood of a residue to be functionally important and thus associated with disease. While the basic idea of a relation between co-evolution and functional sites has been explored before, we have conducted the first systematic and comprehensive analysis of point mutations causing disease in humans with respect to correlated mutations. We included 14,211 distinct positions with known disease-causing point mutations in 1,153 human proteins in our analysis. Our data show that (1) correlated positions are significantly more likely to be disease-associated than expected by chance, and that (2) this signal cannot be explained by conservation patterns of individual sequence positions. Although correlated residues have primarily been used to predict contact sites, our data are in agreement with previous observations that (3) many such correlations do not relate to physical contacts between amino acid residues. Access to our analysis results are provided at http://webclu.bio.wzw.tum.de/~pagel/supplements/correlated-positions/.     

Polynomial algorithms for the Maximal Pairing Problem: efficient phylogenetic targeting on arbitrary trees

Background

The Maximal Pairing Problem (MPP) is the prototype of a class of combinatorial optimization problems that are of considerable interest in bioinformatics: Given an arbitrary phylogenetic tree T and weights ω _xy for the paths between any two pairs of leaves (x, y), what is the collection of edge-disjoint paths between pairs of leaves that maximizes the total weight? Special cases of the MPP for binary trees and equal weights have been described previously; algorithms to solve the general MPP are still missing, however.

Results

We describe a relatively simple dynamic programming algorithm for the special case of binary trees. We then show that the general case of multifurcating trees can be treated by interleaving solutions to certain auxiliary Maximum Weighted Matching problems with an extension of this dynamic programming approach, resulting in an overall polynomial-time solution of complexity Open image in new window (n⁴ log n) w.r.t. the number n of leaves. The source code of a C implementation can be obtained under the GNU Public License from http://www.bioinf.uni-leipzig.de/Software/Targeting. For binary trees, we furthermore discuss several constrained variants of the MPP as well as a partition function approach to the probabilistic version of the MPP.

Conclusions

The algorithms introduced here make it possible to solve the MPP also for large trees with high-degree vertices. This has practical relevance in the field of comparative phylogenetics and, for example, in the context of phylogenetic targeting, i.e., data collection with resource limitations.

     

GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues

Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this paper, we propose an accurate predictor, GraphBind, for identifying nucleic-acid-binding residues on proteins based on an end-to-end graph neural network. Considering that binding sites often behave in highly conservative patterns on local tertiary structures, we first construct graphs based on the structural contexts of target residues and their spatial neighborhood. Then, hierarchical graph neural networks (HGNNs) are used to embed the latent local patterns of structural and bio-physicochemical characteristics for binding residue recognition. We comprehensively evaluate GraphBind on DNA/RNA benchmark datasets. The results demonstrate the superior performance of GraphBind than state-of-the-art methods. Moreover, GraphBind is extended to other ligand-binding residue prediction to verify its generalization capability. Web server of GraphBind is freely available at http://www.csbio.sjtu.edu.cn/bioinf/GraphBind/.     

In situ analysis of gene expression in Xenopus embryos

The molecular anatomy of the vertebrate embryo was systematically analysed through gene expression during early development of the Xenopus frog using whole-mount in situ hybridization. Expression patterns are documented and assembled into the database Axeldb (http://www.dkfz-heidelberg.de/abt0135/axeldb.htm). Synexpression groups representing genes with shared, complex expression pattern that predict molecular pathways involved in patterning and differentiation have been identified. These sets of co-regulated genes show a striking similarity with operons, and may be a key determinant facilitating evolutionary change leading to animal diversity.     

Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble

Background

Vitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.

Results

In this paper, a new predictor called TargetVita is designed and implemented for predicting protein-vitamin binding residues using protein sequences. In TargetVita, features derived from the position-specific scoring matrix (PSSM), predicted protein secondary structure, and vitamin binding propensity are combined to form the original feature space; then, several feature subspaces are selected by performing different feature selection methods. Finally, based on the selected feature subspaces, heterogeneous SVMs are trained and then ensembled for performing prediction.

Conclusions

The experimental results obtained with four separate vitamin-binding benchmark datasets demonstrate that the proposed TargetVita is superior to the state-of-the-art vitamin-specific predictor, and an average improvement of 10% in terms of the Matthews correlation coefficient (MCC) was achieved over independent validation tests. The TargetVita web server and the datasets used are freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetVita or http://www.csbio.sjtu.edu.cn/bioinf/TargetVita.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-297) contains supplementary material, which is available to authorized users.     

Identification of evolutionarily meaningful information within the mammalian RNA editing landscape

A large comparative genomic sequence study has determined the extent of conservation between RNA editing sites within the mammalian evolutionary tree.See related research by Pinto et al., http://genomebiology.com/2014/15/1/R5     

Difficult phylogenetic questions: more data, maybe; better methods, certainly

The combination of molecular sequence data and bioinformatics has revolutionized phylogenetic inference over the past decade, vastly increasing the scope of the evolutionary trees that we are able to infer. A recent paper in BMC Biology describing a new phylogenomic pipeline to help automate the inference of evolutionary trees from public sequence databases provides another important tool in our efforts to derive the Tree of Life. See research article: http://www.biomedcentral.com/1741-7007/9/55     

Bioinformatics Approach to Evaluate Differential Gene Expression of M1/M2 Macrophage Phenotypes and Antioxidant Genes in Atherosclerosis

Atherosclerosis is a pro-inflammatory process intrinsically related to systemic redox impairments. Macrophages play a major role on disease development. The specific involvement of classically activated, M1 (pro-inflammatory), or the alternatively activated, M2 (anti-inflammatory), on plaque formation and disease progression are still not established. Thus, based on meta-data analysis of public micro-array datasets, we compared differential gene expression levels of the human antioxidant genes (HAG) and M1/M2 genes between early and advanced human atherosclerotic plaques, and among peripheric macrophages (with or without foam cells induction by oxidized low density lipoprotein, oxLDL) from healthy and atherosclerotic subjects. Two independent datasets, GSE28829 and GSE9874, were selected from gene expression omnibus (http://www.ncbi.nlm.nih.gov/geo/) repository. Functional interactions were obtained with STRING (http://string-db.org/) and Medusa (http://coot.embl.de/medusa/). Statistical analysis was performed with ViaComplex^® (http://lief.if.ufrgs.br/pub/biosoftwares/viacomplex/) and gene score enrichment analysis (http://www.broadinstitute.org/gsea/index.jsp). Bootstrap analysis demonstrated that the activity (expression) of HAG and M1 gene sets were significantly increased in advance compared to early atherosclerotic plaque. Increased expressions of HAG, M1, and M2 gene sets were found in peripheric macrophages from atherosclerotic subjects compared to peripheric macrophages from healthy subjects, while only M1 gene set was increased in foam cells from atherosclerotic subjects compared to foam cells from healthy subjects. However, M1 gene set was decreased in foam cells from healthy subjects compared to peripheric macrophages from healthy subjects, while no differences were found in foam cells from atherosclerotic subjects compared to peripheric macrophages from atherosclerotic subjects. Our data suggest that, different to cancer, in atherosclerosis there is no M1 or M2 polarization of macrophages. Actually, M1 and M2 phenotype are equally induced, what is an important aspect to better understand the disease progression, and can help to develop new therapeutic approaches.     

PARma: identification of microRNA target sites in AGO-PAR-CLIP data

PARma is a complete data analysis software for AGO-PAR-CLIP experiments to identify target sites of microRNAs as well as the microRNA binding to these sites. It integrates specific characteristics of the experiments into a generative model. The model and a novel pattern discovery tool are iteratively applied to data to estimate seed activity probabilities, cluster confidence scores and to assign the most probable microRNA. Based on differential PAR-CLIP analysis and comparison to RIP-Chip data, we show that PARma is more accurate than existing approaches. PARma is available from http://www.bio.ifi.lmu.de/PARma