ISFOLD: structure prediction of base pairs in non-helical RNA motifs from isostericity signatures in their sequence alignments

The existence and identity of non-Watson-Crick base pairs (bps) within RNA bulges, internal loops, and hairpin loops cannot reliably be predicted by existing algorithms. We have developed the Isfold (Isosteric Folding) program as a tool to examine patterns of nucleotide substitutions from sequence alignments or mutation experiments and identify plausible bp interactions. We infer these interactions based on the observation that each non-Watson-Crick bp has a signature pattern of isosteric substitutions where mutations can be made that preserve the 3D structure. Isfold produces a dynamic representation of predicted bps within defined motifs in order of their probabilities. The software was developed under Windows XP, and is capable of running on PC and MAC with Matlab 7.1 (SP3) or higher. A PC stand-alone version that does not require Matlab also is available. This software and a user manual are freely available at www.ucsf.edu/frankel/isfold.     

Intersect: identification and visualization of overlaps in database search results

The determination of distant evolutionary relationships remains an important biological problem, and distant homologs often appear in statistically insignificant regions of sequence similarity searches. Intersect is a computer program designed to identify and visualize the overlaps between sets of sequences reported by multiple database searches. This capability extends the usefulness of database search results and aids researchers in identifying the individual sequences that best bridge sequence families and superfamilies. AVAILABILITY: The Intersect program is available from the Babbitt laboratory website at http://www.babbittlab.ucsf.edu/software/intersect     

Investigating microRNA-Target Interaction-Supported Tissues in Human Cancer Tissues Based on miRNA and Target Gene Expression Profiling

Recent studies have revealed that a small non-coding RNA, microRNA (miRNA) down-regulates its mRNA targets. This effect is regarded as an important role in various biological processes. Many studies have been devoted to predicting miRNA-target interactions. These studies indicate that the interactions may only be functional in some specific tissues, which depend on the characteristics of an miRNA. No systematic methods have been established in the literature to investigate the correlation between miRNA-target interactions and tissue specificity through microarray data. In this study, we propose a method to investigate miRNA-target interaction-supported tissues, which is based on experimentally validated miRNA-target interactions. The tissue specificity results by our method are in accordance with the experimental results in the literature.

Availability and Implementation

Our analysis results are available at http://tsmti.mbc.nctu.edu.tw/ and http://www.stat.nctu.edu.tw/hwang/tsmti.html.     

Near-native protein loop sampling using nonparametric density estimation accommodating sparcity

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/.     

BiVisu: software tool for bicluster detection and visualization

BiVisu is an open-source software tool for detecting and visualizing biclusters embedded in a gene expression matrix. Through the use of appropriate coherence relations, BiVisu can detect constant, constant-row, constant-column, additive-related as well as multiplicative-related biclusters. The biclustering results are then visualized under a 2D setting for easy inspection. In particular, parallel coordinate (PC) plots for each bicluster are displayed, from which objective and subjective cluster quality evaluation can be performed. Availability: BiVisu has been developed in Matlab and is available at http://www.eie.polyu.edu.hk/~nflaw/Biclustering/.     

Evaluation of methods for detecting conversion events in gene clusters

Background

Several platforms for the analysis of genome-wide association data are available. However, these platforms focus on the evaluation of the genotype inherited by affected (i.e. case) individuals, whereas for some conditions (e.g. birth defects) the genotype of the mothers of affected individuals may also contribute to risk. For such conditions, it is critical to evaluate associations with both the maternal and the inherited (i.e. case) genotype. When genotype data are available for case-parent triads, a likelihood-based approach using log-linear modeling can be used to assess both the maternal and inherited genotypes. However, available software packages for log-linear analyses are not well suited to the analysis of typical genome-wide association data (e.g. including missing data).

Results

An integrated platform, Maternal and Inherited Analyses for Genome-wide Association Studies (MI-GWAS) for log-linear analyses of maternal and inherited genetic effects in large, genome-wide datasets, is described. MI-GWAS uses SAS and LEM software in combination to appropriately format data, perform the log-linear analyses and summarize the results. This platform was evaluated using existing genome-wide data and was shown to perform accurately and relatively efficiently.

Conclusions

The MI-GWAS platform provides a valuable tool for the analysis of association of a phenotype or condition with maternal and inherited genotypes using genome-wide data from case-parent triads. The source code for this platform is freely available at http://www.sph.uth.tmc.edu/sbrr/mi-gwas.htm.     

GIV: A Tool for Genomic Islands Visualization

A Genomic Islands (GI) is a chunk of DNA sequence in a genome whose origin can be traced back to other organisms or viruses. The detection of GIs plays an indispensable role in biomedical research, due to the fact that GIs are highly related to special functionalities such as disease-causing GIs - pathogenicity islands. It is also very important to visualize genomic islands, as well as the supporting features corresponding to the genomic islands in the genome. We have developed a program, Genomic Island Visualization (GIV), which displays the locations of genomic islands in a genome, as well as the corresponding supportive feature information for GIs. GIV was implemented in C++, and was compiled and executed on Linux/Unix operating systems.

Availability

GIV is freely available for non-commercial use at http://www5.esu.edu/cpsc/bioinfo/software/GIV     

Competing endogenous RNA database

A given mRNA can be regulated by interactions with miRNAs and in turn the availability of these miRNAs can be regulated by their interactions with alternate mRNAs. The concept of regulation of a given mRNA by alternate mRNA (competing endogenous mRNA) by virtue of interactions with miRNAs through shared miRNA response elements is poised to become a fundamental genetic regulatory mechanism. The molecular basis of the mRNA-mRNA cross talks is via miRNA response elements, which can be predicted based on both molecular interaction and evolutionary conservation. By examining the co-occurrence of miRNA response elements in the mRNAs on a genome-wide basis we predict competing endogenous RNA for specific mRNAs targeted by miRNAs. Comparison of the mRNAs predicted to regulate PTEN with recently published work, indicate that the results presented within the competing endogenous RNA database (ceRDB) have biological relevance.

Availability

http://www.oncomir.umn.edu/cefinder/     

A Global Comparison of the Human and T. brucei Degradomes Gives Insights about Possible Parasite Drug Targets

We performed a genome-level computational study of sequence and structure similarity, the latter using crystal structures and models, of the proteases of Homo sapiens and the human parasite Trypanosoma brucei. Using sequence and structure similarity networks to summarize the results, we constructed global views that show visually the relative abundance and variety of proteases in the degradome landscapes of these two species, and provide insights into evolutionary relationships between proteases. The results also indicate how broadly these sequence sets are covered by three-dimensional structures. These views facilitate cross-species comparisons and offer clues for drug design from knowledge about the sequences and structures of potential drug targets and their homologs. Two protease groups (“M32” and “C51”) that are very different in sequence from human proteases are examined in structural detail, illustrating the application of this global approach in mining new pathogen genomes for potential drug targets. Based on our analyses, a human ACE2 inhibitor was selected for experimental testing on one of these parasite proteases, TbM32, and was shown to inhibit it. These sequence and structure data, along with interactive versions of the protein similarity networks generated in this study, are available at http://babbittlab.ucsf.edu/resources.html.     

RNAMotifScanX: a graph alignment approach for RNA structural motif identification

RNA structural motifs are recurrent three-dimensional (3D) components found in the RNA architecture. These RNA structural motifs play important structural or functional roles and usually exhibit highly conserved 3D geometries and base-interaction patterns. Analysis of the RNA 3D structures and elucidation of their molecular functions heavily rely on efficient and accurate identification of these motifs. However, efficient RNA structural motif search tools are lacking due to the high complexity of these motifs. In this work, we present RNAMotifScanX, a motif search tool based on a base-interaction graph alignment algorithm. This novel algorithm enables automatic identification of both partially and fully matched motif instances. RNAMotifScanX considers noncanonical base-pairing interactions, base-stacking interactions, and sequence conservation of the motifs, which leads to significantly improved sensitivity and specificity as compared with other state-of-the-art search tools. RNAMotifScanX also adopts a carefully designed branch-and-bound technique, which enables ultra-fast search of large kink-turn motifs against a 23S rRNA. The software package RNAMotifScanX is implemented using GNU C++, and is freely available from http://genome.ucf.edu/RNAMotifScanX.     

Protein-Protein Docking with F2Dock 2.0 and GB-Rerank

Motivation

Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F² Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error.

Results

The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F² Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F² Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F² Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F² Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other.

Availability

The docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dock.shtml. Client: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dockclient.shtml.     

Processing methods for signal suppression of FTMS data

Background

Fourier Transform Mass Spectrometry coupled with Liquid Chromatography(LC-FTMS) has been widely used in proteomics. Past investigation has revealed that there exists an intensity dependent random suppression in peptide elution profiles in LC-FTMS data. The suppression is homogenous for the same peptide but non-homogenous for different peptides. The correction of suppressed profiles and an estimation on the range of suppression are necessary for accurate and reliable quantification using FTMS data.

Results

A software package, Gcorr, is presented. The software corrects peptide profiles that satisfy correction conditions, and it can predict fold change null distributions at different intensity levels. Subsequently, the significance P-values of measured fold changes can be estimated based on the predicted null distributions. We have used an 1:1 LC-FTMS label-free dataset pair collected based on the same sample to verify that our predicted null distributions conforms to that of the observed null distribution.

Conclusions

This software is able to provide suppression correction for peptide profiles, suppression distribution analysis and peptide differential expression analysis in terms of its fold change significance. The software is freely available at http://compgenomics.utsa.edu/Suppression_Study.html.

     

Profile Hidden Markov Models for the Detection of Viruses within Metagenomic Sequence Data

Rapid, sensitive, and specific virus detection is an important component of clinical diagnostics. Massively parallel sequencing enables new diagnostic opportunities that complement traditional serological and PCR based techniques. While massively parallel sequencing promises the benefits of being more comprehensive and less biased than traditional approaches, it presents new analytical challenges, especially with respect to detection of pathogen sequences in metagenomic contexts. To a first approximation, the initial detection of viruses can be achieved simply through alignment of sequence reads or assembled contigs to a reference database of pathogen genomes with tools such as BLAST. However, recognition of highly divergent viral sequences is problematic, and may be further complicated by the inherently high mutation rates of some viral types, especially RNA viruses. In these cases, increased sensitivity may be achieved by leveraging position-specific information during the alignment process. Here, we constructed HMMER3-compatible profile hidden Markov models (profile HMMs) from all the virally annotated proteins in RefSeq in an automated fashion using a custom-built bioinformatic pipeline. We then tested the ability of these viral profile HMMs (“vFams”) to accurately classify sequences as viral or non-viral. Cross-validation experiments with full-length gene sequences showed that the vFams were able to recall 91% of left-out viral test sequences without erroneously classifying any non-viral sequences into viral protein clusters. Thorough reanalysis of previously published metagenomic datasets with a set of the best-performing vFams showed that they were more sensitive than BLAST for detecting sequences originating from more distant relatives of known viruses. To facilitate the use of the vFams for rapid detection of remote viral homologs in metagenomic data, we provide two sets of vFams, comprising more than 4,000 vFams each, in the HMMER3 format. We also provide the software necessary to build custom profile HMMs or update the vFams as more viruses are discovered (http://derisilab.ucsf.edu/software/vFam).     

Recent trends in Lake Ladoga ice cover

This contribution reviews diversity of turbellarian species by biogeographical regions, with comments on species biology. The review draws on the database available at http://www.devbio.umesci.maine.edu/styler/turbellaria. Comparisons between regions suggest that species richness may be at least one order of magnitude higher than the currently reported number of species. In the context of the recent reconstructions of phylogeny of Platyhelminthes based on molecular data, the paper allows inferences as to the history of colonization of freshwaters by turbellarians. Specifically, four, or perhaps six, major invasions of freshwater habitats may have occurred in the Pangean period, each of which gave rise to a monophyletic freshwater taxon. In addition, several occasional invasions by representatives of marine taxa must have taken place.     

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.     

A first test of unattended,acoustic recorders for monitoring Capercaillie Tetrao urogallus lekking activity

Capsule: Automated acoustic recording can be used as a valuable survey technique for Capercaillie Tetrao urogallus leks, improving the quality and quantity of field data for this endangered bird species. However, more development work and testing against traditional methods are needed to establish optimal working practices.

Aims: This study aims to determine whether Capercaillie vocalizations can be recognized in lek recordings, whether this can be automated using readily available software, and whether the number of calls resulting varies with location, weather conditions, date and time of day.

Methods: Unattended recording devices and semi-automated call classification software were used to record and analyse the display calls of Capercaillie at three known lek sites in Scotland over a two-week period.

Results: Capercaillie calls were successfully and rapidly identified within a data set that included the vocalizations of other bird species and environmental noise. Calls could be readily recognized to species level using a combination of unsupervised software and manual analysis. The number of calls varied by time and date, by recorder/microphone location at the lek site, and with weather conditions. This information can be used to better target future acoustic monitoring and improve the quality of existing traditional lek surveys.

Conclusion: Bioacoustic methods provide a practical and cost-effective way to determine habitat occupancy and activity levels by a vocally distinctive bird species. Following further testing alongside traditional counting methods, it could offer a significant new approach towards more effective monitoring of local population levels for Capercaillie and other species of conservation concern.     

The impact of livestock grazing on the spatial pattern of vegetation in north-eastern Patagonia,Argentina

Background: The occurrence of shrub patches, alternating with either bare soil or low herbaceous cover, is a common feature in arid and semi-arid shrublands throughout the world. This patchy pattern of vegetation may result from water limitation, modulated by plant interactions; grazing (offtake and tramping) by livestock may cause further patchiness vegetation structure.

Aims: We hypothesised that vegetation patchiness in the semi-arid shrublands of north-eastern Patagonia would be increased by livestock grazing, but not by positive interactions between adult plants of shrubs and grasses.

Methods: We compared vegetation cover and pattern at three grazing intensities (exclosure, light and heavy grazing) and measured the growth of a representative shrub and grass in the presence and absence of the other to quantify the role of plant-to-plant interactions and its interaction with grazing for vegetation structure.

Results: In the grazing exclosure and in moderately grazed areas, vegetation cover among shrub patches was larger, whereas the top cover of shrubs was lower than in the heavily grazed areas. We did not find any evidence of positive interactions between shrub and grass life forms.

Conclusions: Our results were consistent with the hypothesis that livestock grazing increased the formation of patchy vegetation cover in arid and semi-arid shrublands.     

Protein interactions study through proximity-labeling

Introduction: The proteome is a dynamic system in which protein-protein interactions play a crucial part in shaping the cell phenotype. However, given the current limitations of available technologies to describe the dynamic nature of these interactions, the identification of protein-protein interactions has long been a major challenge in proteomics. In recent years, the development of BioID and APEX, two proximity-tagging technologies, have opened-up new perspectives and have already started to change our conception of protein-protein interactions, and more generally, of the proteome. With a broad range of application encompassing health, these new technologies are currently setting milestones crucial to understand fine cellular mechanisms.

Area covered: In this article, we describe both the recent and the more conventional available tools to study protein-protein interactions, compare the advantages and the limitations of these techniques, and discuss the recent advancements led by the proximity tagging techniques to refine our conception of the proteome.

Expert opinion: The recent development of proximity labeling techniques emphasizes the growing importance of such technologies to decipher cellular mechanism. Although several challenges still need to be addressed, many fields can benefit from these tools and notably the detection of new therapeutic targets for patient care