CGAL: computing genome assembly likelihoods

Assembly algorithms have been extensively benchmarked using simulated data so that results can be compared to ground truth. However, in de novo assembly, only crude metrics such as contig number and size are typically used to evaluate assembly quality. We present CGAL, a novel likelihood-based approach to assembly assessment in the absence of a ground truth. We show that likelihood is more accurate than other metrics currently used for evaluating assemblies, and describe its application to the optimization and comparison of assembly algorithms. Our methods are implemented in software that is freely available at http://bio.math.berkeley.edu/cgal/.     

From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data

RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge are needed. RNAMiner—a multi-level bioinformatics protocol and pipeline—has been developed for such datasets. It includes five steps: Mapping RNA-Seq reads to a reference genome, calculating gene expression values, identifying differentially expressed genes, predicting gene functions, and constructing gene regulatory networks. To demonstrate its utility, we applied RNAMiner to datasets generated from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster cells, and successfully identified differentially expressed genes, clustered them into cohesive functional groups, and constructed novel gene regulatory networks. The RNAMiner web service is available at http://calla.rnet.missouri.edu/rnaminer/index.html.     

Accurate Assignment of Significance to Neuropeptide Identifications Using Monte Carlo K-Permuted Decoy Databases

In support of accurate neuropeptide identification in mass spectrometry experiments, novel Monte Carlo permutation testing was used to compute significance values. Testing was based on k-permuted decoy databases, where k denotes the number of permutations. These databases were integrated with a range of peptide identification indicators from three popular open-source database search software (OMSSA, Crux, and X! Tandem) to assess the statistical significance of neuropeptide spectra matches. Significance p-values were computed as the fraction of the sequences in the database with match indicator value better than or equal to the true target spectra. When applied to a test-bed of all known manually annotated mouse neuropeptides, permutation tests with k-permuted decoy databases identified up to 100% of the neuropeptides at p-value < 10⁻⁵. The permutation test p-values using hyperscore (X! Tandem), E-value (OMSSA) and Sp score (Crux) match indicators outperformed all other match indicators. The robust performance to detect peptides of the intuitive indicator “number of matched ions between the experimental and theoretical spectra” highlights the importance of considering this indicator when the p-value was borderline significant. Our findings suggest permutation decoy databases of size 1×10⁵ are adequate to accurately detect neuropeptides and this can be exploited to increase the speed of the search. The straightforward Monte Carlo permutation testing (comparable to a zero order Markov model) can be easily combined with existing peptide identification software to enable accurate and effective neuropeptide detection. The source code is available at http://stagbeetle.animal.uiuc.edu/pepshop/MSMSpermutationtesting.     

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.     

dCLIP: a computational approach for comparative CLIP-seq analyses

Although comparison of RNA-protein interaction profiles across different conditions has become increasingly important to understanding the function of RNA-binding proteins (RBPs), few computational approaches have been developed for quantitative comparison of CLIP-seq datasets. Here, we present an easy-to-use command line tool, dCLIP, for quantitative CLIP-seq comparative analysis. The two-stage method implemented in dCLIP, including a modified MA normalization method and a hidden Markov model, is shown to be able to effectively identify differential binding regions of RBPs in four CLIP-seq datasets, generated by HITS-CLIP, iCLIP and PAR-CLIP protocols. dCLIP is freely available at http://qbrc.swmed.edu/software/.     

MitProNet: A Knowledgebase and Analysis Platform of Proteome,Interactome and Diseases for Mammalian Mitochondria

Mitochondrion plays a central role in diverse biological processes in most eukaryotes, and its dysfunctions are critically involved in a large number of diseases and the aging process. A systematic identification of mitochondrial proteomes and characterization of functional linkages among mitochondrial proteins are fundamental in understanding the mechanisms underlying biological functions and human diseases associated with mitochondria. Here we present a database MitProNet which provides a comprehensive knowledgebase for mitochondrial proteome, interactome and human diseases. First an inventory of mammalian mitochondrial proteins was compiled by widely collecting proteomic datasets, and the proteins were classified by machine learning to achieve a high-confidence list of mitochondrial proteins. The current version of MitProNet covers 1124 high-confidence proteins, and the remainders were further classified as middle- or low-confidence. An organelle-specific network of functional linkages among mitochondrial proteins was then generated by integrating genomic features encoded by a wide range of datasets including genomic context, gene expression profiles, protein-protein interactions, functional similarity and metabolic pathways. The functional-linkage network should be a valuable resource for the study of biological functions of mitochondrial proteins and human mitochondrial diseases. Furthermore, we utilized the network to predict candidate genes for mitochondrial diseases using prioritization algorithms. All proteins, functional linkages and disease candidate genes in MitProNet were annotated according to the information collected from their original sources including GO, GEO, OMIM, KEGG, MIPS, HPRD and so on. MitProNet features a user-friendly graphic visualization interface to present functional analysis of linkage networks. As an up-to-date database and analysis platform, MitProNet should be particularly helpful in comprehensive studies of complicated biological mechanisms underlying mitochondrial functions and human mitochondrial diseases. MitProNet is freely accessible at http://bio.scu.edu.cn:8085/MitProNet.     

Comparative genomic analysis of clinical and environmental strains provides insight into the pathogenicity and evolution of Vibrio parahaemolyticus

Background

Vibrio parahaemolyticus is a Gram-negative halophilic bacterium. Infections with the bacterium could become systemic and can be life-threatening to immunocompromised individuals. Genome sequences of a few clinical isolates of V. parahaemolyticus are currently available, but the genome dynamics across the species and virulence potential of environmental strains on a genome-scale have not been described before.

Results

Here we present genome sequences of four V. parahaemolyticus clinical strains from stool samples of patients and five environmental strains in Hong Kong. Phylogenomics analysis based on single nucleotide polymorphisms revealed a clear distinction between the clinical and environmental isolates. A new gene cluster belonging to the biofilm associated proteins of V. parahaemolyticus was found in clincial strains. In addition, a novel small genomic island frequently found among clinical isolates was reported. A few environmental strains were found harboring virulence genes and prophage elements, indicating their virulence potential. A unique biphenyl degradation pathway was also reported. A database for V. parahaemolyticus (http://kwanlab.bio.cuhk.edu.hk/vp) was constructed here as a platform to access and analyze genome sequences and annotations of the bacterium.

Conclusions

We have performed a comparative genomics analysis of clinical and environmental strains of V. parahaemolyticus. Our analyses could facilitate understanding of the phylogenetic diversity and niche adaptation of this bacterium.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1135) contains supplementary material, which is available to authorized users.     

Prediction of S-Glutathionylation Sites Based on Protein Sequences

S-glutathionylation, the reversible formation of mixed disulfides between glutathione(GSH) and cysteine residues in proteins, is a specific form of post-translational modification that plays important roles in various biological processes, including signal transduction, redox homeostasis, and metabolism inside cells. Experimentally identifying S-glutathionylation sites is labor-intensive and time consuming, whereas bioinformatics methods provide an alternative way to this problem by predicting S-glutathionylation sites in silico. The bioinformatics approaches give not only candidate sites for further experimental verification but also bio-chemical insights into the mechanism of S-glutathionylation. In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data. Promising results are obtained by our method with an AUC (area under ROC curve) score of 0.879 in 5-fold cross-validation, which demonstrates the predictive power of our proposed method. The datasets used in this work are available at http://csb.shu.edu.cn/SGDB.     

T3_MM: A Markov Model Effectively Classifies Bacterial Type III Secretion Signals

Motivation

Type III Secretion Systems (T3SSs) play important roles in the interaction between gram-negative bacteria and their hosts. T3SSs function by translocating a group of bacterial effector proteins into the host cytoplasm. The details of specific type III secretion process are yet to be clarified. This research focused on comparing the amino acid composition within the N-terminal 100 amino acids from type III secretion (T3S) signal sequences or non-T3S proteins, specifically whether each residue exerts a constraint on residues found in adjacent positions. We used these comparisons to set up a statistic model to quantitatively model and effectively distinguish T3S effectors.

Results

In this study, the amino acid composition (Aac) probability profiles conditional on its sequentially preceding position and corresponding amino acids were compared between N-terminal sequences of T3S and non-T3S proteins. The profiles are generally different. A Markov model, namely T3_MM, was consequently designed to calculate the total Aac conditional probability difference, i.e., the likelihood ratio of a sequence being a T3S or a non-T3S protein. With T3_MM, known T3S and non-T3S proteins were found to well approximate two distinct normal distributions. The model could distinguish validated T3S and non-T3S proteins with a 5-fold cross-validation sensitivity of 83.9% at a specificity of 90.3%. T3_MM was also shown to be more robust, accurate, simple, and statistically quantitative, when compared with other T3S protein prediction models. The high effectiveness of T3_MM also indicated the overall Aac difference between N-termini of T3S and non-T3S proteins, and the constraint of Aac exerted by its preceding position and corresponding Aac.

Availability

An R package for T3_MM is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/T3_MM. T3_MM web server: http://biocomputer.bio.cuhk.edu.hk/T3DB/T3_MM.php.     

RADtyping: An Integrated Package for Accurate De Novo Codominant and Dominant RAD Genotyping in Mapping Populations

Genetic linkage maps are indispensable tools in genetic, genomic and breeding studies. As one of genotyping-by-sequencing methods, RAD-Seq (restriction-site associated DNA sequencing) has gained particular popularity for construction of high-density linkage maps. Current RAD analytical tools are being predominantly used for typing codominant markers. However, no genotyping algorithm has been developed for dominant markers (resulting from recognition site disruption). Given their abundance in eukaryotic genomes, utilization of dominant markers would greatly diminish the extensive sequencing effort required for large-scale marker development. In this study, we established, for the first time, a novel statistical framework for de novo dominant genotyping in mapping populations. An integrated package called RADtyping was developed by incorporating both de novo codominant and dominant genotyping algorithms. We demonstrated the superb performance of RADtyping in achieving remarkably high genotyping accuracy based on simulated and real mapping datasets. The RADtyping package is freely available at http://www2.ouc.edu.cn/mollusk/ detailen.asp?id=727.     

RV-Typer: A Web Server for Typing of Rhinoviruses Using Alignment-Free Approach

Rhinoviruses (RV) are increasingly being reported to cause mild to severe infections of respiratory tract in humans. RV are antigenically the most diverse species of the genus Enterovirus and family Picornaviridae. There are three species of RV (RV-A, -B and -C), with 80, 32 and 55 serotypes/types, respectively. Antigenic variation is the main limiting factor for development of a cross-protective vaccine against RV.Serotyping of Rhinoviruses is carried out using cross-neutralization assays in cell culture. However, these assays become laborious and time-consuming for the large number of strains. Alternatively, serotyping of RV is carried out by alignment-based phylogeny of both protein and nucleotide sequences of VP1. However, serotyping of RV based on alignment-based phylogeny is a multi-step process, which needs to be repeated every time a new isolate is sequenced. In view of the growing need for serotyping of RV, an alignment-free method based on “return time distribution” (RTD) of amino acid residues in VP1 protein has been developed and implemented in the form of a web server titled RV-Typer. RV-Typer accepts nucleotide or protein sequences as an input and computes return times of di-peptides (k = 2) to assign serotypes. The RV-Typer performs with 100% sensitivity and specificity. It is significantly faster than alignment-based methods. The web server is available at http://bioinfo.net.in/RV-Typer/home.html.     

CorSig: A General Framework for Estimating Statistical Significance of Correlation and Its Application to Gene Co-Expression Analysis

With the rapid increase of omics data, correlation analysis has become an indispensable tool for inferring meaningful associations from a large number of observations. Pearson correlation coefficient (PCC) and its variants are widely used for such purposes. However, it remains challenging to test whether an observed association is reliable both statistically and biologically. We present here a new method, CorSig, for statistical inference of correlation significance. CorSig is based on a biology-informed null hypothesis, i.e., testing whether the true PCC (ρ) between two variables is statistically larger than a user-specified PCC cutoff (τ), as opposed to the simple null hypothesis of ρ = 0 in existing methods, i.e., testing whether an association can be declared without a threshold. CorSig incorporates Fisher''s Z transformation of the observed PCC (r), which facilitates use of standard techniques for p-value computation and multiple testing corrections. We compared CorSig against two methods: one uses a minimum PCC cutoff while the other (Zhu''s procedure) controls correlation strength and statistical significance in two discrete steps. CorSig consistently outperformed these methods in various simulation data scenarios by balancing between false positives and false negatives. When tested on real-world Populus microarray data, CorSig effectively identified co-expressed genes in the flavonoid pathway, and discriminated between closely related gene family members for their differential association with flavonoid and lignin pathways. The p-values obtained by CorSig can be used as a stand-alone parameter for stratification of co-expressed genes according to their correlation strength in lieu of an arbitrary cutoff. CorSig requires one single tunable parameter, and can be readily extended to other correlation measures. Thus, CorSig should be useful for a wide range of applications, particularly for network analysis of high-dimensional genomic data.

Software Availability

A web server for CorSig is provided at http://202.127.200.1:8080/probeWeb. R code for CorSig is freely available for non-commercial use at http://aspendb.uga.edu/downloads.     

PubstractHelper: A Web-based Text-Mining Tool for Marking Sentences in Abstracts from PubMed Using Multiple User-Defined Keywords

While a huge amount of information about biological literature can be obtained by searching the PubMed database, reading through all the titles and abstracts resulting from such a search for useful information is inefficient. Text mining makes it possible to increase this efficiency. Some websites use text mining to gather information from the PubMed database; however, they are database-oriented, using pre-defined search keywords while lacking a query interface for user-defined search inputs. We present the PubMed Abstract Reading Helper (PubstractHelper) website which combines text mining and reading assistance for an efficient PubMed search. PubstractHelper can accept a maximum of ten groups of keywords, within each group containing up to ten keywords. The principle behind the text-mining function of PubstractHelper is that keywords contained in the same sentence are likely to be related. PubstractHelper highlights sentences with co-occurring keywords in different colors. The user can download the PMID and the abstracts with color markings to be reviewed later. The PubstractHelper website can help users to identify relevant publications based on the presence of related keywords, which should be a handy tool for their research.

Availability

http://bio.yungyun.com.tw/ATM/PubstractHelper.aspx and http://holab.med.ncku.edu.tw/ATM/PubstractHelper.aspx     

Sequence Maneuverer: tool for sequence extraction from genomes

The availability of genomic sequences of many organisms has opened new challenges in many aspects particularly in terms of genome analysis. Sequence extraction is a vital step and many tools have been developed to solve this issue. These tools are available publically but have limitations with reference to the sequence extraction, length of the sequence to be extracted, organism specificity and lack of user friendly interface. We have developed a java based software package having three modules which can be used independently or sequentially. The tool efficiently extracts sequences from large datasets with few simple steps. It can efficiently extract multiple sequences of any desired length from a genome of any organism. The results are crosschecked by published data.

Availability

URL 1: http://ww3.comsats.edu.pk/bio/ResearchProjects.aspxURL 2: http://ww3.comsats.edu.pk/bio/SequenceManeuverer.aspx     

GOPred: GO Molecular Function Prediction by Combined Classifiers

Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred).     

Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs

Prokaryotic proteins are regulated by pupylation, a type of post-translational modification that contributes to cellular function in bacterial organisms. In pupylation process, the prokaryotic ubiquitin-like protein (Pup) tagging is functionally analogous to ubiquitination in order to tag target proteins for proteasomal degradation. To date, several experimental methods have been developed to identify pupylated proteins and their pupylation sites, but these experimental methods are generally laborious and costly. Therefore, computational methods that can accurately predict potential pupylation sites based on protein sequence information are highly desirable. In this paper, a novel predictor termed as pbPUP has been developed for accurate prediction of pupylation sites. In particular, a sophisticated sequence encoding scheme [i.e. the profile-based composition of k-spaced amino acid pairs (pbCKSAAP)] is used to represent the sequence patterns and evolutionary information of the sequence fragments surrounding pupylation sites. Then, a Support Vector Machine (SVM) classifier is trained using the pbCKSAAP encoding scheme. The final pbPUP predictor achieves an AUC value of 0.849 in10-fold cross-validation tests and outperforms other existing predictors on a comprehensive independent test dataset. The proposed method is anticipated to be a helpful computational resource for the prediction of pupylation sites. The web server and curated datasets in this study are freely available at http://protein.cau.edu.cn/pbPUP/.