Loss-of-function study is an effective approach to research gene functions. However, currently most of such studies have ignored an important problem (in this paper, we call it "off-target" problem), that is, if the target gene is an overlapping gene (A gene whose expressible nucleotides overlaps with that of another one), loss-of-function mutation by deleting the complete open reading frame (ORF) may also cause the gene it overlaps lose function, resulting a phenotype which may be rather different from that of single gene deletion. Therefore, when doing such studies, the loss-of-function mutations should be carefully designed to guarantee only the function of the target gene will be abolished. In this paper, we present PyMut, an easy-to-use web tool for biologists to design such mutations. To the best of our knowledge, PyMut is the first tool that aims to solve the "off-target" problem regarding the overlapping genes. Our web server is freely available at http://www.bioinfo.tsinghua.edu.cn/∼liuke/PyMut/index.html.

A-seq, peak calling has become an important step in transcriptomic sequencing data analysis. However, many of the reported genomic coordinates of transcriptomic peaks are incorrect owing to negligence of the introns. There is currently a lack of a convenient tool to address this problem. Here, we present txCoords, a novel and easy-to-use web application for transcriptomic peak re-mapping. txCoords can be used to correct the incorrectly reported transcriptomic peaks and retrieve the true sequences. It also supports visualization of the re-mapped peaks in a schematic figure or from the UCSC Genome Browser. Our web server is freely available at http://www.bioinfo.tsinghua.edu.cn/txCoords.

UNLABELLED: Large numbers of long intergenic non-coding RNA (lincRNA) have been detected through high-throughput sequencing technology. However, currently we still know very little about their functions. Therefore, a lincRNA function annotation database is needed to facilitate the study in this field. In this article, we present Linc2GO, a web resource that aims to provide comprehensive functional annotations for human lincRNA. MicroRNA-mRNA and microRNA-lincRNA interaction data were integrated to generate lincRNA functional annotations based on the 'competing endogenous RNA hypothesis'. To the best of our knowledge, Linc2GO is the first database that makes use of the 'competing endogenous RNA hypothesis' to predict lincRNA functions. AVAILABILITY: Freely available at http://www.bioinfo.tsinghua.edu.cn/~liuke/Linc2GO/index.html

MOTIVATION: Like most human diseases, tumors are complex traits, the genesis and development of which recruit a number of genes and several important biological processes. As proteins involved in common processes tend to be centralized in the same local area of protein-protein interaction networks, here a novel framework has been developed to identify which areas of the networks are most relevant to a phenotype. RESULTS: These areas termed 'coherent modules' can be regarded as gene sets dynamically defined in the networks. Compared with previous analogous approaches, one critical feature of our method is the optimization of coherent modules for two distinct aspects balanced by tuning a parameter in the framework. First, we seek the low coupling between coherent modules and then maximize the intrinsic similarity within a module. The framework has good expansibility, with classical expression data analysis methods generalized as particular cases. This coherent module approach was applied to an oral cavity tumor dataset with 18 significant coherent modules identified, which could indicate the presence of lymph node metastasis. Further examination shows that most of the modules are responsible for comparatively independent biological processes. Our framework is helpful for the prognosis of tumors and offers a new perspective for tumor research. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Sequence-to-structure alignment in template-based protein structure modeling for remote homologs remains a difficult problem even following the correct recognition of folds. Here we present MICAlign, a sequence-to-structure alignment tool that incorporates multiple sources of information from local structural contexts of template, sequence profiles, predicted secondary structures, solvent accessibilities, potential-like terms (including residue-residue contacts and solvent exposures) and pre-aligned structures and sequences. These features, together with a position-specific gap scheme, were integrated into conditional random fields through which the optimal parameters were automatically learned. MICAlign showed improved alignment accuracy over several other state-of-the-art alignment tools based on comparisons by using independent datasets. AVAILABILITY: Freely available at (http://www.bioinfo.tsinghua.edu.cn/~xiaxf/micalign) for both web server and source code.

BACKGROUND: Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to predict protein functions. RESULTS: The domain context similarity can be a useful index to predict protein function similarity. The prediction accuracy of our method in yeast is between 63%-67%, which outperforms the other methods in terms of ROC curves. CONCLUSION: This paper presents a novel protein function prediction method that combines protein domain composition information and PPI networks. Performance evaluations show that this method outperforms existing methods.

BACKGROUND: Subcellular localization information is one of the key features to protein function research. Locating to a specific subcellular compartment is essential for a protein to function efficiently. Proteins which have multiple localizations will provide more clues. This kind of proteins may take a high proportion, even more than 35%. DESCRIPTION: We have developed a database of proteins with multiple subcellular localizations, designated DBMLoc. The initial release contains 10470 multiple subcellular localization-annotated entries. Annotations are collected from primary protein databases, specific subcellular localization databases and literature texts. All the protein entries are cross-referenced to GO annotations and SwissProt. Protein-protein interactions are also annotated. They are classified into 12 large subcellular localization categories based on GO hierarchical architecture and original annotations. Download, search and sequence BLAST tools are also available on the website. CONCLUSION: DBMLoc is a protein database which collects proteins with more than one subcellular localization annotation. It is freely accessed at http://www.bioinfo.tsinghua.edu.cn/DBMLoc/index.htm.

Abstract Summary: Based on SOAP(Simple Object Access Protocol) technology, the SubLoc server/client suite offers a user-friendly interface for searching and predicting protein subcellular location. Availability:SubLoc SOAP server is available through HTTP protocol at . The client software can be downloaded at . More information is given at . Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Protein palmitoylation, an essential and reversible post-translational modification (PTM), has been implicated in cellular dynamics and plasticity. Although numerous experimental studies have been performed to explore the molecular mechanisms underlying palmitoylation processes, the intrinsic feature of substrate specificity has remained elusive. Thus, computational approaches for palmitoylation prediction are much desirable for further experimental design. RESULTS: In this work, we present NBA-Palm, a novel computational method based on Naïve Bayes algorithm for prediction of palmitoylation site. The training data is curated from scientific literature (PubMed) and includes 245 palmitoylated sites from 105 distinct proteins after redundancy elimination. The proper window length for a potential palmitoylated peptide is optimized as six. To evaluate the prediction performance of NBA-Palm, 3-fold cross-validation, 8-fold cross-validation and Jack-Knife validation have been carried out. Prediction accuracies reach 85.79% for 3-fold cross-validation, 86.72% for 8-fold cross-validation and 86.74% for Jack-Knife validation. Two more algorithms, RBF network and support vector machine (SVM), also have been employed and compared with NBA-Palm. CONCLUSION: Taken together, our analyses demonstrate that NBA-Palm is a useful computational program that provides insights for further experimentation. The accuracy of NBA-Palm is comparable with our previously described tool CSS-Palm. The NBA-Palm is freely accessible from: http://www.bioinfo.tsinghua.edu.cn/NBA-Palm.

We uncovered the underlying energy landscape for a cellular network. We discovered that the energy landscape of the yeast cell-cycle network is funneled towards the global minimum (G0/G1 phase) from the experimentally measured or inferred inherent chemical reaction rates. The funneled landscape is quite robust against random perturbations. This naturally explains robustness from a physical point of view. The ratio of slope versus roughness of the landscape becomes a quantitative measure of robustness of the network. The funneled landscape can be seen as a possible realization of the Darwinian principle of natural selection at the cellular network level. It provides an optimal criterion for network connections and design. Our approach is general and can be applied to other cellular networks.

SUMMARY: The package HMMGEP performs cluster analysis on gene expression data using hidden Markov models. AVAILABILITY: HMMGEP, including the source code, documentation and sample data files, is available at http://www.bioinfo.tsinghua.edu.cn:8080/~rich/hmmgep_download/index.html.

MOTIVATION: Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences. RESULTS: In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals. AVAILABILITY: A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/. SUPPLEMENTARY INFORMATION: Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.