MOTIVATION: Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq). RESULTS: We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples. AVAILABILITY AND IMPLEMENTATION: Cancer classification by neural network. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Protein-protein interactions are essential to cellular and immune function, and in many cases, because of the absence of an experimentally determined structure of the complex, these interactions must be modeled to obtain an understanding of their molecular basis. We present a user-friendly protein docking server, based on the rigid-body docking programs ZDOCK and M-ZDOCK, to predict structures of protein-protein complexes and symmetric multimers. With a goal of providing an accessible and intuitive interface, we provide options for users to guide the scoring and the selection of output models, in addition to dynamic visualization of input structures and output docking models. This server enables the research community to easily and quickly produce structural models of protein-protein complexes and symmetric multimers for their own analysis. AVAILABILITY: The ZDOCK server is freely available to all academic and non-profit users at: http://zdock.umassmed.edu. No registration is required.

Understanding the evolution of a protein, including both close and distant relationships, often reveals insight into its structure and function. Fast and easy access to such up-to-date information facilitates research. We have developed a hierarchical evolutionary classification of all proteins with experimentally determined spatial structures, and presented it as an interactive and updatable online database. ECOD (Evolutionary Classification of protein Domains) is distinct from other structural classifications in that it groups domains primarily by evolutionary relationships (homology), rather than topology (or "fold"). This distinction highlights cases of homology between domains of differing topology to aid in understanding of protein structure evolution. ECOD uniquely emphasizes distantly related homologs that are difficult to detect, and thus catalogs the largest number of evolutionary links among structural domain classifications. Placing distant homologs together underscores the ancestral similarities of these proteins and draws attention to the most important regions of sequence and structure, as well as conserved functional sites. ECOD also recognizes closer sequence-based relationships between protein domains. Currently, approximately 100,000 protein structures are classified in ECOD into 9,000 sequence families clustered into close to 2,000 evolutionary groups. The classification is assisted by an automated pipeline that quickly and consistently classifies weekly releases of PDB structures and allows for continual updates. This synchronization with PDB uniquely distinguishes ECOD among all protein classifications. Finally, we present several case studies of homologous proteins not recorded in other classifications, illustrating the potential of how ECOD can be used to further biological and evolutionary studies.

BACKGROUND: Numerous types of clustering like single linkage and K-means have been widely studied and applied to a variety of scientific problems. However, the existing methods are not readily applicable for the problems that demand high stringency. METHODS: Our method, self consistency grouping, i.e. SCG, yields clusters whose members are closer in rank to each other than to any member outside the cluster. We do not define a distance metric; we use the best known distance metric and presume that it measures the correct distance. SCG does not impose any restriction on the size or the number of the clusters that it finds. The boundaries of clusters are determined by the inconsistencies in the ranks. In addition to the direct implementation that finds the complete structure of the (sub)clusters we implemented two faster versions. The fastest version is guaranteed to find only the clusters that are not subclusters of any other clusters and the other version yields the same output as the direct implementation but does so more efficiently. RESULTS: Our tests have demonstrated that SCG yields very few false positives. This was accomplished by introducing errors in the distance measurement. Clustering of protein domain representatives by structural similarity showed that SCG could recover homologous groups with high precision. CONCLUSIONS: SCG has potential for finding biological relationships under stringent conditions.

Similarity between protein sequences is usually predictive of similarity in structures. However, in some rare cases protein domains with significant sequence similarity adopt different structures. Here, we carry out a survey of protein domain pairs with high sequence similarity (measured by HHsearch probability) and low structural similarity (measured by Dali Z-score), aiming to identify the reasons for this discordance. Besides methodological problems with either sequences or structures of domains, we find and describe novel examples of homologs with structural changes.

UNLABELLED: Profile-based similarity search is an essential step in structure-function studies of proteins. However, inclusion of non-homologous sequence segments into a profile causes its corruption and results in false positives. Profile corruption is common in multidomain proteins, and single domains with long insertions are a significant source of errors. We developed a procedure (HangOut) that, for a single domain with specified insertion position, cleans erroneously extended PSI-BLAST alignments to generate better profiles. AVAILABILITY: HangOut is implemented in Python 2.3 and runs on all Unix-compatible platforms. The source code is available under the GNU GPL license at http://prodata.swmed.edu/HangOut/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Accurate and sensitive performance evaluation is crucial for both effective development of better structure prediction methods based on sequence similarity, and for the comparative analysis of existing methods. Up to date, there has been no satisfactory comprehensive evaluation method that (i) is based on a large and statistically unbiased set of proteins with clearly defined relationships; and (ii) covers all performance aspects of sequence-based structure predictors, such as sensitivity and specificity, alignment accuracy and coverage, and structure template quality. RESULTS: With the aim of designing such a method, we (i) select a statistically balanced set of divergent protein domains from SCOP, and define similarity relationships for the majority of these domains by complementing the best of information available in SCOP with a rigorous SVM-based algorithm; and (ii) develop protocols for the assessment of similarity detection and alignment quality from several complementary perspectives. The evaluation of similarity detection is based on ROC-like curves and includes several complementary approaches to the definition of true/false positives. Reference-dependent approaches use the 'gold standard' of pre-defined domain relationships and structure-based alignments. Reference-independent approaches assess the quality of structural match predicted by the sequence alignment, with respect to the whole domain length (global mode) or to the aligned region only (local mode). Similarly, the evaluation of alignment quality includes several reference-dependent and -independent measures, in global and local modes. As an illustration, we use our benchmark to compare the performance of several methods for the detection of remote sequence similarities, and show that different aspects of evaluation reveal different properties of the evaluated methods, highlighting their advantages, weaknesses, and potential for further development. CONCLUSION: The presented benchmark provides a new tool for a statistically unbiased assessment of methods for remote sequence similarity detection, from various complementary perspectives. This tool should be useful both for users choosing the best method for a given purpose, and for developers designing new, more powerful methods. The benchmark set, reference alignments, and evaluation codes can be downloaded from ftp://iole.swmed.edu/pub/evaluation/.

A new low-dimensional feature representation technique is presented in this paper. Linear discriminant analysis is a popular feature extraction method. However, in the case of high dimensional data, the computational difficulty and the small sample size problem are often encountered. In order to solve these problems, we propose two dimensional direct LDA algorithm named 2D-DLDA, which directly extracts the image scatter matrix from 2D image and uses direct LDA algorithm for face recognition. The ORL face database is used to evaluate the performance of the proposed method. The experimental results indicate that the performance of the proposed method is superior to DLDA