Types Of Biological Information Research Articles

Temporal Protein Complex Identification Based on Dynamic Heterogeneous Protein Information Network Representation Learning.

Protein complexes, as the fundamental units of cellular function and regulation, play a crucial role in understanding the normal physiological functions of cells. Existing methods for protein complex identification attempt to introduce other biological information on top of the protein-protein interaction (PPI) network to assist in evaluating the degree of association between proteins. However, these methods usually treat protein interaction networks as flat homogeneous static networks. They cannot distinguish the roles and importance of different types of biological information, nor can they reflect the dynamic changes of protein complexes. In recent years, heterogeneous network representation learning has achieved great success in processing complex heterogeneous information and mining deep semantics. We thus propose a temporal protein complex identification method based on Dynamic Heterogeneous Protein information network Representation Learning, DHPRL. DHPRL naturally integrates multiple types of heterogeneous biological information in the cellular temporal dimension. It simultaneously models the temporal dynamic properties of proteins and the heterogeneity of biological information to improve the understanding of protein interactions and the accuracy of complex prediction. Firstly, we construct Dynamic Heterogeneous Protein Information Network (DHPIN) by integrating temporal gene expression information and GO attribute information. Then we design a dual-view collaborative contrast mechanism. Specifically, proposing to learn protein representations from two views of DHPIN (1-hop relation view and meta-path view) to model the consistency and specificity between nearest-neighbour bio information and deeper biological semantics. The dynamic PPI network is thereafter re-weighted based on the learned protein representations. Finally, we perform protein identification on the re-weighted dynamic PPI network. Extensive experimental results demonstrate that DHPRL can effectively model complicated biological information and achieve state-of-the-art performance in most cases. The source code and datasets for DHPR are available at https://github.com/LI-jasm/DHPRL.

ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation.

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA–disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA–disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA–disease associations.

Essential Protein Prediction Based on node2vec and XGBoost.

Essential proteins are a vital part of the survival of organisms and cells. Identification of essential proteins lays a solid foundation for understanding protein functions and discovering drug targets. The traditional biological experiments are expensive and time-consuming. Recently, many computational methods have been proposed. However, some noises in the protein-protein interaction (PPI) networks affect the efficiency of essential protein prediction. It is necessary to construct a credible PPI network by using other useful biological information to reduce the effects of these noises. In this article, we proposed a model, Ess-NEXG, to identify essential proteins, which integrates biological information, including orthologous information, subcellular localization information, RNA-Seq information, and PPI network. In our model, first, we constructed a credible weighted PPI network by using different types of biological information. Second, we extracted the topological features of proteins in the constructed weighted PPI network by using the node2vec technique. Last, we used eXtreme Gradient Boosting (XGBoost) to predict essential proteins by using the topological features of proteins. The extensive results show that our model has better performance than other computational methods.

Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data.

In the last two decades, there have been massive advancements in high throughput technologies, which resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. It is possible to unravel biomarkers by comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc. This problem refers to a well-studied problem in the machine learning domain, i.e., the feature selection problem. In biological data analysis, most of the computational feature selection methodologies were taken from other fields, without considering the nature of the biological data. Thus, integrative approaches that utilize the biological knowledge while performing feature selection are necessary for this kind of data. The main idea behind the integrative gene selection process is to generate a ranked list of genes considering both the statistical metrics that are applied to the gene expression data, and the biological background information which is provided as external datasets. One of the main goals of this review is to explore the existing methods that integrate different types of information in order to improve the identification of the biomolecular signatures of diseases and the discovery of new potential targets for treatment. These integrative approaches are expected to aid the prediction, diagnosis, and treatment of diseases, as well as to enlighten us on disease state dynamics, mechanisms of their onset and progression. The integration of various types of biological information will necessitate the development of novel techniques for integration and data analysis. Another aim of this review is to boost the bioinformatics community to develop new approaches for searching and determining significant groups/clusters of features based on one or more biological grouping functions.

Data on biology and demographic parameters of the Aedes albopictus from dengue outbreaks in Klang Valley, Malaysia.

In this article, data on the demographic parameters of the Aedes albopictus were collected from those areas in Shah Alam, Malaysia that had experienced a dengue outbreak. The surveys were conducted from March to December 2017. The eggs of the Ae. albopictus were collected using ovitraps, and were analysed based on the demographic parameters in a controlled environment in an insectarium. The data were comprised of four types of biological information on the life demographic parameters of the Ae. albopictus that were monitored based on specific localities. The data were inferred information regarding egg productivity (n), egg development (%), immature development (days), and survivorship (days).

ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism.

ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review.

Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.

A Blueprint for Systems Biology.

Featured Article: Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 2001;292:929–34.4 By the mid-1990s, it was clear that we would soon be able to sequence the human genome and that this would enable many other types of molecular measurements in cells and tissues. Less apparent was how these different molecular technologies and their data should be integrated to map biological structure and understand function. That is, having systematically sequenced the DNA bases in a genome, could similar systematic concepts and tools be devised to understand the rest of the human biological informational system (RNA, proteins, metabolites, lipids) and their roles in biology? Around that time, one of us (LH) had moved from Caltech to start a new department, called Molecular Biotechnology, in the medical school of the University of Washington. This department was based on the radical idea (at that time) that biology at its core was an information science, and that the path to understanding and integrating the genome and other types of biological information would be enabled only by joining the skills and expertise of biologists with those of leading investigators from engineering, chemistry, and mathematics, as well as the physical and computer sciences. This concept captured the imagination of the other of us (TI), who had studied computer …

Protein complexes identification based on go attributed network embedding

BackgroundIdentifying protein complexes from protein-protein interaction (PPI) network is one of the most important tasks in proteomics. Existing computational methods try to incorporate a variety of biological evidences to enhance the quality of predicted complexes. However, it is still a challenge to integrate different types of biological information into the complexes discovery process under a unified framework. Recently, attributed network embedding methods have be proved to be remarkably effective in generating vector representations for nodes in the network. In the transformed vector space, both the topological proximity and node attributed affinity between different nodes are preserved. Therefore, such attributed network embedding methods provide us a unified framework to integrate various biological evidences into the protein complexes identification process.ResultsIn this article, we propose a new method called GANE to predict protein complexes based on Gene Ontology (GO) attributed network embedding. Firstly, it learns the vector representation for each protein from a GO attributed PPI network. Based on the pair-wise vector representation similarity, a weighted adjacency matrix is constructed. Secondly, it uses the clique mining method to generate candidate cores. Consequently, seed cores are obtained by ranking candidate cores based on their densities on the weighted adjacency matrix and removing redundant cores. For each seed core, its attachments are the proteins with correlation score that is larger than a given threshold. The combination of a seed core and its attachment proteins is reported as a predicted protein complex by the GANE algorithm. For performance evaluation, we compared GANE with six protein complex identification methods on five yeast PPI networks. Experimental results showes that GANE performs better than the competing algorithms in terms of different evaluation metrics.ConclusionsGANE provides a framework that integrate many valuable and different biological information into the task of protein complex identification. The protein vector representation learned from our attributed PPI network can also be used in other tasks, such as PPI prediction and disease gene prediction.

Whole-organism lineage tracing by combinatorial and cumulative genome editing.

Multicellular systems develop from single cells through distinct lineages. However, current lineage-tracing approaches scale poorly to whole, complex organisms. Here, we use genome editing to progressively introduce and accumulate diverse mutations in a DNA barcode over multiple rounds of cell division. The barcode, an array of clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 target sites, marks cells and enables the elucidation of lineage relationships via the patterns of mutations shared between cells. In cell culture and zebrafish, we show that rates and patterns of editing are tunable and that thousands of lineage-informative barcode alleles can be generated. By sampling hundreds of thousands of cells from individual zebrafish, we find that most cells in adult organs derive from relatively few embryonic progenitors. In future analyses, genome editing of synthetic target arrays for lineage tracing (GESTALT) can be used to generate large-scale maps of cell lineage in multicellular systems for normal development and disease.

Optical characterization of epidermal cells and their relationship to DNA recovery from touch samples

The goal of this study was to investigate the relative contributions of different cellular and genetic components to biological samples created by touch or contact with a surface – one of the most challenging forms of forensic evidence. Touch samples were generated by having individuals hold an object for five minutes and analyzed for quantity of intact epidermal cells, extracellular DNA, and DNA from pelleted cell material after elution from the collection swab. Comparisons were made between samples where individuals had washed their hands immediately prior to handling and those where hand washing was not controlled. The vast majority (84-100%) of DNA detected in these touch samples was extracellular and was uncorrelated to the number of epidermal cells detected. Although little to no extracellular or cell pellet-associated DNA was detected when individuals washed their hands prior to substrate handling, we found that a significant number of epidermal cells (between ~5x10 3 and ~1x10 5) could still be recovered from these samples, suggesting that other types of biological information may be present even when no amplifiable nuclear DNA is present. These results help to elucidate the biological context for touch samples and characterize factors that may contribute to patterns of transfer and persistence of genetic material in forensic evidence.

Opticalcharacterization of epidermal cellsand their relationship to DNA recovery fromtouch samples.

The goal of this study was to investigate the relative contributions of different cellular and genetic components to biological samples created by touch or contact with a surface - one of the most challenging forms of forensic evidence. Touch samples were generated by having individuals hold an object for five minutes and analyzed for quantity of intact epidermal cells, extracellular DNA, and DNA from pelleted cell material after elution from the collection swab. Comparisons were made between samples where individuals had washed their hands immediately prior to handling and those where hand washing was not controlled. The vast majority (84-100%) of DNA detected in these touch samples was extracellular and was uncorrelated to the number of epidermal cells detected. Although little to no extracellular or cell pellet-associated DNA was detected when individuals washed their hands prior to substrate handling, we found that a significant number of epidermal cells (between ~5x10 (3) and ~1x10 (5)) could still be recovered from these samples, suggesting that other types of biological information may be present even when no amplifiable nuclear DNA is present. These results help to elucidate the biological context for touch samples and characterize factors that may contribute to patterns of transfer and persistence of genetic material in forensic evidence.

Construction of an ortholog database using the semantic web technology for integrative analysis of genomic data.

Recently, various types of biological data, including genomic sequences, have been rapidly accumulating. To discover biological knowledge from such growing heterogeneous data, a flexible framework for data integration is necessary. Ortholog information is a central resource for interlinking corresponding genes among different organisms, and the Semantic Web provides a key technology for the flexible integration of heterogeneous data. We have constructed an ortholog database using the Semantic Web technology, aiming at the integration of numerous genomic data and various types of biological information. To formalize the structure of the ortholog information in the Semantic Web, we have constructed the Ortholog Ontology (OrthO). While the OrthO is a compact ontology for general use, it is designed to be extended to the description of database-specific concepts. On the basis of OrthO, we described the ortholog information from our Microbial Genome Database for Comparative Analysis (MBGD) in the form of Resource Description Framework (RDF) and made it available through the SPARQL endpoint, which accepts arbitrary queries specified by users. In this framework based on the OrthO, the biological data of different organisms can be integrated using the ortholog information as a hub. Besides, the ortholog information from different data sources can be compared with each other using the OrthO as a shared ontology. Here we show some examples demonstrating that the ortholog information described in RDF can be used to link various biological data such as taxonomy information and Gene Ontology. Thus, the ortholog database using the Semantic Web technology can contribute to biological knowledge discovery through integrative data analysis.

Improving biomarker list stability by integration of biological knowledge in the learning process.

BackgroundThe identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for biomarker discovery using microarray data often provide results with limited overlap. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the outcomes. Thus, a possible approach to improve list stability is to integrate biological information from genomic databases in the learning process; however, a comprehensive assessment based on different types of biological information is still lacking in the literature. In this work we have compared the effect of using different biological information in the learning process like functional annotations, protein-protein interactions and expression correlation among genes.ResultsBiological knowledge has been codified by means of gene similarity matrices and expression data linearly transformed in such a way that the more similar two features are, the more closely they are mapped. Two semantic similarity matrices, based on Biological Process and Molecular Function Gene Ontology annotation, and geodesic distance applied on protein-protein interaction networks, are the best performers in improving list stability maintaining almost equal prediction accuracy.ConclusionsThe performed analysis supports the idea that when some features are strongly correlated to each other, for example because are close in the protein-protein interaction network, then they might have similar importance and are equally relevant for the task at hand. Obtained results can be a starting point for additional experiments on combining similarity matrices in order to obtain even more stable lists of biomarkers. The implementation of the classification algorithm is available at the link: http://www.math.unipd.it/~dasan/biomarkers.html.

Chaotic ant swarm optimization with passive congregation

Chaotic ant swarm optimization (CASO) is a powerful chaos search algorithm for optimization problems, but it is often easy to be premature convergence. To overcome the weakness, this paper presents a CASO with passive congregation (CASOPC). Passive congregation is one type of biological information sharing mechanisms that allow animals to aggregate into groups and help to enhance the exploitation of animals. By introducing passive congregation strategy into the CASO, a modified evolution equation based on the CASO is proposed in the CASOPC. The modified evolution equation cannot only employ the parallel search of all ants and the well exploration ability of the CASO, but also stress and control the exploitation by passive congregation coefficient c in the stage of evolution. Due to linearly increasing c in the CASOPC, the exploration and exploitation ability of ants are well balanced so that premature convergence can be avoided and good performance can be achieved. In order to estimate the capability of the CASOPC, it is tested with a set of 5 benchmark functions with 30 dimensions and compared to the CASO. Experimental results indicate that the CASOPC improves the search performance on the benchmark functions significantly.

Complementarity of network and sequence information in homologous proteins

Summary Traditional approaches for homology detection rely on finding sufficient similarities between protein sequences. Motivated by studies demonstrating that from non-sequence based sources of biological information, such as the secondary or tertiary molecular structure, we can extract certain types of biological knowledge when sequence-based approaches fail, we hypothesize that protein-protein interaction (PPI) network topology and protein sequence might give insights into different slices of biological information. Since proteins aggregate to perform a function instead of acting in isolation, analyzing complex wirings around a protein in a PPI network could give deeper insights into the protein’s role in the inner working of the cell than analyzing sequences of individual genes. Hence, we believe that one could lose much information by focusing on sequence information alone. We examine whether the information about homologous proteins captured by PPI network topology differs and to what extent from the information captured by their sequences. We measure how similar the topology around homologous proteins in a PPI network is and show that such proteins have statistically significantly higher network similarity than nonhomologous proteins. We compare these network similarity trends of homologous proteins with the trends in their sequence identity and find that network similarities uncover almost as much homology as sequence identities. Although none of the two methods, network topology and sequence identity, seems to capture homology information in its entirety, we demonstrate that the two might give insights into somewhat different types of biological information, as the overlap of the homology information that they uncover is relatively low. Therefore, we conclude that similarities of proteins’ topological neighborhoods in a PPI network could be used as a complementary method to sequence-based approaches for identifying homologs, as well as for analyzing evolutionary distance and functional divergence of homologous proteins.

The DNA Barcode Linker

DNA barcoding is based on the use of short DNA sequences to provide taxonomic tags for rapid, efficient identification of biological specimens. Currently, reference databases are being compiled. In the future, it will be important to facilitate access to these databases, especially for nonspecialist users. The method described here provides a rapid, web-based, user-friendly link between the DNA sequence from an unidentified biological specimen and various types of biological information, including the species name. Specifically, we use a customized, Google-type search algorithm to quickly match an unknown DNA sequence to a list of verified DNA barcodes in the reference database. In addition to retrieving the species name, our web tool also provides automatic links to a range of other information about that species. As the DNA barcode database becomes more populated, it will become increasingly important for the broader user community to be able to exploit it for the rapid identification of unknown specimens and to easily obtain relevant biological information about these species. The application presented here meets that need.

전산학의 최신 응용 및 학제 분야인 생명정보학

본 바이오데이터들이 홍수처럼 쏟아지면서 컴퓨터를 활용해 해결해야 할 많은 문제들이 야기되고 있다. 생체분자들과 연관된 정보들을 대량으로 분석하는 전산기술 응용분야인 생명정보학은 학제간 학문분야로서 현재 확고히 자리매김하고 있으며. 구조생물학, 유전체학, 단백질체학, 시스템 생물학, 생물통계학 및 전산학 등 광범위한 학문영역을 포괄한다. 본 총설에서는 생명정보학에 대한 최근 상황과 전반적인 응용분야에 대해 소개하고자 한다. 일반적으로 널리 사용되는 생명정보 및 바이오데이터베이스들의 유형을 살펴보고, 전산학 분야와 긴밀한 관계가 있는 몇가지 생명정보학 응용 분야들을 다루고자 한다. A flood of biological data has caused many challenges in computing. Bioinformatics, the application of computational techniques to analyze the information associated with biomolecules on a large-scale, has now firmly established itself as an interdisciplinary subject in molecular biology, and encompasses a wide range of subject areas from structural biology, genomics, proteomics, systems biology, biostatistics to computer science. In this review, I provide an introduction and overview of the current state of bioinformatics. Looking at the types of biological information and databases that are commonly used, I also deals with some of bioinformatics application domains which are closely related to areas of computer science.

Complementarity of network and sequence information in homologous proteins.

Traditional approaches for homology detection rely on finding sufficient similarities between protein sequences. Motivated by studies demonstrating that from non-sequence based sources of biological information, such as the secondary or tertiary molecular structure, we can extract certain types of biological knowledge when sequence-based approaches fail, we hypothesize that protein-protein interaction (PPI) network topology and protein sequence might give insights into different slices of biological information. Since proteins aggregate to perform a function instead of acting in isolation, analyzing complex wirings around a protein in a PPI network could give deeper insights into the protein's role in the inner working of the cell than analyzing sequences of individual genes. Hence, we believe that one could lose much information by focusing on sequence information alone. We examine whether the information about homologous proteins captured by PPI network topology differs and to what extent from the information captured by their sequences. We measure how similar the topology around homologous proteins in a PPI network is and show that such proteins have statistically significantly higher network similarity than nonhomologous proteins. We compare these network similarity trends of homologous proteins with the trends in their sequence identity and find that network similarities uncover almost as much homology as sequence identities. Although none of the two methods, network topology and sequence identity, seems to capture homology information in its entirety, we demonstrate that the two might give insights into somewhat different types of biological information, as the overlap of the homology information that they uncover is relatively low. Therefore, we conclude that similarities of proteins' topological neighborhoods in a PPI network could be used as a complementary method to sequence-based approaches for identifying homologs, as well as for analyzing evolutionary distance and functional divergence of homologous proteins.

Comparison of morphometric techniques for shapes with few homologous landmarks based on machine-learning approaches to biological discrimination

Biometric analyses are useful tools for the study of organisms, their phylogenetic affiliation, and the pattern and rate of their evolution. Various morphometric techniques have been developed to analyze morphological variation, but methodological choices are often made arbitrarily because quantitative comparisons are lacking or inconclusive. Here we address morphometric quantification of taxa with few unambiguously identifiable landmarks (<15), utilizing ornamented and unornamented gastropod shells. Support vector machines were applied to evaluate classification performances of landmark (LMA), elliptic Fourier (EFA), and semi-landmark analysis (SLM). This evaluation is based on the discrimination of between-group differences relative to within-group variation, and thus allows comparing how the techniques treat different types of biological information. The results suggest that EFA performs slightly better than SLM (and certainly LMA) in discerning a priori identified taxa with unornamented shells, but that SLM is significantly superior to other techniques for ornamented shells. Alignment and homology problems may cause the subtle variations in ornamentation to become blurred as noise in EFA, even though EFA is often cited to be able to deal with complex shapes. Performance of LMA depends entirely on how accurately the structure can be covered with landmarks. Guidelines in choosing a morphometric technique in diverse cases are provided.