Bioinformatics--principles and potential of a new multidisciplinary tool

LncRNA LINC01207 Could Positively Regulate the Development of Colorectal Cancer

Background

LINC01207 expression is associated with colorectal cancer progression. However, the exact role of LINC01207 in colorectal cancer (CRC) is not clear, and further exploration is needed.

Methods

Gene expression data of the GSE34053 database were used to explore the differential expressed genes (DEGs) between colon cancer cells and normal cells. The gene expression profiling interactive analysis (GEPIA) was used to determine the differential expression of LINC01207 between CRC and normal tissues and the association between the expression of LINC01207 and survival in patients with CRC. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were performed to obtain the biological processes and pathways associated with DEGs and LINC01207 coexpressed genes in CRC. The qRT-PCR was used to determine the LINC01207 level in CRC cell lines and tissue samples. CCK-8 assay was employed to measure cell viability and Transwell assay to assess cell invasion and migration.

Results

In this study, a total of 954 DEGs were identified, including 282 upregulated and 672 downregulated genes. LINC01207 was significantly upregulated in CRC samples with a poor prognosis. LINC01207 was also associated with pathways such as ECM-receptor interaction, O-glycan processing, and TNF signaling pathway in CRC. Knockdown of LINC01207 inhibited the migration, invasion, and proliferation of CRC cells.

Conclusion

LINC01207 might act as an oncogene and promote the progression of CRC. Our study suggested that LINC01207 had the potential to be a novel biomarker for CRC detection and a therapeutic target for CRC treatment.

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

Determination of immunodominant scaffolds of Com1 and OmpH antigens of Coxiella burnetii

Today, there is an increasing emphasis on recombinant vaccines to eliminate the side effects of conventional vaccines such as whole-cell bacteria. Query fever is an emerging disease that causes irreparable complications for both humans and domestic animals. The cause of this disease is Coxiella burnetii, a gram-negative intracellular bacteria. In order to determine the most immunodominant epitopes of Com1 and OmpH antigens of C. burnetii, the most reliable bioinformatics tools with high rates of citation in predicting B cell and T cell epitopes were used. Finally, by comparing the results of all servers, the best overlapped epitopes with the highest antigenicity among different servers were selected. In this regard, epitopes in 18-27and 67-82 amino acids residues were introduced for MHCI and MHCII of T cell, respectively, whereas epitope in 16-25 amino acids residues was introduced for B cell of OmpH antigen. The epitopes in the range of 193-202, 100-108 and 215-223 amino acid residues were preferred for MHCI class of T cell, MHCII class of T cell and B cell of Com1 antigen, respectively. For each antigen, some empirical common epitopic regions were introduced, which included both T and B cells epitopes, 53-65 and 102-111 amino acid residues of OmpH antigen as well as 38-54 range of the amino acid of Com1 antigen. All the predicted epitopes were selected based on their high antigenicity scores and number of non-digestive enzymes. To optimize the application of reported epitopes, various orders of epitopes were arranged in three categories of B cell, T cell and common T and B cells epitopes for each antigen. Then, the best immunodominant scaffolds for each antigen were proposed in these categories. The results demonstrated that the scaffold arranged based on B cell epitopes had the highest antigenicity in both antigens.

Feature extraction method for proteins based on Markov tripeptide by compressive sensing

BACKGROUND

In order to capture the vital structural information of the original protein, the symbol sequence was transformed into the Markov frequency matrix according to the consecutive three residues throughout the chain. A three-dimensional sparse matrix sized 20 × 20 × 20 was obtained and expanded to one-dimensional vector. Then, an appropriate measurement matrix was selected for the vector to obtain a compressed feature set by random projection. Consequently, the new compressive sensing feature extraction technology was proposed.

RESULTS

Several indexes were analyzed on the cell membrane, cytoplasm, and nucleus dataset to detect the discrimination of the features. In comparison with the traditional methods of scale wavelet energy and amino acid components, the experimental results suggested the advantage and accuracy of the features by this new method.

CONCLUSIONS

The new features extracted from this model could preserve the maximum information contained in the sequence and reflect the essential properties of the protein. Thus, it is an adequate and potential method in collecting and processing the protein sequence from a large sample size and high dimension.

Teaching the fundamentals of biological data integration using classroom games

This article aims to introduce the nature of data integration to life scientists. Generally, the subject of data integration is not discussed outside the field of computational science and is not covered in any detail, or even neglected, when teaching/training trainees. End users (hereby defined as wet-lab trainees, clinicians, lab researchers) will mostly interact with bioinformatics resources and tools through web interfaces that mask the user from the data integration processes. However, the lack of formal training or acquaintance with even simple database concepts and terminology often results in a real obstacle to the full comprehension of the resources and tools the end users wish to access. Understanding how data integration works is fundamental to empowering trainees to see the limitations as well as the possibilities when exploring, retrieving, and analysing biological data from databases. Here we introduce a game-based learning activity for training/teaching the topic of data integration that trainers/educators can adopt and adapt for their classroom. In particular we provide an example using DAS (Distributed Annotation Systems) as a method for data integration.

Functional genomics via metabolic footprinting: monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry

We sought to test the hypothesis that mutant bacterial strains could be discriminated from each other on the basis of the metabolites they secrete into the medium (their ‘metabolic footprint’), using two methods of ‘global’ metabolite analysis (FT–IR and direct injection electrospray mass spectrometry). The biological system used was based on a published study of Escherichia coli tryptophan mutants that had been analysed and discriminated by Yanofsky and colleagues using transcriptome analysis. Wild-type strains supplemented with tryptophan or analogues could be discriminated from controls using FT–IR of 24 h broths, as could each of the mutant strains in both minimal and supplemented media. Direct injection electrospray mass spectrometry with unit mass resolution could also be used to discriminate the strains from each other, and had the advantage that the discrimination required the use of just two or three masses in each case. These were determined via a genetic algorithm. Both methods are rapid, reagentless, reproducible and cheap, and might beneficially be extended to the analysis of gene knockout libraries.

Providing visualisation support for the analysis of anatomy ontology data

BACKGROUND

Improvements in technology have been accompanied by the generation of large amounts of complex data. This same technology must be harnessed effectively if the knowledge stored within the data is to be retrieved. Storing data in ontologies aids its management; ontologies serve as controlled vocabularies that promote data exchange and re-use, improving analysis. The Edinburgh Mouse Atlas Project stores the developmental stages of the mouse embryo in anatomy ontologies. This project is looking at the use of visual data overviews for intuitive analysis of the ontology data.

RESULTS

A prototype has been developed that visualises the ontologies using directed acyclic graphs in two dimensions, with the ability to study detail in regions of interest in isolation or within the context of the overview. This is followed by the development of a technique that layers individual anatomy ontologies in three-dimensional space, so that relationships across multiple data sets may be mapped using physical links drawn along the third axis.

CONCLUSION

Usability evaluations of the applications confirmed advantages in visual analysis of complex data. This project will look next at data input from multiple sources, and continue to develop the techniques presented to provide intuitive identification of relationships that span multiple ontologies.

In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists

Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new skills and new understanding of genome-scale studies in order to take advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the molecular basis of toxicity. With the advent of high-throughput sequencing and computational methodologies, expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select examples.

Improved pattern for genome-based screening identifies novel cell wall-attached proteins in gram-positive bacteria

With a large number of sequenced microbial genomes available, tools for identifying groups or classes of proteins have become increasingly important. Here we present an improved pattern for the identification of cell wall-attached proteins (CWPs), a group of proteins with diverse and important functions in gram-positive bacteria. This tripartite pattern is based on analysis of 65 previously described cell wall-attached proteins and takes into account the three principal requirements for cell wall sorting; a sortase target region (LPXTGX), a membrane-spanning region, and a charged stop-transfer tail. In five different genomes of gram-positive bacteria, the tripartite pattern identified a total of 35 putative CWPs, 19 of which were novel. The specificity and sensitivity of the tripartite pattern are higher than those of the classical pattern, which is based solely on the sortase target region. Several putative CWPs with atypical sortase target regions were identified. In the complete genome of the important human pathogen Streptococcus pyogenes, the tripartite pattern identified 14 putative CWPs. Seven of the putative S. pyogenes proteins were novel, and two of these were a 5' nucleotidase and a pullulanase. This study represents the first whole-genome screening for CWPs, and we conclude that the tripartite pattern is highly suitable for this purpose. Identification of CWPs using this pattern offers important possibilities in the study of the pathogenesis and physiology of gram-positive bacteria.

The challenges and rewards of integrating diverse neuroscience information

The design of database models and schemas for storing, cross-referencing, and retrieving neuroscience information faces issues that are similar but more complex than most of the other biomedical disciplines, such as genomics and proteonomics. Specifically, the visualization and manipulation of very large and diverse image data, such as digital brain atlases and functional magnetic resonance images, play a unique role in neuroscience while much of the associated information is textually recorded. Nongraphical information can include the annotation of large brain structures ranging from anatomical regions to intracellular structures, the description of cellular functional properties, and their various interrelationships, such as fiber connections. It is necessary that the heterogeneous and distributed types of data be cross-referenced to each other so that this diverse information can be efficiently retrieved, shared, and exchanged among the different neuroscientific disciplines. Continued advances in computers and Internet technologies appear to indicate that increasingly large data sets will be maintained on local or regional file servers and that informational interoperability will be achieved using a networked information system infrastructure. The authors and others have proposed and implemented models of semantically organized information systems that utilize centrally stored and highly structured archival information to index, cross-reference, and retrieve diverse, Web-based data sets.

Recent developments and future directions in computational genomics

Computational genomics is a subfield of computational biology that deals with the analysis of entire genome sequences. Transcending the boundaries of classical sequence analysis, computational genomics exploits the inherent properties of entire genomes by modelling them as systems. We review recent developments in the field, discuss in some detail a number of novel approaches that take into account the genomic context and argue that progress will be made by novel knowledge representation and simulation technologies.

Kleisli: a new tool for data integration in biology

One of the central problems in bioinformatics is data retrieval and integration. The existing biological databases are geographically distributed across the Internet, complex and heterogeneous in data types and data structures, and constantly changing. With the current rapid growth of biomedical data, the challenge is how large volumes of data retrieved from multiple databases can be transformed and integrated automatically and flexibly. This article describes a powerful new tool, the Kleisli system, for complex queries across multiple databases and data integration.

'Genomics': buzzword or reality?

'Genomics' has become a widely used term, covering a range of approaches that make use of the newly acquired wealth of genome data (both on man and on a number of model organisms) to gain new insights and accelerate research. This review attempts to present a clear and balanced view of developments in this field, to describe the four major approaches that contribute to genomics (bioinformatics, genetic analysis of extended populations, large-scale expression studies, functional approaches), and to indicate applications in basic and pharmaceutical research.

Design and realization of an on-line database for multidimensional microscopic images of biological specimens

The BioImage database is a new scientific database for multidimensional microscopic images of biological specimens, which is available through the World Wide Web (WWW). The development of this database has followed an iterative approach, in which requirements and functionality have been revised and extended. The complexity and innovative use of the data meant that technical and biological expertise has been crucial in the initial design of the data model. A controlled vocabulary was introduced to ensure data consistency. Pointers are used to reference information stored in other databases. The data model was built using InfoModeler as a database design tool. The database management system is the Informix Dynamic Server with Universal Data Option. This object-relational system allows the handling of complex data using features such as collection types, inheritance, and user-defined data types. Informix datablades are used to provide additional functionality: the Web Integration Option enables WWW access to the database; the Video Foundation Blade provides functionality for video handling.

B-SPID: an object-relational database architecture to store, retrieve, and manipulate neuroimaging data

We propose a hardware and software architecture to respond to crucial problems in the neuroimaging field: storage, retrieval, and processing of large datasets. The B-SPID project, here discussed, concerns the processing of neuroimages and attached components stored in an object-relational multimedia database management system (DBMS). Advanced bioinformation concepts are exploited in this project such as large scale data storage, high level graphical user interfaces and 3D graphical processing and display of data. Our database implementation is based on standard programming components, runs on several UNIX platforms and is written to be evolutive. Queries on this database are designed to obtain and display from neuroimaging data several types of results (pictures, text, or 3D graphical shapes) on heterogeneous systems.

Drug discovery: past, present and future

New drug discovery from early on involved a trial-and-error approach on naturally derived materials and substances until the end of the nineteenth century. The first half of the twentieth century witnessed systematic pharmacological evaluations of both natural and synthetic compounds. However, most new drugs until the 1970s were discovered by serendipity. With the exponential development of molecular biology on one hand and computer technology on the other, it became possible from 1980 onwards to place drug discovery on a rational basis. Cloning of genes has led to the development of methodologies for specific receptor-directed and enzyme-directed drug discoveries. Advances in recombinant DNA and transgenic technologies have enabled the production of human hormonal and other endogenous biomolecules as new drugs. As we understand more about the co-ordinating and regulating powers of the cerebral cortex during the next century, especially of the frontal lobe, man may be able to use bio-feedback training to voluntarily regulate the release of neurotransmitters, hormones, and other molecules involved in the regulation of various physiological processes in health as well as in disease.