Big Data In Bioinformatics Research Articles

An empirical study of the impact of biological information dissemination in social media on public science literacy

Abstract In this paper, we first establish a locally converged bioinformatics dataset based on gradient sampling and design an optimal data mining control model to improve the accuracy of bioinformatics big data feature mining. The performance of the Compressive Tracking algorithm and Online Bosting algorithm is compared with the mining error as a test index. At the same time, we propose a social media information dissemination algorithm applicable to large-scale social network datasets, taking the degree value of each node as the node’s full influence and comparing and analyzing the dissemination influence of BP-IM, RAND and MC-CELF algorithms. Finally, taking public health big data as the research object, the least squares regression method was used to analyze the influence of the amount of public attention to bioinformatics scientific knowledge on their scientific literacy in different media. The results showed that there was a significant positive correlation between scientific literacy and willingness to engage in science participation behavior on social media when the amount of public attention to scientific information was β =0225, p <0.01. When more people are interested in scientific knowledge of bioinformatics on social media, their scientific literacy will improve.

Big Data in Bioinformatics and Computational Biology: Basic Insights.

The human genome was first sequenced in 1994. It took 10years of cooperation between numerous international research organizations to reveal a preliminary human DNA sequence. Genomics labs can now sequence an entire genome in only a few days. Here, we talk about how the advent of high-performance sequencing platforms has paved the way for Big Data in biology and contributed to the development of modern bioinformatics, which in turn has helped to expand the scope of biology and allied sciences. New technologies and methodologies for the storage, management, analysis, and visualization of big data have been shown to be necessary. Not only does modern bioinformatics have to deal with the challenge of processing massive amounts of heterogeneous data, but it also has to deal with different ways of interpreting and presenting those results, as well as the use of different software programs and file formats. Solutions to these problems are tried to present in this chapter. In order to store massive amounts of data and provide a reasonable period for completing search queries, new database management systems other than relational ones will be necessary. Emerging advance programing approaches, such as machine learning, Hadoop, and MapReduce, aim to provide the capacity to easily construct one's own scripts for data processing and address the issue of the diversity of genomic and proteomic data formats in bioinformatics.

The potential mechanisms of neuroblastoma in children based on bioinformatics big data.

In recent years, miRNAs have become a research hotspot, which is related to the occurrence and development of a variety of malignant tumors, but there are few studies in neuroblastoma. In this study, the differentially expressed microRNAs (miRNAs) in neuroblastoma were identified and analyzed using bioinformatics, and their biological functions and related signaling pathways were examined. The neuroblastoma miRNA chip GSE121513 was obtained from the Gene Expression Omnibus (GEO) database and the data of 95 neuroblastoma samples and normal fetal adrenal neuroblastoma samples were analyzed to screen the differential miRNAs. The target genes of the differentially expressed miRNAs were predicted using |log fold change (FC)| ≥4. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed to construct a protein-protein interaction network and identify the core target genes. A total of 91 differentially expressed miRNAs were identified (P<0.05, |logFC| ≥1), including 52 upregulated and 39 downregulated miRNAs. The target genes of the differential miRNAs (P<0.05, |logFC| ≥4) were pretested, and 602 target genes were obtained. Functional analysis showed that these genes were mainly located in the extracellular matrix region of proteins, and were involved in the negative regulation of cytoplasmic translation, mRNA 3'-untranslated region (UTR) binding, and binding to nucleic acid to inhibit the activity of translation factors. They were also involved in RNA degradation, adhesion pathways, and the phosphatidylinositol-3-kinase (PI3K)-Akt signaling pathway. Ten key target genes were identified via protein interaction network screening. The differential miRNAs may be related to the occurrence of neuroblastoma were screened.

Effect of "Hedyotis Diffusae Herba-Smilacis Glabrae Rhizoma" in treatment of lung adenocarcinoma based on network pharmacology

To explore the mechanism of Hedyotis Diffusae Herba-Smilacis Glabrae Rhizoma(HDH-SGR) in treating lung adenocarcinoma based on big data bioinformatics combined with network pharmacology analysis and molecular docking technology. The chemical components and potential therapeutic targets of HDH-SGR were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). Lung adenocarcinoma-related genes were obtained from The Cancer Genome Atlas(TCGA), Therapeutic Target Database(TTD), Pharmacogenetics and Pharmacogenomics Knowledge Base(PharmGKB), Online Mendelian Inheritance in Man(OMIM), DrugBank, and GeneCards. &quot;Drug component-target&quot; network was constructed using Cytoscape to screen out key compounds. STRING was used to build protein-protein interaction(PPI) network and core targets were screened out by Cytoscape-CytoNCA topology analysis. Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) analyses of target genes were performed by R-clusterProfiler. Finally, key compounds were docked to core target genes using AutoDock. The results showed that 22 active compounds and 499 potential therapeutic targets were obtained from HDH-SGR. A total of 14 332 lung adenocarcinoma-related targets were screened out through six data platforms, including 182 common targets. Fifteen core targets were screened out from the PPI network. GO and KEGG analyses revealed significant enrichment of relevant target genes in various biological processes, cellular functions(e.g., response to lipopolysaccharide, nuclear receptor activity, and ligand-activated transcription factor activity) and close relationship between target genes and non-small cell lung cancer signaling pathways. Based on the results of molecular docking validation, diosgenin, quercetin, naringenin, taxifolin, 2-methoxy-3-methyl-9,10-anthraquinone, stigmasterol, and β-sitosterol were able to bind tightly to the core targets. HDH-SGR can intervene in lung adenocarcinoma through multiple targets and signaling pathways, such as non-small cell lung cancer signaling pathways. The binding of active components in Chinese medicine to key targets is presumedly one of the mechanisms that produce therapeutic effects.

Prioritization and Evaluation of Flooding Tolerance Genes in Soybean [Glycine max (L.) Merr.

Soybean [Glycine max (L.) Merr.] is one of the most important legume crops abundant in edible protein and oil in the world. In recent years there has been increasingly more drastic weather caused by climate change, with flooding, drought, and unevenly distributed rainfall gradually increasing in terms of the frequency and intensity worldwide. Severe flooding has caused extensive losses to soybean production and there is an urgent need to breed strong soybean seeds with high flooding tolerance. The present study demonstrates bioinformatics big data mining and integration, meta-analysis, gene mapping, gene prioritization, and systems biology for identifying prioritized genes of flooding tolerance in soybean. A total of 83 flooding tolerance genes (FTgenes), according to the appropriate cut-off point, were prioritized from 36,705 test genes collected from multidimensional genomic features linking to soybean flooding tolerance. Several validation results using independent samples from SoyNet, genome-wide association study, SoyBase, GO database, and transcriptome databases all exhibited excellent agreement, suggesting these 83 FTgenes were significantly superior to others. These results provide valuable information and contribution to research on the varieties selection of soybean.

Alendronate-induced Perturbation of the Bone Proteome and Microenvironmental Pathophysiology.

Objectives: Bisphosphonates (BPs) are powerful inhibitors of osteoclastogenesis and are used to prevent osteoporotic bone loss and reduce the risk of osteoporotic fracture in patients suffering from postmenopausal osteoporosis. Patients with breast cancer or gynecological malignancies being treated with BPs or those receiving bone-targeted therapy for metastatic prostate cancer are at increased risk of bisphosphonate-related osteonecrosis of the jaw (BRONJ). Although BPs markedly ameliorate osteoporosis, their adverse effects largely limit the clinical application of these drugs. This study focused on providing a deeper understanding of one of the most popular BPs, the alendronate (ALN)-induced perturbation of the bone proteome and microenvironmental pathophysiology.Methods: To understand the molecular mechanisms underlying ALN-induced side-effects, an unbiased and global proteomics approach combined with big data bioinformatics was applied. This was followed by biochemical and functional analyses to determine the clinicopathological mechanisms affected by ALN.Results: The findings from this proteomics study suggest that the RIPK3/Wnt/GSK3/β-catenin signaling pathway is significantly perturbed upon ALN treatment, resulting in abnormal angiogenesis, inflammation, anabolism, remodeling, and mineralization in bone cells in an in vitro cell culture system.Conclusion: Our investigation into potential key signaling mechanisms in response to ALN provides a rational basis for suppressing BP-induced adverse effect and presents various therapeutic strategies.

Molecular docking and big-data bioinformatics predict the mechanisms of action of the active ingredients of Ganoderma lucidum in treating lung cancer

Molecular docking and big-data bioinformatics predict the mechanisms of action of the active ingredients of Ganoderma lucidum in treating lung cancer

The human T‐cell receptor repertoire in health and disease and potential for omics integration

The adaptive immune system arose 600 million years ago in a cold-blooded fish. Over countless generations, our antecedents tuned the function of the T-cell receptor (TCR). The TCR system is arguably the most complex known to science. The TCR evolved hypervariability to fight the hypervariability of pathogens and cancers that look to consume our resources. This review describes the genetics and architecture of the human TCR and highlights surprising new discoveries over the past years that have disproved very old dogmas. The standardization of TCR sequencing data is discussed in preparation for big data bioinformatics and predictive analysis. We next catalogue new signatures and phenomenon discovered by TCR next generation sequencing (NGS) in health and disease and work that remain to be done in this space. Finally, we discuss how TCR NGS can add to immunodiagnostics and integrate with other omics platforms for both a deeper understanding of TCR biology and its use in the clinical setting.

MetaMap: an atlas of metatranscriptomic reads in human disease-related RNA-seq data.

BackgroundWith the advent of the age of big data in bioinformatics, large volumes of data and high-performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts; however, its generic nature also enables the detection of microbial and viral transcripts.FindingsWe developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-human-mapping read fraction. We validated this approach by recapitulating outcomes from six independent, controlled infection experiments of cell line models and compared them with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from more than 17,000 samples from more than 400 studies relevant to human disease using state-of-the-art high-performance computing systems. The resulting data from this large-scale re-analysis are made available in the presented MetaMap resource.ConclusionsOur results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation toward the role of the microbiome in human disease. Additionally, codes to process new datasets and perform statistical analyses are made available.

Emerging trend of big data analytics in bioinformatics: a literature review

Advancement of unparalleled data in bioinformatics over the years is a major concern for storage and management. Such massive data must be handled efficiently to disseminate knowledge. Computational advancements in information technology present feasible analytical solutions to process such data. In this context, the paper is an attempt to highlight the influence of big data in bioinformatics. Some of the concepts emphasised are definition of big data; architectural platforms supporting data analytics; followed by the application of above-mentioned analytical techniques towards complex problems in bioinformatics. The challenges and future prospects of big data analytics in bioinformatics are briefly discussed. This paper provides a comprehensive summary of several data analytical techniques available for bioinformatics researchers and computer scientists.

Computation migration: a new approach to execute big-data bioinformatics workflows

Bioinformatics workflows frequently access various distributed biological data sources and computational analysis tools for data analysis and knowledge discovery. They move large volumes of data from biological data sources to computational analysis tools and follow the traditional data migration approach for workflow execution. However, in the advent of big-data in bioinformatics, moving large volumes of data to computation during workflow execution is no longer feasible. Considering the fact that the size of biological data is continuously growing and is much larger than the computational analysis tool size, moving computation to data in a workflow is a better solution to handle the growing data. In this paper, we therefore propose a computation migration approach to execute bioinformatics workflows. We move computational analysis tools to data sources during workflow execution and demonstrate with workflow patterns that moving computation instead of data yields high performance gains in terms of data-flow and execution time.

Computation migration: a new approach to execute big-data bioinformatics workflows

Bioinformatics workflows frequently access various distributed biological data sources and computational analysis tools for data analysis and knowledge discovery. They move large volumes of data from biological data sources to computational analysis tools and follow the traditional data migration approach for workflow execution. However, in the advent of big-data in bioinformatics, moving large volumes of data to computation during workflow execution is no longer feasible. Considering the fact that the size of biological data is continuously growing and is much larger than the computational analysis tool size, moving computation to data in a workflow is a better solution to handle the growing data. In this paper, we therefore propose a computation migration approach to execute bioinformatics workflows. We move computational analysis tools to data sources during workflow execution and demonstrate with workflow patterns that moving computation instead of data yields high performance gains in terms of data-flow and execution time.

Scalability and Validation of Big Data Bioinformatics Software

This review examines two important aspects that are central to modern big data bioinformatics analysis – software scalability and validity. We argue that not only are the issues of scalability and validation common to all big data bioinformatics analyses, they can be tackled by conceptually related methodological approaches, namely divide-and-conquer (scalability) and multiple executions (validation). Scalability is defined as the ability for a program to scale based on workload. It has always been an important consideration when developing bioinformatics algorithms and programs. Nonetheless the surge of volume and variety of biological and biomedical data has posed new challenges. We discuss how modern cloud computing and big data programming frameworks such as MapReduce and Spark are being used to effectively implement divide-and-conquer in a distributed computing environment. Validation of software is another important issue in big data bioinformatics that is often ignored. Software validation is the process of determining whether the program under test fulfils the task for which it was designed. Determining the correctness of the computational output of big data bioinformatics software is especially difficult due to the large input space and complex algorithms involved. We discuss how state-of-the-art software testing techniques that are based on the idea of multiple executions, such as metamorphic testing, can be used to implement an effective bioinformatics quality assurance strategy. We hope this review will raise awareness of these critical issues in bioinformatics.

Advanced Virus Detection Technologies Interest Group (AVDTIG): Efforts on High Throughput Sequencing (HTS) for Virus Detection.

Several nucleic-acid based technologies have recently emerged with capabilities for broad virus detection. One of these, high throughput sequencing, has the potential for novel virus detection because this method does not depend upon prior viral sequence knowledge. However, the use of high throughput sequencing for testing biologicals poses greater challenges as compared to other newly introduced tests due to its technical complexities and big data bioinformatics. Thus, the Advanced Virus Detection Technologies Users Group was formed as a joint effort by regulatory and industry scientists to facilitate discussions and provide a forum for sharing data and experiences using advanced new virus detection technologies, with a focus on high throughput sequencing technologies. The group was initiated as a task force that was coordinated by the Parenteral Drug Association and subsequently became the Advanced Virus Detection Technologies Interest Group to continue efforts for using new technologies for detection of adventitious viruses with broader participation, including international government agencies, academia, and technology service providers.

Guest Editorial for Special Section on Big Data Computing and Processing in Computational Biology and Bioinformatics

The papers in this special section focus on big data computing in the field of bioinformatics and biocomputing. Big data has emerged as an important application field which has shown its huge impact in different scientific research domains. In particular, the big data bioinformatics applications such as DNA sequence analysis have posed significant challenges to the state-of-the-art processing and computing systems. With the growing explosive data scale, the collection, storage, retrieval, processing, scheduling, and visualization are key big data issues to be tackled. Up to now, many researchers have been seeking high-level parallelism using novel big data computing architectures and processing mechanisms.

ROSEFW-RF: The winner algorithm for the ECBDL’14 big data competition: An extremely imbalanced big data bioinformatics problem

The application of data mining and machine learning techniques to biological and biomedicine data continues to be an ubiquitous research theme in current bioinformatics. The rapid advances in biotechnology are allowing us to obtain and store large quantities of data about cells, proteins, genes, etc., that should be processed. Moreover, in many of these problems such as contact map prediction, the problem tackled in this paper, it is difficult to collect representative positive examples. Learning under these circumstances, known as imbalanced big data classification, may not be straightforward for most of the standard machine learning methods.In this work we describe the methodology that won the ECBDL’14 big data challenge for a bioinformatics big data problem. This algorithm, named as ROSEFW-RF, is based on several MapReduce approaches to (1) balance the classes distribution through random oversampling, (2) detect the most relevant features via an evolutionary feature weighting process and a threshold to choose them, (3) build an appropriate Random Forest model from the pre-processed data and finally (4) classify the test data. Across the paper, we detail and analyze the decisions made during the competition showing an extensive experimental study that characterize the way of working of our methodology. From this analysis we can conclude that this approach is very suitable to tackle large-scale bioinformatics classifications problems.

Intuitive web-based experimental design for high-throughput biomedical data.

Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the future. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model.

What is a gene for?

The word “gene” means different things to different people, and can even be used in multiple ways by the same individual. In this review, I follow a particular thread running through Griffith and Stotz’s “Genetics and Philosophy: an introduction”, which is the way that methods of investigation influence the way we define the concept of “gene”, from nineteen century breeding experiments to twenty-first century big data bioinformatics. These different views lead to a set of gene concepts, which only partially overlap each other, each of which picks up on a different part of gene behaviour, function or scientific utility. This plurality of concepts carries over to the use of the concept of “information” in biology, where the non-overlapping concepts can be connected to whether you view the genome as a blueprint for development, a response to environmental triggers, an engine of heritability, or a document of history.

Impact of Biological Big Data in Bioinformatics

In an era of high-throughput sequencing, drilling of biological data to extract hidden valued information plays an important role in making critical decisions across every branch of science, whether it is genomics or proteomics or metabolomics or personal medicine. For example, the genome sequence of the patients contains much valuable information about the myriad of disease causes, easy extraction of which from the sequences will enable science to develop patientspecific medicine, thereby accelerating curing process and minimizing drug’s side effects. Today, true solutions of the several problems in the biological field are hidden in the analysis of exponentially increasing data, so-called Big Data. Big data has become currently hot and open issue for the biological community to handle, collect, store, analyze and manage such vast amount of data. Due to this, the computing Big Data has become the new paradigm of the science and big data in bioinformatics. While, big data is playing central role in the continuity of the progress of research in the biological field, but it presents challenges in terms of scalability, complexity, privacy and security. Big Data in the biological field have revolutionizing power to bring dramatic changes in our current understanding about several solved and unanswered problems. With these emerging big data, bioinformatics field is also evolving continuously. This paper presents the concept of biological big data and its associated challenges in the bioinformatics field which will provides a snapshot of the importance of biological big data in the bioinformatics future research.

Mining of protein–protein interfacial residues from massive protein sequential and spatial data

It is a great challenge to process big data in bioinformatics. In this paper, we addressed the problem of identifying protein–protein interfacial residues from massive protein structural data. A protein set, comprising 154 993 residues, was analyzed. We applied the three-dimensional alpha shape modeling to the search of surface and interfacial residues in this set, and adopted the spatially neighboring residue profiles to characterize each residue. These residue profiles, which revealed the sequential and spatial information of proteins, translated the original data into a large matrix. After vertically and horizontally refining this matrix, we comparably implemented a series of popular learning procedures, including neuro-fuzzy classifiers (NFCs), CART, neighborhood classifiers (NECs), extreme learning machines (ELMs) and naive Bayesian classifiers (NBCs), to predict the interfacial residues, aiming to investigate the sensitivity of these massive structural data to different learning mechanisms. As a consequence, ELMs, CART and NFCs performed better in terms of computational costs; NFCs, NBCs and ELMs provided favorable prediction accuracies. Overall, NFCs, NBCs and ELMs are favourable choices for fastly and accurately handling this type of data. More importantly, the marginal differences between the prediction performances of these methods imply the insensitivity of this type of data to different learning mechanisms.