Huge Amount Of Genomic Data Research Articles

Genome evolution in intracellular parasites: Microsporidia and Apicomplexa.

Microsporidia and Apicomplexa are eukaryotic, single-celled, intracellular parasites with huge public health and economic importance. Typically, these parasites are studied separately, emphasizing their uniqueness and diversity. In this review, we explore the huge amount of genomic data that has recently become available for the two groups. We compare and contrast their genome evolution and discuss how their transitions to intracellular life may have shaped it. In particular, we explore genome reduction and compaction, genome expansion and ploidy, gene shuffling and rearrangements, and the evolution of centromeres and telomeres.

Application of Bioinformatics resources for mining of simple sequence repeats (SSRs) marker in plant genomes: An Overview

In the case of plants, the prediction of simple sequence repeats (SSRs) is important for the purpose of gene mapping, biodiversity study and detection of genes with desired characters. In addition to this, due to their polymorphic nature and distribution throughout the genome, it is considered as an ideal marker in plants. However, the traditional methods for the detection of SSR-based polymorphism cause difficultly. As an alternative method, the bioinformatics approaches have been used extensively in the study of these molecular markers, in a economic way. The next generation sequencing (NGS) techniques generate a huge amount of genomic data which is stored in the public databases in the form of whole genome and EST. This provides the opportunity to implement the bioinformatics tools to predict and annotate the SSR associated with desired gene of the plants. Several computational programs along with the pipelines have been developed to detect the SSR sequences automatically by using the genomic information from the database. In this review, the application and availability of specific bioinformatics resources and the methods of application involved for SSR discovery have been presented by taking suitable examples from the literature.

Actionable data for precision oncology: Framing trustworthy evidence for exploratory research and clinical diagnostics.

Huge amounts of genomic data produced by researchers around the world undermine data-centred discovery and therapeutic development. This paper considers how researchers make decisions about the actionability of specific datasets and the conditions that allow such data to be trusted. We discuss the case of COSMIC, a leading cancer genomics database which aggregates a large amount of sources. We research what the actionability of cancer data means in different situations of use, contrasting exploratory and diagnostics research. They highlight different questions and concerns upon genomic data use in medical research. At the same time, strategies and justifications pursued to evaluate and re-use can also share important similarities. To explain differences and similarities, we argue for an understanding of actionability and trust in data that depends on the goals and resources within the situation of inquiry, and the social epistemology of standards.

WBTC: a new approach for efficient storage of genomic data

With the improvement in high-throughput genome sequencing technology, huge amount of genomic data are generated every day. These data are used in numerous applications: sequence alignment, drug discovery and personalized medicine, etc. To efficiently handle genome data for storage, processing, and transmission, some specific genomic data compression approach is a need of today. In this paper, a hybrid approach-WBTC (Word Based Compression Technique) based on statistical and substitution model is proposed for genome compression. WBTC can support genomic data in raw forms as well as Fasta/Multi-fasta file formats. WBTC is a lossless genome compression algorithm in which searching is possible without full decompression. Experiments show that the proposed algorithm-WBTC outperforms in comparison to other state-of-the-art algorithms with respect to compression ratio, compression time, decompression time, compression memory and decompression memory.

Predatory protists.

The vast majority of eukaryotic life is made up of single cells commonly referred to as protists. In this primer, Leander provides an introduction to predatory protists - cells that eat other cells. This lifestyle, in particular the use of phagocytosis, makes endosymbiosis possible and enabled the evolution of complex cells.

A computational framework to analyze human genomes

The advent of genomic technologies has led to the current genomic era. Large-scale human genome projects have resulted in a huge amount of genomic data. Analyzing human genomes is a challenging task including a number of key steps from short read alignment, variant calling, and variant annotating. In this paper, the state-of-the-art computational methods and databases for each step will be analyzed to suggest a practical and efficient guideline for whole human genome analyses. This paper also discusses frameworks to combine variants from various genome analysis pipelines to obtain reliable variants. Finally, we will address advantages as well as discordances of widely-used variant annotation methods to evaluate the clinical significance of variants. The review will empower bioinformaticians to efficiently perform human genome analyses, and more importantly, help genetic consultants understand and properly interpret mutations for clinical purposes.

A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph

BackgroundCancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task.ResultsConsidering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types.ConclusionsThe final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment.AvailabilityThe source code can be obtained at https://github.com/weiba/Subdyquency.

Smart Data for Genomic Information Systems: the SILE Method

During the last two decades, data generated by Next Generation Sequencing Technologies have revolutionized our understanding of human biology and improved the study on how changes (variations) in the DNA are involved in the risk of suffering a certain disease. A huge amount of genomic data is publicly available and frequently used by the research community in order to extract meaningful and reliable gene-disease relationships. However, the management of this exponential growth of data has become a challenge for biologists. Under such a Big Data problem perspective, they are forced to delve into a lake of complex data spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality; but when data are used to solve a concrete problem only a small part of that “data lake” is really significant; this is what we call the “smart” data perspective. By using conceptual models and the principles of data quality management, adapted to the genomic domain, we propose a systematic approach called SILE method to move from a Big Data to a Smart Data perspective. The aim of this approach is to populate an Information System with genomic data which are accessible, informative and actionable enough to extract valuable knowledge.

GTRAC: fast retrieval from compressed collections of genomic variants.

The dramatic decrease in the cost of sequencing has resulted in the generation of huge amounts of genomic data, as evidenced by projects such as the UK10K and the Million Veteran Project, with the number of sequenced genomes ranging in the order of 10 K to 1 M. Due to the large redundancies among genomic sequences of individuals from the same species, most of the medical research deals with the variants in the sequences as compared with a reference sequence, rather than with the complete genomic sequences. Consequently, millions of genomes represented as variants are stored in databases. These databases are constantly updated and queried to extract information such as the common variants among individuals or groups of individuals. Previous algorithms for compression of this type of databases lack efficient random access capabilities, rendering querying the database for particular variants and/or individuals extremely inefficient, to the point where compression is often relinquished altogether. We present a new algorithm for this task, called GTRAC, that achieves significant compression ratios while allowing fast random access over the compressed database. For example, GTRAC is able to compress a Homo sapiens dataset containing 1092 samples in 1.1 GB (compression ratio of 160), while allowing for decompression of specific samples in less than a second and decompression of specific variants in 17 ms. GTRAC uses and adapts techniques from information theory, such as a specialized Lempel-Ziv compressor, and tailored succinct data structures. The GTRAC algorithm is available for download at: https://github.com/kedartatwawadi/GTRAC CONTACT: : kedart@stanford.edu Supplementary data are available at Bioinformatics online.

SparkBWA: Speeding Up the Alignment of High-Throughput DNA Sequencing Data.

Next-generation sequencing (NGS) technologies have led to a huge amount of genomic data that need to be analyzed and interpreted. This fact has a huge impact on the DNA sequence alignment process, which nowadays requires the mapping of billions of small DNA sequences onto a reference genome. In this way, sequence alignment remains the most time-consuming stage in the sequence analysis workflow. To deal with this issue, state of the art aligners take advantage of parallelization strategies. However, the existent solutions show limited scalability and have a complex implementation. In this work we introduce SparkBWA, a new tool that exploits the capabilities of a big data technology as Spark to boost the performance of one of the most widely adopted aligner, the Burrows-Wheeler Aligner (BWA). The design of SparkBWA uses two independent software layers in such a way that no modifications to the original BWA source code are required, which assures its compatibility with any BWA version (future or legacy). SparkBWA is evaluated in different scenarios showing noticeable results in terms of performance and scalability. A comparison to other parallel BWA-based aligners validates the benefits of our approach. Finally, an intuitive and flexible API is provided to NGS professionals in order to facilitate the acceptance and adoption of the new tool. The source code of the software described in this paper is publicly available at https://github.com/citiususc/SparkBWA, with a GPL3 license.

GDC 2: Compression of large collections of genomes.

The fall of prices of the high-throughput genome sequencing changes the landscape of modern genomics. A number of large scale projects aimed at sequencing many human genomes are in progress. Genome sequencing also becomes an important aid in the personalized medicine. One of the significant side effects of this change is a necessity of storage and transfer of huge amounts of genomic data. In this paper we deal with the problem of compression of large collections of complete genomic sequences. We propose an algorithm that is able to compress the collection of 1092 human diploid genomes about 9,500 times. This result is about 4 times better than what is offered by the other existing compressors. Moreover, our algorithm is very fast as it processes the data with speed 200 MB/s on a modern workstation. In a consequence the proposed algorithm allows storing the complete genomic collections at low cost, e.g., the examined collection of 1092 human genomes needs only about 700 MB when compressed, what can be compared to about 6.7 TB of uncompressed FASTA files. The source code is available at http://sun.aei.polsl.pl/REFRESH/index.php?page=projects&project=gdc&subpage=about.

RNASurface: fast and accurate detection of locally optimal potentially structured RNA segments

During the past decade, new classes of non-coding RNAs (ncRNAs) and their unexpected functions were discovered. Stable secondary structure is the key feature of many non-coding RNAs. Taking into account huge amounts of genomic data, development of computational methods to survey genomes for structured RNAs remains an actual problem, especially when homologous sequences are not available for comparative analysis. Existing programs scan genomes with a fixed window by efficiently constructing a matrix of RNA minimum free energies. A wide range of lengths of structured RNAs necessitates the use of many different window lengths that substantially increases the output size and computational efforts. In this article, we present an algorithm RNASurface to efficiently scan genomes by constructing a matrix of significance of RNA secondary structures and to identify all locally optimal structured RNA segments up to a predefined size. RNASurface significantly improves precision of identification of known ncRNA in Bacillus subtilis. RNASurface C source code is available from http://bioinf.fbb.msu.ru/RNASurface/downloads.html.

Exploring Database Keyword Search for Association Studies between Genetic Variants and Diseases

Keyword search plays a critical role for researchers in bioinformatics to retrieve structured, semi-structured, and unstructured data. In addition, in order to fully exploit the rich repository of biological databases, data mining has drawn increasing attention of researchers. An interesting issue is to examine the possible relationship between database keyword search (DB KWS) and in- depth database exploration (or data mining) in the context of bioinformatics, and in particular, the potential contribution of DB KWS for data mining. However, so far there is no known systematic investigation on this relationship. In this paper, we provide a preliminary discussion on how we can take advantage of DB KWS for in-depth exploration of biological databases, and describe a case study on the association between genetic variants and diseases. The case study is motivated from the fact that the advent of high throughput sequencing technologies have facilitated in generating a huge amount of genomic data. A wealth of genomic information in the form of publicly available databases is underutilized as a potential resource for uncovering functionally relevant markers underlying complex human traits. The discovery of genetic associations is an important factor in the understanding of human illness to derive disease pathways and a plethora of other information such as the disease-gene associations, the variants associated with the diseases etc. A database was curated of the genome wide association studies, and an algorithm inspired by DBXplorer was used to implement the keyword search over the database in JAVA. The case study further proposes ways to include the association rule mining as a data mining technique, which is useful for discovering interesting relationships hidden in large data sets, to further investigate the results of the keyword search when done with different yet sensible combinations of disease and genes. We believe that such an integrated study to explore the potential of how bioinformatics can take advantage of both techniques in a single bioinformatics application would be a very interesting issue of both theoretical and practical importance.

Modelling knowledge strategy for solving the DNA sequence annotation problem through CommonKADS methodology

Finding the genes that exist within a DNA sequence and assigning them biological features and functions is one of the biggest challenges of Genomics. This task, called annotation, has to be as accurate and reliable as possible, because this information will be applied in other researches. Ideally, each sequence should be annotated and validated by a human expert, who has the knowledge to infer the most appropriate annotation. Nevertheless, the huge amount of genomic data produced by the new sequencing technologies prevents this practice. Developing expert systems that are able to annotate sequences automatically and emulate the expert involvement in certain key points of the process would enhance the annotation quality. In this work, the CommonKADS methodology is innovatively applied for this purpose. It is used to structure and model the knowledge required to build an expert system able to deal with the functional part of sequence annotation, i.e. establishing the biological purpose of the sequence. This approach provides the first general framework for the aforementioned problem, which can be easily extended to related issues.

Niche specific amino acid features within the core genes of the genus Shewanella.

Shewanella species are found to dwell in various ecological niches. The widespread habitation where they live requires specific adaptations. Recent advances in genomic approaches, such as in sequencing technologies, generate huge amount of genomic data that lend support towards understanding the microbial evolution and diversity through comparative study. In this manuscript, we discuss a comparative analysis of core genes of phylogenetically related twelve members from the genus Shewanella. Phylogenetic analysis based on the core genes, differentiated two subgroups of the genus, one group comprises of species characterized as highpressure cold-adapted while the other group is characterized as mesophilic pressure-sensitive species. By analyzing the differences of amino acid composition of these two groups, we have identified the specific trend of amino acid usage that has been adopted by the psychro-peizo-tolerant Shewanella species. The functional categories have also been recognized which are responsible for rendering the particular amino acid compositional pattern in psychropeizophilic Shewanella species facilitating their niche adaptation.

Whole-genome and whole-exome sequencing in neurological diseases

Genetic risk factors that underlie many rare and common neurological disorders remain poorly understood because of the multifactorial and heterogeneous nature of these complex traits. With the decreasing cost of massively parallel sequencing technologies, whole-genome and whole-exome sequencing will soon allow the characterization of the full spectrum of sequence and structural variants present in each individual. Methods are being developed to parse the huge amount of genomic data and to sift out which variants are associated with diseases. Numerous challenges are inherent in the identification of rare and common variants that have a role in complex neurological diseases, and tools are being developed to overcome these challenges. Given that genomic data will soon be the main driver towards the goal of personalized medicine, future developments in the production and interpretation of data, as well as in ethics and counselling, will be needed for whole-genome and whole-exome sequencing to be used as informative tools in a clinical setting.

Coping with genetic diversity: the contribution of pathogen and human genomics to modern vaccinology

Vaccine development faces major difficulties partly because of genetic variation in both infectious organisms and humans. This causes antigenic variation in infectious agents and a high interindividual variability in the human response to the vaccine. The exponential growth of genome sequence information has induced a shift from conventional culture-based to genome-based vaccinology, and allows the tackling of challenges in vaccine development due to pathogen genetic variability. Additionally, recent advances in immunogenetics and genomics should help in the understanding of the influence of genetic factors on the interindividual and interpopulation variations in immune responses to vaccines, and could be useful for developing new vaccine strategies. Accumulating results provide evidence for the existence of a number of genes involved in protective immune responses that are induced either by natural infections or vaccines. Variation in immune responses could be viewed as the result of a perturbation of gene networks; this should help in understanding how a particular polymorphism or a combination thereof could affect protective immune responses. Here we will present: i) the first genome-based vaccines that served as proof of concept, and that provided new critical insights into vaccine development strategies; ii) an overview of genetic predisposition in infectious diseases and genetic control in responses to vaccines; iii) population genetic differences that are a rationale behind group-targeted vaccines; iv) an outlook for genetic control in infectious diseases, with special emphasis on the concept of molecular networks that will provide a structure to the huge amount of genomic data.

Ecological Genomics and Epidemiology

The huge amount of genomic data now becoming available offers both opportunities and challenges for epidemiologists. In this “preview” of likely developments as the field of ecological genomics evolves and merges with epidemiology, we discuss how epidemiology can use new information about genetic sequences and gene expression to form predictions about epidemic features and outcomes and for understanding host resistance and pathogen evolution. DNA sequencing is now complete for some hosts and several pathogens. Microarrays make it possible to measure gene expression simultaneously for thousands of genes. These tools will contribute to plant disease epidemiology by providing information about which resistance or pathogenicity genes are present in individuals and populations, what genes other than those directly involved in resistance and virulence are important in epidemics, the role of the phenotypic status of hosts and pathogens, and the role of the status of the environmental metagenome. Conversely, models of group dynamics supplied by population biology and ecology may be used to interpret gene expression within individual organisms and in populations of organisms. Genomic tools have great potential for improving understanding of resistance gene evolution and the durability of resistance. For example, DNA sequence analysis can be used to evaluate whether an arms race model of co-evolution is supported. Finally, new genomic tools will make it possible to consider the landscape ecology of epidemics in terms of host resistance both as determined by genotype and as expressed in host phenotypes in response to the biotic and abiotic environment. Host phenotype mixtures can be modeled and evaluated, with epidemiological predictions based on phenotypic characteristics such as physiological age and status in terms of induced systemic resistance or systemic acquired resistance.

The German cDNA network: cDNAs, functional genomics and proteomics.

Among the greatest challenges facing biology today is the exploitation of huge amounts of genomic data, and their conversion into functional information about the proteins encoded. For example, the large-scale cDNA sequencing project of the German cDNA Consortium is providing vast numbers of open reading frames (ORFs) encoding novel proteins of completely unknown function. As a first step towards their characterization we have tagged over 500 of these with the green fluorescent protein (GFP), and examined the subcellular localizations of these fusion proteins in living cells. These data have allowed us to classify the proteins into subcellular groups which determines the next step towards a detailed functional characterization. To make further use of these GFP-tagged constructs, a series of functional assays have been designed and implemented to assess the effect of these novel proteins on processes such as cell growth, cell death, and protein transport. Functional assays with such a large set of molecules is only possible by automation. Therefore, we have developed, and adapted, functional assays for use by robotic liquid handling stations and reading stations. A transport assay allows to identify proteins which localize to distinct organelles of the secretory pathway and have the potential to be new regulators in protein transport, a proliferation assay helps identifying proteins that stimulate or repress mitosis. Further assays to monitor the effects of the proteins in apoptosis and signal transduction pathways are in progress. Integrating the functional information that is generated in the assays with data from expression profiling and further functional genomics and proteomics approaches, will ultimately allow us to identify functional networks of proteins in a morphological context, and will greatly contribute to our understanding of cell function.

A Keyword Recommendation System for GenBank

With the progress of Human Genome Project, a huge amount of genomic data is stored in various databases and is open to the public through Internet. The size of these databases grows rapidly and, for example, the size of GenBank1, which is a well-known DNA sequence database, reaches more than 5 million entries. However, as the size of database grows, it becomes more difficult for users to retrieve necessary data. Many of genome databases consist of full text data and the keyword search method is widely used for their retrieval. Hence, when a user submits a simple query with a general keyword, he receives a large number of search results and is obliged to add another keywords to reduce the number of results. In this paper, we propose a keyword recommendation system[1, 2] that recommends proper keywords to the user and show a prototype where the technique is applied for GenBank. Advantages of our system are as follows.