Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data

BackgroundCluster analysis is an important technique for the exploratory analysis of biological data. Such data is often high-dimensional, inherently noisy and contains outliers. This makes clustering challenging. Mixtures are versatile and powerful statistical models which perform robustly for clustering in the presence of noise and have been successfully applied in a wide range of applications.ResultsPyMix - the Python mixture package implements algorithms and data structures for clustering with basic and advanced mixture models. The advanced models include context-specific independence mixtures, mixtures of dependence trees and semi-supervised learning. PyMix is licenced under the GNU General Public licence (GPL). PyMix has been successfully used for the analysis of biological sequence, complex disease and gene expression data.ConclusionsPyMix is a useful tool for cluster analysis of biological data. Due to the general nature of the framework, PyMix can be applied to a wide range of applications and data sets.

Application Programming Interfaces (APIs) define how Web services, middle-wares, frameworks, and libraries communicate with their clients. An API that conforms to REpresentational State Transfer (REST) design principles is known as REST API. At present, it is an industry-standard for interaction among Web services. There exist mainly three categories of APIs: public, partner, and private. Public APIs are designed for external consumers, whereas partner APIs are designed aiming at organizational partners. In contrast, private APIs are designed solely for internal use. The API quality matters regardless of their category and intended consumers. To assess the (linguistic) design of APIs, researchers defined linguistic patterns (i.e., best API design practices) and linguistic antipatterns (i.e., poor API design practices.) APIs that follow linguistic patterns are easy to understand, use, and maintain. In this study, we analyze and compare the design quality of public, partner, and private APIs. More specifically, we made a large survey by analyzing and performing the detection of nine linguistic patterns and their corresponding antipatterns on more than 2,500 end-points from 37 APIs. Our results suggest that (1) public, partner, and private APIs lack quality linguistic design, (2) among the three API categories, private APIs lack linguistic design the most, and (3) end-points are amorphous, contextless, and non-descriptive in partner APIs. End-points have contextless design and poor documentation regardless of the API categories.

Statistical analysis of biological data from preoperational-postoperational industrial water quality monitoring

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

Knowledge-based systems (KBS) and intelligent computing techniques (ICT) are widely used in the field of bioinformatics. A number of techniques have been applied for the interpretation and analysis of biological data. KBS consists of case based reasoning (CBR) whereas ICT consists of artificial neural network (ANN), fuzzy logic (FL), data mining techniques (DMT), swarm intelligence (SI) and evolutionary algorithm (EA). The aim of this review is to introduce briefly the various KBS and ICT and their integration such as ANN-DMT, CBR-DMT, EA-ANN, EA-DMT, EA-ANN-DMT, ANN-SI, ANN-FL, ANN-SI-EA-FL, EA-FL, EA-SI etc. which are applied in the field of bioinformatics. This study is exhibited in the form of a table which includes application area, intelligent technique used, machine learning task and the database/databank used. This study will help the novice researchers to choose appropriate KBS and ICT to deal with the challenges encountered during representation, integration, analysis, interpretation and management of biological data.

Data clustering is a major data mining technique and has been shown to be useful in a wide variety of domains, including medical and biological statistical data analysis. A non trivial application of cluster analysis occurs in the identification of different subpopulations of particles in large-sized heterogeneous flow-cytometric data. Mixture-model based clustering has been several times applied in the past to medical and biological data analysis; to our knowledge, however, non of these applications was involved with flow-cytometric data. We claim, that utilizing the probabilities mixture model offers several advantages compared to other proposed flow-cytometric data clustering approaches. We apply this model in order to gate lymphocytes in peripheral blood, which is a necessary first-step procedure when dealing with various hematological diseases diagnoses, such as lymphocytic leukemias and lymphoma.KeywordsMixture ModelBayesian NetworkData ClusterSupervise ClusterGating LymphocyteThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Recently API (Application Programming Interface) is becoming more popular for developers. When software is designed, most of the time, developers need to use APIs to manage a specific task. Developers use various kinds of APIs. Some of them are built by themselves and some are used from public APIs. API is a set of functions and procedures that allows another program or application to get access to features or data. Public APIs are open in public networks; developers collect these APIs depending on their specific needs. Developers need to interact with other software, as a result, a developer can conduct specific task without authorization to access the entirety of the software. It definitely reduces our loads at the same time introduces risks. In the end every developer wants to ensure security to his/her application. Commonly used public APIs are not enough secure to provide security to confidential data. We focused on these public APIs that are commonly used by developers. We tested a set of public APIs in our security lab and we have found many vulnerabilities that are highly alarming for developers who are going to use these API. In this paper we have tried to introduce the current status of vulnerable APIs. Moreover, several relationships exist between API vulnerabilities. In this paper we have also discussed the dependencies and relationships between API vulnerabilities.

Public participation in research, also known as citizen science, is being increasingly adopted for the analysis of biological volumetric data. Researchers working in this domain are applying online citizen science as a scalable distributed data analysis approach, with recent research demonstrating that non-experts can productively contribute to tasks such as the segmentation of organelles in volume electron microscopy data. This, alongside the growing challenge to rapidly process the large amounts of biological volumetric data now routinely produced, means there is increasing interest within the research community to apply online citizen science for the analysis of data in this context. Here, we synthesise core methodological principles and practices for applying citizen science for analysis of biological volumetric data. We collate and share the knowledge and experience of multiple research teams who have applied online citizen science for the analysis of volumetric biological data using the Zooniverse platform (www.zooniverse.org). We hope this provides inspiration and practical guidance regarding how contributor effort via online citizen science may be usefully applied in this domain.

The growing interest in chemogenomics approaches over the last years has led to an increasing amount of data regarding chemical and the corresponding biological activity space. The resulting data, collected in either in-house or public databases, need to be analyzed efficiently to speed-up the increasingly difficult task of drug discovery. Unfortunately, the discovery of new chemical entities or new targets for known drugs ('drug repurposing') is not suitable to a fully automated analysis or a simple drill down process. Visual interactive interfaces that allow to explore chemical space in a systematic manner and facilitate analytical reasoning can help to overcome these problems. Scaffold Hunter is a tool for the visual analysis of chemical compound databases that provides integrated visualization and analysis of biological activity data and fosters the interactive exploration of data imported from a variety of sources. We describe the features and illustrate the use by means of an exemplary analysis workflow.

BackgroundA significant problem in the study of mechanisms of an organism's development is the elucidation of interrelated factors which are making an impact on the different levels of the organism, such as genes, biological molecules, cells, and cell systems. Numerous sources of heterogeneous data which exist for these subsystems are still not integrated sufficiently enough to give researchers a straightforward opportunity to analyze them together in the same frame of study. Systematic application of data integration methods is also hampered by a multitude of such factors as the orthogonal nature of the integrated data and naming problems.ResultsHere we report on a new version of BiologicalNetworks, a research environment for the integral visualization and analysis of heterogeneous biological data. BiologicalNetworks can be queried for properties of thousands of different types of biological entities (genes/proteins, promoters, COGs, pathways, binding sites, and other) and their relations (interactions, co-expression, co-citations, and other). The system includes the build-pathways infrastructure for molecular interactions/relations and module discovery in high-throughput experiments. Also implemented in BiologicalNetworks are the Integrated Genome Viewer and Comparative Genomics Browser applications, which allow for the search and analysis of gene regulatory regions and their conservation in multiple species in conjunction with molecular pathways/networks, experimental data and functional annotations.ConclusionsThe new release of BiologicalNetworks together with its back-end database introduces extensive functionality for a more efficient integrated multi-level analysis of microarray, sequence, regulatory, and other data. BiologicalNetworks is freely available at http://www.biologicalnetworks.org.

Biological data analysis is typically implemented using a pipeline that combines many data analysis tools and meta-databases. These pipelines must scale to very large datasets, and therefore often require parallel and distributed computing. There are many infrastructure systems for data-intensive computing. However, most biological data analysis pipelines do not leverage these systems. An important challenge is therefore to integrate biological data analysis frameworks with data-intensive computing infrastructure systems. In this paper, we describe how we have extended data-intensive computing systems to support unmodified biological data analysis tools. We also describe four approaches for integrating the extended systems with biological data analysis frameworks, and discuss challenges for such integration on production platforms. Our results demonstrate how biological data analysis pipelines can benefit from infrastructure systems for data-intensive computing.

Application Programming Interfaces (APIs) are software tools that help different programs work together. APIs can improve an organization's presence on the Web with tools that integrate various useful, popular programs. This study aimed to identify appropriate web-based APIs used by the most popular public library websites for presentation on Iranian public libraries’ websites. For this purpose, we conducted this study in two stages: In stage one, Web APIs were identified by reviewing the websites of the top public libraries in the world. Then, in stage two, using the obtained results, important Web APIs were selected utilizing experts' opinions (the heuristic method). In stage one, the 30 Web APIs in two categories were identified: 10 public Web APIs and 20 private Web APIs. Then, in stage two, 7 public APIs and 17 private Web APIs for these websites were selected, based on expert analysis. The results of this study can be used to improve the design of public library websites and enhance the communication of such websites' presence on the Web.

Application Programming Interfaces (APIs) are software tools that help different programs work together. APIs can improve an organization's presence on the Web with tools that integrate various useful, popular programs. This study aimed to identify appropriate web-based APIs used by the most popular public library websites for presentation on Iranian public libraries’ websites. For this purpose, we conducted this study in two stages: In stage one, Web APIs were identified by reviewing the websites of the top public libraries in the world. Then, in stage two, using the obtained results, important Web APIs were selected utilizing experts' opinions (the heuristic method). In stage one, the 30 Web APIs in two categories were identified: 10 public Web APIs and 20 private Web APIs. Then, in stage two, 7 public APIs and 17 private Web APIs for these websites were selected, based on expert analysis. The results of this study can be used to improve the design of public library websites and enhance the communication of such websites' presence on the Web.

Substantial progress has been made in the field of "omics" research (e.g., Genomics, Transcriptomics, Proteomics, and Metabolomics), leading to a vast amount of biological data. In order to represent large biological data sets in an easily interpretable manner, this information is frequently visualized as graphs, i.e., a set of nodes and edges. Nodes are representations of biological molecules and edges connect the nodes depicting some kind of relationship. Obviously, there is a high demand for computer-based assistance for both visualization and analysis of biological data, which are often heterogeneous and retrieved from different sources. This chapter focuses on software tools that assist in visual exploration and analysis of biological networks. Global requirements for such programs are discussed. Utilization of visualization software is exemplified using the widely used Cytoscape tool. Additional information about the use of Cytoscape is provided in the Notes section. Furthermore, special features of alternative software tools are highlighted in order to assist researchers in the choice of an adequate program for their specific requirements.

Computational Approaches to Examine Cellular SignallingNetworks