Insulin synthesis is a complex and essential biological process involving a series of interconnected subprocesses. We present a model of the translational process and post-translational modifications occurring in insulin biosynthesis. Using Colored Petri Nets (CPNs), we accurately represent sets of interactions among components such as amino acids, polypeptide chains, and other cellular compounds. This CPN framework captures the intricate state changes within biological processes and establishes a theoretical foundation for modeling and analyzing other critical biological processes.

The short variant discovery is one of the most important steps into genomics studies since it allows genetic variants identification that influences the emergence and evolution of some diseases. Specifically, cancer can be associated with germline variants present in small populations, such as somatic variants located in tumor cells. Therefore, it is necessary to implement workflows that allow data analysis resulting from the new generation sequencing while taking advantage of the resources available in HPC infrastructures. This work presents the PIPEMB-WDL workflow for HPC infrastructure to integrate the short variant discovery for germline and somatic calling, including pre-processing and variants refinement steps, following the best practices of GATK4. This workflow was developed using emerging technologies in current development like WDL and Cromwell engine. The challenges we address in this paper are integrating and deploying container technologies, workload manager technologies, Cromwell, and WDL in our HPC infrastructure.

Context: Data-intensive systems, a.k.a. big data systems (BDS), are software systems that handle a large volume of data in the presence of performance quality attributes, such as scalability and availability. Before the advent of big data management systems (e.g. Cassandra) and frameworks (e.g. Spark), organizations had to cope with large data volumes with custom-tailored solutions. In particular, a decade ago, Tecgraf/PUC-Rio developed a system to monitor truck fleet in real-time and proactively detect events from the positioning data received. Over the years, the system evolved into a complex and large obsolescent code base involving a costly maintenance process. Goal: We report our experience on replacing a legacy BDS with a microservice-based event-driven system. Method: We applied action research, investigating the reasons that motivate the adoption of a microservice-based event-driven architecture, intervening to define the new architecture, and documenting the challenges and lessons learned. Results: We perceived that the resulting architecture enabled easier maintenance and faultisolation. However, the myriad of technologies and the complex data flow were perceived as drawbacks. Based on the challenges faced, we highlight opportunities to improve the design of big data reactive systems. Conclusions: We believe that our experience provides helpful takeaways for practitioners modernizing systems with data-intensive requirements.

Fragment assembly is a current fundamental problem in bioinformatics. In the absence of a reference genome sequence that could guide the whole process, a de Bruijn Graph data structure has been considered to improve the computational processing. Notably, we need to count on a broad set of k-mers, biological sequences substrings. However, the construction of a de Bruijn Graph has a high computational cost, primarily due to main memory consumption. Some approaches use external memory processing to achieve feasibility. These solutions generate all k-mers with high redundancy, increasing the number of managed data and, consequently, the number of I/O operations. This work proposes a new approach for de Bruijn Graph construction that does not need to generate all k-mers. The solution enables to reduce computational requirements and execution feasibility.

In Bioinformatics, the reproducibility of experiments is a fundamental principle to which the data provenance significantly contributes through the acquisition and management of information about the trajectory of the data in a workflow. In addition to the data provenance, aspects such as program configurations and the entire computational environment must be considered to achieve this goal. Cloud computing can provide computational resources, hiding technical details and providing an accessible and configurable on-demand environment for researchers. Cloud federation enables the broad distribution of services and a flexible combination of computing power. Considering this particular scenario, we propose a management platform that collects the provenance data of Bioinformatics workflows allowing portability between different clouds in federated clouds using the Infrastructure as a Service model. This platform is composed of tools that support running these workflows with data provenance captured in NoSQL databases. The retrospective data provenance is captured according to the PROV-DM norms. The findings indicate that the proposed platform can be used for some of the most commoninly available clouds.

In recent years, graph database systems have become very popular and been deployed mainly in situations where the relationship between data is significant, such as in social networks. Although they do not require a particular schema design, a data model contributes to their consistency. Designing diagrams is an approach to satisfying this demand for a conceptual data model. While researchers and companies have been developing concepts and notations for graph database modeling, their notations focus on their specific implementations. In this article, the authors propose a diagram to address this lack of a generic and comprehensive notation for graph databases modeling, named GRAPHED (Graph Description Diagram for Graph Databases). The authors verified the effectiveness and compatibility of GRAPHED in two case studies: fraud identification, and a biological network model.

[Context] Data is being collected at an unprecedented scale. Data sets are becoming so large and complex that traditionally engineered systems may be inadequate to deal with them. While software engineering comprises a large set of approaches to support engineering robust software systems, there is no comprehensive overview of approaches that have been proposed and/or applied in the context of engineering big data systems. [Goal] This study aims at surveying existing research on big data software engineering to unveil and characterize the development approaches and major contributions. [Method] We conducted a systematic mapping study, identifying 52 related research papers, dated from 2011 to 2016. We classified and analyzed the identified approaches, their objectives, application domains, development lifecycle phase, and type of contribution. [Results] As a result, we outline the current state of the art and gaps on employing software engineering approaches to develop big data systems. For instance, we observed that the major challenges are in the area of software architecture and that more experimentation is needed to assess the classified approaches. [Conclusion] The results of this systematic mapping provide an overview on existing approaches to support building big data systems and helps to steer future research based on the identified gaps.

Many scientific experiments in Bioinformatics are executed as computational workflows. Frequently, it is necessary to re-run an experiment under the original circumstances in which it was run to recognize and validate it. Data provenance concerns the origin of data. Knowing the data source facilitates the understanding and analysis of the results, by detailing and documenting the history and the paths of the input data, from the beginning to the end of an experiment. Therefore, in this context, data provenance can be applied when experimenting traceability. This document presents AProvBio, an architecture that can perform the data provenance of scientific experiments in bioinformatics automatically, using the provenance data model PROV-DM and in a graph database. The architecture can perform the automatic provenance type prospectively, retrospectively and with user-defined data. Thus, the architecture stores and captures information obtained during the execution of the data generation processes with user-defined data information, such as features and versions of the programs used. A graph model, based on the PROV-DM model, was proposed for storing the data provenance. The PROV-DM can be represented by a graph, it allows for a more natural modelling, as well as expressing queries at a more natural level, and the implementation of efficient algorithms to perform specific operations.

Computer science solutions for molecular biology problems are often presented in the form of workflows. There is a set of activities performed by different processing entities through managed tasks. Knowledge about the data trajectory throughout a given workflow enables reproducibility by data provenance. In order to reproduce an in silico bioinformatics experiment one must consider other aspects besides those steps followed by a workflow. Indeed, the computational settings in which the involved programs run is a requirement for reproducibility. Cloud computing technology may hide the technical details and make it easier for the user to set up such an on-demand environment. NoSQL database systems have also gained popularity, particularly in the cloud. Considering this particular scenario, we have planned and executed a research study about a bioinformatics workflow running in an IaaS cloud computing environment. We have persisted provenance data according to the PROV-DM model, using different types of NoSQL database systems. We present in this paper some preliminary results from our research work, where we have explored the characteristics of several NoSQL database systems to persist provenance data.

K-mer Mapping, an internal process for many de novo genome fragments assembly methods, constitutes a computational challenge due to its high main memory consumption. We present in this paper a study of indexing methods to deal with this problem, considering plant genome assembling. We propose an ad-hoc I/O cost model to analyze the performance of B+- tree and hashing index structures. We use indexes to detect duplicate k-mers and improve the execution time. An actual RDBMS implementation for experiments with a sugarcane data set shows that one can obtain considerable performance gains while reducing RAM requirements.

Many research projects in bioinformatics may be viewed as scientific workflows. Biologists often run multiple times the same workflow with different parameters in order to refine their data analysis. These executions generate a large volume of files with different formats, which need to be stored for future evaluations. New database models, like NoSQL systems, could be considered to deal with large volumes of data, particularly in distributed systems. This work presents a data replication impact assessment from the execution of scientific workflows for two NoSQL database management systems: Cassandra and MongoDB.

This work considers a scientific experiment as a computational workflow. Provenance models store details of each workflow execution, including produced data, computational tools parameters and their versions, among others. This way, scientists can review details of a particular workflow execution, compare information generated among different executions and plan new ones efficiently. In the bioinformatics domain, particularly in the presence of large volumes of data, persistency of those data generated during the workflow execution is still a research challenge. In this article, we consider a study on provenance data storage for bioinformatics in a document-oriented NoSQL database system. We present data modeling issues and discuss an actual implementation into MongoDB.

Persistency of genomic data brings many research challenges. Among them, alternatives to the commonly used relational database systems should be evaluated, to deal with large volumes of data, in particular writing operations and specific modelling issues. In this paper, we present a NoSQL approach to treat genomic data (Cassandra database system), and evaluate both persistency and I/O operations in experiments with real data. Our results show that this approach enables to store, access and manage genomic data with performance gains, when compared to relational database systems.

Many scientific experiments are designed as computational workflows in bioinformatics. However, the amount of data generated increases at every phase of each execution, hindering the identification of the source and the transformation of data. Therefore, it has become necessary to create new tools to store data provenance, mainly which resources and parameters were used to generate the results, among other information, to validate and publish the experiment. In this paper, we propose to use graph database to store data provenance using the PROV-DM model of bioinformatics workflows. To validate the model, we developed a simulator that worked as a logbook to capture data provenance. A workflow with real genomic data showed that very little additional data should be stored, which means that our provenance model can be easily included in genome projects.

Many scientific experiments are designed as computational workflows in the bioinformatics domain, which facilitates implementation and analysis. However, the amount of data generated increases at every phase of each execution, hindering the identification of the source and the data transformation. Therefore, it has become necessary to create new tools to verify automatically which resources and parameters were used to generate the results, among other information to validate and publish the experiment. This functionality of automatically capturing data provenance has been receiving attention in the scientific community, primarily with regard to bioinformatics projects, due the fact that the same workflow is executed several times with different parameters and versions of the tools. In this paper, we propose to use relational schema to automatically store data provenance using the PROV-DM model for workflows in bioinformatics projects.

In this work, we used the PROV-DM model to manage data provenance in workflows of genome projects. This provenance model allows the storage of details of one workflow execution, e.g., raw and produced data and computational tools, their versions and parameters. Using this model, biologists can access details of one particular execution of a workflow, compare results produced by different executions, and plan new experiments more efficiently. In addition to this, a provenance simulator was created, which facilitates the inclusion of provenance data of one genome project workflow execution. Finally, we discuss one case study, which aims to identify genes involved in specific metabolic pathways of Bacillus cereus, as well as to compare this isolate with other phylogenetic related bacteria from the Bacillus group. B. cereus is an extremophilic bacteria, collected in warm water in the Midwestern Region of Brazil, its DNA samples having been sequenced with an NGS machine.

MOTIVATION: Many analyses in modern biological research are based on comparisons between biological sequences, resulting in functional, evolutionary and structural inferences. When large numbers of sequences are compared, heuristics are often used resulting in a certain lack of accuracy. In order to improve and validate results of such comparisons, we have performed radical all-against-all comparisons of 4 million protein sequences belonging to the RefSeq database, using an implementation of the Smith-Waterman algorithm. This extremely intensive computational approach was made possible with the help of World Community Grid, through the Genome Comparison Project. The resulting database, ProteinWorldDB, which contains coordinates of pairwise protein alignments and their respective scores, is now made available. Users can download, compare and analyze the results, filtered by genomes, protein functions or clusters. ProteinWorldDB is integrated with annotations derived from Swiss-Prot, Pfam, KEGG, NCBI Taxonomy database and gene ontology. The database is a unique and valuable asset, representing a major effort to create a reliable and consistent dataset of cross-comparisons of the whole protein content encoded in hundreds of completely sequenced genomes using a rigorous dynamic programming approach. AVAILABILITY: The database can be accessed through http://proteinworlddb.org

Most autonomic database systems do not explicit their decision rationale behind tuning activities. Consequently, users may not trust some of the automatic tuning decisions. In this paper we propose a rather transparent strategy, that provides feedback to database administrators, based on information extracted from the database log. The proposed approach consists in transforming log results into a user-friendly knowledge representation, based on the graphical representation for OWL. This model provides users with the rationale behind system decisions, adds semantics to the database self-tuning actions, and provides useful provenance information about the whole process.

In-silico experiments require molecular biological knowledge to be mapped into a data model. In a previous work, we have elicited a list of requirements to be attended by a conceptual language for the molecular biology domain. In this paper, we present the evolution of this research by introducing a such language that encompasses most of the identified biological domain requirements. The language offers logical constraints expressions that augments the semantics of data and relationships types, as well as monotonic inheritance. Those aspects address the uncertainty and variability of biological knowledge shortening the gap between scientists mental model and traditional conceptual models.

Bio-AXS is an object-oriented framework tool that aims at integrating genomic databases as well as related applications. This approach provides the expected flexibility, reusability and extensibility requirements of this domain. We present here an overview of Bio-AXS implementation issues that show how this tool may be effectively used in practice.