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Journal articles on the topic 'Bioinformatics workflow'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Jackson, Michael, Kostas Kavoussanakis, and Edward W. J. Wallace. "Using prototyping to choose a bioinformatics workflow management system." PLOS Computational Biology 17, no. 2 (2021): e1008622. http://dx.doi.org/10.1371/journal.pcbi.1008622.

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Workflow management systems represent, manage, and execute multistep computational analyses and offer many benefits to bioinformaticians. They provide a common language for describing analysis workflows, contributing to reproducibility and to building libraries of reusable components. They can support both incremental build and re-entrancy—the ability to selectively re-execute parts of a workflow in the presence of additional inputs or changes in configuration and to resume execution from where a workflow previously stopped. Many workflow management systems enhance portability by supporting th

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2

Bedő, Justin. "BioShake: a Haskell EDSL for bioinformatics workflows." PeerJ 7 (July 9, 2019): e7223. http://dx.doi.org/10.7717/peerj.7223.

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Typical bioinformatics analyses comprise of long running computational workflows. An important part of reproducible research is the management and execution of these workflows to allow robust execution and to minimise errors. BioShake is an embedded domain specific language in Haskell for specifying and executing computational workflows for bioinformatics that significantly reduces the possibility of errors occurring. Unlike other workflow frameworks, BioShake raises many properties to the type level allowing the correctness of a workflow to be statically checked during compilation, catching e

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Kožusznik, Jan, Petr Bainar, Jana Klímová, et al. "SPIM workflow manager for HPC." Bioinformatics 35, no. 19 (2019): 3875–76. http://dx.doi.org/10.1093/bioinformatics/btz140.

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Abstract Summary Here we introduce a Fiji plugin utilizing the HPC-as-a-Service concept, significantly mitigating the challenges life scientists face when delegating complex data-intensive processing workflows to HPC clusters. We demonstrate on a common Selective Plane Illumination Microscopy image processing task that execution of a Fiji workflow on a remote supercomputer leads to improved turnaround time despite the data transfer overhead. The plugin allows the end users to conveniently transfer image data to remote HPC resources, manage pipeline jobs and visualize processed results directly

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4

Conery, John S., Julian M. Catchen, and Michael Lynch. "Rule-based workflow management for bioinformatics." VLDB Journal 14, no. 3 (2005): 318–29. http://dx.doi.org/10.1007/s00778-005-0153-9.

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Köster, Johannes, and Sven Rahmann. "Snakemake—a scalable bioinformatics workflow engine." Bioinformatics 34, no. 20 (2018): 3600. http://dx.doi.org/10.1093/bioinformatics/bty350.

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Koster, J., and S. Rahmann. "Snakemake--a scalable bioinformatics workflow engine." Bioinformatics 28, no. 19 (2012): 2520–22. http://dx.doi.org/10.1093/bioinformatics/bts480.

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Simopoulos, Caitlin M. A., Zhibin Ning, Xu Zhang, et al. "pepFunk: a tool for peptide-centric functional analysis of metaproteomic human gut microbiome studies." Bioinformatics 36, no. 14 (2020): 4171–79. http://dx.doi.org/10.1093/bioinformatics/btaa289.

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Abstract Motivation Enzymatic digestion of proteins before mass spectrometry analysis is a key process in metaproteomic workflows. Canonical metaproteomic data processing pipelines typically involve matching spectra produced by the mass spectrometer to a theoretical spectra database, followed by matching the identified peptides back to parent-proteins. However, the nature of enzymatic digestion produces peptides that can be found in multiple proteins due to conservation or chance, presenting difficulties with protein and functional assignment. Results To combat this challenge, we developed pep

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Bhardwaj, Vivek, Steffen Heyne, Katarzyna Sikora, et al. "snakePipes: facilitating flexible, scalable and integrative epigenomic analysis." Bioinformatics 35, no. 22 (2019): 4757–59. http://dx.doi.org/10.1093/bioinformatics/btz436.

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Abstract Summary Due to the rapidly increasing scale and diversity of epigenomic data, modular and scalable analysis workflows are of wide interest. Here we present snakePipes, a workflow package for processing and downstream analysis of data from common epigenomic assays: ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq. snakePipes enables users to assemble variants of each workflow and to easily install and upgrade the underlying tools, via its simple command-line wrappers and yaml files. Availability and implementation snakePipes can be installed via conda: `conda in

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Theil, Sebastien, and Etienne Rifa. "rANOMALY: AmplicoN wOrkflow for Microbial community AnaLYsis." F1000Research 10 (January 7, 2021): 7. http://dx.doi.org/10.12688/f1000research.27268.1.

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Bioinformatic tools for marker gene sequencing data analysis are continuously and rapidly evolving, thus integrating most recent techniques and tools is challenging. We present an R package for data analysis of 16S and ITS amplicons based sequencing. This workflow is based on several R functions and performs automatic treatments from fastq sequence files to diversity and differential analysis with statistical validation. The main purpose of this package is to automate bioinformatic analysis, ensure reproducibility between projects, and to be flexible enough to quickly integrate new bioinformat

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Dijkstra, Maurits J. J., Atze J. van der Ploeg, K. Anton Feenstra, Wan J. Fokkink, Sanne Abeln, and Jaap Heringa. "Tailor-made multiple sequence alignments using the PRALINE 2 alignment toolkit." Bioinformatics 35, no. 24 (2019): 5315–17. http://dx.doi.org/10.1093/bioinformatics/btz572.

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Abstract Summary PRALINE 2 is a toolkit for custom multiple sequence alignment workflows. It can be used to incorporate sequence annotations, such as secondary structure or (DNA) motifs, into the alignment scoring, as well as to customize many other aspects of a progressive multiple alignment workflow. Availability and implementation PRALINE 2 is implemented in Python and available as open source software on GitHub: https://github.com/ibivu/PRALINE/.

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Cheng, Gong, Quan Lu, Ling Ma, Guocai Zhang, Liang Xu, and Zongshan Zhou. "BGDMdocker: a Docker workflow for data mining and visualization of bacterial pan-genomes and biosynthetic gene clusters." PeerJ 5 (November 30, 2017): e3948. http://dx.doi.org/10.7717/peerj.3948.

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Recently, Docker technology has received increasing attention throughout the bioinformatics community. However, its implementation has not yet been mastered by most biologists; accordingly, its application in biological research has been limited. In order to popularize this technology in the field of bioinformatics and to promote the use of publicly available bioinformatics tools, such as Dockerfiles and Images from communities, government sources, and private owners in the Docker Hub Registry and other Docker-based resources, we introduce here a complete and accurate bioinformatics workflow b

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Yuen, Denis, Louise Cabansay, Andrew Duncan, et al. "The Dockstore: enhancing a community platform for sharing reproducible and accessible computational protocols." Nucleic Acids Research 49, W1 (2021): W624—W632. http://dx.doi.org/10.1093/nar/gkab346.

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Abstract Dockstore (https://dockstore.org/) is an open source platform for publishing, sharing, and finding bioinformatics tools and workflows. The platform has facilitated large-scale biomedical research collaborations by using cloud technologies to increase the Findability, Accessibility, Interoperability and Reusability (FAIR) of computational resources, thereby promoting the reproducibility of complex bioinformatics analyses. Dockstore supports a variety of source repositories, analysis frameworks, and language technologies to provide a seamless publishing platform for authors to create a

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13

Damkliang, Kasikrit, Pichaya Tandayya, Unitsa Sangket, and Ekawat Pasomsub. "Integrated Automatic Workflow for Phylogenetic Tree Analysis Using Public Access and Local Web Services." Journal of Integrative Bioinformatics 13, no. 1 (2016): 7–22. http://dx.doi.org/10.1515/jib-2016-287.

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SummaryAt the present, coding sequence (CDS) has been discovered and larger CDS is being revealed frequently. Approaches and related tools have also been developed and upgraded concurrently, especially for phylogenetic tree analysis. This paper proposes an integrated automatic Taverna workflow for the phylogenetic tree inferring analysis using public access web services at European Bioinformatics Institute (EMBL-EBI) and Swiss Institute of Bioinformatics (SIB), and our own deployed local web services. The workflow input is a set of CDS in the Fasta format. The workflow supports 1,000 to 20,000

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Emami Khoonsari, Payam, Pablo Moreno, Sven Bergmann, et al. "Interoperable and scalable data analysis with microservices: applications in metabolomics." Bioinformatics 35, no. 19 (2019): 3752–60. http://dx.doi.org/10.1093/bioinformatics/btz160.

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Abstract Motivation Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. Results We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for

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Chao Zhou. "A Workflow Developing and Executing Environment for Bioinformatics." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 3 (2013): 850–57. http://dx.doi.org/10.4156/aiss.vol5.issue3.99.

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Ewels, Philip, Felix Krueger, Max Käller, and Simon Andrews. "Cluster Flow: A user-friendly bioinformatics workflow tool." F1000Research 5 (December 6, 2016): 2824. http://dx.doi.org/10.12688/f1000research.10335.1.

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Pipeline tools are becoming increasingly important within the field of bioinformatics. Using a pipeline manager to manage and run workflows comprised of multiple tools reduces workload and makes analysis results more reproducible. Existing tools require significant work to install and get running, typically needing pipeline scripts to be written from scratch before running any analysis. We present Cluster Flow, a simple and flexible bioinformatics pipeline tool designed to be quick and easy to install. Cluster Flow comes with 40 modules for common NGS processing steps, ready to work out of the

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Ewels, Philip, Felix Krueger, Max Käller, and Simon Andrews. "Cluster Flow: A user-friendly bioinformatics workflow tool." F1000Research 5 (May 2, 2017): 2824. http://dx.doi.org/10.12688/f1000research.10335.2.

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Pipeline tools are becoming increasingly important within the field of bioinformatics. Using a pipeline manager to manage and run workflows comprised of multiple tools reduces workload and makes analysis results more reproducible. Existing tools require significant work to install and get running, typically needing pipeline scripts to be written from scratch before running any analysis. We present Cluster Flow, a simple and flexible bioinformatics pipeline tool designed to be quick and easy to install. Cluster Flow comes with 40 modules for common NGS processing steps, ready to work out of the

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18

Goderis, Antoon, Paul Fisher, Andrew Gibson, et al. "Benchmarking workflow discovery: a case study from bioinformatics." Concurrency and Computation: Practice and Experience 21, no. 16 (2009): 2052–69. http://dx.doi.org/10.1002/cpe.1447.

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19

Mondelli, Maria Luiza, Thiago Magalhães, Guilherme Loss, et al. "BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments." PeerJ 6 (August 29, 2018): e5551. http://dx.doi.org/10.7717/peerj.5551.

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Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require high-performance computing techniques and can benefit from specialized technologies such as Scientific Workflow Management Systems and databases. In this work, we present BioWorkbench, a framework for managing and analyzing bioinformatics experiments. This framework automatically collects provenance data, including both performance data from workflow execution and dat

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Linke, Burkhard, Robert Giegerich, and Alexander Goesmann. "Conveyor: a workflow engine for bioinformatic analyses." Bioinformatics 27, no. 7 (2011): 903–11. http://dx.doi.org/10.1093/bioinformatics/btr040.

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Kahsay, Robel, Jeet Vora, Rahi Navelkar, et al. "GlyGen data model and processing workflow." Bioinformatics 36, no. 12 (2020): 3941–43. http://dx.doi.org/10.1093/bioinformatics/btaa238.

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Abstract Summary Glycoinformatics plays a major role in glycobiology research, and the development of a comprehensive glycoinformatics knowledgebase is critical. This application note describes the GlyGen data model, processing workflow and the data access interfaces featuring programmatic use case example queries based on specific biological questions. The GlyGen project is a data integration, harmonization and dissemination project for carbohydrate and glycoconjugate-related data retrieved from multiple international data sources including UniProtKB, GlyTouCan, UniCarbKB and other key resour

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Sarkadi, Balazs, Istvan Liko, Gabor Nyiro, Peter Igaz, Henriett Butz, and Attila Patocs. "Analytical Performance of NGS-Based Molecular Genetic Tests Used in the Diagnostic Workflow of Pheochromocytoma/Paraganglioma." Cancers 13, no. 16 (2021): 4219. http://dx.doi.org/10.3390/cancers13164219.

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Next Generation Sequencing (NGS)-based methods are high-throughput and cost-effective molecular genetic diagnostic tools. Targeted gene panel and whole exome sequencing (WES) are applied in clinical practice for assessing mutations of pheochromocytoma/paraganglioma (PPGL) associated genes, but the best strategy is debated. Germline mutations of at the least 18 PPGL genes are present in approximately 20–40% of patients, thus molecular genetic testing is recommended in all cases. We aimed to evaluate the analytical and clinical performances of NGS methods for mutation detection of PPGL-associate

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Puig, Oscar, Eugene Joseph, Malgorzata Jaremko, et al. "Comprehensive next generation sequencing assay and bioinformatic pipeline for identifying pathogenic variants associated with hereditary cancers." Journal of Clinical Oncology 35, no. 15_suppl (2017): e13105-e13105. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e13105.

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e13105 Background: Diagnosis of hereditary cancer syndromes involves time-consuming comprehensive clinical and laboratory work-up, however, timely and accurate diagnosis is pivotal to the clinical management of cancer patients. Germline genetic testing has shown to facilitate the diagnostic process, allowing for identification and management of individuals at risk for inherited cancers. However, the laboratory diagnostics process requires not only development and validation of comprehensive gene panels to improve diagnostic yields, but a quality driven workflow including an end-to-end bioinfor

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Thi Nhung, Doan, and Bui Van Ngoc. "Bioinformatic approaches for analysis of coral-associated bacteria using R programming language." Vietnam Journal of Biotechnology 18, no. 4 (2021): 733–43. http://dx.doi.org/10.15625/1811-4989/18/4/15320.

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Recent advances in metagenomics and bioinformatics allow the robust analysis of the composition and abundance of microbial communities, functional genes, and their metabolic pathways. So far, there has been a variety of computational/statistical tools or software for analyzing microbiome, the common problems that occurred in its implementation are, however, the lack of synchronization and compatibility of output/input data formats between such software. To overcome these challenges, in this study context, we aim to apply the DADA2 pipeline (written in R programming language) instead of using a

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Lee, Michael D. "GToTree: a user-friendly workflow for phylogenomics." Bioinformatics 35, no. 20 (2019): 4162–64. http://dx.doi.org/10.1093/bioinformatics/btz188.

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Abstract Summary Genome-level evolutionary inference (i.e. phylogenomics) is becoming an increasingly essential step in many biologists’ work. Accordingly, there are several tools available for the major steps in a phylogenomics workflow. But for the biologist whose main focus is not bioinformatics, much of the computational work required—such as accessing genomic data on large scales, integrating genomes from different file formats, performing required filtering, stitching different tools together etc.—can be prohibitive. Here I introduce GToTree, a command-line tool that can take any combina

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Piras, Marco Enrico, Luca Pireddu, and Gianluigi Zanetti. "wft4galaxy: a workflow testing tool for galaxy." Bioinformatics 33, no. 23 (2017): 3805–7. http://dx.doi.org/10.1093/bioinformatics/btx461.

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Wercelens, Polyane, Waldeyr da Silva, Fernanda Hondo, et al. "Bioinformatics Workflows With NoSQL Database in Cloud Computing." Evolutionary Bioinformatics 15 (January 2019): 117693431988997. http://dx.doi.org/10.1177/1176934319889974.

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Scientific workflows can be understood as arrangements of managed activities executed by different processing entities. It is a regular Bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of Bioinformatics experiments, various aspects should be

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Sohn, Bong-Ki, Keon-Myung Lee, and Hak-Joon Kim. "A Multiagent System for Workflow-Based Bioinformatics Tool Integration." International Journal of Fuzzy Logic and Intelligent Systems 3, no. 2 (2003): 133–37. http://dx.doi.org/10.5391/ijfis.2003.3.2.133.

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Plesniewicz, Gerald, and Baurzhan Karabekov. "Specifying temporal knowledge for workflows ontologies." Open Computer Science 6, no. 1 (2016): 226–31. http://dx.doi.org/10.1515/comp-2016-0020.

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AbstractA workflow is an automation of a process, in which participants (people or programs) are involved in activities for solving a set of tasks according to certain rules and constraints in order to attain a common goal. The concept of workflow appeared in business informatics. Currently the workflow techniques are used in many other fields such as medical informatics, bioinformatics, automation of scientific research, computer-aided design and manufacturing, etc. An ontology is a formal description (in terms of concepts, entities, their properties and relationships) of knowledge for solvin

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Chakroborti, Debasish, Banani Roy, and Sristy Sumana Nath. "Designing for Recommending Intermediate States in A Scientific Workflow Management System." Proceedings of the ACM on Human-Computer Interaction 5, EICS (2021): 1–29. http://dx.doi.org/10.1145/3457145.

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To process a large amount of data sequentially and systematically, proper management of workflow components (i.e., modules, data, configurations, associations among ports and links) in a Scientific Workflow Management System (SWfMS) is inevitable. Managing data with provenance in a SWfMS to support reusability of workflows, modules, and data is not a simple task. Handling such components is even more burdensome for frequently assembled and executed complex workflows for investigating large datasets with different technologies (i.e., various learning algorithms or models). However, a great many

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Palmblad, Magnus, Anna-Lena Lamprecht, Jon Ison, and Veit Schwämmle. "Automated workflow composition in mass spectrometry-based proteomics." Bioinformatics 35, no. 4 (2018): 656–64. http://dx.doi.org/10.1093/bioinformatics/bty646.

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Phillips, Jason R., Daniel L. Svoboda, Arpit Tandon, et al. "BMDExpress 2: enhanced transcriptomic dose-response analysis workflow." Bioinformatics 35, no. 10 (2018): 1780–82. http://dx.doi.org/10.1093/bioinformatics/bty878.

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Claeys, M., V. Storms, H. Sun, T. Michoel, and K. Marchal. "MotifSuite: workflow for probabilistic motif detection and assessment." Bioinformatics 28, no. 14 (2012): 1931–32. http://dx.doi.org/10.1093/bioinformatics/bts293.

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34

Mariette, Jérôme, Frédéric Escudié, Philippe Bardou, et al. "Jflow: a workflow management system for web applications." Bioinformatics 32, no. 3 (2015): 456–58. http://dx.doi.org/10.1093/bioinformatics/btv589.

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Sztromwasser, Paweł, Kjell Petersen, and Pál Puntervoll. "Data partitioning enables the use of standard SOAP Web Services in genome-scale workflows." Journal of Integrative Bioinformatics 8, no. 2 (2011): 95–114. http://dx.doi.org/10.1515/jib-2011-163.

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Summary Biological databases and computational biology tools are provided by research groups around the world, and made accessible on the Web. Combining these resources is a common practice in bioinformatics, but integration of heterogeneous and often distributed tools and datasets can be challenging. To date, this challenge has been commonly addressed in a pragmatic way, by tedious and error-prone scripting. Recently however a more reliable technique has been identified and proposed as the platform that would tie together bioinformatics resources, namely Web Services. In the last decade the W

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36

Ahmed, Azza E., Phelelani T. Mpangase, Sumir Panji, et al. "Organizing and running bioinformatics hackathons within Africa: The H3ABioNet cloud computing experience." AAS Open Research 1 (April 18, 2018): 9. http://dx.doi.org/10.12688/aasopenres.12847.1.

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The need for portable and reproducible genomics analysis pipelines is growing globally as well as in Africa, especially with the growth of collaborative projects like the Human Health and Heredity in Africa Consortium (H3Africa). The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for portable, reproducible pipelines adapted to heterogeneous compute environments, and for the nurturing of technical expertise in workflow languages and containerization technologies. To address this need, in 2016 H3ABioNet arranged its first Cloud Computing and Reproducible Workflows Ha

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Ma, Xiaoxia, Yijun Meng, Pu Wang, Zhonghai Tang, Huizhong Wang, and Tian Xie. "Bioinformatics-assisted, integrated omics studies on medicinal plants." Briefings in Bioinformatics 21, no. 6 (2019): 1857–74. http://dx.doi.org/10.1093/bib/bbz132.

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Abstract The immense therapeutic and economic values of medicinal plants have attracted increasing attention from the worldwide researchers. It has been recognized that production of the authentic and high-quality herbal drugs became the prerequisite for maintaining the healthy development of the traditional medicine industry. To this end, intensive research efforts have been devoted to the basic studies, in order to pave a way for standardized authentication of the plant materials, and bioengineering of the metabolic pathways in the medicinal plants. In this paper, the recent advances of omic

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Huang, Yu, Jian Yong Wang, Xiao Mei Wei, and Bin Hu. "Bioinfo-Kit: A Sharing Software Tool for Bioinformatics." Applied Mechanics and Materials 472 (January 2014): 466–69. http://dx.doi.org/10.4028/www.scientific.net/amm.472.466.

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In bioinformatics research, for the lacks of an effective algorithm-integrating mechanism and a friendly graphical user interface of the toolkits in the field of biological data processing and analyzing, the authors designed and implemented a sharing software system Bioinfo-Kit based on the Java 2 Platform Enterprise Edition (J2EE), which provides 1) a general application development interface to integrate or bridge other programs and 2) a workflow mechanism to operate them and make them talk easily. In addition, a module for biological data (multiple electrodes data, biomedical data and etc.)

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Anslan, Sten, R. Henrik Nilsson, Christian Wurzbacher, Petr Baldrian, Leho Tedersoo, and Mohammad Bahram. "Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding." MycoKeys 39 (September 10, 2018): 29–40. http://dx.doi.org/10.3897/mycokeys.39.28109.

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Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data se

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Ahmed, Azza E., Phelelani T. Mpangase, Sumir Panji, et al. "Organizing and running bioinformatics hackathons within Africa: The H3ABioNet cloud computing experience." AAS Open Research 1 (August 7, 2019): 9. http://dx.doi.org/10.12688/aasopenres.12847.2.

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The need for portable and reproducible genomics analysis pipelines is growing globally as well as in Africa, especially with the growth of collaborative projects like the Human Health and Heredity in Africa Consortium (H3Africa). The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for portable, reproducible pipelines adapted to heterogeneous computing environments, and for the nurturing of technical expertise in workflow languages and containerization technologies. Building on the network’s Standard Operating Procedures (SOPs) for common genomic analyses, H3ABioNet

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Jeong, E., M. Nagasaki, E. Ikeda, Y. Sekiya, A. Saito, and S. Miyano. "CSO validator: improving manual curation workflow for biological pathways." Bioinformatics 27, no. 17 (2011): 2471–72. http://dx.doi.org/10.1093/bioinformatics/btr395.

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Kano, Yoshinobu, Paul Dobson, Mio Nakanishi, Jun'ichi Tsujii, and Sophia Ananiadou. "Text mining meets workflow: linking U-Compare with Taverna." Bioinformatics 26, no. 19 (2010): 2486–87. http://dx.doi.org/10.1093/bioinformatics/btq464.

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Gouet, P., and E. Courcelle. "ENDscript: a workflow to display sequence and structure information." Bioinformatics 18, no. 5 (2002): 767–68. http://dx.doi.org/10.1093/bioinformatics/18.5.767.

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Peleg, M., I. Yeh, and R. B. Altman. "Modelling biological processes using workflow and Petri Net models." Bioinformatics 18, no. 6 (2002): 825–37. http://dx.doi.org/10.1093/bioinformatics/18.6.825.

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Wrede, Fredrik, and Andreas Hellander. "Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning." Bioinformatics 35, no. 24 (2019): 5199–206. http://dx.doi.org/10.1093/bioinformatics/btz420.

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Abstract Motivation Discrete stochastic models of gene regulatory network models are indispensable tools for biological inquiry since they allow the modeler to predict how molecular interactions give rise to nonlinear system output. Model exploration with the objective of generating qualitative hypotheses about the workings of a pathway is usually the first step in the modeling process. It involves simulating the gene network model under a very large range of conditions, due to the large uncertainty in interactions and kinetic parameters. This makes model exploration highly computational deman

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Nasir, Waqas, Alejandro Gomez Toledo, Fredrik Noborn, et al. "SweetNET: A Bioinformatics Workflow for Glycopeptide MS/MS Spectral Analysis." Journal of Proteome Research 15, no. 8 (2016): 2826–40. http://dx.doi.org/10.1021/acs.jproteome.6b00417.

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Fiannaca, Antonino, Massimo La Rosa, Salvatore Gaglio, Riccardo Rizzo, and Alfonso Urso. "An ontological-based knowledge organization for bioinformatics workflow management system." EMBnet.journal 18, B (2012): 110. http://dx.doi.org/10.14806/ej.18.b.570.

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Li, Jing, Zengliu Su, Ze-Qiang Ma, et al. "A Bioinformatics Workflow for Variant Peptide Detection in Shotgun Proteomics." Molecular & Cellular Proteomics 10, no. 5 (2011): M110.006536. http://dx.doi.org/10.1074/mcp.m110.006536.

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