Bash Script Research Articles

GVAF: generalized, flexible filtering software for annotated variant files.

In the rapidly advancing field of genomics, many tools have been developed to interpret genetic variants using next-generation sequencing (NGS) data. However, these tools often produce annotated variant files in different formats, which require specific software or programming skills to filter and analyze. To provide a filtering tool that can be used with diverse variant annotation tools without requiring specific software or programming skills. We developed Germline Variant Annotation and Filtering (GVAF), a command-line software tool that can handle annotated variant files in any table-shaped format. GVAF offers powerful filtering operations without the need for additional software or programming expertise. Built on the Java framework and bash scripts, it provides extensive features, including flexible filtering rules, recognition of genotype-related fields from variant call format (VCF) files, and customizable result generation. GVAF also integrates easily into existing data analysis pipelines. Compared to other tools, GVAF offers a broader range of functionalities, making it more flexible and intuitive for managing annotated variant files. This GVAF software and online manual is publicly available at https://www.sysbiolab.org/gvaf for academic users and is designed to streamline the variant interpretation process, aiding researchers in producing meaningful results.

ChronoBak: Automated cost-effective incremental backup software for large-scale data

Background: Data backup is an important component of data protection and management. It is especially imperative to back up large-scale data to guard against losses due to hardware failure, unauthorized access, software/system malfunction, and unintended deletion. However, businesses and organizations suffer from the financial investment required for data backup and recovery systems. Health-care systems suffer from data breaches and hacking incidents, which increase annually. Therefore, a cost-effective and highly secure backup approach is necessary. Methods: Seven different backup scripts were written in bash within Linux, and Crontab was used as the core chronological scheduler to execute the backup scripts on specific dates and times. These bash scripts, together with Crontab as their scheduler, were used to implement automated incremental backups on a Unix/Linux-based system. Results: ChronoBak is a collection of bash scripts that are compiled and executed through Crontab, which is a standard Unix/Linux function used to schedule hourly, daily, weekly, and monthly maintenance routines using Linux scripts. This strategy enabled us to implement cost-effective incremental backups within any time range. Conclusion: We developed ChronoBak software, which executes an automated incremental backup that is scheduled efficiently and systematically using standard and hidden directories, thereby providing an additional layer of security against cyber threats and system malfunctions.

EQTL-Detect: nextflow-based pipeline for eQTL detection in modular format with sharable and parallelizable scripts

Abstract Bioinformatic pipelines are becoming increasingly complex with the ever-accumulating amount of Next-generation sequencing (NGS) data. Their orchestration is difficult with a simple Bash script, but bioinformatics workflow managers such as Nextflow provide a framework to overcome respective problems. This study used Nextflow to develop a bioinformatic pipeline for detecting expression quantitative trait loci (eQTL) using a DSL2 Nextflow modular syntax, to enable sharing the huge demand for computing power as well as data access limitation across different partners often associated with eQTL studies. Based on the results from a test run with pilot data by measuring the required runtime and computational resources, the new pipeline should be suitable for eQTL studies in large scale analyses.

Detection using Intrusion Detection System (IDS) and SMS Gateway Controller

Intrusion Detection System and SMS controller with Snort using SMS Gateway on public networks. The need for an attack detection system that can assist administrators in monitoring the network, so that administrators can cope with threats quickly and the network can operate optimally again, the purpose of this study is to facilitate network administrators in preventing and detecting attacks. this has been tested against ping of death attacks, sql injection, sysflooding attacks and port scanning. From the results of testing the system that has been tested, the system has been able to detect and prevent it quickly via SMS controller integrated with gammu as an sms gateway, as for information sent to the network administrator in the form of attack alerts in real time via SMS, the result of attack detection in the form of 4 (four) types of information, namely attack time, attack type, destination IP, and source IP. Blocking attacks is done by sending blocking commands via an sms controller that is integrated with php scripts, bash scripting and iptables, blocking attacks successfully executed after the gammu server receives an SMS reply from the network administrator in an average period of 40 seconds.

Introduction of the Capsules environment to support further growth of the SBGrid structural biology software collection.

The expansive scientific software ecosystem, characterized by millions of titles across various platforms and formats, poses significant challenges in maintaining reproducibility and provenance in scientific research. The diversity of independently developed applications, evolving versions and heterogeneous components highlights the need for rigorous methodologies to navigate these complexities. In response to these challenges, the SBGrid team builds, installs and configures over 530 specialized software applications for use in the on-premises and cloud-based computing environments of SBGrid Consortium members. To address the intricacies of supporting this diverse application collection, the team has developed the Capsule Software Execution Environment, generally referred to as Capsules. Capsules rely on a collection of programmatically generated bash scripts that work together to isolate the runtime environment of one application from all other applications, thereby providing a transparent cross-platform solution without requiring specialized tools or elevated account privileges for researchers. Capsules facilitate modular, secure software distribution while maintaining a centralized, conflict-free environment. The SBGrid platform, which combines Capsules with the SBGrid collection of structural biology applications, aligns with FAIR goals by enhancing the findability, accessibility, interoperability and reusability of scientific software, ensuring seamless functionality across diverse computing environments. Its adaptability enables application beyond structural biology into other scientific fields.

A Literature Survey on System Security and Network Vulnerability Assessment

This abstract outlines a detailed study on improving network security through vulnerability assessment and penetration testing, using tools like Nessus, Nmap, Metasploit, on the Kali Linux OS. The methodology involves identifying vulnerabilities, conducting simulated attacks, and developing a bash script to address false positives from Nessus scans. The research aims to enhance network security, provide practical recommendations, and contribute to cybersecurity practices, with a focus on both internal and external network security. Keywords: Vulnerability Assessment, Pentest, Nessus, Metasploit, Kali Linux

Nextflow vs. plain bash: different approaches to the parallelization of SNP calling from the whole genome sequence data.

This study compared computational approaches to parallelization of an SNP calling workflow. The data comprised DNA from five Holstein-Friesian cows sequenced with the Illumina platform. The pipeline consisted of quality control, alignment to the reference genome, post-alignment, and SNP calling. Three approaches to parallelization were compared: (i) a plain Bash script in which a pipeline for each cow was executed as separate processes invoked at the same time, (ii) a Bash script wrapped in a single Nextflow processand (iii) a Nextflow script with each component of the pipeline defined as a separate process. The results demonstrated that on average, the multi-process Nextflow script performed 15-27% faster depending on the number of assigned threads, with the biggest execution time advantage over the plain Bash approach observed with 10 threads. In terms of RAM usage, the most substantial variation was observed for the multi-process Nextflow, for which it increased with the number of assigned threads, while RAM consumption of the other setups did not depend much on the number of threads assigned for computations. Due to intermediate and log files generated, disk usage was markedly higher for the multi-process Nextflow than for the plain Bash and for the single-process Nextflow.

Assessment Automation of Complex Student Programming Assignments

Grading student programming assignments is not an easy task. This task is even more challenging when talking about complex programming assignments at university graduate level. By complex assignments, we mean assignments where students have to program a complete application from scratch. For example, building a complete web application with a client and server side, whereby the application uses multiple threads that gather data from some external service (like the REST service, IoT sensors, etc.), processes these data and store them in some storage (e.g., a database), implements a custom protocol over a socket or something similar, implements their own REST/SOAP/GraphQL service, then sends or receives JMS/MQTT/WebSocket messages, etc. Such assignments give students an inside view of building real Internet applications. On the other hand, assignments like these take a long time to be tested and graded manually, e.g., up to 1 h per student. To speed up the assessment process, there are different automation possibilities that can check for the correctness of some application parts without endangering the grading quality. In this study, different possibilities of automation are described that have been improved over several years. This process takes advantage of unit testing, bash scripting, and other methods. The main goal of this study is to define an assessment process that can be used to grade complex programming assignments, with concrete examples of what and how to automate. This process involves assignment preparation for automation, plagiarism (i.e., better said similarity) detection, performing an automatic check of the correctness of each programming assignment, conducting an analysis of the obtained data, the awarding of points (grading) for each programming assignment, and other such activities. We also discuss what the downsides of automation are and why it is not possible to completely automate the grading process.

Automated management of AWS instances for training.

Amazon Web Services (AWS) instances provide a convenient way to run training on complex 'omics data analysis workflows without requiring participants to install software packages or store large data volumes locally. However, efficiently managing dozens of instances is challenging for training providers. We present a set of Bash scripts that make it quick and easy to manage Linux AWS instances pre-configured with all the software analysis tools and data needed for a course, and accessible using encrypted login keys and optional domain names. Creating over 30 instances takes 10-15minutes. A comprehensive online tutorial describes how to set up and use an AWS account and the scripts, and how to customise AWS instance templates with other software tools and data. We anticipate that others offering similar training may benefit from using the scripts regardless of the analyses being taught.

PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data

BackgroundDetection of exotic plant pathogens and preventing their entry and establishment are critical for the protection of agricultural systems while securing the global trading of agricultural commodities. High-throughput sequencing (HTS) has been applied successfully for plant pathogen discovery, leading to its current application in routine pathogen detection. However, the analysis of massive amounts of HTS data has become one of the major challenges for the use of HTS more broadly as a rapid diagnostics tool. Several bioinformatics pipelines have been developed to handle HTS data with a focus on plant virus and viroid detection. However, there is a need for an integrative tool that can simultaneously detect a wider range of other plant pathogens in HTS data, such as bacteria (including phytoplasmas), fungi, and oomycetes, and this tool should also be capable of generating a comprehensive report on the phytosanitary status of the diagnosed specimen.ResultsWe have developed an open-source bioinformatics pipeline called PhytoPipe (Phytosanitary Pipeline) to provide the plant pathology diagnostician community with a user-friendly tool that integrates analysis and visualization of HTS RNA-seq data. PhytoPipe includes quality control of reads, read classification, assembly-based annotation, and reference-based mapping. The final product of the analysis is a comprehensive report for easy interpretation of not only viruses and viroids but also bacteria (including phytoplasma), fungi, and oomycetes. PhytoPipe is implemented in Snakemake workflow with Python 3 and bash scripts in a Linux environment. The source code for PhytoPipe is freely available and distributed under a BSD-3 license.ConclusionsPhytoPipe provides an integrative bioinformatics pipeline that can be used for the analysis of HTS RNA-seq data. PhytoPipe is easily installed on a Linux or Mac system and can be conveniently used with a Docker image, which includes all dependent packages and software related to analyses. It is publicly available on GitHub at https://github.com/healthyPlant/PhytoPipe and on Docker Hub at https://hub.docker.com/r/healthyplant/phytopipe.

CONIPHER: a computational framework for scalable phylogenetic reconstruction with error correction.

Intratumor heterogeneity provides the fuel for the evolution and selection of subclonal tumor cell populations. However, accurate inference of tumor subclonal architecture and reconstruction of tumor evolutionary histories from bulk DNA sequencing data remains challenging. Frequently, sequencing and alignment artifacts are not fully filtered out from cancer somatic mutations, and errors in the identification of copy number alterations or complex evolutionary events (e.g., mutation losses) affect the estimated cellular prevalence of mutations. Together, such errors propagate into the analysis of mutation clustering and phylogenetic reconstruction. In this Protocol, we present a new computational framework, CONIPHER (COrrecting Noise In PHylogenetic Evaluation and Reconstruction), that accurately infers subclonal structure and phylogenetic relationships from multisample tumor sequencing, accounting for both copy number alterations and mutation errors. CONIPHER has been used to reconstruct subclonal architecture and tumor phylogeny from multisample tumors with high-depth whole-exome sequencing from the TRACERx421 dataset, as well as matched primary-metastatic cases. CONIPHER outperforms similar methods on simulated datasets, and in particular scales to a large number of tumor samples and clones, while completing in under 1.5 h on average. CONIPHER enables automated phylogenetic analysis that can be effectively applied to large sequencing datasets generated with different technologies. CONIPHER can be run with a basic knowledge of bioinformatics and R and bash scripting languages.

TUMME 2023: Tsinghua University Minnesota Master Equation program. New version announcement

This is an updated version of TUMME (Tsinghua University Minnesota Master Equation program), which is a computer program for setting up and solving master equations for chemical kinetics of unimolecular and bimolecular reactions. The master equation is discretized in terms of energy bins, and rate constants are calculated from chemically significant eigenmodes. TUMME has interfaces to Gaussian 16, Polyrate 2023, and MSTor 2023 output files that allow the master equation code to obtain the microcanonical flux coefficients needed for the kernel of the master equation as calculated by conventional transition state theory (TST), variational transition state theory (VTST) with various tunneling methods, or multi-structural or multi-path VTST (MS-VTST or MP-VTST) with various tunneling methods. The tunneling methods supported include zero-curvature tunneling (ZCT), small-curvature tunneling (SCT), large-curvature tunneling (LCT), and microcanonically optimized multidimensional tunneling (μOMT). For mechanisms involving only unimolecular isomerization (no bimolecular pairs), TUMME can set up and solve a conservative master equation for both rate constants and time-dependent energy-bin populations. For mechanisms involving bimolecular pairs, TUMME 2023 can set up and solve two kinds of master equation: (i) a nonconservative master equation for calculating rate constants of bimolecular reactions and (ii) a conservative master equation that includes bimolecular association in the transition matrix and that can be used for calculating the time evolution of the concentration of a pseudo-first-order bimolecular reactant. TUMME is written in double precision with Python 3; quadruple and octuple precision are also available for some subtasks in C++. The Python code can run in serial or parallel (MP or MPI), and C++ code can run on a single processor or on multiple processors with OpenMP. The program includes a manual and a tutorial. New version program summaryProgram Title: TUMME 2023CPC Library link to program files:https://doi.org/10.17632/whcnvm2mc9.2Developer's repository link:https://doi.org/10.5281/zenodo.7943283Licensing Provisions: Apache-2.0 for the program and CC-BY-4.0 for the manualProgramming languages: Python 3 and C++Does the new version supersede the previous version?: YesJournal reference of previous version: Comput. Phys. Commun. 270 (2022) 108140 [1]External libraries: Numpy, Scipy, Numba, mpi4py (optional), modified mpack (optional), qd (optional), omp (optional)Interfaces to other programs: Gaussian 16 [2], Polyrate 2023 [3], and/or MSTor 2023 [4]Nature of problem: Characterize and calculate rate constants for a temperature-dependent and pressure-dependent complex reaction systemSolution method: Solve the energy-grained master equation based on the chemically significant eigenmodes to get phenomenological rate constants and time evolutions of the populations [1,5–7].Reason for new version: The new version increases the functionality and accuracy, and it offers an easier way to run the program. In addition, some bugs have been corrected.Summary of revisions:1.Added an option (available in high precision) to treat pseudo-first-order bimolecular reactions to obtain the time evolution of the reactants and intermediates. [8]2.Added an inverse-Laplace-transform option in the REACTION block.3.Abandoned rounding the sizes of energy bins to an integer wavenumber (ESOT).4.Changed the way to run TUMME by providing an executable bash script.5.Slightly adjusted the strategy for eigenmode mergers to deal with some unexpected cases.6.Adjusted the output for the partition functions.7.Added options for large-curvature tunneling (LCT) and microcanonically optimized multidimensional tunneling (μOMT).8.Added the keyword pyrfile to support a Polyrate 2023 [3] file that ends with a suffix .fu100.9.Fixed some bugs.10.Updated the manual and added a tutorial to the program distribution, so the distribution now has both a manual and a tutorial.

Image data harmonization tools for the analysis of post-traumatic epilepsy development in preclinical multisite MRI studies

Preclinical MRI studies have been utilized for the discovery of biomarkers that predict post-traumatic epilepsy (PTE). However, these single site studies often lack statistical power due to limited and homogeneous datasets. Therefore, multisite studies, such as the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), are developed to create large, heterogeneous datasets that can lead to more statistically significant results. EpiBioS4Rx collects preclinical data internationally across sites, including the United States, Finland, and Australia. However, in doing so, there are robust normalization and harmonization processes that are required to obtain statistically significant and generalizable results. This work describes the tools and procedures used to harmonize multisite, multimodal preclinical imaging data acquired by EpiBioS4Rx. There were four main harmonization processes that were utilized, including file format harmonization, naming convention harmonization, image coordinate system harmonization, and diffusion tensor imaging (DTI) metrics harmonization. By using Python tools and bash scripts, the file formats, file names, and image coordinate systems are harmonized across all the sites. To harmonize DTI metrics, values are estimated for each voxel in an image to generate a histogram representing the whole image. Then, the Quantitative Imaging Toolkit (QIT) modules are utilized to scale the mode to a value of one and depict the subsequent harmonized histogram. The standardization of file formats, naming conventions, coordinate systems, and DTI metrics are qualitatively assessed. The histograms of the DTI metrics were generated for all the individual rodents per site. For inter-site analysis, an average of the individual scans was calculated to create a histogram that represents each site. In order to ensure the analysis can be run at the level of individual animals, the sham and TBI cohort were analyzed separately, which depicted the same harmonization factor. The results demonstrate that these processes qualitatively standardize the file formats, naming conventions, coordinate systems, and DTI metrics of the data. This assists in the ability to share data across the study, as well as disseminate tools that can help other researchers to strengthen the statistical power of their studies and analyze data more cohesively.

GOgetter: A pipeline for summarizing and visualizing GO slim annotations for plant genetic data.

The functional annotation of genes is a crucial component of genomic analyses. A common way to summarize functional annotations is with hierarchical gene ontologies, such as the Gene Ontology (GO) Resource. GO includes information about the cellular location, molecular function(s), and products/processes that genes produce or are involved in. For a set of genes, summarizing GO annotations using pre-defined, higher-order terms (GO slims) is often desirable in order to characterize the overall function of the data set, and it is impractical to do this manually. The GOgetter pipeline consists of bash and Python scripts. From an input FASTA file of nucleotide gene sequences, it outputs text and image files that list (1) the best hit for each input gene in a set of reference gene models, (2) all GO terms and annotations associated with those hits, and (3) a summary and visualization of GO slim categories for the data set. These output files can be queried further and analyzed statistically, depending on the downstream need(s). GO annotations are a widely used "universal language" for describing gene functions and products. GOgetter is a fast and easy-to-implement pipeline for obtaining, summarizing, and visualizing GO slim categories associated with a set of genes.

Construction of a Real-Time Forecast Model for Coastal Engineering and Processes Nested in a Basin Scale Model

A numerical model was developed using Delft3D to simulate the circulation dynamics in Port Everglades, FL, and the adjacent coastal area. The model was nested within the HYCOM (Hybrid Coordinate Ocean Model), while meteorological data were obtained from the NARR (North American Regional Reanalysis) model. To evaluate the model, model outputs were compared with observed data from the NOAA. Calibration experiments were conducted on the model parameters, including the bottom friction, wind forcings, and vertical layer specification. These experiments revealed that implementing a 10-layer model slightly improved the vertical stratification, while the utilization of 2-D wind data resulted in more pronounced surface layer characteristics in temperature and velocity profiles and employing moderate values of the Chezy coefficient produced optimal outcomes for the bottom roughness parameter. The model demonstrated satisfactory performance across major parameters, including water level, salinity, temperature, and currents. A real-time forecast system has been constructed with this nested model, providing up to 3-day forecasts that are updated daily. To facilitate automated forecasting without manual intervention, an automation system has been developed using a combination of bash, MATLAB, and Python scripts. This study provides a comprehensive documentation of the concepts and detailed methods involved in developing a real-time forecast model for estuarine and coastal regions.

Rigour and reproducibility in perinatal and paediatric epidemiologic research using big data.

Rigour and reproducibility in perinatal and paediatric epidemiologic research using big data.

An automatic and accurate deep learning-based neuroimaging pipeline for theneonatal brain.

Accurate segmentation of neonatal brain tissues and structures is crucial for studying normal development and diagnosing early neurodevelopmental disorders. However, there is a lack of an end-to-end pipeline for automated segmentation and imaging analysis of the normal and abnormal neonatal brain. To develop and validate a deep learning-based pipeline for neonatal brain segmentation and analysis of structural magnetic resonance images (MRI). Two cohorts were enrolled in the study, including cohort 1 (582 neonates from the developing Human Connectome Project) and cohort 2 (37 neonates imaged using a 3.0-tesla MRI scanner in our hospital).We developed a deep leaning-based architecture capable of brain segmentation into 9 tissues and 87 structures. Then, extensive validations were performed for accuracy, effectiveness, robustness and generality of the pipeline. Furthermore, regional volume and cortical surface estimation were measured through in-house bash script implemented in FSL (Oxford Centre for Functional MRI of the Brain Software Library) to ensure reliability of the pipeline. Dice similarity score (DSC), the 95th percentile Hausdorff distance (H95) and intraclass correlation coefficient (ICC) were calculated to assess the quality of our pipeline. Finally, we finetuned and validated our pipeline on 2-dimensional thick-slice MRI in cohorts 1 and 2. The deep learning-based model showed excellent performance for neonatal brain tissue and structural segmentation, with the best DSC and the 95th percentile Hausdorff distance (H95) of 0.96 and 0.99mm, respectively. In terms of regional volume and cortical surface analysis, our model showed good agreement with ground truth. The ICC values for the regional volume were all above 0.80. Considering the thick-slice image pipeline, the same trend was observed for brain segmentation and analysis. The best DSC and H95 were 0.92 and 3.00mm, respectively. The regional volumes and surface curvature had ICC values just below 0.80. We propose an automatic, accurate, stable and reliable pipeline for neonatal brain segmentation and analysis from thin and thick structural MRI. The external validation showed very good reproducibility of the pipeline.

Bash in the Wild: Language Usage, Code Smells, and Bugs

The Bourne-again shell (Bash) is a prevalent scripting language for orchestrating shell commands and managing resources in Unix-like environments. It is one of the mainstream shell dialects that is available on most GNU Linux systems. However, the unique syntax and semantics of Bash could easily lead to unintended behaviors if carelessly used. Prior studies primarily focused on improving the reliability of Bash scripts or facilitating writing Bash scripts; there is yet no empirical study on the characteristics of Bash programs written in reality, e.g., frequently used language features, common code smells, and bugs. In this article, we perform a large-scale empirical study of Bash usage, based on analyses over one million open source Bash scripts found in Github repositories. We identify and discuss which features and utilities of Bash are most often used. Using static analysis, we find that Bash scripts are often error-prone, and the error-proneness has a moderately positive correlation with the size of the scripts. We also find that the most common problem areas concern quoting, resource management, command options, permissions, and error handling. We envision that these findings can be beneficial for learning Bash and future research that aims to improve shell and command-line productivity and reliability.

LAVA 1.0: A general-purpose python toolkit for calculation of material properties with LAMMPS and VASP

We introduce LAVA, a general-purpose python toolkit to provide user-friendly and high throughput calculations for material properties using both LAMMPS and VASP. It contains a set of classes as well as pre-processing and post-processing functions to prepare, execute and extract information from LAMMPS/VASP simulations. An overview of the program structure is provided. The current version contains modules to calculate elastic and mechanical properties such as lattice constant, cohesive energy, cold curve, elastic constants, bulk and shear modulus, volume conserving/non-conserving deformation path, vacancy/interstitial formation energy, surface energy, stacking fault energy, melting point, radial distribution function, and thermal expansion. These functionalities are demonstrated for different interatomic potentials and DFT calculations in Al. Program summaryProgram title: LAVACPC Library link to program files:https://doi.org/10.17632/6grh8x4hgt.1Licensing provisions: BSD 3-Clause LicenseProgramming language: PythonNature of problem: This program provides user-friendly and high throughput calculations for material properties via two widely-used LAMMPS and VASP code.Solution method: LAVA can prepare the input scripts, extract, calculate and even plot the material properties from molecular dynamics simulations and density functional calculations together with bash and python scripts.

Client Applications and Server-Side Docker for Management of RNASeq and/or VariantSeq Workflows and Pipelines of the GPRO Suite.

The GPRO suite is an in-progress bioinformatic project for -omics data analysis. As part of the continued growth of this project, we introduce a client- and server-side solution for comparative transcriptomics and analysis of variants. The client-side consists of two Java applications called "RNASeq" and "VariantSeq" to manage pipelines and workflows based on the most common command line interface tools for RNA-seq and Variant-seq analysis, respectively. As such, "RNASeq" and "VariantSeq" are coupled with a Linux server infrastructure (named GPRO Server-Side) that hosts all dependencies of each application (scripts, databases, and command line interface software). Implementation of the Server-Side requires a Linux operating system, PHP, SQL, Python, bash scripting, and third-party software. The GPRO Server-Side can be installed, via a Docker container, in the user's PC under any operating system or on remote servers, as a cloud solution. "RNASeq" and "VariantSeq" are both available as desktop (RCP compilation) and web (RAP compilation) applications. Each application has two execution modes: a step-by-step mode enables each step of the workflow to be executed independently, and a pipeline mode allows all steps to be run sequentially. "RNASeq" and "VariantSeq" also feature an experimental, online support system called GENIE that consists of a virtual (chatbot) assistant and a pipeline jobs panel coupled with an expert system. The chatbot can troubleshoot issues with the usage of each tool, the pipeline jobs panel provides information about the status of each computational job executed in the GPRO Server-Side, while the expert system provides the user with a potential recommendation to identify or fix failed analyses. Our solution is a ready-to-use topic specific platform that combines the user-friendliness, robustness, and security of desktop software, with the efficiency of cloud/web applications to manage pipelines and workflows based on command line interface software.