Biology subjects

Jalili, V.

Publications and source records attributed to Jalili, V..

4 recordsLinked to original sources

bioRxiv2021

Rescuing Biologically Relevant Consensus Regions Across Replicated Samples

MotivationProtein-DNA binding sites of ChIP-seq experiments are identified where the binding affinity is significant based on a given threshold. The choice of the threshold is a trade-off between conservative region identification and discarding weak, but true binding sites. ResultsWe argue the biological relevance of weak binding sites and the information they add when rescued. The sites are rescued using MSPC, which exploits replicates to lower the threshold required to identify a binding site while keeping a low false-positive rate. We extend MSPC to call consensus regions across any number of replicated samples, accounting for differences between biological and technical replicates. We observed several master transcription regulators (e.g., SP1 and GATA3) and HDAC2-GATA1 regulatory networks on rescued regions. Availability and implementationAn implementation of the proposed method and the scripts to reproduce the performed analysis are freely available at https://genometric.github.io/MSPC/, MSPC is distributed as a command-line application, an R package available from Bioconductor (https://doi.org/doi:10.18129/B9.bioc.rmspc), and a C# library.

bioinformatics↗

bioRxiv2020

Adding software to package management systems can increase their citation by 280%

A growing number of biomedical methods and protocols are being disseminated as open-source software packages. When put in concert with other packages, they can execute in-depth and comprehensive computational pipelines. Therefore, their integration with other software packages plays a prominent role in their adoption in addition to their availability. Accordingly, package management systems are developed to standardize the discovery and integration of software packages. Here we study the impact of package management systems on software dissemination and their scholarly recognition. We study the citation pattern of more than 18,000 scholarly papers referenced by more than 23,000 software packages hosted by Bioconda, Bioconductor, BioTools, and ToolShed--the package management systems primarily used by the Bioinformatics community. Our results suggest that there is significant evidence that the scholarly papers citation count increases after their respective software was published to package management systems. Additionally, our results show that the impact of different package management systems on the scholarly papers recognition is of the same magnitude. These results may motivate scientists to distribute their software via package management systems, facilitating the composition of computational pipelines and helping reduce redundancy in package development. Significance StatementSoftware packages are the building blocks of computational pipelines. A myriad of packages are developed; however, the lack of integration and discovery standards hinders their adoption, leaving most scientists scholarly contributions unrecognized. Package management systems are developed to facilitate software dissemination and integration. However, developing software to meet their code and packaging standards is an involved process. Therefore, our study results on the significant impact of the package management systems on scholarly papers recognition can motivate scientists to invest in disseminating their software via package management systems. Dissemination of more software via package management systems will lead to a more straightforward composition of computational pipelines and less redundancy in software packages.

bioinformatics↗

bioRxiv2020

Accessible, Reproducible, and Scalable Machine Learning for Biomedicine

Supervised machine learning, where the goal is to predict labels of new instances by training on labeled data, has become an essential tool in biomedical data analysis. To make supervised machine learning more accessible to biomedical scientists, we have developed Galaxy-ML, a platform that enables scientists to perform end-to-end reproducible machine learning analyses at large scale using only a web browser. Galaxy-ML extends Galaxy, a biomedical computational workbench used by tens of thousands of scientists across the world, with a machine learning tool suite that supports end-to-end analysis.

bioinformatics↗

bioRxiv2018

Cloud Bursting Galaxy: Federated Identity and Access Management

MotivationLarge biomedical datasets, such as those from genomics and imaging, are increasingly being stored on commercial and institutional cloud computing platforms. This is because cloud-scale computing resources, from robust backup to highspeed data transfer to scalable compute and storage, are needed to make these large datasets usable. However, one challenge for large-scale biomedical data on the cloud is providing secure access, especially when datasets are distributed across platforms. While there are open Web protocols for secure authentication and authorization, these protocols are not in wide use in bioinformatics and are difficult to use for even technologically sophisticated users.\n\nResultsWe have developed a generic and extensible approach for securely accessing biomedical datasets distributed across cloud computing platforms. Our approach combines OpenID Connect and OAuth2, best-practice Web protocols for authentication and authorization, together with Galaxy (https://galaxyproject.org), a web-based computational workbench used by thousands of scientists across the world. With our enhanced version of Galaxy, users can access and analyze data distributed across multiple cloud computing providers without any special knowledge of access/authorization protocols. Our approach does not require users to share permanent credentials (e.g., username, password, API key), instead relying on automatically-generated temporary tokens that refresh as needed. Our approach is generalizable to most identity providers and cloud computing platforms. To the best of our knowledge, Galaxy is the only computational workbench where users can access biomedical datasets across multiple cloud computing platforms using best-practice Web security approaches and thereby minimize risks of unauthorized data access and credential use.\n\nAvailability and ImplementationFreely available for academic and commercial use under the open-source Academic Free License (https://opensource.org/licenses/AFL-3.0) from the following Github repositories: https://github.com/galaxyproject/galaxy and https://github.com/galaxyproject/cloudauthz\n\nContactjalili@ohsu.edu, goecksj@ohsu.edu

bioinformatics↗