Ozturk, A. | Search bioRxiv

bioRxiv2025

FindBacksplice: a Tool for Locating Circular RNA Backsplice Coordinates

Circular RNAs (circRNAs) are generated through back-splicing, a process where a backsplice junction (BSJ) is formed, based on the circRNAs unique sequence. BSJs are highly conserved and can be mapped to chromosomal coordinates. Current platforms determine these coordinates from bulk RNA sequencing data. We aimed to develop a tool capable of determining backsplice coordinates based on BSJ sequences for any species and genome version. MotivationcircRNAs are emerging as an important regulator of cellular differentiation and other biologically important processes. Common tools for circRNA analyses require a circular RNAs backsplice coordinates. These can be accessed from public databases. However, the coordinates are specific to a version of a species genome, and are unavailable for many model organisms. ResultsWe have developed a Python-based, command line tool, FindBacksplice, which produces backsplice coordinates for any available genome, based on a circRNAs BSJ sequence. Implemented in Python, this script is integrated with BLAST for use in existing pipelines. We were able to find valid locations of backsplices for known human BSJs in the rat genome and produce backsplice coordinates for use in existing pipelines. Availability and implementationFindBacksplice is available at github.com/m-kraljevic/findbacksplice

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bioRxiv2019

DolphinNext: A distributed data processing platform for high throughput genomics

The emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) are transforming biological research. The dramatic increase in the volume of data makes analysis the main bottleneck for scientific discovery. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for a highly-parallel, flexible, portable, and reproducible data processing frameworks. Flexibility ensures that pipelines can support a variety of applications without requiring one-off modifications. Portability ensures that users can leverage computationally available resources and work within economic constraints. Reproducibility warrants credibility to the results and is particularly challenging in the face of the sheer volume of data and the complexity of processing pipelines that vary widely between users.\n\nSeveral platforms currently exist for the design and execution of complex pipelines (e.g. Galaxy, GenePattern, GeneProf). Unfortunately, these platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, Nextflow was implemented to simplify portable, scalable, and reproducible scientific pipelines using containers. We have used Nextflow capabilities as leverage and developed a user interface, DolphinNext, for creating, deploying, and executing complex Nextflow pipelines for high throughput data processing. The guiding principle of DolphinNext is to facilitate the building and deployment of complex pipelines using a modular approach implemented in a graphical interface. DolphinNext provides: 1. A drag and drop user interface that abstracts pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. A user interface to monitor pipeline execution that allows the re-initiation of pipelines at intermediate steps 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Seamless portability to distributed computational environments such as high-performance clusters or cloud computing environments.

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