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easyfm: An easy software suite for file manipulation of Next Generation Sequencing data on desktops
  • Hyungtaek Jung,
  • Brendan Jeon,
  • Daniel Ortiz-Barrientos
Hyungtaek Jung
The University of Queensland
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Brendan Jeon
The University of Queensland
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Daniel Ortiz-Barrientos
The University of Queenland
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Storing and manipulating Next Generation Sequencing (NGS) file formats for understanding biological phenomena is an essential but difficult task in the life sciences. Yet, most methods for analysing NGS data require complex command-line tools in high-performance computing (HPC) or web-based servers and have not yet been implemented in comprehensive, easy-to-use software. Here we present easyfm (easy file manipulation), a free standalone Graphical User Interface (GUI) software with Python support that can be used to facilitate the rapid discovery of target sequences (or user’s interest) in NGS datasets for novice users (more accessible to biologists). It enables them to perform end-to-end reproducible data analyses using a desktop application (Windows, Mac and Linux). Unlike existing tools, the GUI-based easyfm is not dependent on any HPC system and can be operated without an internet connection. For user-friendliness and convenience, easyfm was developed with four work modules and a secondary GUI window, covering different aspects of NGS data analysis, including post-processing, filtering, format conversion, generating results, real-time log, and help. In combination with the executable tools (BLAST+ and BLAT) and Python, easyfm allows the user to set analysis parameters, select/extract regions of interest, examine the input and output results, and convert to a wide range of file formats. To help augment the functionality of existing web-based and command-line tools, easyfm, a self-contained program, comes with extensive documentation (https://github.com/TaekAndBrendan/easyfm). This specific benefit allows easyfm to seamlessly integrate visual and interactive representations of NGS files, supporting a wider scope of bioinformatics applications in the life sciences.

Peer review status:UNDER REVIEW

01 Dec 2021Submitted to Ecology and Evolution
01 Dec 2021Assigned to Editor
01 Dec 2021Submission Checks Completed
02 Dec 2021Reviewer(s) Assigned