Technical Note
Version 1
This version is not peer-reviewed
SeqMate: A Novel Large Language Model Pipeline for Automating RNA Sequencing
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and
Version 1
: Received: 30 June 2024 / Approved: 1 July 2024 / Online: 2 July 2024 (13:30:51 CEST)
How to cite: Mondal, D.; Inamdar, A. SeqMate: A Novel Large Language Model Pipeline for Automating RNA Sequencing. Preprints 2024, 2024070160. https://doi.org/10.20944/preprints202407.0160.v1 Mondal, D.; Inamdar, A. SeqMate: A Novel Large Language Model Pipeline for Automating RNA Sequencing. Preprints 2024, 2024070160. https://doi.org/10.20944/preprints202407.0160.v1
Abstract
RNA sequencing techniques, like bulk RNA-seq and Single Cell (sc) RNA-seq, are critical tools for the biologist looking to analyze the genetic activity/transcriptome of a tissue or cell during an experimental procedure. Platforms like Illumina's next-generation sequencing (NGS) are used to produce the raw data for this experimental procedure. This raw FASTQ data must then be prepared via a complex series of data manipulations by bioinformaticians. This process currently takes place on an unwieldy textual user interface like a terminal/command line that requires the user to install and import multiple program packages, preventing the untrained biologist from initiating data analysis. Open-source platforms like Galaxy have produced a more user-friendly pipeline, yet the visual interface remains cluttered and highly technical, remaining uninviting for the natural scientist. To address this, SeqMate is a user-friendly tool that allows for one-click analytics by utilizing the power of a large language model (LLM) to automate both data preparation and analysis (differential expression, trajectory analysis, etc). Furthermore, by utilizing the power of generative AI, SeqMate is also capable of analyzing such findings and producing written reports of upregulated/downregulated/user-prompted genes with sources cited from known repositories like PubMed, PDB, and Uniprot.
Keywords
bioinformatics; natural language processing; automation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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