4.7 Article

Bioinformatics for wet-lab scientists: practical application in sequencing analysis

Journal

BMC GENOMICS
Volume 24, Issue 1, Pages -

Publisher

BMC
DOI: 10.1186/s12864-023-09454-7

Keywords

ChIP-seq; RNA-seq; ATAC-seq; Integrated data analysis; Transcriptional networks

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Researchers can utilize bioinformatic tools to analyze genomics data, explore different types of sequencing data, gain a deeper understanding of molecular interactions underlying transcriptional regulation, and validate new hypotheses through computational simulations. This article introduces a series of freely available web-based platforms and bioinformatic tools that can be combined to analyze genomics data.
BackgroundGenomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth.MainA likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments.ConclusionIntegrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico.

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