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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Bioinformatics for wet-lab scientists: practical application in sequencing analysis.

Vera Laub1, Kavi Devraj2,3, Lena Elias2

  • 1Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany. vera.laub@kgu.de.

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|July 7, 2023
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Summary

This study introduces user-friendly bioinformatics tools for analyzing genomics data, enabling wetlab researchers to explore next-generation sequencing data without extensive programming knowledge. These resources facilitate deeper biological insights from public and private datasets.

Keywords:
ATAC-seqChIP-seqIntegrated data analysisRNA-seqTranscriptional networks

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Vast amounts of genomics data are publicly available but often underutilized after initial publication.
  • Wetlab researchers may lack formal bioinformatics training, hindering their ability to analyze complex sequencing data.
  • Under-exploitation of deposited data represents a missed opportunity for scientific discovery.

Purpose of the Study:

  • To present accessible, web-based bioinformatics tools for analyzing next-generation sequencing (NGS) data.
  • To empower researchers without extensive programming experience to interrogate public genomics datasets.
  • To provide flexible analysis pipelines for diverse research questions.

Main Methods:

  • A curated selection of predominantly web-based bioinformatics platforms and tools is presented.
  • Tools are combinable into analysis pipelines for various NGS data types.
  • Emphasis is placed on usability for researchers with limited prior programming knowledge.

Main Results:

  • Freely available tools and pipelines are described for interrogating genomics data.
  • Exemplary analysis routes and alternative tool combinations are provided.
  • The approach allows application to existing public data or comparison with new experimental results.

Conclusions:

  • Integrating ChIP-seq, RNA-seq, and ATAC-seq data deepens understanding of transcriptional regulation.
  • These integrated analyses aid in forming and in silico pre-testing of new biological hypotheses.
  • Accessible bioinformatics tools democratize the analysis of complex genomic datasets.