A multi-tool recipe to identify regions of protein-DNA binding and their influence on associated gene expression.
Daniel E CarlinKassi KosnickiSara GaramszegiTrey IdekerHelga ThorvaldsdóttirMichael ReichJill MesirovPublished in: F1000Research (2017)
One commonly performed bioinformatics task is to infer functional regulation of transcription factors by observing differential expression under a knockout, and integrating DNA binding information of that transcription factor. However, until now, this task has required dedicated bioinformatics support to perform the necessary data integration. GenomeSpace provides a protocol, or "recipe", and a user interface with inter-operating software tools to identify protein occupancies along the genome from a ChIP-seq experiment and associated differentially regulated genes from a RNA-Seq experiment. By integrating RNA-Seq and ChIP-seq analyses, a user is easily able to associate differing expression phenotypes with changing epigenetic landscapes.
Keyphrases
- rna seq
- dna binding
- transcription factor
- single cell
- gene expression
- high throughput
- genome wide identification
- dna methylation
- genome wide
- binding protein
- poor prognosis
- protein protein
- circulating tumor cells
- randomized controlled trial
- amino acid
- healthcare
- machine learning
- big data
- long non coding rna
- deep learning