AI Article Synopsis
- Methods for analyzing ChIP-seq data typically rely on aligning short reads to a reference genome, but this isn't always feasible if a reference is unavailable.
- The authors introduce a new approach that uses de novo assembly combined with statistical tests for motif discovery, which doesn't require a reference genome.
- Their validation with human, mouse, and fly data shows that this method performs better than traditional alignment-based approaches using related species.
Article Abstract
Methods for the analysis of chromatin immunoprecipitation sequencing (ChIP-seq) data start by aligning the short reads to a reference genome. While often successful, they are not appropriate for cases where a reference genome is not available. Here we develop methods for de novo analysis of ChIP-seq data. Our methods combine de novo assembly with statistical tests enabling motif discovery without the use of a reference genome. We validate the performance of our method using human and mouse data. Analysis of fly data indicates that our method outperforms alignment based methods that utilize closely related species.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579611 | PMC |
| http://dx.doi.org/10.1186/s13059-015-0756-4 | DOI Listing |
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