DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data

1 Citation

John M. Gaspar and Ronald P. Hart, DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data, BMC Bioinformatics (2017) 18:528.

https://doi.org/10.1186/s12859-017-1909-0

2 Summary

A new approach (DMRfinder) for the identification of differentially methylated regions (DMRS) of the DNA based on whole genome bisulfite sequencing (BSSEQ) data is proposed. The new approach is compared with one an existing approach (DSS).

3 Study outcomes

The paper does not provide results allowing a quantiative performance assessement. It compares DMRfinder with DSS based on examples where the outcome does not conicide. Moreover, the statistics of frequency and lengths of the predicted DMRs are reported.

3.1 Further outcomes

• The predicted DMRs of DMRfinder are much shorter than the predicted DMRs of DSS.
• Only a small overlap of the DMRs predicted by both approaches was found (86.9% had no overlap).

4 Study design and evidence level

4.1 General aspects

The paper presents a new approach (MethCP) and at the same times provides several analyses for comparing the performance of the new approach with existing algorithms. Such a study setting is very frequently found in the literature although it has a high risk for biased outcomes. One reason for such a bias might be that typically application examples are selected to nicely demonstrate performance benefits. Moreover, new approaches are often established if existing methods have minor performance in a new application setup. For such a setup, a new approach then has good chances to outperform and it remains rather unclear how performance comparisons translates to new application settings.

5 Further comments and aspects

None.

6 References

None.