Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus

1 Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus

David E. Condon, Phu V. Tran, Yu-Chin Lien, Jonathan Schug, Michael K. Georgieff, Rebecca A. Simmons and Kyoung-Jae Won, Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus, 2018, BMC Bioinformatics, 19:31.

https://doi.org/10.1186/s12859-018-2037-1

1.1 Summary

The paper considers identification of differentially methylated regions (DMRs) from bisulfite sequencing data (BSSEQ). A new package (defiant) is introduced. The paper claims that shows superior performance to other approaches as shown in analyses of a series of benchmarking tests on artificial and real data.

1.2 Study outcomes

List the paper results concerning method comparison and benchmarking:

1.2.1 Outcome O1

The performance of ...

Outcome O1 is presented as Figure X in the original publication.

1.2.2 Outcome O2

...

Outcome O2 is presented as Figure X in the original publication.

1.2.3 Outcome On

...

Outcome On is presented as Figure X in the original publication.

1.2.4 Further outcomes

If intended, you can add further outcomes here.

1.3 Study design and evidence level

1.3.1 General aspects

• The paper presents a new approach (DMRcaller) 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 but 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.

1.3.2 Design for Outcome O1

• 16 "benchmark" data sets were analyzed taken from [27]
• Configuration parameters were chosen ...
• ...

1.3.3 Design for Outcome O2

• The outcome was generated for ...
• Configuration parameters were chosen ...
• ...

...

1.3.4 Design for Outcome O

• The outcome was generated for ...
• Configuration parameters were chosen ...
• ...

1.5 References

[27] Jühling F, Kretzmer H, Bernhart SH, Otto C, Stadler PF, Hoffmann S. Metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data. Genome Res. 2015; https://doi.org/10.1101/gr.196394.11