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

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=== Study design and evidence level ===
 
=== Study design and evidence level ===
 
==== General aspects ====
 
==== General aspects ====
You can describe general design aspects here.
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* 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.
The study designs for describing specific outcomes are listed in the following subsections:
 
  
 
==== Design for Outcome O1 ====
 
==== Design for Outcome O1 ====

Revision as of 14:02, 25 January 2019

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 ...
  • ...

1.4 Further comments and aspects

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