Difference between revisions of "Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus"
(→General aspects) |
(→General aspects) |
||
| Line 32: | Line 32: | ||
=== Study design and evidence level === | === Study design and evidence level === | ||
==== General aspects ==== | ==== General aspects ==== | ||
| − | * The paper presents a new approach ( | + | * The paper presents a new approach (defiant) 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. |
==== Design for Outcome O1 ==== | ==== Design for Outcome O1 ==== | ||
Revision as of 14:02, 25 January 2019
Contents
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 (defiant) 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
