PMID- 31888469
OWN - NLM
STAT- MEDLINE
DCOM- 20200506
LR  - 20200506
IS  - 1471-2164 (Electronic)
IS  - 1471-2164 (Linking)
VI  - 20
IP  - Suppl 12
DP  - 2019 Dec 30
TI  - normGAM: an R package to remove systematic biases in genome architecture mapping 
      data.
PG  - 1006
LID - 10.1186/s12864-019-6331-8 [doi]
LID - 1006
AB  - BACKGROUND: The genome architecture mapping (GAM) technique can capture 
      genome-wide chromatin interactions. However, besides the known systematic biases 
      in the raw GAM data, we have found a new type of systematic bias. It is necessary 
      to develop and evaluate effective normalization methods to remove all systematic 
      biases in the raw GAM data. RESULTS: We have detected a new type of systematic 
      bias, the fragment length bias, in the genome architecture mapping (GAM) data, 
      which is significantly different from the bias of window detection frequency 
      previously mentioned in the paper introducing the GAM method but is similar to 
      the bias of distances between restriction sites existing in raw Hi-C data. We 
      have found that the normalization method (a normalized variant of the linkage 
      disequilibrium) used in the GAM paper is not able to effectively eliminate the 
      new fragment length bias at 1 Mb resolution (slightly better at 30 kb 
      resolution). We have developed an R package named normGAM for eliminating the new 
      fragment length bias together with the other three biases existing in raw GAM 
      data, which are the biases related to window detection frequency, mappability, 
      and GC content. Five normalization methods have been implemented and included in 
      the R package including Knight-Ruiz 2-norm (KR2, newly designed by us), 
      normalized linkage disequilibrium (NLD), vanilla coverage (VC), sequential 
      component normalization (SCN), and iterative correction and eigenvector 
      decomposition (ICE). CONCLUSIONS: Based on our evaluations, the five 
      normalization methods can eliminate the four biases existing in raw GAM data, 
      with VC and KR2 performing better than the others. We have observed that the 
      KR2-normalized GAM data have a higher correlation with the KR-normalized Hi-C 
      data on the same cell samples indicating that the KR-related methods are better 
      than the others for keeping the consistency between the GAM and Hi-C experiments. 
      Compared with the raw GAM data, the normalized GAM data are more consistent with 
      the normalized distances from the fluorescence in situ hybridization (FISH) 
      experiments. The source code of normGAM can be freely downloaded from 
      http://dna.cs.miami.edu/normGAM/.
FAU - Liu, Tong
AU  - Liu T
AD  - Department of Computer Science, University of Miami, 1365 Memorial Drive, P.O. 
      Box 248154, Coral Gables, FL, 33124, USA.
FAU - Wang, Zheng
AU  - Wang Z
AD  - Department of Computer Science, University of Miami, 1365 Memorial Drive, P.O. 
      Box 248154, Coral Gables, FL, 33124, USA. zheng.wang@miami.edu.
LA  - eng
GR  - R15 GM120650/GM/NIGMS NIH HHS/United States
PT  - Comparative Study
PT  - Journal Article
DEP - 20191230
PL  - England
TA  - BMC Genomics
JT  - BMC genomics
JID - 100965258
RN  - 0 (Chromatin)
SB  - IM
MH  - Bias
MH  - Chromatin/chemistry/genetics
MH  - Chromosome Mapping/*methods
MH  - Contig Mapping
MH  - Genome/genetics
MH  - Reproducibility of Results
MH  - *Software
PMC - PMC6936146
OTO - NOTNLM
OT  - 3D genome structure
OT  - GAM
OT  - Genome architecture mapping
OT  - Hi-C
OT  - Normalization
OT  - Systematic biases
COIS- The authors declare that they have no competing interests.
EDAT- 2020/01/01 06:00
MHDA- 2020/05/07 06:00
PMCR- 2019/12/30
CRDT- 2020/01/01 06:00
PHST- 2020/01/01 06:00 [entrez]
PHST- 2020/01/01 06:00 [pubmed]
PHST- 2020/05/07 06:00 [medline]
PHST- 2019/12/30 00:00 [pmc-release]
AID - 10.1186/s12864-019-6331-8 [pii]
AID - 6331 [pii]
AID - 10.1186/s12864-019-6331-8 [doi]
PST - epublish
SO  - BMC Genomics. 2019 Dec 30;20(Suppl 12):1006. doi: 10.1186/s12864-019-6331-8.