Cai, Zhengqiu

Nabavi, Sheida

Tonellato, Peter J

Harvard University - DASH 

 Analysis of sequence-based copy number variation detection toolsfor cancer studies  (Article begins on next page)The Harvard community has made this article openly available.Please share how this access benefits you. Your story matters.Citation Nabavi, Sheida, Zhengqiu Cai, and Peter J Tonellato. 2013.“Analysis of sequence-based copy number variation detectiontools for cancer studies.” AMIA Summits on Translational ScienceProceedings 2013 (1): 124.Accessed February 19, 2015 3:04:12 PM ESTCitable Link http://nrs.harvard.edu/urn-3:HUL.InstRepos:11879407Terms of Use This article was downloaded from Harvard University's DASHrepository, and is made available under the terms and conditionsapplicable to Other Posted Material, as set forth athttp://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAAAnalysis of sequence-based copy number variation detection tools for cancer studies  Sheida Nabavi, Zhengqiu Cai and Peter J Tonellato Center for Biomedical Informatics, Harvard Medical School  Background: Copy number variations (CNVs) can play an important role in tumor genesis and growth through amplification of oncogenes and loss of tumor suppressor genes. Consequently, identification of cancer specific somatic CNVs can provide insight into cancer diagnosis, prognosis and treatment.  Recent advances in high throughput next generation sequencing (NGS) technologies have created an opportunity for  detecting  CNVs  with  higher  accuracy  and  resolution  than  previous  technologies  such  as  array-comparative  genomic  hybridization.  As  a  result,  several  sequence-based  CNV  detection  methods  and software  applications  have  been  developed  to  take  advantage  of  these  technologies.  However,  no comprehensive comparison of the sensitivity and precision of the most recent sequence-based tools has been  performed.  The  results  of  this  work  indicate  the  performance  characteristics  of  the  recent sequence-based  CNV  detection  tools,  which  can  facilitate  selection  of  an  appropriate  tool  for  cancer studies and serve as a guide to develop new algorithms that address current limitations. Results: In this work, we analyze the performance of six new publically available sequence-based CNV detection  tools  (SegSeq,  CNVer,  CNVnator,  FREEC,  CNV-seq  and  ReadDepth),  using  six  synthesized datasets, characterized by different coverage values (0.5, 2, 5, 10, 20 and 50) and contained known CNVs, and eight low coverage breast cancer cell line (MCF7, T47D, BT474, ZR75-1, BT20, MDA-MB-231, MDA-MB-468 and HCC1143) NGS datasets.  The synthesized NGS datasets are used to accurately compute sensitivity (Figure 1a), precision and breakpoint accuracy of the tools while increasing sequence coverage. Breast cancer cell line NGS datasets are used to calculate sensitivity and precision of the tools (Figure 1b), analyze statistical characteristics of the  detected  CNVs  and  compare  the  computational requirements and costs of the tools employing default and a range of the key parameters’ values.  Conclusion:  The  sensitivity  and  breakpoint  accuracy  of CNV detection tools, using synthesized and breast cancer cell  lines  NGS  datasets,  indicate  tools  that  employ  more computationally  complex  and  elaborate  detection algorithms;  and  tools  that  incorporate  information embedded  in  paired-end  data  and  depth  of  coverage, perform better than other tools (in average 30% increase in  sensitivity  and  6%  increase  in  break  point  accuracy. However, the computational cost of the better performing tools is higher than the other methods (about one order of magnitude).  Also, this study shows that the precision of current CNV detection tools is still low (43% in average) and  indicates  the  likelihood  of  high  false  positive  rate. There is also a disturbing lack of CNV prediction consensus across  tools  –  even  those  high  performing  methods. Current  methods  for  detecting  CNV  by  analysis  of  NGS sequence of cancer genomes do not yet provide the level of accuracy  and  robustness  required  to  predict  CNVs  with high  confidence.    This  study  demonstrates  the  need  for further work on both the quality of NGS data and on the development  and  refinement  of  sequence-based  CNV detection algorithms.  Figure  1.  (a)  Sensitivity  of  the  tools  vs.  coverage using synthesized NGS datasets. (b) Sensitivity and precision of the tools against MCF7 cell line (a) (b) 124

Analysis of sequence-based copy number variation detection tools for cancer studies

http://dash.harvard.edu/bitstream/handle/1/11879407/3814465.pdf?sequence=1

Analysis of sequence-based copy number variation detection tools for cancer studies

Abstract

Similar works

Full text

Available Versions

Harvard University - DASH