You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
Mendeley readers
Title |
MixClone: a mixture model for inferring tumor subclonal populations
|
---|---|
Published in |
BMC Genomics, January 2015
|
DOI | 10.1186/1471-2164-16-s2-s1 |
Pubmed ID | |
Authors |
Yi Li, Xiaohui Xie |
Abstract |
Tumor genomes are often highly heterogeneous, consisting of genomes from multiple subclonal types. Complete characterization of all subclonal types is a fundamental need in tumor genome analysis. With the advancement of next-generation sequencing, computational methods have recently been developed to infer tumor subclonal populations directly from cancer genome sequencing data. Most of these methods are based on sequence information from somatic point mutations, However, the accuracy of these algorithms depends crucially on the quality of the somatic mutations returned by variant calling algorithms, and usually requires a deep coverage to achieve a reasonable level of accuracy. |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 6% |
Unknown | 33 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 12 | 34% |
Researcher | 4 | 11% |
Student > Bachelor | 3 | 9% |
Student > Doctoral Student | 2 | 6% |
Student > Master | 2 | 6% |
Other | 4 | 11% |
Unknown | 8 | 23% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 13 | 37% |
Computer Science | 4 | 11% |
Biochemistry, Genetics and Molecular Biology | 3 | 9% |
Mathematics | 2 | 6% |
Engineering | 2 | 6% |
Other | 4 | 11% |
Unknown | 7 | 20% |