Name
Title | ||
|---|---|---|
LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer. |
Abstract | ||
|---|---|---|
BackgroundThe increasing availability of resequencing data has led to a better understanding of the most important genes in cancer development. Nevertheless, the mutational landscape of many tumor types is heterogeneous and encompasses a long tail of potential driver genes that are systematically excluded by currently available methods due to the low frequency of their mutations. We developed LowMACA (Low frequency Mutations Analysis via Consensus Alignment), a method that combines the mutations of various proteins sharing the same functional domains to identify conserved residues that harbor clustered mutations in multiple sequence alignments. LowMACA is designed to visualize and statistically assess potential driver genes through the identification of their mutational hotspots. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1186/s12859-016-0935-7 | BMC Bioinformatics |
Keywords | Field | DocType |
Driver Mutation, Pfam Domain, Driver Gene, Mutual Exclusivity, Clustal Omega | Protein family,Gene,Biology,Bioinformatics,DNA Mutational Analysis,Genetics,DNA microarray,Cancer,Mutually exclusive events,Mutation | Journal |
Volume | Issue | ISSN |
17 | 1 | 1471-2105 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giorgio E. M. Melloni | 1 | 0 | 0.68 |
| Stefano de Pretis | 2 | 4 | 1.53 |
| Laura Riva | 3 | 3 | 0.84 |
| Mattia Pelizzola | 4 | 41 | 3.25 |
| Arnaud Ceol | 5 | 568 | 36.57 |
| Jole Costanza | 6 | 21 | 5.98 |
| Heiko Müller | 7 | 12 | 2.52 |
| Luca Zammataro | 8 | 2 | 1.81 |