Name
Abstract | ||
|---|---|---|
Reliably identifying genomic rearrangements and interpreting their impact is a key step in understanding their role in human cancers and inherited genetic diseases. Many short read algorithmic approaches exist but all have appreciable false negative rates. A common approach is to evaluate the union of multiple tools increasing sensitivity, followed by filtering to retain specificity. Here we describe an application framework for the rapid generation of structural variant consensus, unique in its ability to visualize the genetic impact and context as well as process both genome and transcriptome data. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/bioinformatics/bty621 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Annotation,Information retrieval,Computer science,Merge (version control) | Journal | 35 |
Issue | ISSN | Citations |
3 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 7 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caralyn Reisle | 1 | 0 | 0.34 |
| Karen L Mungall | 2 | 0 | 0.34 |
| Caleb Choo | 3 | 2 | 0.80 |
| Daniel Paulino | 4 | 3 | 0.77 |
| Dustin W Bleile | 5 | 0 | 0.34 |
| Amir Muhammadzadeh | 6 | 0 | 0.34 |
| Andrew J Mungall | 7 | 0 | 0.34 |
| Richard A. Moore | 8 | 19 | 1.38 |
| Inna Shlafman | 9 | 0 | 0.34 |
| Robin Coope | 10 | 10 | 1.04 |
| Stephen Pleasance | 11 | 0 | 0.34 |
| Yussanne Ma | 12 | 0 | 0.34 |
| Steven J. M. Jones | 13 | 578 | 71.55 |