Abstract
In this paper, we present a graph search approach for identifying arbitrarily complex structural genomic variation. Our method leverages the ability of long reads (e.g. from Pacific Biosciences platforms) to span multiple breakpoints of complicated local rearrangements, allowing us to resolve small-scale complexities that may be overlooked by other tools. We applied our method to a subset of NA12878 germline events using two long read datasets and demonstrate, with a concordance rate of 88.4% between the two sets, an increased ability to denote complex events over baseline calls from short read data. In a majority of the regions analyzed we detected small complexities that flank the breakpoints of larger events, including small insertions, inversions, and duplicated sequences. These patterns of complexity match known mechanisms associated with DNA replication and structural variant formation, and showcase the ability of our approach to efficiently unravel such events. Our method automatically classifies complex structural variant calls as a combination of nested or adjacent reference transformations, allowing users to identify specific structure types of interest. Additionally, an output report is generated for each event with interactive visual representations of the rearrangement.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 181-187 |
| Number of pages | 7 |
| Volume | 2017-January |
| ISBN (Electronic) | 9781509030491 |
| DOIs | |
| State | Published - Dec 15 2017 |
| Event | 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States Duration: Nov 13 2017 → Nov 16 2017 |
Other
| Other | 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 |
|---|---|
| Country | United States |
| City | Kansas City |
| Period | 11/13/17 → 11/16/17 |
Fingerprint
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
Cite this
Unraveling complex local genomic rearrangements from long-read data. / Stephens, Zachary D.; Iyer, Ravishankar K.; Wang, Chen; Kocher, Jean-Pierre.
Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 181-187.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Unraveling complex local genomic rearrangements from long-read data
AU - Stephens, Zachary D.
AU - Iyer, Ravishankar K.
AU - Wang, Chen
AU - Kocher, Jean-Pierre
PY - 2017/12/15
Y1 - 2017/12/15
N2 - In this paper, we present a graph search approach for identifying arbitrarily complex structural genomic variation. Our method leverages the ability of long reads (e.g. from Pacific Biosciences platforms) to span multiple breakpoints of complicated local rearrangements, allowing us to resolve small-scale complexities that may be overlooked by other tools. We applied our method to a subset of NA12878 germline events using two long read datasets and demonstrate, with a concordance rate of 88.4% between the two sets, an increased ability to denote complex events over baseline calls from short read data. In a majority of the regions analyzed we detected small complexities that flank the breakpoints of larger events, including small insertions, inversions, and duplicated sequences. These patterns of complexity match known mechanisms associated with DNA replication and structural variant formation, and showcase the ability of our approach to efficiently unravel such events. Our method automatically classifies complex structural variant calls as a combination of nested or adjacent reference transformations, allowing users to identify specific structure types of interest. Additionally, an output report is generated for each event with interactive visual representations of the rearrangement.
AB - In this paper, we present a graph search approach for identifying arbitrarily complex structural genomic variation. Our method leverages the ability of long reads (e.g. from Pacific Biosciences platforms) to span multiple breakpoints of complicated local rearrangements, allowing us to resolve small-scale complexities that may be overlooked by other tools. We applied our method to a subset of NA12878 germline events using two long read datasets and demonstrate, with a concordance rate of 88.4% between the two sets, an increased ability to denote complex events over baseline calls from short read data. In a majority of the regions analyzed we detected small complexities that flank the breakpoints of larger events, including small insertions, inversions, and duplicated sequences. These patterns of complexity match known mechanisms associated with DNA replication and structural variant formation, and showcase the ability of our approach to efficiently unravel such events. Our method automatically classifies complex structural variant calls as a combination of nested or adjacent reference transformations, allowing users to identify specific structure types of interest. Additionally, an output report is generated for each event with interactive visual representations of the rearrangement.
UR - http://www.scopus.com/inward/record.url?scp=85046277710&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046277710&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2017.8217647
DO - 10.1109/BIBM.2017.8217647
M3 - Conference contribution
VL - 2017-January
SP - 181
EP - 187
BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -