TY - GEN
T1 - Qualitative Comparison of Selected Indel Detection Methods for RNA-Seq Data
AU - Slosarek, Tamara
AU - Kraus, Milena
AU - Schapranow, Matthieu P.
AU - Boettinger, Erwin
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - RNA sequencing (RNA-Seq) provides both gene expression and sequence information, which can be exploited for a joint approach to explore cell processes in general and diseases caused by genomic variants in particular. However, the identification of insertions and deletions (indels) from RNA-Seq data, which for instance play a significant role in the development, detection, and treatment of cancer, still poses a challenge. In this paper, we present a qualitative comparison of selected methods for indel detection from RNA-Seq data. More specifically, we benchmarked two promising aligners and two filter methods on simulated as well as on real RNA-Seq data. We conclude that in cases where reliable detection of indels is crucial, e.g. in a clinical setting, the usage of our pipeline setup is superior to other state-of-the-art approaches.
AB - RNA sequencing (RNA-Seq) provides both gene expression and sequence information, which can be exploited for a joint approach to explore cell processes in general and diseases caused by genomic variants in particular. However, the identification of insertions and deletions (indels) from RNA-Seq data, which for instance play a significant role in the development, detection, and treatment of cancer, still poses a challenge. In this paper, we present a qualitative comparison of selected methods for indel detection from RNA-Seq data. More specifically, we benchmarked two promising aligners and two filter methods on simulated as well as on real RNA-Seq data. We conclude that in cases where reliable detection of indels is crucial, e.g. in a clinical setting, the usage of our pipeline setup is superior to other state-of-the-art approaches.
KW - Indels
KW - RNA-Seq
KW - Variant calling
UR - https://www.scopus.com/pages/publications/85065835326
U2 - 10.1007/978-3-030-17938-0_16
DO - 10.1007/978-3-030-17938-0_16
M3 - Conference contribution
AN - SCOPUS:85065835326
SN - 9783030179373
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 166
EP - 177
BT - Bioinformatics and Biomedical Engineering - 7th International Work-Conference, IWBBIO 2019, Proceedings
A2 - Rojas, Ignacio
A2 - Valenzuela, Olga
A2 - Ortuño, Francisco
A2 - Rojas, Fernando
A2 - Ortuño, Francisco
PB - Springer Verlag
T2 - 7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019
Y2 - 8 May 2019 through 10 May 2019
ER -