publication . Article . Other literature type . 2019

RNAIndel: discovering somatic coding indels from tumor RNA-Seq data

Kohei Hagiwara; Liang Ding; Michael N Edmonson; Stephen V Rice; Scott Newman; John Easton; Juncheng Dai; Soheil Meshinchi; Rhonda E Ries; Michael Rusch; ...
Restricted
  • Published: 08 Oct 2019 Journal: Bioinformatics, volume 36, pages 4,231-4,231 (issn: 1367-4803, eissn: 1460-2059, Copyright policy)
  • Publisher: Oxford University Press (OUP)
Abstract
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Reliable identification of expressed somatic insertions/deletions (indels) is an unmet need due to artifacts generated in PCR-based RNA-Seq library preparation and the lack of normal RNA-Seq data, presenting analytical challenges for discovery of somatic indels in tumor transcriptome.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We present RNAIndel, a tool for predicting somatic, germline and artifact indels from tumor RNA-Seq data. RNAIndel leverages features derived from indel sequence context and biological effect in a machine-learning framewor...
Subjects
free text keywords: Statistics and Probability, Computational Theory and Mathematics, Biochemistry, Molecular Biology, Computational Mathematics, Computer Science Applications
Funded by
NIH| Center for Precision Medicine in Leukemia (CPML)
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 3P50GM115279-03S1
  • Funding stream: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
Download fromView all 3 versions
Bioinformatics
Article . 2019
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http://dx.doi.org/10.1093/bioi...
Other literature type . 2019
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Article . 2020
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27 references, page 1 of 2

1. Piskol, R., Ramaswami, G., and Li, J.B. (2013) Reliable identification of genomic variants from RNA-Seq data. Am J Hum Genet., 93, 641‒651. [OpenAIRE]

2. Wang., C., Davila, J.I., Baheti, S., Bhagwate, A.V., Wang, X., Kocher, J.P., Slager, S.L., Feldman, A.L., Novak, A.J., Cerhan, J.R., et al. (2014) RVboost: RNA-seq variant prioritization using a boosting 4. Oikkonen, L. and Lise, S. (2017) Making the most of RNA-seq: pre-processing sequencing data with Opossum for reliable SNP variant detection. Wellcome Open Res., 10.12688/wellcomeopenres.10501.2.

5. Sun, Z., Bhagwate, A., Prodduturi, N., Yang, P. and Kocher J.A. (2017) Indel detection from RNAseq data: tool evaluation and strategies for accurate detection of actionable mutations. Brief Bioinform., 18, 973-983.

6. Fang, H., Wu, Y., Narzisi, G., O'Rawe, J.A., Barrón, L.T., Rosenbaum, J., Ronemus, M., Iossifov, I., Schatz, M.C. and Lyon, G.J. (2014) Reducing INDEL calling errors in whole genome and exome sequencing data. Genome Med., 10.1186/s13073-014-0089-z.

7. Ng, P.C., Levy, S., Huang, J., Stockwell, T.B., Walenz, B.P., Li, K., Axelrod, N., Busam, D.A., Strausberg, R.L. and Venter, J.C. (2008) Genetic variation in an individual human exome. PLoS Genet., 4, e1000160.

8. Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M. and Gingeras, T.R. (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29, 15-21.

9. Rusch, M., Nakitandwe, J., Shurtleff, S., Newman, S., Zhang, Z., Edmonson, M.N., Parker, M., Jiao, Y., Ma, X., Liu, Y., et al. (2018) Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome. Nat Commun., 10.1038/s41467-018-06485-7. [OpenAIRE]

10. Ma, X., Liu, Y., Liu, Y., Alexandrov, L.B., Edmonson, M.N., Gawad, C., Zhou, X., Li, Y., Rusch, M.C., Easton, J., et al. (2018) Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature, 555, 371-376. [OpenAIRE]

11. Bolouri, H., Farrar, J.E., Triche, T Jr., Ries, R.E., Lim, E.L., Alonzo, T.A., Ma, Y., Moore, R., Mungall, A.J., Marra, M.A., et al. (2018) The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions. Nat Med., 1, 103-112.

12. Saunders, C.T., Wong, W.S., Swamy, S., Becq, J., Murray, L.J. and Cheetham, R.K. (2012) Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics, 28, 1811-1817.

13. Hand, D.J. and Till, R.J. (2001) A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine Learning, 45, 171-186.

14. DePristo, M.A., Banks, E., Poplin, R., Garimella, K.V., Maguire, J.R., Hartl, C., Philippakis, A.A., del Angel, G., Rivas, M.A., Hanna, M. et al. (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet., 10.1038/ng.806.

15. Edmonson, M.N., Zhang, J., Yan, C., Finney, R.P., Meerzaman, D.M. and Buetow, K.H. (2011)

16. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011) Scikit-learn: Machine Learning in Python.

17. Danecek, P., Auton, A., Abecasis, G., Albers, C.A., Banks, E., DePristo, M.A., Handsaker, R.E., Lunter, G., Marth, G.T., Sherry, S.T., et al. (2011) The variant call format and VCFtools.

27 references, page 1 of 2
Abstract
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Reliable identification of expressed somatic insertions/deletions (indels) is an unmet need due to artifacts generated in PCR-based RNA-Seq library preparation and the lack of normal RNA-Seq data, presenting analytical challenges for discovery of somatic indels in tumor transcriptome.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We present RNAIndel, a tool for predicting somatic, germline and artifact indels from tumor RNA-Seq data. RNAIndel leverages features derived from indel sequence context and biological effect in a machine-learning framewor...
Subjects
free text keywords: Statistics and Probability, Computational Theory and Mathematics, Biochemistry, Molecular Biology, Computational Mathematics, Computer Science Applications
Funded by
NIH| Center for Precision Medicine in Leukemia (CPML)
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 3P50GM115279-03S1
  • Funding stream: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
Download fromView all 3 versions
Bioinformatics
Article . 2019
Provider: Crossref
http://dx.doi.org/10.1093/bioi...
Other literature type . 2019
Provider: Datacite
Bioinformatics
Article . 2020
Provider: Crossref
27 references, page 1 of 2

1. Piskol, R., Ramaswami, G., and Li, J.B. (2013) Reliable identification of genomic variants from RNA-Seq data. Am J Hum Genet., 93, 641‒651. [OpenAIRE]

2. Wang., C., Davila, J.I., Baheti, S., Bhagwate, A.V., Wang, X., Kocher, J.P., Slager, S.L., Feldman, A.L., Novak, A.J., Cerhan, J.R., et al. (2014) RVboost: RNA-seq variant prioritization using a boosting 4. Oikkonen, L. and Lise, S. (2017) Making the most of RNA-seq: pre-processing sequencing data with Opossum for reliable SNP variant detection. Wellcome Open Res., 10.12688/wellcomeopenres.10501.2.

5. Sun, Z., Bhagwate, A., Prodduturi, N., Yang, P. and Kocher J.A. (2017) Indel detection from RNAseq data: tool evaluation and strategies for accurate detection of actionable mutations. Brief Bioinform., 18, 973-983.

6. Fang, H., Wu, Y., Narzisi, G., O'Rawe, J.A., Barrón, L.T., Rosenbaum, J., Ronemus, M., Iossifov, I., Schatz, M.C. and Lyon, G.J. (2014) Reducing INDEL calling errors in whole genome and exome sequencing data. Genome Med., 10.1186/s13073-014-0089-z.

7. Ng, P.C., Levy, S., Huang, J., Stockwell, T.B., Walenz, B.P., Li, K., Axelrod, N., Busam, D.A., Strausberg, R.L. and Venter, J.C. (2008) Genetic variation in an individual human exome. PLoS Genet., 4, e1000160.

8. Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M. and Gingeras, T.R. (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29, 15-21.

9. Rusch, M., Nakitandwe, J., Shurtleff, S., Newman, S., Zhang, Z., Edmonson, M.N., Parker, M., Jiao, Y., Ma, X., Liu, Y., et al. (2018) Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome. Nat Commun., 10.1038/s41467-018-06485-7. [OpenAIRE]

10. Ma, X., Liu, Y., Liu, Y., Alexandrov, L.B., Edmonson, M.N., Gawad, C., Zhou, X., Li, Y., Rusch, M.C., Easton, J., et al. (2018) Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature, 555, 371-376. [OpenAIRE]

11. Bolouri, H., Farrar, J.E., Triche, T Jr., Ries, R.E., Lim, E.L., Alonzo, T.A., Ma, Y., Moore, R., Mungall, A.J., Marra, M.A., et al. (2018) The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions. Nat Med., 1, 103-112.

12. Saunders, C.T., Wong, W.S., Swamy, S., Becq, J., Murray, L.J. and Cheetham, R.K. (2012) Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics, 28, 1811-1817.

13. Hand, D.J. and Till, R.J. (2001) A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine Learning, 45, 171-186.

14. DePristo, M.A., Banks, E., Poplin, R., Garimella, K.V., Maguire, J.R., Hartl, C., Philippakis, A.A., del Angel, G., Rivas, M.A., Hanna, M. et al. (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet., 10.1038/ng.806.

15. Edmonson, M.N., Zhang, J., Yan, C., Finney, R.P., Meerzaman, D.M. and Buetow, K.H. (2011)

16. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011) Scikit-learn: Machine Learning in Python.

17. Danecek, P., Auton, A., Abecasis, G., Albers, C.A., Banks, E., DePristo, M.A., Handsaker, R.E., Lunter, G., Marth, G.T., Sherry, S.T., et al. (2011) The variant call format and VCFtools.

27 references, page 1 of 2
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