ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification
Whole exome sequencing has facilitated the discovery of causal genetic variants associated with human diseases at deep coverage and low cost. In particular, the detection of somatic mutations from tumor/normal pairs has provided insights into the cancer genome. Although there is an abundance of publ...
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| Vydáno v: | PLOS ONE Ročník 10; číslo 8; s. e0135800 |
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United States
Public Library of Science (PLoS)
13.08.2015
Public Library of Science |
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Whole exome sequencing has facilitated the discovery of causal genetic variants associated with human diseases at deep coverage and low cost. In particular, the detection of somatic mutations from tumor/normal pairs has provided insights into the cancer genome. Although there is an abundance of publicly-available software for the detection of germline and somatic variants, concordance is generally limited among variant callers and alignment algorithms. Successful integration of variants detected by multiple methods requires in-depth knowledge of the software, access to high-performance computing resources, and advanced programming techniques. We present ExScalibur, a set of fully automated, highly scalable and modulated pipelines for whole exome data analysis. The suite integrates multiple alignment and variant calling algorithms for the accurate detection of germline and somatic mutations with close to 99% sensitivity and specificity. ExScalibur implements streamlined execution of analytical modules, real-time monitoring of pipeline progress, robust handling of errors and intuitive documentation that allows for increased reproducibility and sharing of results and workflows. It runs on local computers, high-performance computing clusters and cloud environments. In addition, we provide a data analysis report utility to facilitate visualization of the results that offers interactive exploration of quality control files, read alignment and variant calls, assisting downstream customization of potential disease-causing mutations. ExScalibur is open-source and is also available as a public image on Amazon cloud. |
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| AbstractList | Whole exome sequencing has facilitated the discovery of causal genetic variants associated with human diseases at deep coverage and low cost. In particular, the detection of somatic mutations from tumor/normal pairs has provided insights into the cancer genome. Although there is an abundance of publicly-available software for the detection of germline and somatic variants, concordance is generally limited among variant callers and alignment algorithms. Successful integration of variants detected by multiple methods requires in-depth knowledge of the software, access to high-performance computing resources, and advanced programming techniques. We present ExScalibur, a set of fully automated, highly scalable and modulated pipelines for whole exome data analysis. The suite integrates multiple alignment and variant calling algorithms for the accurate detection of germline and somatic mutations with close to 99% sensitivity and specificity. ExScalibur implements streamlined execution of analytical modules, real-time monitoring of pipeline progress, robust handling of errors and intuitive documentation that allows for increased reproducibility and sharing of results and workflows. It runs on local computers, high-performance computing clusters and cloud environments. In addition, we provide a data analysis report utility to facilitate visualization of the results that offers interactive exploration of quality control files, read alignment and variant calls, assisting downstream customization of potential disease-causing mutations. ExScalibur is open-source and is also available as a public image on Amazon cloud. Whole exome sequencing has facilitated the discovery of causal genetic variants associated with human diseases at deep coverage and low cost. In particular, the detection of somatic mutations from tumor/normal pairs has provided insights into the cancer genome. Although there is an abundance of publicly-available software for the detection of germline and somatic variants, concordance is generally limited among variant callers and alignment algorithms. Successful integration of variants detected by multiple methods requires in-depth knowledge of the software, access to high-performance computing resources, and advanced programming techniques. We present ExScalibur, a set of fully automated, highly scalable and modulated pipelines for whole exome data analysis. The suite integrates multiple alignment and variant calling algorithms for the accurate detection of germline and somatic mutations with close to 99% sensitivity and specificity. ExScalibur implements streamlined execution of analytical modules, real-time monitoring of pipeline progress, robust handling of errors and intuitive documentation that allows for increased reproducibility and sharing of results and workflows. It runs on local computers, high-performance computing clusters and cloud environments. In addition, we provide a data analysis report utility to facilitate visualization of the results that offers interactive exploration of quality control files, read alignment and variant calls, assisting downstream customization of potential disease-causing mutations. ExScalibur is open-source and is also available as a public image on Amazon cloud.Whole exome sequencing has facilitated the discovery of causal genetic variants associated with human diseases at deep coverage and low cost. In particular, the detection of somatic mutations from tumor/normal pairs has provided insights into the cancer genome. Although there is an abundance of publicly-available software for the detection of germline and somatic variants, concordance is generally limited among variant callers and alignment algorithms. Successful integration of variants detected by multiple methods requires in-depth knowledge of the software, access to high-performance computing resources, and advanced programming techniques. We present ExScalibur, a set of fully automated, highly scalable and modulated pipelines for whole exome data analysis. The suite integrates multiple alignment and variant calling algorithms for the accurate detection of germline and somatic mutations with close to 99% sensitivity and specificity. ExScalibur implements streamlined execution of analytical modules, real-time monitoring of pipeline progress, robust handling of errors and intuitive documentation that allows for increased reproducibility and sharing of results and workflows. It runs on local computers, high-performance computing clusters and cloud environments. In addition, we provide a data analysis report utility to facilitate visualization of the results that offers interactive exploration of quality control files, read alignment and variant calls, assisting downstream customization of potential disease-causing mutations. ExScalibur is open-source and is also available as a public image on Amazon cloud. |
| Audience | Academic |
| Author | Volchenboum, Samuel Huang, Lei Andrade, Jorge Bartom, Elizabeth Kang, Wenjun Onel, Kenan Bao, Riyue Hernandez, Kyle |
| AuthorAffiliation | 2 Department of Pediatrics, The University of Chicago, Chicago, Illinois, United States of America 3 Computation Institute, The University of Chicago, Chicago, Illinois, United States of America 1 Center for Research Informatics, The University of Chicago, Chicago, Illinois, United States of America Children's Medical Research Institute, AUSTRALIA |
| AuthorAffiliation_xml | – name: 2 Department of Pediatrics, The University of Chicago, Chicago, Illinois, United States of America – name: Children's Medical Research Institute, AUSTRALIA – name: 3 Computation Institute, The University of Chicago, Chicago, Illinois, United States of America – name: 1 Center for Research Informatics, The University of Chicago, Chicago, Illinois, United States of America |
| Author_xml | – sequence: 1 fullname: Bao, Riyue – sequence: 2 orcidid: 0000-0001-8267-644x fullname: Hernandez, Kyle – sequence: 3 fullname: Huang, Lei – sequence: 4 fullname: Kang, Wenjun – sequence: 5 orcidid: 0000-0002-5618-2582 fullname: Bartom, Elizabeth – sequence: 6 fullname: Onel, Kenan – sequence: 7 fullname: Volchenboum, Samuel – sequence: 8 orcidid: 0000-0003-3210-2186 fullname: Andrade, Jorge |
| BackLink | https://cir.nii.ac.jp/crid/1874243915436908416$$DView record in CiNii https://www.ncbi.nlm.nih.gov/pubmed/26271043$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1158_2159_8290_CD_16_0607 crossref_primary_10_1016_j_csbj_2017_10_001 crossref_primary_10_3389_fmolb_2023_1169109 crossref_primary_10_1002_humu_23275 crossref_primary_10_1038_s41598_018_25022_6 crossref_primary_10_1002_2211_5463_13261 crossref_primary_10_1158_1078_0432_CCR_24_2785 crossref_primary_10_7717_peerj_11724 crossref_primary_10_1093_annonc_mdx682 crossref_primary_10_1186_s12859_019_2791_8 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: RB KH LH SV JA. Performed the experiments: RB KH LH WK. Analyzed the data: RB KH LH. Contributed reagents/materials/analysis tools: KO EB. Wrote the paper: RB KH LH WK EB KO SV JA. Developed and implemented the pipelines and software: RB KH. Evaluated the pipelines: RB KH LH. Built Amazon EC2 environments: WK. Competing Interests: The authors have declared that no competing interests exist. Current address: Department of Biochemistry and Molecular Genetics, Northwestern University, Chicago, Illinois, United States of America |
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