Fastq2vcf: A concise and transparent pipeline for whole-exome sequencing data analyses

BMC Research Notes

Volumn 8, Issue 1, 2015, Pages

  Gao, Xiaoyi ^a   Xu, Jianpeng ^a   Starmer, Joshua ^b,c  

^a UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

Automation; Next generation sequencing; Pipeline; Variant calling; Whole exome sequencing

Indexed keywords

COMPUTER PROGRAM; EXOME; HIGH THROUGHPUT SEQUENCING; HUMAN; PROCEDURES; QUALITY CONTROL; REPRODUCIBILITY;

BENCHMARKING; EXOME; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; QUALITY CONTROL; REPRODUCIBILITY OF RESULTS; SOFTWARE;

EID: 84925865899     PISSN: None     EISSN: 17560500     Source Type: Journal    
DOI: 10.1186/s13104-015-1027-x     Document Type: Article

References (17)

1. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43(5):491-8.
2. Lam HY, Pan C, Clark MJ, Lacroute P, Chen R, Haraksingh R, et al. Detecting and annotating genetic variations using the HugeSeq pipeline. Nat Biotechnol. 2012;30(3):226-9.
3. Blankenberg D, Von Kuster G, Coraor N, Ananda G, Lazarus R, Mangan M, et al. Galaxy: a web-based genome analysis tool for experimentalists. Curr Protoc Mol Biol. 2010;Chapter 19:Unit 19 10 11-21.
4. Rubio-Camarillo M, Gomez-Lopez G, Fernandez JM, Valencia A, Pisano DG. RUbioSeq: a suite of parallelized pipelines to automate exome variation and bisulfite-seq analyses. Bioinformatics. 2013;29(13):1687-9.
5. D'Antonio M, D'Onorio De Meo P, Paoletti D, Elmi B, Pallocca M, Sanna N, et al. WEP: a high-performance analysis pipeline for whole-exome data. BMC Bioinformatics. 2013;14 Suppl 7:S11.
6. Karczewski KJ, Fernald GH, Martin AR, Snyder M, Tatonetti NP, Dudley JT. STORMSeq: an open-source, user-friendly pipeline for processing personal genomics data in the cloud. PLoS One. 2014;9(1):e84860.
7. Fischer M, Snajder R, Pabinger S, Dander A, Schossig A, Zschocke J, et al. SIMPLEX: cloud-enabled pipeline for the comprehensive analysis of exome sequencing data. PLoS One. 2012;7(8):e41948.
8. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754-60.
9. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078-9.
10. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297-303.
11. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, et al. From FastQ data to high confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinformatics. 2013;11(1110):11 10 11-11 10 33.
12. Wei Z, Wang W, Hu P, Lyon GJ, Hakonarson H. SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data. Nucleic Acids Res. 2011;39(19):e132.
13. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164.
14. McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics. 2010;26(16):2069-70.
15. McCarthy DJ, Humburg P, Kanapin A, Rivas MA, Gaulton K, Cazier JB, et al. Choice of transcripts and software has a large effect on variant annotation. Genome Med. 2014;6(3):26.
16. Falk MJ, Zhang Q, Nakamaru-Ogiso E, Kannabiran C, Fonseca-Kelly Z, Chakarova C, et al. NMNAT1 mutations cause Leber congenital amaurosis. Nat Genet. 2012;44(9):1040-5.
17. Collins FS, Tabak LA. Policy: NIH plans to enhance reproducibility. Nature. 2014;505(7485):612-3.