164 related articles for article (PubMed ID: 22238694)
1. Customisation of the exome data analysis pipeline using a combinatorial approach.
Pattnaik S; Vaidyanathan S; Pooja DG; Deepak S; Panda B
PLoS One; 2012; 7(1):e30080. PubMed ID: 22238694
[TBL] [Abstract][Full Text] [Related]
2. Machine learning random forest for predicting oncosomatic variant NGS analysis.
Pellegrino E; Jacques C; Beaufils N; Nanni I; Carlioz A; Metellus P; Ouafik L
Sci Rep; 2021 Nov; 11(1):21820. PubMed ID: 34750410
[TBL] [Abstract][Full Text] [Related]
3. WEP: a high-performance analysis pipeline for whole-exome data.
D'Antonio M; D'Onorio De Meo P; Paoletti D; Elmi B; Pallocca M; Sanna N; Picardi E; Pesole G; Castrignanò T
BMC Bioinformatics; 2013; 14 Suppl 7(Suppl 7):S11. PubMed ID: 23815231
[TBL] [Abstract][Full Text] [Related]
4. Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers.
Hofmann AL; Behr J; Singer J; Kuipers J; Beisel C; Schraml P; Moch H; Beerenwinkel N
BMC Bioinformatics; 2017 Jan; 18(1):8. PubMed ID: 28049408
[TBL] [Abstract][Full Text] [Related]
5. Consensus Genotyper for Exome Sequencing (CGES): improving the quality of exome variant genotypes.
Trubetskoy V; Rodriguez A; Dave U; Campbell N; Crawford EL; Cook EH; Sutcliffe JS; Foster I; Madduri R; Cox NJ; Davis LK
Bioinformatics; 2015 Jan; 31(2):187-93. PubMed ID: 25270638
[TBL] [Abstract][Full Text] [Related]
6. A survey of tools for variant analysis of next-generation genome sequencing data.
Pabinger S; Dander A; Fischer M; Snajder R; Sperk M; Efremova M; Krabichler B; Speicher MR; Zschocke J; Trajanoski Z
Brief Bioinform; 2014 Mar; 15(2):256-78. PubMed ID: 23341494
[TBL] [Abstract][Full Text] [Related]
7. DNAscan: personal computer compatible NGS analysis, annotation and visualisation.
Iacoangeli A; Al Khleifat A; Sproviero W; Shatunov A; Jones AR; Morgan SL; Pittman A; Dobson RJ; Newhouse SJ; Al-Chalabi A
BMC Bioinformatics; 2019 Apr; 20(1):213. PubMed ID: 31029080
[TBL] [Abstract][Full Text] [Related]
8. A comparative study of k-spectrum-based error correction methods for next-generation sequencing data analysis.
Akogwu I; Wang N; Zhang C; Gong P
Hum Genomics; 2016 Jul; 10 Suppl 2(Suppl 2):20. PubMed ID: 27461106
[TBL] [Abstract][Full Text] [Related]
9. Impact of post-alignment processing in variant discovery from whole exome data.
Tian S; Yan H; Kalmbach M; Slager SL
BMC Bioinformatics; 2016 Oct; 17(1):403. PubMed ID: 27716037
[TBL] [Abstract][Full Text] [Related]
10. A three-caller pipeline for variant analysis of cancer whole-exome sequencing data.
Liu ZK; Shang YK; Chen ZN; Bian H
Mol Med Rep; 2017 May; 15(5):2489-2494. PubMed ID: 28447726
[TBL] [Abstract][Full Text] [Related]
11. An integrated approach for analyzing clinical genomic variant data from next-generation sequencing.
Crowgey EL; Stabley DL; Chen C; Huang H; Robbins KM; Polson SW; Sol-Church K; Wu CH
J Biomol Tech; 2015 Apr; 26(1):19-28. PubMed ID: 25649353
[TBL] [Abstract][Full Text] [Related]
12. Comparing the performance of selected variant callers using synthetic data and genome segmentation.
Bian X; Zhu B; Wang M; Hu Y; Chen Q; Nguyen C; Hicks B; Meerzaman D
BMC Bioinformatics; 2018 Nov; 19(1):429. PubMed ID: 30453880
[TBL] [Abstract][Full Text] [Related]
13. A Comparison of Variant Calling Pipelines Using Genome in a Bottle as a Reference.
Cornish A; Guda C
Biomed Res Int; 2015; 2015():456479. PubMed ID: 26539496
[TBL] [Abstract][Full Text] [Related]
14. Challenges in exome analysis by LifeScope and its alternative computational pipelines.
Pranckevičiene E; Rančelis T; Pranculis A; Kučinskas V
BMC Res Notes; 2015 Sep; 8():421. PubMed ID: 26346699
[TBL] [Abstract][Full Text] [Related]
15. Next-generation sequencing using a pre-designed gene panel for the molecular diagnosis of congenital disorders in pediatric patients.
Lim EC; Brett M; Lai AH; Lee SP; Tan ES; Jamuar SS; Ng IS; Tan EC
Hum Genomics; 2015 Dec; 9():33. PubMed ID: 26666243
[TBL] [Abstract][Full Text] [Related]
16. Improving mapping and SNP-calling performance in multiplexed targeted next-generation sequencing.
Elsharawy A; Forster M; Schracke N; Keller A; Thomsen I; Petersen BS; Stade B; Stähler P; Schreiber S; Rosenstiel P; Franke A
BMC Genomics; 2012 Aug; 13():417. PubMed ID: 22913592
[TBL] [Abstract][Full Text] [Related]
17. SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing.
Spinella JF; Mehanna P; Vidal R; Saillour V; Cassart P; Richer C; Ouimet M; Healy J; Sinnett D
BMC Genomics; 2016 Nov; 17(1):912. PubMed ID: 27842494
[TBL] [Abstract][Full Text] [Related]
18. Detection of runs of homozygosity from whole exome sequencing data: state of the art and perspectives for clinical, population and epidemiological studies.
Pippucci T; Magi A; Gialluisi A; Romeo G
Hum Hered; 2014; 77(1-4):63-72. PubMed ID: 25060270
[TBL] [Abstract][Full Text] [Related]
19. Accuracy and reproducibility of somatic point mutation calling in clinical-type targeted sequencing data.
Karimnezhad A; Palidwor GA; Thavorn K; Stewart DJ; Campbell PA; Lo B; Perkins TJ
BMC Med Genomics; 2020 Oct; 13(1):156. PubMed ID: 33059707
[TBL] [Abstract][Full Text] [Related]
20. AMLVaran: a software approach to implement variant analysis of targeted NGS sequencing data in an oncological care setting.
Wünsch C; Banck H; Müller-Tidow C; Dugas M
BMC Med Genomics; 2020 Feb; 13(1):17. PubMed ID: 32019565
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
