BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

181 related articles for article (PubMed ID: 31888441)

  • 1. VariFAST: a variant filter by automated scoring based on tagged-signatures.
    Zhang H; Wang K; Zhou J; Chen J; Xu Y; Wang D; Li X; Sun R; Zhang M; Wang Z; Shi Y
    BMC Bioinformatics; 2019 Dec; 20(Suppl 22):713. PubMed ID: 31888441
    [TBL] [Abstract][Full Text] [Related]  

  • 2. FVC as an adaptive and accurate method for filtering variants from popular NGS analysis pipelines.
    Ren Y; Kong Y; Zhou X; Genchev GZ; Zhou C; Zhao H; Lu H
    Commun Biol; 2022 Sep; 5(1):975. PubMed ID: 36114280
    [TBL] [Abstract][Full Text] [Related]  

  • 3. FIREVAT: finding reliable variants without artifacts in human cancer samples using etiologically relevant mutational signatures.
    Kim H; Lee AJ; Lee J; Chun H; Ju YS; Hong D
    Genome Med; 2019 Dec; 11(1):81. PubMed ID: 31847917
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 123VCF: an intuitive and efficient tool for filtering VCF files.
    Eidi M; Abdolalizadeh S; Moeini S; Garshasbi M; Zahiri J
    BMC Bioinformatics; 2024 Feb; 25(1):68. PubMed ID: 38350858
    [TBL] [Abstract][Full Text] [Related]  

  • 5. tarSVM: Improving the accuracy of variant calls derived from microfluidic PCR-based targeted next generation sequencing using a support vector machine.
    Gillies CE; Otto EA; Vega-Warner V; Robertson CC; Sanna-Cherchi S; Gharavi A; Crawford B; Bhimma R; Winkler C; ; ; Kang HM; Sampson MG
    BMC Bioinformatics; 2016 Jun; 17(1):233. PubMed ID: 27287006
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Systematic benchmark of state-of-the-art variant calling pipelines identifies major factors affecting accuracy of coding sequence variant discovery.
    Barbitoff YA; Abasov R; Tvorogova VE; Glotov AS; Predeus AV
    BMC Genomics; 2022 Feb; 23(1):155. PubMed ID: 35193511
    [TBL] [Abstract][Full Text] [Related]  

  • 7. VEF: a variant filtering tool based on ensemble methods.
    Zhang C; Ochoa I
    Bioinformatics; 2020 Apr; 36(8):2328-2336. PubMed ID: 31873730
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data.
    Kumaran M; Subramanian U; Devarajan B
    BMC Bioinformatics; 2019 Jun; 20(1):342. PubMed ID: 31208315
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Halvade somatic: Somatic variant calling with Apache Spark.
    Decap D; de Schaetzen van Brienen L; Larmuseau M; Costanza P; Herzeel C; Wuyts R; Marchal K; Fostier J
    Gigascience; 2022 Jan; 11(1):. PubMed ID: 35022699
    [TBL] [Abstract][Full Text] [Related]  

  • 11. JWES: a new pipeline for whole genome/exome sequence data processing, management, and gene-variant discovery, annotation, prediction, and genotyping.
    Ahmed Z; Renart EG; Mishra D; Zeeshan S
    FEBS Open Bio; 2021 Sep; 11(9):2441-2452. PubMed ID: 34370400
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. ICR142 Benchmarker: evaluating, optimising and benchmarking variant calling performance using the ICR142 NGS validation series.
    Ruark E; Holt E; Renwick A; Münz M; Wakeling M; Ellard S; Mahamdallie S; Yost S; Rahman N
    Wellcome Open Res; 2018; 3():108. PubMed ID: 30483600
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing data.
    do Valle ÍF; Giampieri E; Simonetti G; Padella A; Manfrini M; Ferrari A; Papayannidis C; Zironi I; Garonzi M; Bernardi S; Delledonne M; Martinelli G; Remondini D; Castellani G
    BMC Bioinformatics; 2016 Nov; 17(Suppl 12):341. PubMed ID: 28185561
    [TBL] [Abstract][Full Text] [Related]  

  • 15. VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering.
    Gézsi A; Bolgár B; Marx P; Sarkozy P; Szalai C; Antal P
    BMC Genomics; 2015 Oct; 16():875. PubMed ID: 26510841
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Improving somatic exome sequencing performance by biological replicates.
    Cebeci YE; Erturk RA; Ergun MA; Baysan M
    BMC Bioinformatics; 2024 Mar; 25(1):124. PubMed ID: 38519906
    [TBL] [Abstract][Full Text] [Related]  

  • 17. ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.
    Li J; Jew B; Zhan L; Hwang S; Coppola G; Freimer NB; Sul JH
    PLoS Comput Biol; 2019 Dec; 15(12):e1007556. PubMed ID: 31851693
    [TBL] [Abstract][Full Text] [Related]  

  • 18. GATK hard filtering: tunable parameters to improve variant calling for next generation sequencing targeted gene panel data.
    De Summa S; Malerba G; Pinto R; Mori A; Mijatovic V; Tommasi S
    BMC Bioinformatics; 2017 Mar; 18(Suppl 5):119. PubMed ID: 28361668
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SeqMule: automated pipeline for analysis of human exome/genome sequencing data.
    Guo Y; Ding X; Shen Y; Lyon GJ; Wang K
    Sci Rep; 2015 Sep; 5():14283. PubMed ID: 26381817
    [TBL] [Abstract][Full Text] [Related]  

  • 20. ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification.
    Bao R; Hernandez K; Huang L; Kang W; Bartom E; Onel K; Volchenboum S; Andrade J
    PLoS One; 2015; 10(8):e0135800. PubMed ID: 26271043
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.