BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

304 related articles for article (PubMed ID: 25887570)

  • 1. SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines.
    Leung WY; Marschall T; Paudel Y; Falquet L; Mei H; Schönhuth A; Maoz Moss TY
    BMC Genomics; 2015 Mar; 16(1):238. PubMed ID: 25887570
    [TBL] [Abstract][Full Text] [Related]  

  • 2. svclassify: a method to establish benchmark structural variant calls.
    Parikh H; Mohiyuddin M; Lam HY; Iyer H; Chen D; Pratt M; Bartha G; Spies N; Losert W; Zook JM; Salit M
    BMC Genomics; 2016 Jan; 17():64. PubMed ID: 26772178
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A comprehensive benchmarking of WGS-based deletion structural variant callers.
    Sarwal V; Niehus S; Ayyala R; Kim M; Sarkar A; Chang S; Lu A; Rajkumar N; Darfci-Maher N; Littman R; Chhugani K; Soylev A; Comarova Z; Wesel E; Castellanos J; Chikka R; Distler MG; Eskin E; Flint J; Mangul S
    Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35753701
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Benchmarking long-read aligners and SV callers for structural variation detection in Oxford nanopore sequencing data.
    Helal AA; Saad BT; Saad MT; Mosaad GS; Aboshanab KM
    Sci Rep; 2024 Mar; 14(1):6160. PubMed ID: 38486064
    [TBL] [Abstract][Full Text] [Related]  

  • 5. RAPTR-SV: a hybrid method for the detection of structural variants.
    Bickhart DM; Hutchison JL; Xu L; Schnabel RD; Taylor JF; Reecy JM; Schroeder S; Van Tassell CP; Sonstegard TS; Liu GE
    Bioinformatics; 2015 Jul; 31(13):2084-90. PubMed ID: 25686638
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Comparison of multiple algorithms to reliably detect structural variants in pears.
    Liu Y; Zhang M; Sun J; Chang W; Sun M; Zhang S; Wu J
    BMC Genomics; 2020 Jan; 21(1):61. PubMed ID: 31959124
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Evaluating nanopore sequencing data processing pipelines for structural variation identification.
    Zhou A; Lin T; Xing J
    Genome Biol; 2019 Nov; 20(1):237. PubMed ID: 31727126
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies.
    Du X; Li L; Liang F; Liu S; Zhang W; Sun S; Sun Y; Fan F; Wang L; Liang X; Qiu W; Fan G; Wang O; Yang W; Zhang J; Xiao Y; Wang Y; Wang D; Qu S; Chen F; Huang J
    Genomics Proteomics Bioinformatics; 2022 Feb; 20(1):192-204. PubMed ID: 33662625
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Oxford Nanopore and Bionano Genomics technologies evaluation for plant structural variation detection.
    Canaguier A; Guilbaud R; Denis E; Magdelenat G; Belser C; Istace B; Cruaud C; Wincker P; Le Paslier MC; Faivre-Rampant P; Barbe V
    BMC Genomics; 2022 Apr; 23(1):317. PubMed ID: 35448948
    [TBL] [Abstract][Full Text] [Related]  

  • 10. DELLY: structural variant discovery by integrated paired-end and split-read analysis.
    Rausch T; Zichner T; Schlattl A; Stütz AM; Benes V; Korbel JO
    Bioinformatics; 2012 Sep; 28(18):i333-i339. PubMed ID: 22962449
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Detection of somatic structural variants from short-read next-generation sequencing data.
    Gong T; Hayes VM; Chan EKF
    Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32379294
    [TBL] [Abstract][Full Text] [Related]  

  • 12. iSVP: an integrated structural variant calling pipeline from high-throughput sequencing data.
    Mimori T; Nariai N; Kojima K; Takahashi M; Ono A; Sato Y; Yamaguchi-Kabata Y; Nagasaki M
    BMC Syst Biol; 2013; 7 Suppl 6(Suppl 6):S8. PubMed ID: 24564972
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Robust and exact structural variation detection with paired-end and soft-clipped alignments: SoftSV compared with eight algorithms.
    Bartenhagen C; Dugas M
    Brief Bioinform; 2016 Jan; 17(1):51-62. PubMed ID: 25998133
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Leveraging long read sequencing from a single individual to provide a comprehensive resource for benchmarking variant calling methods.
    Mu JC; Tootoonchi Afshar P; Mohiyuddin M; Chen X; Li J; Bani Asadi N; Gerstein MB; Wong WH; Lam HY
    Sci Rep; 2015 Sep; 5():14493. PubMed ID: 26412485
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Structural variation detection using next-generation sequencing data: A comparative technical review.
    Guan P; Sung WK
    Methods; 2016 Jun; 102():36-49. PubMed ID: 26845461
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparison of structural variant callers for massive whole-genome sequence data.
    Joe S; Park JL; Kim J; Kim S; Park JH; Yeo MK; Lee D; Yang JO; Kim SY
    BMC Genomics; 2024 Mar; 25(1):318. PubMed ID: 38549092
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Toolkit for automated and rapid discovery of structural variants.
    Soylev A; Kockan C; Hormozdiari F; Alkan C
    Methods; 2017 Oct; 129():3-7. PubMed ID: 28583483
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Gustaf: Detecting and correctly classifying SVs in the NGS twilight zone.
    Trappe K; Emde AK; Ehrlich HC; Reinert K
    Bioinformatics; 2014 Dec; 30(24):3484-90. PubMed ID: 25028727
    [TBL] [Abstract][Full Text] [Related]  

  • 19. MetaSV: an accurate and integrative structural-variant caller for next generation sequencing.
    Mohiyuddin M; Mu JC; Li J; Bani Asadi N; Gerstein MB; Abyzov A; Wong WH; Lam HY
    Bioinformatics; 2015 Aug; 31(16):2741-4. PubMed ID: 25861968
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Benchmarking Oxford Nanopore read alignment-based insertion and deletion detection in crop plant genomes.
    Yildiz G; Zanini SF; Afsharyan NP; Obermeier C; Snowdon RJ; Golicz AA
    Plant Genome; 2023 Jun; 16(2):e20314. PubMed ID: 36988043
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.