Literature DB >> 28598576

Semi-automated cancer genome analysis using high-performance computing.

Giuliano Crispatzu1,2, Pranav Kulkarni1, Mohammad R Toliat3, Peter Nürnberg3,4, Marco Herling2,4, Carmen D Herling5, Peter Frommolt1.   

Abstract

Next-generation sequencing (NGS) has turned from a new and experimental technology into a standard procedure for cancer genome studies and clinical investigation. While a multitude of software packages for cancer genome data analysis have been made available, these need to be combined into efficient analytical workflows that cover multiple aspects relevant to a clinical environment and that deliver handy results within a reasonable time frame. Here, we introduce QuickNGS Cancer as a new suite of bioinformatics pipelines that is focused on cancer genomics and significantly reduces the analytical hurdles that still limit a broader applicability of NGS technology, particularly to clinically driven research. QuickNGS Cancer allows a highly efficient analysis of a broad variety of NGS data types, specifically considering cancer-specific issues, such as biases introduced by tumor impurity and aneuploidy or the assessment of genomic variations regarding their biomedical relevance. It delivers highly reproducible analysis results ready for interpretation within only a few days after sequencing, as shown by a reanalysis of 140 tumor/normal pairs from The Cancer Genome Atlas (TCGA) in which QuickNGS Cancer detected a significant number of mutations in key cancer genes missed by a well-established mutation calling pipeline. Finally, QuickNGS Cancer obtained several unexpected mutations in leukemias that could be confirmed by Sanger sequencing.
© 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  analysis pipeline; cancer genomics; medical bioinformatics; next-generation sequencing

Mesh:

Year:  2017        PMID: 28598576     DOI: 10.1002/humu.23275

Source DB:  PubMed          Journal:  Hum Mutat        ISSN: 1059-7794            Impact factor:   4.878


  6 in total

Review 1.  Music of metagenomics-a review of its applications, analysis pipeline, and associated tools.

Authors:  Bilal Wajid; Faria Anwar; Imran Wajid; Haseeb Nisar; Sharoze Meraj; Ali Zafar; Mustafa Kamal Al-Shawaqfeh; Ali Riza Ekti; Asia Khatoon; Jan S Suchodolski
Journal:  Funct Integr Genomics       Date:  2021-10-18       Impact factor: 3.410

Review 2.  Use of Next-Generation Sequencing for Identifying Mitochondrial Disorders.

Authors:  Shafi Mahmud; Suvro Biswas; Shamima Afrose; Mohasana Akter Mita; Md Robiul Hasan; Mst Sharmin Sultana Shimu; Gobindo Kumar Paul; Sanghyun Chung; Md Abu Saleh; Sultan Alshehri; Momammed M Ghoneim; Maha Alruwaily; Bonglee Kim
Journal:  Curr Issues Mol Biol       Date:  2022-02-27       Impact factor: 2.976

3.  Actionable perturbations of damage responses by TCL1/ATM and epigenetic lesions form the basis of T-PLL.

Authors:  A Schrader; G Crispatzu; S Oberbeck; P Mayer; S Pützer; J von Jan; E Vasyutina; K Warner; N Weit; N Pflug; T Braun; E I Andersson; B Yadav; A Riabinska; B Maurer; M S Ventura Ferreira; F Beier; J Altmüller; M Lanasa; C D Herling; T Haferlach; S Stilgenbauer; G Hopfinger; M Peifer; T H Brümmendorf; P Nürnberg; K S J Elenitoba-Johnson; S Zha; M Hallek; R Moriggl; H C Reinhardt; M-H Stern; S Mustjoki; S Newrzela; P Frommolt; M Herling
Journal:  Nat Commun       Date:  2018-02-15       Impact factor: 14.919

4.  Specific Glioma Prognostic Subtype Distinctions Based on DNA Methylation Patterns.

Authors:  Xueran Chen; Chenggang Zhao; Zhiyang Zhao; Hongzhi Wang; Zhiyou Fang
Journal:  Front Genet       Date:  2019-09-12       Impact factor: 4.599

5.  Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB).

Authors:  Rasmus Krempel; Pranav Kulkarni; Annie Yim; Ulrich Lang; Bianca Habermann; Peter Frommolt
Journal:  BMC Bioinformatics       Date:  2018-04-24       Impact factor: 3.169

Review 6.  Challenges in the Setup of Large-scale Next-Generation Sequencing Analysis Workflows.

Authors:  Pranav Kulkarni; Peter Frommolt
Journal:  Comput Struct Biotechnol J       Date:  2017-10-25       Impact factor: 7.271

  6 in total

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