Realizing personalized medicine requires integrating diverse data types with bioinformatics.The most vital data are genomic information for individuals that are from advanced next-generation sequencing(NGS) technologi...Realizing personalized medicine requires integrating diverse data types with bioinformatics.The most vital data are genomic information for individuals that are from advanced next-generation sequencing(NGS) technologies at present.The technologies continue to advance in terms of both decreasing cost and sequencing speed with concomitant increase in the amount and complexity of the data.The prodigious data together with the requisite computational pipelines for data analysis and interpretation are stressors to IT infrastructure and the scientists conducting the work alike.Bioinformatics is increasingly becoming the rate-limiting step with numerous challenges to be overcome for translating NGS data for personalized medicine.We review some key bioinformatics tasks,issues,and challenges in contexts of IT requirements,data quality,analysis tools and pipelines,and validation of biomarkers.展开更多
The next generation sequencing （NGS） is an important process which assures inexpen- sive organization of vast size of raw sequence dataset over any traditional sequencing systems or methods. Various aspects of NGS s...The next generation sequencing （NGS） is an important process which assures inexpen- sive organization of vast size of raw sequence dataset over any traditional sequencing systems or methods. Various aspects of NGS such as template preparation, sequencing imaging and genome alignment and assembly outline the genome sequencing and align- ment. Consequently, de Bruijn graph （dBG） is an important mathematical tool that graphically analyzes how the orientations are constructed in groups of nucleotides. Basi- cally, dBG describes the formation of the genome segments in circular iterative fashions. Some pivotal dBG-based de novo algorithms and software packages such as T-IDBA, Oases, IDBA-tran, Euler, Velvet, ABYSS, AllPaths, SOAPde novo and SOAPde novo2 are illustrated in this paper. Consequently, overlap layout consensus （OLC） graph-based algorithms also play vital role in NGS assembly. Some important OLC-based algorithms such as MIRA3, CABOG, Newbler, Edena, Mosaik and SHORTY are portrayed in this paper. It has been experimented that greedy graph-based algorithms and software pack- ages are also vital for proper genome dataset assembly. A few algorithms named SSAKE, SHARCGS and VCAKE help to perform proper genome sequencing.展开更多
文摘Realizing personalized medicine requires integrating diverse data types with bioinformatics.The most vital data are genomic information for individuals that are from advanced next-generation sequencing(NGS) technologies at present.The technologies continue to advance in terms of both decreasing cost and sequencing speed with concomitant increase in the amount and complexity of the data.The prodigious data together with the requisite computational pipelines for data analysis and interpretation are stressors to IT infrastructure and the scientists conducting the work alike.Bioinformatics is increasingly becoming the rate-limiting step with numerous challenges to be overcome for translating NGS data for personalized medicine.We review some key bioinformatics tasks,issues,and challenges in contexts of IT requirements,data quality,analysis tools and pipelines,and validation of biomarkers.
文摘The next generation sequencing （NGS） is an important process which assures inexpen- sive organization of vast size of raw sequence dataset over any traditional sequencing systems or methods. Various aspects of NGS such as template preparation, sequencing imaging and genome alignment and assembly outline the genome sequencing and align- ment. Consequently, de Bruijn graph （dBG） is an important mathematical tool that graphically analyzes how the orientations are constructed in groups of nucleotides. Basi- cally, dBG describes the formation of the genome segments in circular iterative fashions. Some pivotal dBG-based de novo algorithms and software packages such as T-IDBA, Oases, IDBA-tran, Euler, Velvet, ABYSS, AllPaths, SOAPde novo and SOAPde novo2 are illustrated in this paper. Consequently, overlap layout consensus （OLC） graph-based algorithms also play vital role in NGS assembly. Some important OLC-based algorithms such as MIRA3, CABOG, Newbler, Edena, Mosaik and SHORTY are portrayed in this paper. It has been experimented that greedy graph-based algorithms and software pack- ages are also vital for proper genome dataset assembly. A few algorithms named SSAKE, SHARCGS and VCAKE help to perform proper genome sequencing.
