Genome sequencing survey and identification of SSR of Lycium ruthenicum

2020 ◽  
Author(s):  
Defang Zhang
Keyword(s):  
Genome Sequencing ◽  
Genomic Research ◽  
Western China ◽  
Population Genetic Study ◽  
Next Generation Sequencing Technology ◽  
Study Results ◽  
Lycium Ruthenicum ◽  
Repeat Rate ◽  
Generation Sequencing ◽  
Active Substances

Abstract Background Lycium ruthenicum had high economic and ecological role in western China due to the high content of active substances and tolerance to drought and salinity stress. But its genomic information was lack, which seriously affected the next breeding and forestation. We surveyed the genomic size and developed SSRs of L. ruthenicum based on the next generation sequencing technology to lay a theoretical foundation for next genomic research in this study. Results Totally 451,721,828 bp raw data were generated, 4,596,439 scaffolds were obtained after assembly. The estimated genome size of L. ruthenicum was 3,249.33 Mb, the heterozygosity rate was 1.13%, and repeat rate was 73.13%. Totally 958,619 SSRs were identified. The average SSRs density were 163.95 SSRs/Mb, the dinucleotide repeat motif accounted for larger proportion in all motifs, the AT/AT, AC/GT and AG/CT are dominant repeat motifs in L. ruthenicum genome. Conclusion These results could lay a foundation for next genome sequencing. And SSR data could alarge the molecular resources for L. ruthenicum and relatives, such as genetic mapping, QTL and population genetic study.

scholarly journals Human whole genome sequencing in South Africa

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brigitte Glanzmann ◽  
Tracey Jooste ◽  
Samira Ghoor ◽  
Richard Gordon ◽  
Rizwana Mia ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
South African ◽  
Genome Sequencing ◽  
Genomic Research ◽  
Whole Genome ◽  
Human Dna ◽  
Whole Genomes ◽  
World Class ◽  
Average Sequencing Depth ◽  
Generation Sequencing

AbstractThe advent and evolution of next generation sequencing has considerably impacted genomic research. Until recently, South African researchers were unable to access affordable platforms capable of human whole genome sequencing locally and DNA samples had to be exported. Here we report the whole genome sequences of the first six human DNA samples sequenced and analysed at the South African Medical Research Council’s Genomics Centre. We demonstrate that the data obtained is of high quality, with an average sequencing depth of 36.41, and that the output is comparable to data generated internationally on a similar platform. The Genomics Centre creates an environment where African researchers are able to access world class facilities, increasing local capacity to sequence whole genomes as well as store and analyse the data.

scholarly journals Human Whole Genome Sequencing in South Africa

2020 ◽  
Author(s):  
Brigitte Glanzmann ◽  
Tracey Jooste ◽  
Samira Ghoor ◽  
Richard Gordon ◽  
Rizwana Mia ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
South African ◽  
Genome Sequencing ◽  
Genomic Research ◽  
Whole Genome ◽  
Human Dna ◽  
Whole Genomes ◽  
World Class ◽  
Average Sequencing Depth ◽  
Generation Sequencing

ABSTRACTThe advent and evolution of next generation sequencing has considerably impacted genomic research. Until recently, South African researchers were unable to access affordable platforms capable of human whole genome sequencing locally and DNA samples had to be exported. Here we report the whole genome sequences of the first six human DNA samples sequenced and analysed at the South African Medical Research Council’s Genomics Centre. We demonstrate that the data obtained is of high quality, with an average sequencing depth of 36.41, and that the output is comparable to data generated internationally on a similar platform. The Genomics Centre creates an environment where African researchers are able to access world class facilities, increasing local capacity to sequence whole genomes as well as store and analyse the data.

Development and Application of Computational Methods in Phage Display Technology

2020 ◽  
Vol 26 (42) ◽  
pp. 7672-7693 ◽  
Author(s):  
Bifang He ◽  
Anthony Mackitz Dzisoo ◽  
Ratmir Derda ◽  
Jian Huang
Keyword(s):  
Phage Display ◽  
Computational Methods ◽  
Peptide Ligands ◽  
Next Generation ◽  
Phage Display Technology ◽  
Next Generation Sequencing Technology ◽  
Screening Experiments ◽  
Display Technology ◽  
Comprehensive Search ◽  
Generation Sequencing

Background: Phage display is a powerful and versatile technology for the identification of peptide ligands binding to multiple targets, which has been successfully employed in various fields, such as diagnostics and therapeutics, drug-delivery and material science. The integration of next generation sequencing technology with phage display makes this methodology more productive. With the widespread use of this technique and the fast accumulation of phage display data, databases for these data and computational methods have become an indispensable part in this community. This review aims to summarize and discuss recent progress in the development and application of computational methods in the field of phage display. Methods: We undertook a comprehensive search of bioinformatics resources and computational methods for phage display data via Google Scholar and PubMed. The methods and tools were further divided into different categories according to their uses. Results: We described seven special or relevant databases for phage display data, which provided an evidence-based source for phage display researchers to clean their biopanning results. These databases can identify and report possible target-unrelated peptides (TUPs), thereby excluding false-positive data from peptides obtained from phage display screening experiments. More than 20 computational methods for analyzing biopanning data were also reviewed. These methods were classified into computational methods for reporting TUPs, for predicting epitopes and for analyzing next generation phage display data. Conclusion: The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.

Next-Generation Sequencing: An Emerging Tool for Drug Designing

2019 ◽  
Vol 25 (31) ◽  
pp. 3350-3357 ◽  
Author(s):  
Pooja Tripathi ◽  
Jyotsna Singh ◽  
Jonathan A. Lal ◽  
Vijay Tripathi
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput ◽  
Large Scale ◽  
Massively Parallel Sequencing ◽  
Genomic Research ◽  
Biological Research ◽  
Next Generation ◽  
Human Welfare ◽  
Drug Designing ◽  
Generation Sequencing

Background: With the outbreak of high throughput next-generation sequencing (NGS), the biological research of drug discovery has been directed towards the oncology and infectious disease therapeutic areas, with extensive use in biopharmaceutical development and vaccine production. Method: In this review, an effort was made to address the basic background of NGS technologies, potential applications of NGS in drug designing. Our purpose is also to provide a brief introduction of various Nextgeneration sequencing techniques. Discussions: The high-throughput methods execute Large-scale Unbiased Sequencing (LUS) which comprises of Massively Parallel Sequencing (MPS) or NGS technologies. The Next geneinvolved necessarily executes Largescale Unbiased Sequencing (LUS) which comprises of MPS or NGS technologies. These are related terms that describe a DNA sequencing technology which has revolutionized genomic research. Using NGS, an entire human genome can be sequenced within a single day. Conclusion: Analysis of NGS data unravels important clues in the quest for the treatment of various lifethreatening diseases and other related scientific problems related to human welfare.

The Influence of Memory-Aware Computation on Distributed BLAST

2019 ◽  
Vol 14 (2) ◽  
pp. 157-163
Author(s):  
Majid Hajibaba ◽  
Mohsen Sharifi ◽  
Saeid Gorgin
Keyword(s):  
Search Time ◽  
Genomic Research ◽  
Local Alignment ◽  
Negative Effects ◽  
Sequencing Technologies ◽  
Percent Improvement ◽  
Fast Processing ◽  
Search Tool ◽  
Memory Awareness ◽  
Generation Sequencing

Background: One of the pivotal challenges in nowadays genomic research domain is the fast processing of voluminous data such as the ones engendered by high-throughput Next-Generation Sequencing technologies. On the other hand, BLAST (Basic Local Alignment Search Tool), a longestablished and renowned tool in Bioinformatics, has shown to be incredibly slow in this regard. Objective: To improve the performance of BLAST in the processing of voluminous data, we have applied a novel memory-aware technique to BLAST for faster parallel processing of voluminous data. Method: We have used a master-worker model for the processing of voluminous data alongside a memory-aware technique in which the master partitions the whole data in equal chunks, one chunk for each worker, and consequently each worker further splits and formats its allocated data chunk according to the size of its memory. Each worker searches every split data one-by-one through a list of queries. Results: We have chosen a list of queries with different lengths to run insensitive searches in a huge database called UniProtKB/TrEMBL. Our experiments show 20 percent improvement in performance when workers used our proposed memory-aware technique compared to when they were not memory aware. Comparatively, experiments show even higher performance improvement, approximately 50 percent, when we applied our memory-aware technique to mpiBLAST. Conclusion: We have shown that memory-awareness in formatting bulky database, when running BLAST, can improve performance significantly, while preventing unexpected crashes in low-memory environments. Even though distributed computing attempts to mitigate search time by partitioning and distributing database portions, our memory-aware technique alleviates negative effects of page-faults on performance.

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