Scale-up development of high-performance polymer matrix for DNA sequencing analysis

2006 ◽  
Vol 27 (19) ◽  
pp. 3712-3723 ◽  
Author(s):  
Fen Wan ◽  
Weidong He ◽  
Jun Zhang ◽  
Qicong Ying ◽  
Benjamin Chu
Keyword(s):  
Dna Sequencing ◽  
Polymer Matrix ◽  
High Performance ◽  
Scale Up ◽  
Sequencing Analysis ◽  
High Performance Polymer ◽  
Dna Sequencing Analysis

Human c-fms proto-oncogene: comparative analysis with an abnormal allele

1985 ◽  
Vol 5 (2) ◽  
pp. 422-426
Author(s):  
J S Verbeek ◽  
A J Roebroek ◽  
A M van den Ouweland ◽  
H P Bloemers ◽  
W J Van de Ven
Keyword(s):  
Comparative Analysis ◽  
Dna Sequencing ◽  
Sequencing Analysis ◽  
Base Pairs ◽  
Small Deletion ◽  
Viral Oncogene ◽  
Close Proximity ◽  
Genetic Sequences ◽  
Dna Sequencing Analysis

The organization of the human c-fms proto-oncogene has been determined and compared with an abnormal allele. The human v-fms homologous genetic sequences are dispersed discontinuously and colinearly with the viral oncogene over a DNA region of ca. 32 kilobase pairs. The abnormal c-fms locus contains a small deletion in its 3' portion. DNA sequencing analysis indicated that it was 426 base pairs in size and located in close proximity to a putative c-fms exon.

scholarly journals Comparison of Angelica Species Roots Using Taste Sensor and DNA Sequencing Analysis

2012 ◽  
Vol 27 (6) ◽  
pp. 37-42 ◽  
Author(s):  
Young Hwa Kim ◽  
Goya Choi ◽  
Hye Won Lee ◽  
Gwan Ho Lee ◽  
Seong Wook Chae ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Sequencing Analysis ◽  
Dna Sequencing Analysis ◽  
Taste Sensor

scholarly journals Workstation benchmark of Spark Capable Genome Analysis ToolKit 4 Variant Calling

2020 ◽  
Author(s):  
Marcus H. Hansen ◽  
Anita T. Simonsen ◽  
Hans B. Ommen ◽  
Charlotte G. Nyvold
Keyword(s):  
Dna Sequencing ◽  
Genome Analysis ◽  
High Speed ◽  
High Performance ◽  
Variant Calling ◽  
Amplicon Sequencing ◽  
Targeted Sequencing ◽  
Sequencing Analysis ◽  
Genome Analysis Toolkit ◽  
Order Of Magnitude

AbstractBackgroundRapid and practical DNA-sequencing processing has become essential for modern biomedical laboratories, especially in the field of cancer, pathology and genetics. While sequencing turn-over time has been, and still is, a bottleneck in research and diagnostics, the field of bioinformatics is moving at a rapid pace – both in terms of hardware and software development. Here, we benchmarked the local performance of three of the most important Spark-enabled Genome analysis toolkit 4 (GATK4) tools in a targeted sequencing workflow: Duplicate marking, base quality score recalibration (BQSR) and variant calling on targeted DNA sequencing using a modest hyperthreading 12-core single CPU and a high-speed PCI express solid-state drive.ResultsCompared to the previous GATK version the performance of Spark-enabled BQSR and HaplotypeCaller is shifted towards a more efficient usage of the available cores on CPU and outperforms the earlier GATK3.8 version with an order of magnitude reduction in processing time to analysis ready variants, whereas MarkDuplicateSpark was found to be thrice as fast. Furthermore, HaploTypeCallerSpark and BQSRPipelineSpark were significantly faster than the equivalent GATK4 standard tools with a combined ∼86% reduction in execution time, reaching a median rate of ten million processed bases per second, and duplicate marking was reduced ∼42%. The called variants were found to be in close agreement between the Spark and non-Spark versions, with an overall concordance of 98%. In this setup, the tools were also highly efficient when compared execution on a small 72 virtual CPU/18-node Google Cloud cluster.ConclusionIn conclusion, GATK4 offers practical parallelization possibilities for DNA sequence processing, and the Spark-enabled tools optimize performance and utilization of local CPUs. Spark utilizing GATK variant calling is several times faster than previous GATK3.8 multithreading with the same multi-core, single CPU, configuration. The improved opportunities for parallel computations not only hold implications for high-performance cluster, but also for modest laboratory or research workstations for targeted sequencing analysis, such as exome, panel or amplicon sequencing.

Comparative View of In Silico DNA Sequencing Analysis Tools

2011 ◽  
pp. 207-221 ◽  
Author(s):  
Sissades Tongsima ◽  
Anunchai Assawamakin ◽  
Jittima Piriyapongsa ◽  
Philip J. Shaw
Keyword(s):  
Dna Sequencing ◽  
In Silico ◽  
Sequencing Analysis ◽  
Analysis Tools ◽  
Dna Sequencing Analysis

scholarly journals Butyrylcholinesterase (BCHE) Genotyping for Post-Succinylcholine Apnea in an Australian Population

2003 ◽  
Vol 49 (8) ◽  
pp. 1297-1308 ◽  
Author(s):  
Tina Yen ◽  
Brian N Nightingale ◽  
Jennifer C Burns ◽  
David R Sullivan ◽  
Peter M Stewart
Keyword(s):  
Dna Sequencing ◽  
Gene Mutations ◽  
University Teaching ◽  
Sequencing Analysis ◽  
Direct Dna Sequencing ◽  
Bche Activity ◽  
Mutation Screen ◽  
Sequencing Studies ◽  
Dna Sequencing Analysis ◽  
K Variant

Abstract Background: Measurement of plasma butyrylcholinesterase (BChE) activity and inhibitor-based phenotyping are standard methods for identifying patients who experience post-succinylcholine (SC) apnea attributable to inherited variants of the BChE enzyme. Our aim was to develop PCR-based assays for BCHE mutation detection and implement them for routine diagnostic use at a university teaching hospital. Methods: Between 1999 and 2002, we genotyped 65 patients referred after prolonged post-SC apnea. Five BCHE gene mutations were analyzed. Competitive oligo-priming (COP)-PCR was used for flu-1, flu-2, and K-variant and direct DNA sequencing analysis for dibucaine and sil-1 mutations. Additional DNA sequencing of BCHE coding regions was provided when the five-mutation screen was negative or mutation findings were inconsistent with enzyme activity. Results: Genotyping identified 52 patients with primary hypocholinesterasemia attributable to BCHE mutations, and in 44 individuals the abnormalities were detected by the five-mutation screen (detection rate, 85%). Additional sequencing studies revealed mutations in eight other patients, including five with novel mutations. The most common genotype abnormality was compound homozygous dibucaine and homozygous K-variant mutations. No simple homozygotes were found. Of the remaining 13 patients, 3 had normal BChE activity and gene, and 10 were diagnosed with hypocholinesterasemia unrelated to BCHE gene abnormalities. Conclusion: A five-mutation screen for investigation of post-SC apnea identified BCHE gene abnormalities for 80% of a referral population. Six new BCHE mutations were identified by sequencing studies of 16 additional patients.

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