Title |
PVT: An Efficient Computational Procedure to Speed up Next-generation Sequence Analysis
|
---|---|
Published in |
BMC Bioinformatics, June 2014
|
DOI | 10.1186/1471-2105-15-167 |
Pubmed ID | |
Authors |
Ranjan Kumar Maji, Arijita Sarkar, Sunirmal Khatua, Subhasis Dasgupta, Zhumur Ghosh |
Abstract |
High-throughput Next-Generation Sequencing (NGS) techniques are advancing genomics and molecular biology research. This technology generates substantially large data which puts up a major challenge to the scientists for an efficient, cost and time effective solution to analyse such data. Further, for the different types of NGS data, there are certain common challenging steps involved in analysing those data. Spliced alignment is one such fundamental step in NGS data analysis which is extremely computational intensive as well as time consuming. There exists serious problem even with the most widely used spliced alignment tools. TopHat is one such widely used spliced alignment tools which although supports multithreading, does not efficiently utilize computational resources in terms of CPU utilization and memory. Here we have introduced PVT (Pipelined Version of TopHat) where we take up a modular approach by breaking TopHat's serial execution into a pipeline of multiple stages, thereby increasing the degree of parallelization and computational resource utilization. Thus we address the discrepancies in TopHat so as to analyze large NGS data efficiently. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 33% |
France | 1 | 11% |
India | 1 | 11% |
United Kingdom | 1 | 11% |
Unknown | 3 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 7 | 78% |
Practitioners (doctors, other healthcare professionals) | 1 | 11% |
Members of the public | 1 | 11% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 1 | 3% |
France | 1 | 3% |
Australia | 1 | 3% |
Sweden | 1 | 3% |
United States | 1 | 3% |
Philippines | 1 | 3% |
Unknown | 34 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 20 | 50% |
Student > Ph. D. Student | 7 | 18% |
Student > Master | 3 | 8% |
Student > Doctoral Student | 2 | 5% |
Student > Bachelor | 2 | 5% |
Other | 5 | 13% |
Unknown | 1 | 3% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 17 | 43% |
Biochemistry, Genetics and Molecular Biology | 7 | 18% |
Computer Science | 7 | 18% |
Engineering | 2 | 5% |
Environmental Science | 1 | 3% |
Other | 3 | 8% |
Unknown | 3 | 8% |