↓ Skip to main content

PVT: An Efficient Computational Procedure to Speed up Next-generation Sequence Analysis

Overview of attention for article published in BMC Bioinformatics, June 2014
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

twitter
9 X users

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
40 Mendeley
citeulike
4 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 June 2014.
All research outputs
#7,878,286
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#3,082
of 7,454 outputs
Outputs of similar age
#75,183
of 230,571 outputs
Outputs of similar age from BMC Bioinformatics
#60
of 155 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 230,571 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 155 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.