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X Demographics
Mendeley readers
Attention Score in Context
| Title |
VISPA2: a scalable pipeline for high-throughput identification and annotation of vector integration sites
|
|---|---|
| Published in |
BMC Bioinformatics, November 2017
|
| DOI | 10.1186/s12859-017-1937-9 |
| Pubmed ID | |
| Authors |
Giulio Spinozzi, Andrea Calabria, Stefano Brasca, Stefano Beretta, Ivan Merelli, Luciano Milanesi, Eugenio Montini |
| Abstract |
Bioinformatics tools designed to identify lentiviral or retroviral vector insertion sites in the genome of host cells are used to address the safety and long-term efficacy of hematopoietic stem cell gene therapy applications and to study the clonal dynamics of hematopoietic reconstitution. The increasing number of gene therapy clinical trials combined with the increasing amount of Next Generation Sequencing data, aimed at identifying integration sites, require both highly accurate and efficient computational software able to correctly process "big data" in a reasonable computational time. Here we present VISPA2 (Vector Integration Site Parallel Analysis, version 2), the latest optimized computational pipeline for integration site identification and analysis with the following features: (1) the sequence analysis for the integration site processing is fully compliant with paired-end reads and includes a sequence quality filter before and after the alignment on the target genome; (2) an heuristic algorithm to reduce false positive integration sites at nucleotide level to reduce the impact of Polymerase Chain Reaction or trimming/alignment artifacts; (3) a classification and annotation module for integration sites; (4) a user friendly web interface as researcher front-end to perform integration site analyses without computational skills; (5) the time speedup of all steps through parallelization (Hadoop free). We tested VISPA2 performances using simulated and real datasets of lentiviral vector integration sites, previously obtained from patients enrolled in a hematopoietic stem cell gene therapy clinical trial and compared the results with other preexisting tools for integration site analysis. On the computational side, VISPA2 showed a > 6-fold speedup and improved precision and recall metrics (1 and 0.97 respectively) compared to previously developed computational pipelines. These performances indicate that VISPA2 is a fast, reliable and user-friendly tool for integration site analysis, which allows gene therapy integration data to be handled in a cost and time effective fashion. Moreover, the web access of VISPA2 ( http://openserver.itb.cnr.it/vispa/ ) ensures accessibility and ease of usage to researches of a complex analytical tool. We released the source code of VISPA2 in a public repository ( https://bitbucket.org/andreacalabria/vispa2 ). |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 70 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 22 | 31% |
| Student > Ph. D. Student | 11 | 16% |
| Student > Bachelor | 6 | 9% |
| Student > Master | 6 | 9% |
| Other | 4 | 6% |
| Other | 6 | 9% |
| Unknown | 15 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 17 | 24% |
| Agricultural and Biological Sciences | 14 | 20% |
| Medicine and Dentistry | 6 | 9% |
| Engineering | 5 | 7% |
| Computer Science | 3 | 4% |
| Other | 10 | 14% |
| Unknown | 15 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 10 March 2018.
All research outputs
#17,920,654
of 23,008,860 outputs
Outputs from BMC Bioinformatics
#5,968
of 7,315 outputs
Outputs of similar age
#305,737
of 438,185 outputs
Outputs of similar age from BMC Bioinformatics
#102
of 150 outputs
Altmetric has tracked 23,008,860 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,315 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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We're also able to compare this research output to 150 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.