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HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data

Overview of attention for article published in BMC Bioinformatics, November 2017
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  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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Title
HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data
Published in
BMC Bioinformatics, November 2017
DOI 10.1186/s12859-017-1923-2
Pubmed ID
Authors

Marco Beccuti, Elisa Genuardi, Greta Romano, Luigia Monitillo, Daniela Barbero, Mario Boccadoro, Marco Ladetto, Raffaele Calogero, Simone Ferrero, Francesca Cordero

Abstract

Mantle Cell Lymphoma (MCL) is a B cell aggressive neoplasia accounting for about the 6% of all lymphomas. The most common molecular marker of clonality in MCL, as in other B lymphoproliferative disorders, is the ImmunoGlobulin Heavy chain (IGH) rearrangement, occurring in B-lymphocytes. The patient-specific IGH rearrangement is extensively used to monitor the Minimal Residual Disease (MRD) after treatment through the standardized Allele-Specific Oligonucleotides Quantitative Polymerase Chain Reaction based technique. Recently, several studies have suggested that the IGH monitoring through deep sequencing techniques can produce not only comparable results to Polymerase Chain Reaction-based methods, but also might overcome the classical technique in terms of feasibility and sensitivity. However, no standard bioinformatics tool is available at the moment for data analysis in this context. In this paper we present HashClone, an easy-to-use and reliable bioinformatics tool that provides B-cells clonality assessment and MRD monitoring over time analyzing data from Next-Generation Sequencing (NGS) technique. The HashClone strategy-based is composed of three steps: the first and second steps implement an alignment-free prediction method that identifies a set of putative clones belonging to the repertoire of the patient under study. In the third step the IGH variable region, diversity region, and joining region identification is obtained by the alignment of rearrangements with respect to the international ImMunoGenetics information system database. Moreover, a provided graphical user interface for HashClone execution and clonality visualization over time facilitate the tool use and the results interpretation. The HashClone performance was tested on the NGS data derived from MCL patients to assess the major B-cell clone in the diagnostic samples and to monitor the MRD in the real and artificial follow up samples. Our experiments show that in all the experimental settings, HashClone was able to correctly detect the major B-cell clones and to precisely follow them in several samples showing better accuracy than the state-of-art tool.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 29%
Student > Ph. D. Student 5 14%
Student > Master 5 14%
Student > Bachelor 3 9%
Other 2 6%
Other 4 11%
Unknown 6 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 26%
Agricultural and Biological Sciences 4 11%
Medicine and Dentistry 4 11%
Engineering 3 9%
Nursing and Health Professions 2 6%
Other 5 14%
Unknown 8 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 24 September 2020.
All research outputs
#6,392,102
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#2,380
of 7,400 outputs
Outputs of similar age
#123,416
of 441,082 outputs
Outputs of similar age from BMC Bioinformatics
#41
of 152 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 67% 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 441,082 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 71% of its contemporaries.
We're also able to compare this research output to 152 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 71% of its contemporaries.