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X Demographics
Mendeley readers
Attention Score in Context
| Title |
TIA: algorithms for development of identity-linked SNP islands for analysis by massively parallel DNA sequencing
|
|---|---|
| Published in |
BMC Bioinformatics, April 2018
|
| DOI | 10.1186/s12859-018-2133-2 |
| Pubmed ID | |
| Authors |
M. Heath Farris, Andrew R. Scott, Pamela A. Texter, Marta Bartlett, Patricia Coleman, David Masters |
| Abstract |
Single nucleotide polymorphisms (SNPs) located within the human genome have been shown to have utility as markers of identity in the differentiation of DNA from individual contributors. Massively parallel DNA sequencing (MPS) technologies and human genome SNP databases allow for the design of suites of identity-linked target regions, amenable to sequencing in a multiplexed and massively parallel manner. Therefore, tools are needed for leveraging the genotypic information found within SNP databases for the discovery of genomic targets that can be evaluated on MPS platforms. The SNP island target identification algorithm (TIA) was developed as a user-tunable system to leverage SNP information within databases. Using data within the 1000 Genomes Project SNP database, human genome regions were identified that contain globally ubiquitous identity-linked SNPs and that were responsive to targeted resequencing on MPS platforms. Algorithmic filters were used to exclude target regions that did not conform to user-tunable SNP island target characteristics. To validate the accuracy of TIA for discovering these identity-linked SNP islands within the human genome, SNP island target regions were amplified from 70 contributor genomic DNA samples using the polymerase chain reaction. Multiplexed amplicons were sequenced using the Illumina MiSeq platform, and the resulting sequences were analyzed for SNP variations. 166 putative identity-linked SNPs were targeted in the identified genomic regions. Of the 309 SNPs that provided discerning power across individual SNP profiles, 74 previously undefined SNPs were identified during evaluation of targets from individual genomes. Overall, DNA samples of 70 individuals were uniquely identified using a subset of the suite of identity-linked SNP islands. TIA offers a tunable genome search tool for the discovery of targeted genomic regions that are scalable in the population frequency and numbers of SNPs contained within the SNP island regions. It also allows the definition of sequence length and sequence variability of the target region as well as the less variable flanking regions for tailoring to MPS platforms. As shown in this study, TIA can be used to discover identity-linked SNP islands within the human genome, useful for differentiating individuals by targeted resequencing on MPS technologies. |
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 % |
|---|---|---|
| United Kingdom | 1 | 33% |
| Unknown | 2 | 67% |
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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 17% |
| Researcher | 1 | 8% |
| Other | 1 | 8% |
| Student > Doctoral Student | 1 | 8% |
| Unknown | 7 | 58% |
| Readers by discipline | Count | As % |
|---|---|---|
| Arts and Humanities | 1 | 8% |
| Environmental Science | 1 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Agricultural and Biological Sciences | 1 | 8% |
| Computer Science | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 6 | 50% |
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 16 April 2018.
All research outputs
#17,944,820
of 23,041,514 outputs
Outputs from BMC Bioinformatics
#5,970
of 7,318 outputs
Outputs of similar age
#238,928
of 329,169 outputs
Outputs of similar age from BMC Bioinformatics
#72
of 106 outputs
Altmetric has tracked 23,041,514 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 7,318 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 106 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.