| Title |
ILP-based maximum likelihood genome scaffolding
|
|---|---|
| Published in |
BMC Bioinformatics, September 2014
|
| DOI | 10.1186/1471-2105-15-s9-s9 |
| Pubmed ID | |
| Authors |
James Lindsay, Hamed Salooti, Ion Măndoiu, Alex Zelikovsky |
| Abstract |
Interest in de novo genome assembly has been renewed in the past decade due to rapid advances in high-throughput sequencing (HTS) technologies which generate relatively short reads resulting in highly fragmented assemblies consisting of contigs. Additional long-range linkage information is typically used to orient, order, and link contigs into larger structures referred to as scaffolds. Due to library preparation artifacts and erroneous mapping of reads originating from repeats, scaffolding remains a challenging problem. In this paper, we provide a scalable scaffolding algorithm (SILP2) employing a maximum likelihood model capturing read mapping uncertainty and/or non-uniformity of contig coverage which is solved using integer linear programming. A Non-Serial Dynamic Programming (NSDP) paradigm is applied to render our algorithm useful in the processing of larger mammalian genomes. To compare scaffolding tools, we employ novel quantitative metrics in addition to the extant metrics in the field. We have also expanded the set of experiments to include scaffolding of low-complexity metagenomic samples. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 8% |
| Sweden | 1 | 4% |
| Unknown | 22 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 32% |
| Professor > Associate Professor | 5 | 20% |
| Student > Ph. D. Student | 5 | 20% |
| Student > Master | 3 | 12% |
| Other | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 1 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 44% |
| Computer Science | 7 | 28% |
| Biochemistry, Genetics and Molecular Biology | 3 | 12% |
| Mathematics | 2 | 8% |
| Unknown | 2 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 December 2014.
All research outputs
#13,866,700
of 23,498,099 outputs
Outputs from BMC Bioinformatics
#4,286
of 7,400 outputs
Outputs of similar age
#115,391
of 240,499 outputs
Outputs of similar age from BMC Bioinformatics
#69
of 116 outputs
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