SCOP: a novel scaffolding algorithm based on contig classification and optimization
Bibliographic record
Abstract
MOTIVATION: Scaffolding is an essential step during the de novo sequence assembly process to infer the direction and order relationships between the contigs and make the sequence assembly results more continuous and complete. However, scaffolding still faces the challenges of repetitive regions in genome, sequencing errors and uneven sequencing depth. Moreover, the accuracy of scaffolding greatly depends on the quality of contigs. Generally, the existing scaffolding methods construct a scaffold graph, and then optimize the graph by deleting spurious edges. Nevertheless, due to the wrong joints between contigs, some correct edges connecting contigs may be deleted. RESULTS: In this study, we present a novel scaffolding method SCOP, which is the first method to classify the contigs and utilize the vertices and edges to optimize the scaffold graph. Specially, SCOP employs alignment features and GC-content of paired reads to evaluate the quality of contigs (vertices), and divide the contigs into three types (True, Uncertain and Misassembled), and then optimizes the scaffold graph based on the classification of contigs together with the alignment of edges. The experiment results on the datasets of GAGE-A and GAGE-B demonstrate that SCOP performs better than 12 other competing scaffolders. AVAILABILITY AND IMPLEMENTATION: SCOP is publicly available for download at https://github.com/bioinfomaticsCSU/SCOP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".