Konnector: Connecting paired-end reads using a bloom filter de Bruijn graph
Bibliographic record
Abstract
Paired-end sequencing yields a read from each end of a DNA fragment, typically leaving a gap of unsequenced nucleotides in the middle. Closing this gap using information from other reads in the same sequencing experiment offers the potential to generate longer “pseudo-reads” using short read sequencing platforms. Such long reads may benefit downstream applications such as de novo sequence assembly, gap filling, and variant detection. With these possible applications in mind, we have developed Konnector, a software tool to fill in the nucleotides of the sequence gap between read pairs by navigating a de Bruijn graph. Konnector represents the de Bruijn graph using a Bloom filter, a probabilistic and memory-efficient data structure. Our implementation is able to store the de Bruijn graph using a mean 1.5 bytes of memory per k-mer, which represents a marked improvement over the typical hash table data structure. The memory usage per k-mer is independent of the k-mer length, enabling application of the tool to large genomes. We report the performance of the tool on simulated and experimental datasets, and discuss its utility for downstream analysis. Availability-Konnector is open-source software, free for academic use, released under the British Columbia Cancer Agency's academic license. The tool is included with ABySS version 1.5.2 and later, and is available for download from http://www.bcgsc.ca/platform/bioinfo/software/abyss.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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 source (direct Gemma or distilled Codex), 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".