LAMPS: an analysis pipeline for sequence-specific ligation-mediated amplification reads
Bibliographic record
Abstract
OBJECTIVE: Ligation-Mediated Amplification (LMA) is a versatile biochemical tool for amplifying selected DNA sequences. LMA has increased in popularity due to its integration within chromosome conformation capture (5C) and chromatin immunoprecipitation (2C-ChIP) methodologies. The output of either 5C or 2C-ChIP protocols is a single-read sequencing library of ligated primer pairs that may or may not be multiplexed. While many computational tools currently exist for read mapping and analysis, these tools neither fully support multiplexed libraries nor provide qualitative reporting on the LMA primers involved. Typically, the task of library demultiplexing or primer analysis is offloaded on to the user. Our aim was to develop an easy-to-use pipeline for processing (multiplexed) single-read sequencing data produced by sequence-specific LMA. RESULTS: Here, we describe the Ligation-mediated Amplified, Multiplexed Primer-pair Sequence (LAMPS) analysis pipeline. LAMPS facilitates the analysis of multiplexed LMA sequencing data and provides a thorough assessment of a library's reads for a variety of experimental parameters (e.g., primer-pair efficiency). The standardized output of LAMPS allows for easy integration with downstream analyses, such as data track visualization on a genome browser. LAMPS is made publicly available on GitHub: https://github.com/BlanchetteLab/LAMPS.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.027 |
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".