NGS++: a library for rapid prototyping of epigenomics software tools
Bibliographic record
Abstract
MOTIVATION: The development of computational tools to enable testing and analysis of high-throughput-sequencing data is essential to modern genomics research. However, although multiple frameworks have been developed to facilitate access to these tools, comparatively little effort has been made at implementing low-level programming libraries to increase the speed and ease of their development. RESULTS: We propose NGS++, a programming library in C++11 specialized in manipulating both next-generation sequencing (NGS) datasets and genomic information files. This library allows easy integration of new formats and rapid prototyping of new functionalities with a focus on the analysis of genomic regions and features. It offers a powerful, yet versatile and easily extensible interface to read, write and manipulate multiple genomic file formats. By standardizing the internal data structures and presenting a common interface to the data parser, NGS++ offers an effective framework for epigenomics tool development. AVAILABILITY: NGS++ was written in C++ using the C++11 standard. It requires minimal efforts to build and is well-documented via a complete docXygen guide, online documentation and tutorials. Source code, tests, code examples and documentation are available via the website at http://www.ngsplusplus.ca and the github repository at https://github.com/NGS-lib/NGSplusplus. CONTACT: nicolas.gevry@usherbrooke.ca or arnaud.droit@crchuq.ulaval.ca.
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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".