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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.046 |
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".