btllib: A C++ library with Python interface forefficient genomic sequence processing
Bibliographic record
Abstract
Bioinformaticians often do not have software engineering training or background, and software quality is not the top priority of research groups due to limited time and funding Additionally, one-off scripts or code is frequently written to perform a specific task instead of reusing existing code. This could be because the pre-existing computer programming code is either not well written, not widely available, insufficiently documented, inefficient, or not general enough. This practice leads to lower quality and non-reusable code. As bioinformatics analyses are increasingly complex and deal with ever more data, high quality code is needed to handle the complexities of the analyses reliably and productively. The solution to this is well designed and documented libraries. For example, SeqAn Not all programmers are well versed in C++, so for users of widely used and accessible higher level programming languages such as Python, Biopython (Cock et al., 2009) is available as a set of Python modules with implementations of commonly needed algorithms. Here, we present the btllib library as an addition to this ecosystem with the goal of providing highly efficient, scalable, and ergonomic implementations of bioinformatics algorithms and data structures.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.069 |
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".