Atria: An Ultra-fast and Accurate Trimmer for Adapter and Quality Trimming
Bibliographic record
Abstract
Abstract Background As Next Generation Sequencing takes a dominant role in terms of output capacity and sequence length, adapters attached to the reads and low-quality bases hinder the performance of downstream analysis directly and implicitly, such as producing false-positive single nucleotide polymorphisms (SNP), and generating fragmented assemblies. A fast trimming algorithm is in demand to remove adapters precisely, especially in read tails with relatively low quality. Findings We present a trimming program named Atria. Atria matches the adapters in paired reads and finds possible overlapped regions with a super-fast and carefully designed byte-based matching algorithm ( O(n) time with O(1) space). Atria also implements multi-threading in both sequence processing and file compression and supports single-end reads. Conclusions Atria performs favorably in various trimming and runtime benchmarks of both simulated and real data with other cutting-edge trimmers. We also provide an ultra-fast and lightweight byte-based matching algorithm. The algorithm can be used in a broad range of short-sequence matching applications, such as primer search and seed scanning before alignment. Availability & Implementation The Atria executables, source code, and benchmark scripts are available at https://github.com/cihga39871/Atria under the MIT license.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".