Auto Sequencer: A DNA Sequence Alignment and Assembly Tool
Bibliographic record
Abstract
The process of determining the exact order of nucleotides in DNA is a crucial component of a wide varietyof research applications known as DNA sequencing. Over the last fifty years, several DNA sequencingtechnologies have been well characterized through their nature and the kind of output they provide. Evenwith significant advances in DNA sequencing technology, sequencing and assembly of large pieces ofDNA remains a complex task. It requires sequencing small reads of DNA at a time, and performing DNAsequence assembly to merge the individual pieces into a single contiguous sequence. DNA sequenceassembly, albeit tedious and time consuming, is a process in which short DNA sequence fragments aremerged into longer fragments in the attempt to reconstruct the original DNA sequence. This is usuallyachieved by manually identifying sequence overlaps between two reads before aligning them intoone contiguous sequence. Then, with the aid of online tools or software, this contiguous sequence istranslated into protein sequence. While this process may only take a few minutes, the complexity ofsequence translation and assembly can be driven by two major challenges: finding the most reasonableoverlap in sequences that may contain repeats or low quality regions, and outputting both nucleotideand protein sequence in an easy to use, comprehensive output. To facilitate this process, we introducean all-in-one tool: Auto Sequencer. This user-friendly tool can combine and translate raw DNA sequencefiles by finding the most reasonable overlap between them displaying outputs in flexible formats.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.037 |
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".