SeqTailor: a user-friendly webserver for the extraction of DNA or protein sequences from next-generation sequencing data
Bibliographic record
Abstract
ABSTRACT Human whole-genome sequencing generally reveals about 4,000,000 genetic variants, including 20,000 coding variants, in each individual studied. These data are mostly stored as VCF-format files. Although many variant analysis methods accept VCF files as input, many other tools require DNA or protein sequences, particularly for splicing prediction, sequence alignment, phylogenetic analysis, and structure prediction. However, there is currently no existing online tool for extracting DNA or protein sequences for genomic variants from VCF files with user-defined parameters in a user-friendly, efficient, and standardized manner. We developed the SeqTailor webserver to bridge this gap. It can be used for the rapid extraction of (1) DNA sequences around genetic variants, with customizable window sizes, from the hg19 or hg38 human reference genomes; and (2) protein sequences encoded by the DNA sequences around genetic variants, with built-in SnpEff annotation and customizable window sizes, from human canonical transcripts. The SeqTailor webserver streamlines the sequence extraction process, and accelerates the analysis of genetic variant data with software requiring DNA or protein sequences. SeqTailor will facilitate the study of human genomic variation, by increasing the feasibility of sequence-based analysis and prediction. The SeqTailor webserver is freely available from http://shiva.rockefeller.edu/SeqTailor/ .
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.104 | 0.102 |
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".