Sequence data analysis and preprocessing for oligo probe design in microbial genomes
Bibliographic record
Abstract
A good oligo probe design in DNA microarray experiments is crucial to obtain the better results of gene expression analysis. However, sequence data from a very large microbial genome or pan-genome will produce a reduced number of oligos and affect the design quality if processed by a probe designer. Gene redundancies and discrepancies across resources of the same species or strain and their sequence similarity and homology are responsible for the poor quantity of oligos designed. We addressed these issues and problems with sequences and introduced the concept of open reading frame (ORF) sequence segmentation from which quality oligos can be selected. Analysis and pre-processing of sequence data were performed using our Perl-based pipeline ORF-Purger 2.0. ORFs were purged of redundancy, discrepancy, invalidity, overlapping, similarity and, optionally, homology, such that the quantity and quality of oligos to be designed were drastically improved. Probe integrity was proposed as the first probe selection criterion since the fully physical availability of all possible probes corresponding to their targets in a nucleic acid sample is necessary for a best probe design.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".