Systematic Recovery and Analysis of Full-ORF Human cDNA Clones
Bibliographic record
Abstract
The Mammalian Gene Collection (MGC) consortium (http://mgc.nci.nih.gov) seeks to establish publicly available collections of full-ORF cDNAs for several organisms of significance to biomedical research, including human. To date over 15,200 human cDNA clones containing full-length open reading frames (ORFs) have been identified via systematic expressed sequence tag (EST) analysis of a diverse set of cDNA libraries; however, further systematic EST analysis is no longer an efficient method for identifying new cDNAs. As part of our involvement in the MGC program, we have developed a scalable method for targeted recovery of cDNA clones to facilitate recovery of genes absent from the MGC collection. First, cDNA is synthesized from various RNAs, followed by polymerase chain reaction (PCR) amplification of transcripts in 96-well plates using gene-specific primer pairs flanking the ORFs. Amplicons are cloned into a sequencing vector, and full-length sequences are obtained. Sequences are processed and assembled using Phred and Phrap, and analyzed using Consed and a number of bioinformatics methods we have developed. Sequences are compared with the Reference Sequence (RefSeq) database, and validation of sequence discrepancies is attempted using other sequence databases including dbEST and dbSNP. Clones with identical sequence to RefSeq or containing only validated changes will become part of the MGC human gene collection. Clones containing novel splice variants or polymorphisms have also been identified. Our approach to clone recovery, applied at large scale, has the potential to recover many and possibly most of the genes absent from the MGC collection.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".