Analysis of the role of gene regulatory elements in human health and evolution
Bibliographic record
Abstract
When the first draft of the human genome was published, scientists labeled all non-genic regions (>95% of the genome) as "junk DNA" since they were not predicted to encode for proteins, the molecular building blocks of life. This proved to be a hasty conclusion because it turns out that much of what was considered to be "junk" comprises important functional elements, such as transposons, retrotransposons, non-coding RNAs, and gene regulatory elements (GREs). "Junk" DNA is now simply referred to as "non-coding DNA" to indicate that though they are not expected to encode for protein, they may indeed still serve some important function. Francis Crick's central dogma of molecular biology provides the now standard pathway of gene activation. It states that a gene (a DNA subsequence of the genome) is first transcribed into messenger RNA (mRNA) which is in turn translated into protein. A subset of GREs, often referred to as cis-elements, are involved in regulating the transcriptional and translational aspects of gene activation. In 1975, King & Wilson speculated that differences in gene activation patterns underlie phenotypic (observable physical or biochemical characteristics) variation within and between species [1]. With the advent of large-scale computational and experimental techniques for the identification of GREs it is becoming increasingly tractable to test their speculation. In this thesis, I present computational techniques for the genome-wide identification of a sub-class of GREs known as microRNA target sites, and evidences for their important role in human health and disease. Further, I also describe and discuss the results of a quantitative population genomics approach to analyze human population patterns of sequence variation in another sub-class of GREs known as transcription factor binding sites (TFBSs). These results extend current knowledge of the role of GREs in human evolution, and also point to the importance of continued research in the area of evolution of gene regulation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".