Computational approaches for identification of active regulatory regions in plant genomes
Bibliographic record
Abstract
Identifying mechanisms governing gene regulation is important for improving gene expression in plants. Past computational approaches for studying regulatory associations specially to identify protein-DNA interactions suffered due to unavailability of huge amount of genetic data required to identify such loci at a global level. However, recent advancement in genomic technologies, such as ChIP-seq, ATACseq and RNAseq is driving this discovery process with unprecedented pace. In the current work, we integrate multiple different omics data to build regulatory network in maize and rice genome. ATACseq, RNAseq, Bind-n-seq datasets were generated in-house and complemented with other data obtained from public resources such as DNAseI and H3K27ac for different developmental stages of rice. A robust computational pipeline was built to identify open chromatin regions using ATACseq data which was then associated with genome-wide binding site for specific transcription factors. The motifs for these transcription factors were discovered de novo using bind-n-seq method. The discovered motifs where used to predict the binding sites across genome. These putative binding sites falling in open chromatin regions were then analyzed in context of differentially expressed genes in various developmental stages. This integrative analysis has led us to identify various active regulatory regions in cis-promoter as well as distal enhancers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".