Analyze What Papers Actually Reported

Visual workflow — map PRJEB107732 vaTE-siRNAs to AGO1, TCP, ARF, NAC ORFs and ORMV

Required resources & reproducibility

• Raw data: ENA project PRJEB107732 (download with enaBrowserTools/aspera) (PRJEB107732 data).
• References: Arabidopsis TAIR10 CDS/annotation, ORMV reference genome (GenBank/RefSeq), TE annotation (TAIR/RepBase), ShortStack/Bowtie indexes.
• Software (recommended): FastP/cutadapt, Bowtie v1.3.1, ShortStack (for sRNA cluster calling & multimapping-aware assignment), samtools, bedtools, deepTools (for coverage), DESeq2 (R) for differential small RNA counts, Python3 with pandas/biopython/plotly for plotting and table generation, Snakemake or Nextflow for pipeline reproducibility.
• Compute: mapping of many small RNA libraries is light-to-moderate (tens to low hundreds of CPU-hours depending on sample count); use HPC or cloud with 32–64 GB RAM per worker for large batches.

Detailed commands (example snippets)

# 1) trim adapters with cutadapt
cutadapt -a TGGAATTCTCGGGTGCCAAGG -m 18 -M 30 -o sample.trim.fastq.gz sample.fastq.gz

# 2) build Bowtie index (TAIR10 CDS + ORMV + TE fasta concatenated)
bowtie-build combined_refs.fa combined_idx

# 3) map allowing 0 mismatches and report all valid alignments
bowtie -v 0 -a --best --strata --sam combined_idx sample.trim.fastq.gz > sample.sam

# 4) convert, sort, index
samtools view -bS sample.sam | samtools sort -o sample.sorted.bam
samtools index sample.sorted.bam

# 5) count reads per ORF (bedtools intersect / featureCounts or custom script that respects short-RNA strand rules)

QC & interpretation caveats (epistemic humility)

• PRJEB107732 data are total small RNAs (not AGO1-IP). Presence of a sRNA mapping to AGO1/TCP/ARF/NAC ORFs does not prove AGO1‑loading or slicing; AGO1 association should be inferred cautiously using 5' U/21-nt biases and, if possible, validated by AGO1-IP or degradome data (AGO1 loading caution).
• vaTE-siRNAs often multi-map to repeated TE sequences; multi-mapping introduces uncertainty about the exact origin—report both uniquely mapped and multi-mapped proportional assignments.
• Statistical power varies by ORF: AGO1 is a single gene (long ORF) while a family like TCP/NAC includes many genes; correct multiple testing and account for ORF length and expression-level biases.
• If you need causal claims about AGO1 loading or slicing of targets, plan orthogonal experiments: AGO1-IP small RNA sequencing, PARE/degradome sequencing, and reporter assays to validate slicing at predicted sites (the PRJEB107732 paper used such orthogonal evidence in a nematode context) (AGO1-RIP validation example).

Planned plots (Plotly-ready) — examples to be generated after raw data processing

1. Length distribution stacked barplot (counts of 18–30 nt by sample; 21-nt highlighted).
2. Per-ORF barplots: 21‑nt RPM for AGO1, each TCP/ARF/NAC gene, and ORMV ORFs across conditions (mock, diseased, recovered).
3. Per-ORF coverage heatmap: position (x-axis) vs sample (y-axis) showing normalized coverage (log scale) for 21-nt reads.
4. 5' nucleotide composition stacked bars for AGO1/TCP/ARF/NAC-mapped reads (to estimate AGO1-loading propensity via 5' U enrichment).
5. Volcano plot of fold change (infected vs mock) vs adjusted p-value for per‑ORF 21‑nt counts (DESeq2 results).

Selected supporting literature (method & interpretation guidance)

• vaTE-siRNAs / PRJEB107732
• AGO1 loading proxies
• AGO1 pockets & siRNA amplification
• miR168 length effects on AGO1

Confidence, limitations, and what would change conclusions

Confidence in an initial mapping-based inference that certain ORFs are enriched for 21‑nt vaTE-siRNAs is high provided reads uniquely map and 21‑nt/5'U features are enriched; however, confidence that those reads are AGO1-loaded and cause slicing is moderate-to-low without AGO1-IP or degradome (PARE) validation. If AGO1-IP libraries (or degradome evidence of cleavage at predicted sites) are available and show enrichment, conclusions strengthen markedly. Conversely, if mapped reads predominantly multi-map to TEs or lack 5'U/21-nt features, AGO1 association is unlikely and the hypothesis is weakened.

Quick bioinformatics helper (one-line description)

Next steps I can run for you

1. Run the full reproducible pipeline on PRJEB107732 now (download, QC, mapping, counts, DE testing, interactive plots) — click the button below.
2. If you already have a subset of samples you care about (e.g., recovered vs diseased), upload sample list and I will prioritize those.
3. If you want AGO1‑IP or degradome integration, provide accessions and I will integrate them to test AGO1-loading and slicing hypotheses.

If you want, I will produce the exact Plotly graphs (length distributions, per-ORF barplots, coverage heatmaps, 5' nucleotide composition) as interactive HTML outputs after I run the pipeline; otherwise I can provide the Snakemake workflow and runnable command list for you to run locally or on an HPC.