{ "a_galaxy_workflow": "true", "annotation": "", "comments": [], "format-version": "0.1", "name": "TAL1 (auto-extract, post-fix)", "report": { "markdown": "# TAL1 peaks to candidate regulated genes \u2014 extractable rebuild\n\nThe erythroid (G1E) vs megakaryocyte lineage switch falls out of **differential TAL1 ChIP binding**. This is the **workflow-extractable** build: every step is a tool or collection operation, every displayed result is a real tool output, and the read inputs are structured as nested collections so peak-calling is **one mapped step** instead of per-sample branches.\n\nThree things that broke extraction in the first-pass version are fixed here:\n\n- **Reads are two `list:list` collections** (outer = condition, inner = replicate), not six independent uploads. Bowtie2 maps over them; **MACS2 is a single mapped step** that pools replicates per condition automatically (see below).\n- **Candidate-gene lists are `Group` tool outputs**, not bash `sort -u`.\n- **The distance-to-TSS figure is built by tools** (`bedtools closest` \u2192 `awk` bins \u2192 `Datamash` \u2192 `Multi-Join` \u2192 `heatmap2`), not a pasted Python matrix.\n\n## Reads as nested collections \u2192 one mapped MACS2 step\n\nThe reads are uploaded as two `list:list` collections:\n\n```\ntreatment_reads : list:list { G1E:{r1}, mega:{r1, r2} }\ncontrol_reads : list:list { G1E:{r1}, mega:{r1, r2} }\n```\n\nBowtie2 maps over each (mm10) \u2192 BAM `list:list` with structure preserved. Then **a single MACS2 `callpeak`** maps the two `list:list` over its two pooling inputs (treatment / control are `multiple=\"true\"`): the **outer list maps** (one job per condition) and the **inner list reduces** (replicates pool). So the G1E (1 replicate) vs megakaryocyte (2 replicates, pooled) asymmetry is handled structurally, with **zero element-addressing** \u2014 one MACS2 step, output a `list` of two condition peak sets:\n\n```\nBowtie2 [map-over] \u2192 treatment_bams / control_bams (list:list)\nMACS2 [outer-map condition + inner-reduce replicates, treatment&control linked on condition]\n \u2192 peaks : list { G1E: narrowPeak (261), mega: narrowPeak (150) }\n```\n\nPooling the megakaryocyte replicates for **both** treatment and control is what lifts the mega peak count from a single-replicate ~17 to **150** (deep TAL1 vs shallow input otherwise scales MACS2's libraries down and kills sensitivity).\n\n## Differential binding \u2192 the lineage switch\n\nThe two condition peak sets are compared with `bedtools intersect`: **common** (39), **G1E-only** (222), **megakaryocyte-only** (110). Each class's peaks are intersected with strand-aware promoter windows (RefSeq TSS \u22121000/+500) and the overlapping genes pulled out with the **`Group`** tool \u2014 reproducing the lineage contrast as datasets in the graph:\n\n| Class | Promoter-bound candidate genes (`Group` output) |\n|---|---|\n| **Common** | `Cbfa2t3` (ETO2, a TAL1-complex member), `Pf4` |\n| **G1E-only** | **`Gata1`** (erythroid master TF locus), `Cpox`, `Tfr2`, `Pdcd4`, `Cep55`, `Il9r`, `Noc3l`, `Eml3`, `Zfyve27`, `Pga5`, `Mir101b`, `Mir5114` |\n| **Megakaryocyte-only** | **`Fli1`** (megakaryocyte master TF), **`Tal1`** (autoregulation), `Dock8`, `Lgi1` |\n\n```galaxy\nhistory_dataset_display(output=\"Group on dataset 35\")\n```\n\n```galaxy\nhistory_dataset_display(output=\"Group on dataset 36\")\n```\n\n`Gata1` bound only in G1E and `Fli1` only in megakaryocyte is the erythroid\u2194megakaryocyte switch. (G1E is a **GATA1-null** erythroid line, so this is differential TAL1 binding *at the Gata1 locus*, not GATA1 activity \u2014 the differential-binding contrast stands.)\n\n## Most TAL1 peaks are distal (enhancer-dominated)\n\nFor each peak class, `bedtools closest` (ties=first, one row per peak) gives the distance to the nearest TSS; `awk` bins it, `Datamash` counts peaks per bin, `Multi-Join` assembles a bins\u00d7class matrix, and `heatmap2` renders it \u2014 all on-graph tools:\n\n```galaxy\nhistory_dataset_as_image(output=\"heatmap2 on dataset 63\")\n```\n\nThe mass sits in the 1\u201350 kb bins (peak of G1E-only at 10\u201350 kb), i.e. TAL1 occupancy is enhancer-dominated, with the promoter-proximal (<1 kb) minority carrying the master-regulator calls above.\n\n## What extracts \u2014 and the one honest residual\n\n- **Reads (2 `list:list`) \u2192 Bowtie2 map-over \u2192 one MACS2 mapped step \u2192 bedtools \u2192 Group \u2192 datamash \u2192 heatmap2** are all real tool/collection jobs. The alignment front-end and the entire figure + gene-list tails extract cleanly, and the `list:list` restructure means **MACS2 is a single mapped step with no element-addressing** \u2014 strictly better than per-condition fixed inputs.\n- **The irreducible residual is the two-way comparison itself.** \"G1E vs megakaryocyte\" picks two specific elements out of the `peaks` list (via `Extract Dataset`) to feed the pairwise `bedtools intersect`. That is an inherent 2-condition set comparison \u2014 it has no map-over form inside a single notebook, so those two steps extract as **condition-pinned** (`G1E` / `mega`). The workflow *runs*; it just isn't sample-agnostic at the comparison seam. 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