scientific-writing

name

scientific-writing

description

Generate publication-ready methods sections, figure legends, supplementary tables, and data availability statements from ENCODE analysis provenance. Implements the scientific documentation standards requiring complete metadata reporting. Use when the user needs to write methods, generate figure legends, create supplementary tables, draft data availability statements, compile tool citations, or auto-generate any publication text from their ENCODE analysis. Trigger on: methods section, figure legend, supplementary table, data availability, tool citations, publication writing, manuscript, write methods, methods draft, write up, write-up, paper writing, reproducible methods.

Scientific Writing from ENCODE Provenance

Generate publication-quality scientific writing from ENCODE analysis records. This skill integrates with data-provenance and cite-encode to auto-generate methods from logged pipeline runs. Every generated section follows rigorous scientific documentation standards -- complete reporting of all experimental and computational parameters with zero ambiguity.

When to Use

• User wants to write publication-ready methods sections, figure legends, or data availability statements
• User asks about "methods section", "figure legend", "scientific writing", or "manuscript preparation"
• User needs to auto-generate methods text from logged provenance/analysis steps
• User wants templates for supplementary tables, Key Resources Tables, or tool citation formatting
• Example queries: "write a methods section for my ChIP-seq analysis", "generate a figure legend for my heatmap", "format my data availability statement"

Overview

Most methods sections in genomics papers are incomplete. They omit software versions, skip reference file details, conflate technical and biological replicates, and use phrases like "default parameters" without stating what those defaults are. Reviewers catch these omissions, and readers cannot reproduce the analysis.

This skill solves the problem by generating methods text directly from the provenance chain. When every processing step has been logged (via data-provenance), the methods section writes itself. When metadata has been captured from ENCODE (via track-experiments), the experimental details are already recorded. This skill assembles these records into publication-ready prose, figure legends, supplementary tables, and data availability statements.

This standard is not aspirational -- it is the minimum bar for reproducible science.

Scientific Documentation Standards -- Required Metadata

Every methods section MUST report the following fields. Omitting any of these fields produces an incomplete methods section that reviewers will flag and readers cannot reproduce.

Field	Example	Why Required
Library preparation	TruSeq ChIP	Affects fragment size distribution and GC bias
Biological replicates	n=2 per condition	Statistical power and reproducibility
Cells/nuclei per replicate	50,000 cells	Input sufficiency for the assay
Sequencing reads	30M paired-end	Coverage depth determines sensitivity
Read length	2x150 bp	Alignment accuracy and mappability
Paired/single-end	Paired-end	Fragment size estimation, structural variants
Sequencer	NovaSeq 6000	Quality profile, error model, binning
Lab/batch	Snyder Lab, Stanford	Batch effect awareness
Reference genome	GRCh38/hg38	Coordinate system for all downstream analysis
Gene annotation	GENCODE v44	Gene definitions change between versions
ENCODE accessions	ENCSR133RZO	Exact data provenance for reproducibility
Blacklist version	ENCODE Blacklist v2	Artifact exclusion affects all peak-based analyses

How to Populate These Fields

# Track the experiment to capture metadata
encode_track_experiment(accession="ENCSR...", fetch_publications=True)

# Get full experiment details
encode_get_experiment(accession="ENCSR...")

# Get file-level metadata
encode_get_file_info(accession="ENCFF...")

# Get provenance for derived files
encode_get_provenance(file_path="/path/to/derived/file.bed")

Methods Section Templates

Each template below is a fill-in-the-blank paragraph that reads like a real methods section. Bracketed fields [like this] are populated from ENCODE metadata and provenance records. Every template follows these documentation standards.

ChIP-seq Methods

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) data for
[target] in [biosample] were obtained from the ENCODE Project (ENCODE
Project Consortium 2020) under accession [ENCSR accession]. [Library
preparation method] libraries were prepared from [number] biological
replicates ([cells/nuclei] per replicate) and sequenced on an Illumina
[sequencer model] to generate [read count]M [paired-end/single-end]
reads of [read length] bp per replicate.

Raw reads were assessed with FastQC (v[version]; Andrews 2010) and
trimmed with Trim Galore (v[version]; Krueger 2015) to remove adapter
sequences and low-quality bases (Phred < 20). Trimmed reads were aligned
to the [organism] reference genome ([assembly]) using BWA-MEM (v[version];
Li 2013) with default parameters. Duplicate reads were marked and removed
using Picard MarkDuplicates (v[version]; Broad Institute). Reads with
mapping quality < 30 were excluded using samtools (v[version]; Danecek
et al. 2021). Reads mapping to ENCODE Blacklist v2 regions (Amemiya et al.
2019) were removed using bedtools intersect (v[version]; Quinlan & Hall
2010).

Peaks were called using MACS2 (v[version]; Zhang et al. 2008) with
parameters [--broad for broad marks / -q 0.05 for narrow marks]. For
narrow-peak targets, IDR analysis (Li et al. 2011) was performed on
replicate peak sets with a threshold of [0.05]. Signal tracks (fold
change over control) were generated using MACS2 bdgcmp and converted
to bigWig format using bedGraphToBigWig (Kent et al. 2010). Of [N]
called peaks, [N] ([%]) passed IDR filtering and [N] ([%]) remained
after blacklist removal.

ATAC-seq Methods

Assay for Transposase-Accessible Chromatin with sequencing (ATAC-seq) data
for [biosample] were obtained from the ENCODE Project (ENCODE Project
Consortium 2020) under accession [ENCSR accession]. [Number] biological
replicates of [cells/nuclei] [cells/nuclei] each were transposed with
Tn5 transposase ([library kit]) and sequenced on an Illumina [sequencer]
to generate [read count]M [paired-end/single-end] reads of [read length]
bp per replicate.

Raw reads were assessed with FastQC (v[version]; Andrews 2010) and
adapter-trimmed with Trim Galore (v[version]; Krueger 2015). Trimmed
reads were aligned to [assembly] using Bowtie2 (v[version]; Langmead &
Salzberg 2012) with parameters --very-sensitive -X 2000 --no-mixed
--no-discordant. Mitochondrial reads were removed. Duplicate reads were
removed using Picard MarkDuplicates (v[version]; Broad Institute). Reads
with mapping quality < 30 were excluded. Tn5 transposase offset
correction was applied (+4 bp on the positive strand, -5 bp on the
negative strand; Buenrostro et al. 2013). ENCODE Blacklist v2 regions
(Amemiya et al. 2019) were excluded.

Peaks were called using MACS2 (v[version]; Zhang et al. 2008) with
parameters --nomodel --shift -75 --extsize 150 --keep-dup all -q 0.05.
Nucleosome-free fragments (< 150 bp) were used for peak calling. Signal
tracks were generated as fold change over background. TSS enrichment
score was [value] (threshold >= 6; ENCODE data standards; Yan et al. 2020). Of [N]
called peaks, [N] ([%]) passed quality filtering.

RNA-seq Methods

RNA sequencing (RNA-seq) data for [biosample] were obtained from the
ENCODE Project (ENCODE Project Consortium 2020) under accession [ENCSR
accession]. Total RNA was extracted from [number] biological replicates
and [library preparation method] libraries were prepared. Libraries were
sequenced on an Illumina [sequencer] to generate [read count]M
[paired-end/single-end] reads of [read length] bp per replicate.

Raw reads were assessed with FastQC (v[version]; Andrews 2010) and
MultiQC (v[version]; Ewels et al. 2016). Adapter sequences were trimmed
with Trim Galore (v[version]; Krueger 2015). Reads were aligned to
[assembly] with [GENCODE annotation version] gene annotations using STAR
(v[version]; Dobin et al. 2013) in two-pass mode. Gene-level
quantification was performed using RSEM (v[version]; Li & Dewey 2011)
for expected counts and TPM values. Transcript-level quantification was
obtained with Kallisto (v[version]; Bray et al. 2016). Mapping rate was
[%] and rRNA contamination was [%] (thresholds: mapping 70-90%, rRNA <
10%; Conesa et al. 2016; ENCODE data standards). Replicate Pearson correlation was [r value]
(threshold >= 0.9).

Differential expression analysis was performed using DESeq2 (v[version];
Love et al. 2014) in R (v[version]). The design formula was [~ batch +
condition]. Genes with |log2 fold change| > [threshold] and Benjamini-
Hochberg adjusted p-value < [threshold] were considered differentially
expressed. Of [N] genes tested, [N] ([%]) were significantly
upregulated and [N] ([%]) were significantly downregulated.

WGBS Methods

Whole-genome bisulfite sequencing (WGBS) data for [biosample] were
obtained from the ENCODE Project (ENCODE Project Consortium 2020) under
accession [ENCSR accession]. Genomic DNA from [number] biological
replicates was bisulfite-converted using [conversion kit] and sequenced
on an Illumina [sequencer] to generate [read count]M [paired-end/
single-end] reads of [read length] bp per replicate.

Raw reads were assessed with FastQC (v[version]; Andrews 2010) and
trimmed with Trim Galore (v[version]; Krueger 2015) in --rrbs mode
[if applicable] or standard mode. Trimmed reads were aligned to
[assembly] using Bismark (v[version]; Krueger & Andrews 2011) with
Bowtie2 as the underlying aligner. Duplicate reads were removed using
Bismark deduplicate_bismark. Methylation calls were extracted using
MethylDackel (v[version]; https://github.com/dpryan79/MethylDackel)
with parameters [--minDepth X --mergeContext]. Bisulfite conversion
efficiency was [%] (threshold >= 98%; ENCODE data standards). CpG sites with
coverage >= [X]x were retained for analysis ([N] CpGs, [%] of all
genomic CpGs).

Differentially methylated regions (DMRs) were identified using DMRcate
(v[version]; Peters et al. 2021) with parameters [lambda, C]. CpG
sites within ENCODE Blacklist v2 regions (Amemiya et al. 2019) were
excluded prior to DMR calling.

Hi-C Methods

Hi-C data for [biosample] were obtained from the ENCODE Project (ENCODE
Project Consortium 2020) under accession [ENCSR accession]. Chromatin
was crosslinked, digested with [restriction enzyme], proximity-ligated,
and sequenced on an Illumina [sequencer] to generate [read count]M
[paired-end] reads of [read length] bp across [number] biological
replicates.

Read pairs were aligned to [assembly] using BWA-MEM (v[version]; Li
2013) and processed with pairtools (v[version]; Open2C et al. 2024) for
pair classification, deduplication, and filtering. Valid interaction
pairs (cis-reads with MAPQ >= 30) were retained. Contact matrices were
generated using cooler (v[version]; Abdennur & Mirny 2020) at
resolutions of [1kb, 5kb, 10kb, 25kb, 50kb, 100kb, 250kb, 500kb, 1Mb].
Matrices were balanced using iterative correction (ICE; Imakaev et al.
2012). Cis/trans ratio was [%] (threshold > 60%) and long-range cis
interactions (> 20kb) comprised [%] of total (threshold > 40%; Yardimci
et al. 2019).

Chromatin loops were called using HiCCUPS (Juicer v[version]; Durand
et al. 2016) at [resolutions] with default FDR thresholds.
Topologically associating domains (TADs) were identified using [method]
at [resolution]. [N] loops and [N] TADs were identified.

CUT&RUN Methods

Cleavage Under Targets and Release Using Nuclease (CUT&RUN) data for
[target] in [biosample] were obtained from the ENCODE Project (ENCODE
Project Consortium 2020) under accession [ENCSR accession]. [Number]
biological replicates of [cells/nuclei] each were incubated with
[antibody] and processed with protein A-MNase. Released DNA fragments
were sequenced on an Illumina [sequencer] to generate [read count]M
[paired-end] reads of [read length] bp per replicate.

Raw reads were assessed with FastQC (v[version]; Andrews 2010) and
trimmed with Trim Galore (v[version]; Krueger 2015). Reads were aligned
to [assembly] using Bowtie2 (v[version]; Langmead & Salzberg 2012) with
parameters --very-sensitive --no-mixed --no-discordant -X 700. Spike-in
reads (E. coli or S. cerevisiae) were aligned separately and used for
calibration normalization ([spike-in method]). Duplicate reads were
removed with Picard MarkDuplicates (v[version]; Broad Institute). ENCODE
Blacklist v2 and suspect list regions (Amemiya et al. 2019; Nordin et
al. 2023) were excluded.

Peaks were called using SEACR (v[version]; Meers et al. 2019) in
[stringent/relaxed] mode with [IgG control / numeric threshold]. Note:
CUT&RUN has different QC profiles than ChIP-seq; standard ChIP-seq
metrics (FRiP, NSC, RSC) should not be directly applied (Nordin et al.
2023). [N] peaks were identified across replicates.

Single-cell RNA-seq Methods

Single-cell RNA sequencing (scRNA-seq) data for [biosample] were obtained
from the ENCODE Project (ENCODE Project Consortium 2020) under accession
[ENCSR accession]. [Cell/nuclei isolation method] was performed on
[number] biological replicates. Libraries were prepared using [10x
Genomics Chromium / Smart-seq2 / other platform] targeting [N] cells per
replicate, and sequenced on an Illumina [sequencer] to generate [read
count]M reads per sample.

Raw reads were processed with [CellRanger v[version] (10x Genomics) /
STARsolo (STAR v[version]; Dobin et al. 2013)] to generate gene-cell
count matrices against [assembly] with [GENCODE annotation version].
Ambient RNA contamination was estimated and removed using [SoupX
(v[version]; Young & Behjati 2020) / CellBender (v[version]; Fleming
et al. 2023)]. Doublets were detected and removed using [Scrublet
(v[version]; Wolock et al. 2019) / DoubletFinder (v[version]; McGinnis
et al. 2019)]. Cells with [< N genes, > N% mitochondrial reads, < N
UMIs] were excluded.

Data were normalized using [SCTransform (v[version]; Hafemeister & Satija
2019) / scran (v[version]; Lun et al. 2016)] and integrated across
samples using [Harmony (v[version]; Korsunsky et al. 2019) / scVI
(v[version]; Lopez et al. 2018)]. Dimensionality reduction was performed
using PCA ([N] components) followed by UMAP (n_neighbors=[N],
min_dist=[value]; McInnes et al. 2018). Clustering was performed using
the Leiden algorithm (resolution=[value]; Traag et al. 2019) implemented
in [Seurat v[version] (Hao et al. 2024) / Scanpy v[version] (Wolf et
al. 2018)]. Cell types were annotated based on [marker genes / reference
mapping / automated annotation with method]. Final dataset comprised [N]
cells across [N] clusters representing [N] cell types.

Figure Legend Templates

Genome Browser Screenshot

Figure [N]. Genome browser view of [mark/signal] at the [gene name]
locus ([chr]:[start]-[end], [assembly]). Tracks shown from top to
bottom: [track 1 description, e.g., "H3K27ac ChIP-seq signal in
pancreatic islets (ENCSR...)"], [track 2], [track 3]. Signal tracks
represent fold change over input control. [Gene models from GENCODE
v[version] are shown at bottom.] [Shaded region highlights the
[promoter/enhancer/regulatory element] of interest.] Data were obtained
from the ENCODE Project (ENCODE Project Consortium 2020).

Heatmap

Figure [N]. Heatmap of [signal type, e.g., "H3K27ac ChIP-seq signal"]
across [N] [regions/genes/peaks] in [N] [samples/conditions/cell types].
Rows represent [individual peaks / genes / genomic regions], sorted by
[k-means clustering (k=[N]) / hierarchical clustering (method=[ward.D2],
distance=[euclidean]) / signal intensity]. Columns represent [samples /
cell types / conditions]. Color scale indicates [log2 fold change /
z-scored signal / CPM / RPKM] with [color scheme, e.g., "blue-white-red
diverging scale, range [-2, 2]"]. [Normalization method: quantile /
library size / spike-in.] Heatmap was generated using [deepTools
computeMatrix + plotHeatmap (v[version]; Ramirez et al. 2016) /
ComplexHeatmap (v[version]; Gu et al. 2016) / pheatmap (v[version];
Kolde 2019)].

Volcano Plot

Figure [N]. Volcano plot of differentially expressed genes between
[condition A] and [condition B] in [biosample/cell type]. X-axis shows
log2 fold change; y-axis shows -log10 adjusted p-value (Benjamini-
Hochberg correction). Significance thresholds: |log2FC| > [threshold]
(vertical dashed lines) and adjusted p-value < [threshold] (horizontal
dashed line). Red points: [N] significantly upregulated genes. Blue
points: [N] significantly downregulated genes. Gray points: [N] non-
significant genes. [Selected genes are labeled.] Differential expression
analysis was performed using [DESeq2 / edgeR / limma-voom] (v[version]).
[N] total genes were tested.

Hi-C Contact Map

Figure [N]. Hi-C contact frequency map of [region, e.g., "chromosome 7:
25-30 Mb"] in [biosample] at [resolution, e.g., "10 kb"] resolution.
Upper triangle: [observed / Knight-Ruiz balanced / ICE-normalized]
contact frequencies displayed on a [log / linear] color scale. [Lower
triangle: [comparison condition / O/E ratio / difference map].] [Loops
identified by HiCCUPS are marked with [circles/squares].] [TAD
boundaries identified by [method] are shown as [lines/triangles].]
Contact matrix was generated using [cooler (Abdennur & Mirny 2020) /
Juicer (Durand et al. 2016)] from [N]M valid read pairs. Data were
obtained from ENCODE accession [ENCSR...].

UMAP / tSNE

Figure [N]. [UMAP / tSNE] projection of [N] single cells from
[biosample] colored by [cluster identity / gene expression / sample
origin / cell cycle phase]. [UMAP parameters: n_neighbors=[N],
min_dist=[value], computed on [N] principal components. / tSNE
parameters: perplexity=[N], computed on [N] principal components.]
[N] clusters were identified using the [Leiden / Louvain] algorithm
(resolution=[value]). Cell types were annotated based on [canonical
marker gene expression / reference-based mapping using [SingleR /
Azimuth / scArches] / manual curation]. [Inset: expression of [gene
name] across clusters, showing enrichment in [cell type].]

Venn / UpSet Diagram

Figure [N]. [Venn diagram / UpSet plot] showing overlap of [peak sets /
gene lists / regulatory elements] across [N] [conditions / cell types /
datasets]. Set definitions: [Set A] = [N] [peaks/genes] from [source],
[Set B] = [N] [peaks/genes] from [source][, Set C = ...]. Overlaps were
computed using [bedtools intersect (minimum [N] bp overlap) / exact gene
ID matching]. [N] [peaks/genes] ([%]) were shared across all sets; [N]
([%]) were unique to [Set A]. [Statistical significance of overlap was
assessed using [Fisher exact test / hypergeometric test / permutation
test (N=[iterations])]; p = [value].] [UpSet plot generated with
UpSetR (v[version]; Conway et al. 2017).]

Peak Genomic Distribution Bar Chart

Figure [N]. Genomic feature distribution of [N] [peak type, e.g.,
"H3K27ac peaks"] in [biosample]. Peaks were annotated using ChIPseeker
(v[version]; Yu et al. 2015) with [GENCODE v[version] / TxDb annotation
version] gene models. Bars show percentage of peaks overlapping each
genomic feature category: promoter (<= [N] kb from TSS, [%]), 5' UTR
([%]), 3' UTR ([%]), exon ([%]), intron ([%]), downstream (<= [N] kb,
[%]), and intergenic ([%]). [Background genomic distribution is shown
for comparison (gray bars).] [A second panel shows the distribution of
peak distance to nearest TSS.]

Supplementary Table Templates

Table S1: Experiment Metadata

A master table listing all ENCODE experiments used in the study.

Column	Description	Source
ENCODE Accession	ENCSR identifier	encode_get_experiment
Assay	Assay type (ChIP-seq, ATAC-seq, etc.)	encode_get_experiment
Target	Antibody target (for ChIP/CUT&RUN)	encode_get_experiment
Biosample	Tissue or cell type	encode_get_experiment
Organism	Species	encode_get_experiment
Assembly	Genome build	encode_get_experiment
Lab	Submitting laboratory	encode_get_experiment
Biological Replicates	Number of bio reps	encode_get_experiment
Sequencing Reads	Total reads per replicate	File metadata
Read Length	bp, paired/single	File metadata
Sequencer	Instrument model	encode_get_experiment
Library	Library preparation method	encode_get_experiment
Date Released	Release date on ENCODE portal	encode_get_experiment
Audit Status	ENCODE quality audit level	encode_get_experiment

Generate with:

encode_export_data(format="csv")

Table S2: Quality Control Metrics

Assay-specific QC metrics for all experiments. Columns depend on the assay type.

ChIP-seq columns: Accession, Target, Total reads, Mapped reads, Mapping rate, Duplicate rate, NRF, PBC1, PBC2, NSC, RSC, FRiP, Peak count, IDR peaks

ATAC-seq columns: Accession, Total reads, Mapped reads, Mapping rate, Mitochondrial rate, Duplicate rate, NRF, TSS enrichment, Fragment size distribution (NFR/mono/di), FRiP, Peak count

RNA-seq columns: Accession, Total reads, Mapped reads, Mapping rate, rRNA rate, Exonic rate, Intronic rate, Intergenic rate, Genes detected (TPM > 1), Replicate correlation (Pearson r)

WGBS columns: Accession, Total reads, Mapped reads, Mapping rate, Bisulfite conversion rate, CpG coverage (mean), CpGs at 1x, CpGs at 5x, CpGs at 10x, Global methylation (%)

Hi-C columns: Accession, Total read pairs, Valid pairs, Cis pairs (%), Trans pairs (%), Long-range cis (%), Short-range cis (%), Duplicate rate, Library complexity

QC thresholds (include as table footnotes):

• ChIP-seq: FRiP >= 1%, NSC > 1.05, RSC > 0.8, NRF >= 0.8 (Landt et al. 2012)
• ATAC-seq: TSS enrichment >= 5 (GRCh38), >= 6 (hg19), >= 10 (mm10); nucleosomal ladder visible (ENCODE data standards; Yan et al. 2020)
• RNA-seq: Mapping 70-90%, rRNA < 10%, replicate r >= 0.9 (Conesa et al. 2016; ENCODE data standards)
• WGBS: Conversion >= 98%, CpG coverage >= 10x for DMRs (ENCODE data standards)
• Hi-C: Cis/trans > 60%, long-range cis > 40% (Yardimci et al. 2019)

Table S3: Differential Results

For differential expression, differential accessibility, or differential methylation analyses.

Column	Description
Feature ID	Gene ID / Peak ID / CpG ID
Gene Symbol	Gene name (for expression)
Chromosome	chr
Start	Start coordinate
End	End coordinate
log2 Fold Change	Effect size
Standard Error	SE of log2FC
Stat	Test statistic
P-value	Raw p-value
Adjusted P-value	BH-corrected FDR
Base Mean / Mean Signal	Average expression or signal

Table S4: Peak / Region Catalog

For aggregated or filtered peak sets.

Column	Description
Chromosome	chr
Start	Start coordinate (0-based)
End	End coordinate
Name	Peak identifier
Score	Scaled score (0-1000)
Signal Value	Fold enrichment or signal
P-value	-log10(p-value)
Q-value	-log10(q-value)
Summit	Distance from start to summit
Source Accession	ENCODE file accession
Biosample	Source tissue/cell type

Table S5: Software Versions

Every tool used in the analysis with version and citation.

Column	Description
Software	Tool name
Version	Exact version string
Purpose	What it was used for
Citation	Publication reference
DOI	Digital Object Identifier
URL	Download or documentation URL

Data Availability Statements

Standard ENCODE Data Availability

All sequencing data used in this study are publicly available through
the ENCODE Project portal (https://www.encodeproject.org) under the
following experiment accessions: [ENCSR list]. Processed files including
[peak calls / gene quantifications / contact matrices / methylation
calls] used in this analysis are available under file accessions [ENCFF
list]. ENCODE data are released under unrestricted use policy with no
embargo period (ENCODE Project Consortium 2020).

Derived Data Availability

All derived data generated in this study, including [filtered peak sets /
merged catalogs / differential results / aggregated matrices], are
available at [repository URL / GEO accession / Zenodo DOI]. Complete
analysis scripts, provenance logs, and software environment
specifications are available at [GitHub URL]. A detailed description of
all processing steps, parameters, and software versions is provided in
Supplementary Table S[N].

Code Availability

Code used for all analyses in this study is available at [GitHub URL].
The repository includes [Nextflow / Snakemake / Bash / R / Python]
scripts for [list major analyses], along with the complete software
environment specification ([Docker image / conda environment YAML /
renv.lock]). All computational tools and their versions are listed in
Supplementary Table S[N].

GEO Cross-Reference (when applicable)

Raw sequencing data generated in this study have been deposited in the
Gene Expression Omnibus (GEO) under accession [GSE number]. Processed
data are available at the same accession. Previously published ENCODE
data used in this study are available at https://www.encodeproject.org
under accessions listed in Supplementary Table S1.

Tool Citation Reference

Every bioinformatics tool used in an analysis must be cited with its full publication reference. The following table covers the most commonly used tools in ENCODE-based analyses.

Alignment and Read Processing

Tool	Citation	Journal	Year	DOI
BWA-MEM	Li H	arXiv	2013	10.48550/arXiv.1303.3997
Bowtie2	Langmead B & Salzberg SL	Nature Methods	2012	10.1038/nmeth.1923
STAR	Dobin A et al.	Bioinformatics	2013	10.1093/bioinformatics/bts635
HISAT2	Kim D et al.	Nature Biotechnology	2019	10.1038/s41587-019-0201-4
minimap2	Li H	Bioinformatics	2018	10.1093/bioinformatics/bty191
samtools	Danecek P et al.	GigaScience	2021	10.1093/gigascience/giab008
Picard	Broad Institute	--	--	broadinstitute.github.io/picard
sambamba	Tarasov A et al.	Bioinformatics	2015	10.1093/bioinformatics/btv098

Quality Control

Tool	Citation	Journal	Year	DOI
FastQC	Andrews S	Babraham Bioinformatics	2010	bioinformatics.babraham.ac.uk
MultiQC	Ewels P et al.	Bioinformatics	2016	10.1093/bioinformatics/btw354
Trim Galore	Krueger F	Babraham Bioinformatics	2015	github.com/FelixKrueger/TrimGalore
fastp	Chen S et al.	Bioinformatics	2018	10.1093/bioinformatics/bty560
Preseq	Daley T & Smith AD	Nature Methods	2013	10.1038/nmeth.2375
Qualimap	Okonechnikov K et al.	Bioinformatics	2016	10.1093/bioinformatics/btv566

Peak Calling and Signal Analysis

Tool	Citation	Journal	Year	DOI
MACS2	Zhang Y et al.	Genome Biology	2008	10.1186/gb-2008-9-9-r137
IDR	Li Q et al.	Annals of Applied Statistics	2011	10.1214/11-AOAS466
SEACR	Meers MP et al.	Epigenetics & Chromatin	2019	10.1186/s13072-019-0287-4
Hotspot2	John S et al.	--	2022	github.com/Altius/hotspot2
HOMER	Heinz S et al.	Molecular Cell	2010	10.1016/j.molcel.2010.05.004
F-Seq2	Zhao H et al.	Bioinformatics	2020	10.1093/bioinformatics/btab273
deepTools	Ramirez F et al.	Nucleic Acids Research	2016	10.1093/nar/gkw257
bedtools	Quinlan AR & Hall IM	Bioinformatics	2010	10.1093/bioinformatics/btq033
bedGraphToBigWig	Kent WJ et al.	Bioinformatics	2010	10.1093/bioinformatics/btq351

RNA-seq Quantification and Differential Expression

Tool	Citation	Journal	Year	DOI
RSEM	Li B & Dewey CN	BMC Bioinformatics	2011	10.1186/1471-2105-12-323
Kallisto	Bray NL et al.	Nature Biotechnology	2016	10.1038/nbt.3519
Salmon	Patro R et al.	Nature Methods	2017	10.1038/nmeth.4197
featureCounts	Liao Y et al.	Bioinformatics	2014	10.1093/bioinformatics/btt656
HTSeq	Anders S et al.	Bioinformatics	2015	10.1093/bioinformatics/btu638
DESeq2	Love MI et al.	Genome Biology	2014	10.1186/s13059-014-0550-8
edgeR	Robinson MD et al.	Bioinformatics	2010	10.1093/bioinformatics/btp616
limma	Ritchie ME et al.	Nucleic Acids Research	2015	10.1093/nar/gkv007

Bisulfite Sequencing / Methylation

Tool	Citation	Journal	Year	DOI
Bismark	Krueger F & Andrews SR	Bioinformatics	2011	10.1093/bioinformatics/btr167
MethylDackel	Ryan DP	GitHub	--	github.com/dpryan79/MethylDackel
DMRcate	Peters TJ et al.	Epigenetics & Chromatin	2021	10.1186/s13072-021-00428-1
bsmap	Xi Y & Li W	BMC Bioinformatics	2009	10.1186/1471-2105-10-232
methylKit	Akalin A et al.	Genome Biology	2012	10.1186/gb-2012-13-10-r87

3D Genome / Hi-C

Tool	Citation	Journal	Year	DOI
pairtools	Open2C et al.	PLOS Computational Biology	2024	10.1371/journal.pcbi.1012164
cooler	Abdennur N & Mirny LA	Bioinformatics	2020	10.1093/bioinformatics/btz540
Juicer	Durand NC et al.	Cell Systems	2016	10.1016/j.cels.2016.07.002
HiCCUPS	Rao SSP et al.	Cell	2014	10.1016/j.cell.2014.11.021
FAN-C	Kruse K et al.	Genome Biology	2020	10.1186/s13059-020-02215-9
Mustache	Roayaei Ardakany A et al.	Genome Biology	2020	10.1186/s13059-020-02167-0
HiGlass	Kerpedjiev P et al.	Genome Biology	2018	10.1186/s13059-018-1486-1

Single-Cell Analysis

Tool	Citation	Journal	Year	DOI
Seurat	Hao Y et al.	Nature Biotechnology	2024	10.1038/s41587-023-01767-y
Scanpy	Wolf FA et al.	Genome Biology	2018	10.1186/s13059-017-1382-0
SCTransform	Hafemeister C & Satija R	Genome Biology	2019	10.1186/s13059-019-1874-1
scran	Lun ATL et al.	Genome Biology	2016	10.1186/s13059-016-0947-7
Harmony	Korsunsky I et al.	Nature Methods	2019	10.1038/s41592-019-0619-0
scVI	Lopez R et al.	Nature Methods	2018	10.1038/s41592-018-0229-2
CellRanger	10x Genomics	--	--	support.10xgenomics.com
STARsolo	Kaminow B et al.	Cell Genomics	2021	10.1016/j.xgen.2021.100004
Scrublet	Wolock SL et al.	Cell Systems	2019	10.1016/j.cels.2018.11.005
DoubletFinder	McGinnis CS et al.	Cell Systems	2019	10.1016/j.cels.2019.03.003
SoupX	Young MD & Behjati S	GigaScience	2020	10.1093/gigascience/giaa151
SingleR	Aran D et al.	Nature Immunology	2019	10.1038/s41590-018-0276-y

Annotation and Enrichment

Tool	Citation	Journal	Year	DOI
ChIPseeker	Yu G et al.	Bioinformatics	2015	10.1093/bioinformatics/btv145
GREAT	McLean CY et al.	Nature Biotechnology	2010	10.1038/nbt.1630
clusterProfiler	Wu T et al.	Innovation	2021	10.1016/j.xinn.2021.100141
GSEA	Subramanian A et al.	PNAS	2005	10.1073/pnas.0506580102
Enrichr	Kuleshov MV et al.	Nucleic Acids Research	2016	10.1093/nar/gkw377
DAVID	Huang DW et al.	Nature Protocols	2009	10.1038/nprot.2008.211
ChromHMM	Ernst J & Kellis M	Nature Methods	2012	10.1038/nmeth.1906
liftOver	Kent WJ et al.	Genome Research	2002	10.1101/gr.229102

Visualization

Tool	Citation	Journal	Year	DOI
IGV	Robinson JT et al.	Nature Biotechnology	2011	10.1038/nbt.1754
ggplot2	Wickham H	Springer	2016	ISBN: 978-3-319-24277-4
ComplexHeatmap	Gu Z et al.	Bioinformatics	2016	10.1093/bioinformatics/btw313
Gviz	Hahne F & Ivanek R	Methods in Molecular Biology	2016	10.1007/978-1-4939-3578-9_16
pyGenomeTracks	Lopez-Delisle L et al.	Bioinformatics	2021	10.1093/bioinformatics/btaa692
HiGlass	Kerpedjiev P et al.	Genome Biology	2018	10.1186/s13059-018-1486-1
UpSetR	Conway JR et al.	Bioinformatics	2017	10.1093/bioinformatics/btx364

Workflow Management

Tool	Citation	Journal	Year	DOI
Nextflow	Di Tommaso P et al.	Nature Biotechnology	2017	10.1038/nbt.3820
Snakemake	Molder F et al.	F1000Research	2021	10.12688/f1000research.29032.2
Docker	Merkel D	Linux Journal	2014	--
Singularity	Kurtzer GM et al.	PLOS ONE	2017	10.1371/journal.pone.0177459

Auto-Generation from Provenance

The provenance chain logged by the data-provenance skill contains all information needed to auto-generate a complete methods section. The workflow is:

Step 1: Query Provenance for a Derived File

encode_get_provenance(file_path="/path/to/derived/final_peaks.bed")

This returns the full chain: derived_file -> processing_steps -> source ENCODE files.

Step 2: Extract Tool Names, Versions, Parameters

From each provenance record, extract:

• tool_used field: tool name and version
• parameters field: command-line arguments or function calls
• source_accessions field: ENCODE experiment and file accessions

Step 3: Map to Methods Template

Match each tool to the appropriate methods template section:

• BWA-MEM / Bowtie2 / STAR -> Alignment paragraph
• MACS2 / SEACR / Hotspot2 -> Peak calling paragraph
• DESeq2 / edgeR -> Differential analysis paragraph
• bedtools / samtools -> Filtering paragraph

Step 4: Fill in Metadata from Tracked Experiment

encode_get_experiment(accession="ENCSR...")

Populate template fields: biosample, target, lab, replicates, sequencer, read length, read count, library preparation.

Step 5: Generate Citations for All Tools

Cross-reference every tool in the provenance chain against the Tool Citation Reference table above. Compile a complete bibliography.

Step 6: Assemble and Review

Combine all sections into a complete methods draft:

1. Data acquisition paragraph (ENCODE sources)
2. Processing paragraphs (one per major step)
3. Analysis paragraphs (differential, enrichment, etc.)
4. Data availability statement
5. Supplementary table definitions
6. Complete tool citations

Review the draft against the documentation standards checklist to ensure no required fields are missing.

Example: Auto-Generated from Provenance

Given a provenance chain:

final_peaks.bed
  <- tool: bedtools v2.31.0, params: intersect -v blacklist
  <- filtered_peaks.bed
       <- tool: awk, params: '$7 >= 4.5'
       <- ENCFF123ABC.bed (IDR thresholded peaks, ENCSR456DEF)

Auto-generated methods:

H3K27ac ChIP-seq data for human pancreas were obtained from the ENCODE
Project (ENCODE Project Consortium 2020) under accession ENCSR456DEF.
IDR-thresholded peaks (ENCFF123ABC) aligned to GRCh38 were selected for
downstream analysis. Peaks were filtered to retain only those with MACS2
signal value >= 4.5 (retaining 34,521 of 45,231 peaks; 76.3%). Peaks
overlapping ENCODE Blacklist v2 regions (Amemiya et al. 2019) were
excluded using bedtools intersect (v2.31.0; Quinlan & Hall 2010),
yielding 34,198 peaks (99.1% of filtered set) for downstream analysis.

Pitfalls

Critical Omissions That Reviewers Will Flag

1. Not reporting software versions: "Reads were aligned with STAR" is unacceptable. Reviewers require "STAR v2.7.11a (Dobin et al. 2013)." Every tool needs name, version, and citation.

2. Missing biological replicate counts: "ChIP-seq was performed" does not tell readers whether results are from 1 replicate or 5. Always state: "n=[N] biological replicates per condition."

3. Confusing technical vs biological replicates: Technical replicates (same sample, sequenced twice) are NOT biological replicates (independent biological samples). Reporting "4 replicates" when 2 are technical inflates apparent statistical power.

4. Not specifying genome assembly version: "Reads were aligned to the human genome" is ambiguous. hg19, hg38, GRCh37, GRCh38, T2T-CHM13 are all different. Always specify: "GRCh38/hg38."

5. Using "default parameters" without stating what those defaults are: "MACS2 was run with default parameters" is not reproducible because defaults change between versions. State the actual parameter values.

6. Missing blacklist filtering mention: Peaks or signal tracks without blacklist filtering contain artifact regions. Always report: "ENCODE Blacklist v2 regions (Amemiya et al. 2019) were excluded."

7. Not reporting IDR threshold for ChIP-seq: IDR analysis is standard for ENCODE ChIP-seq. Report: "IDR threshold of 0.05" or whichever threshold was used.

8. Omitting spike-in normalization details for CUT&RUN: CUT&RUN relies on spike-in normalization. Report the spike-in organism, calibration method, and scaling factor.

9. Not distinguishing paired-end from single-end in methods: PE and SE reads produce different fragment size estimates, alignment rates, and duplicate detection. Always specify.

10. Omitting gene annotation version: GENCODE v38 and v44 define different gene sets. "Genes were annotated using GENCODE" is insufficient -- state the exact version.

11. Not reporting filtering statistics: Every filtering step should report input count, output count, and percentage retained. "Peaks were filtered" without numbers is uninformative.

12. Missing normalization method: "Signal tracks were compared across samples" -- how? Library-size normalization? Spike-in? RPKM? RPM? This changes interpretation.

Literature Foundation

Reference	Year	Journal	Key Contribution	Citations
Landt et al.	2012	Genome Research	ChIP-seq reporting guidelines and quality standards; established FRiP, NSC, RSC thresholds	~3,400
ENCODE Consortium	2020	Nature	ENCODE Phase 3 expanded encyclopaedias; defined current data standards and uniform pipelines	~1,200
Conesa et al.	2016	Genome Biology	RNA-seq best practices survey; defined mapping rate, rRNA, and correlation thresholds	~4,500
Wilkinson et al.	2016	Scientific Data	FAIR data principles (Findable, Accessible, Interoperable, Reusable); framework for data sharing	~8,000
Sandve et al.	2013	PLOS Computational Biology	Ten simple rules for reproducible computational research; foundational reproducibility guide	~1,800
Baker	2016	Nature	1,500 scientists lift the lid on reproducibility; established scale of reproducibility crisis	~3,200
Amemiya et al.	2019	Scientific Reports	ENCODE Blacklist: identification and exclusion of artifact regions across genome assemblies	~1,372
Buenrostro et al.	2013	Nature Methods	ATAC-seq method development; defined TSS enrichment and nucleosomal fragment thresholds	~5,000
Foox et al.	2021	Genome Biology	WGBS benchmarking and quality standards; bisulfite conversion thresholds	~200
Yardimci et al.	2019	Genome Biology	Hi-C quality metrics; cis/trans ratio and long-range cis thresholds	~150
Nordin et al.	2023	Genome Biology	CUT&RUN/CUT&Tag QC: different profiles from ChIP-seq; suspect list regions	~50

Integration

This skill produces...	Feed into...	Using tool/skill
Methods section text	Manuscript draft	Publication submission
Figure legends	Manuscript figures	visualization-workflow outputs
Data availability statements	Manuscript appendix	cite-encode accession lists
Supplementary table templates	Manuscript supplements	track-experiments → encode_export_data
Tool citation paragraphs	Bibliography	cite-encode → BibTeX/RIS export

Walkthrough: Generating Publication-Ready Methods for an ENCODE Analysis

Goal: Auto-generate a publication-ready methods section describing ENCODE data acquisition, processing, and analysis, with complete tool citations and reproducible parameters. Context: Journal methods sections require precise documentation of every tool, version, parameter, and data source. This skill automates that process using tracked provenance data.

Step 1: Review tracked experiments

encode_list_tracked()

Expected output:

{
  "experiments": [
    {"accession": "ENCSR000AKA", "assay": "Histone ChIP-seq", "notes": "GM12878 H3K27ac"},
    {"accession": "ENCSR637ENO", "assay": "ATAC-seq", "notes": "GM12878 accessibility"}
  ]
}

Step 2: Get provenance for derived files

encode_get_provenance(file_path="/data/analysis/enhancers_filtered.bed")

Expected output:

{
  "file": "/data/analysis/enhancers_filtered.bed",
  "tool": "bedtools v2.31.0",
  "sources": [{"accession": "ENCFF001ABC"}],
  "description": "Blacklist-filtered H3K27ac peaks"
}

Step 3: Get collection summary

encode_summarize_collection()

Step 4: Draft methods paragraph

Using provenance data, generate: "H3K27ac ChIP-seq data for GM12878 (ENCSR000AKA) were obtained from the ENCODE Portal (ENCODE Project Consortium, 2012). IDR-thresholded peaks (ENCFF001ABC) were filtered against the ENCODE blacklist v2 (Amemiya et al., 2019) using bedtools v2.31.0 (Quinlan & Hall, 2010)."

Integration with downstream skills

• Provenance data from → data-provenance provides tool versions and parameters
• Citations from → cite-encode provide proper references
• QC metrics from → quality-assessment document quality thresholds used
• Pipeline details from → pipeline-guide describe processing workflows

Code Examples

1. List tracked experiments for methods

encode_list_tracked()

Expected output:

{
  "experiments": [
    {"accession": "ENCSR000AKA", "assay": "Histone ChIP-seq"}
  ]
}

2. Get provenance for a derived file

encode_get_provenance(file_path="/data/peaks_filtered.bed")

Expected output:

{
  "file": "/data/peaks_filtered.bed",
  "tool": "bedtools v2.31.0",
  "sources": [{"accession": "ENCFF001ABC"}]
}

3. Get experiment citations for bibliography

encode_get_citations(accession="ENCSR000AKA")

Expected output:

{
  "citations": [{"pmid": "29126249", "title": "ENCODE encyclopedia", "year": 2012}]
}

Related Skills

Skill	Relationship
data-provenance	Source of provenance records for methods auto-generation; log every operation
cite-encode	Citation formatting and ENCODE data use policy compliance
quality-assessment	QC metrics to include in methods sections and Table S2
pipeline-guide	Pipeline parameters for methods text; links to assay-specific pipeline skills
publication-trust	Verify integrity of cited publications before including in references
pipeline-chipseq	ChIP-seq pipeline parameters for methods template
pipeline-atacseq	ATAC-seq pipeline parameters for methods template
pipeline-rnaseq	RNA-seq pipeline parameters for methods template
pipeline-wgbs	WGBS pipeline parameters for methods template
pipeline-hic	Hi-C pipeline parameters for methods template
pipeline-cutandrun	CUT&RUN pipeline parameters for methods template
visualization-workflow	Figure generation workflow to pair with figure legends
peak-annotation	Peak annotation details for methods and figure legends
batch-analysis	Multi-experiment analyses requiring comprehensive methods

Presenting Results

When generating scientific writing, present:

1. Complete methods paragraph(s) in a code block for easy copy-paste. Every paragraph should be self-contained and ready for insertion into a manuscript.

2. Documentation standards checklist showing which required metadata fields are covered vs. missing:

   [x] Library preparation: TruSeq ChIP
   [x] Biological replicates: n=2
   [x] Sequencing reads: 42.3M PE
   [ ] Cells/nuclei per replicate: NOT FOUND -- check experiment metadata
   [x] Read length: 76 bp
   ...
   

3. Citation list of all tools referenced in the methods, formatted for the target reference manager (BibTeX, RIS, or inline text).

4. Supplementary table structure with column headers and example rows, ready to populate.

5. Data availability statement draft customized to the specific ENCODE accessions used.

6. Missing information flags: If any required documentation fields cannot be populated from available metadata, flag them explicitly with suggestions for how to obtain them (e.g., "Cells per replicate not available in ENCODE metadata -- check the associated publication or contact the submitting lab").

For the request: "$ARGUMENTS"