The code for Daugherty, et al 2017 - Chromatin accessibility dynamics reveal novel functional enhancers in C. elegans
| Figure | Panel | Comments | Program name |
|---|---|---|---|
| 1 | A/B | General pipeline | ATACPipeline_combined.sh |
| pyadapter_trim.py | |||
| random_split_fastq.pl | |||
| get consensus peaks | reRunningWithoutL1s.sh | ||
| splitMACS2SubPeaks.pl | |||
| smart_merge.py | |||
| C | Differential, as well as 1B | 30JanDiffBind_allButL1_metaPeaks_withGDNA.R | |
| D & E | Gene plots | examples.R | |
| support for the gene plots | gvizSupportFunctions_noLog.R | ||
| F & G | How genes were connected to peaks; genes were then sorted using command line sort, and gene lists copied over to Gorilla | connectingPeaksAndTss_withConcentrations.sh | |
| Many of these combinations were ad hoc, so this is harder to understand | selectTerms_ggPlot2_EEvL3_allConnectedATACPeakTotalChange_GOProcess_splitup_selectGroupedTerms.R | ||
| S1 | A | Plots from Picard in the General pipeline | From above (1 A/B) |
| B | jaccardOfRegions.R | ||
| C/D | Volcano plots | plotDifferences.24Mar2015_volcano.R | |
| E&F | Many of these combinations were ad hoc, so this is harder to understand | selectTerms_ggPlot2_L3vYA_allConnectedATACPeakTotalChange_GOProcess_splitup_selectGroupedTerms | |
| S2 | A/B | description and how each of the remaining scripts was called; mini pipeline | whatWasDone |
| called in the above | trim_galore_SE.sh | ||
| called in the above | mapqFilterAndStrandSpecificSplit.sh | ||
| called in the above | GROseq_alignment_bowtie2.sh | ||
| called in the above | getConsensusPeaks_2BioReps_version4.sh | ||
| called in the above | deDup.sh | ||
| Plotting of the ATAC and GRO-seq data | plottingPromAccessibilityVsGroSignalRefChen.R | ||
| C/D | All the scripts for RNA-seq analysis | runningUcscAlignments.sh | |
| All the scripts for RNA-seq analysis | topHat2_RNAseq_alignmentForModEncodeData_36bp_PE.sh | ||
| All the scripts for RNA-seq analysis | topHat2_RNAseq_alignmentForModEncodeData_76bpReads_SE.sh | ||
| All the scripts for RNA-seq analysis | quantifyTophat2ResultsWithHtSeq_RefSeq.sh | ||
| Plotting of the ATAC and RNA-seq data | See plottingPromAccessibilityVsGroSignalRefChen.R | ||
| 2 | A | Step by step of what was run | ExactlyWhatIdid.txt |
| all of the various scripts | chromHMM_scripts | ||
| Generating the null distribution | getEnrichments_justBash.sh | ||
| Measuring intersection with the null | createShuffledBedDirectory_mappableSubtractBlacklistAndGDNAPeaks_parallel.sh | ||
| actually generating the bar plots | plottingAllTogetherInTheirStates.R | ||
| B | Standard NGS plot code | Example command: ngs.plot.r -G ce10 -R bed -C ConfigVsInput.txt -O L3_HM_SignalVsInput_atL3ATACPeaks_localScale -P 0 -IN 1 -GO max -N 0.33 -VLN 0 -CO blue:red | |
| How the Chen TSSs were used to replace the canonical TSS | replaceFeaturesWherePossible_byName.py | ||
| C/D | Getting the data | generatingChangeMatrixData.sh | |
| plotting | chreatingChromHMMStateChangeHeatMaps.R | ||
| E | Example plot | examples.R | |
| S3 | A-C | See ChromHMM code above | N/A |
| plottingStackedBarPlots.R | |||
| D/E | Getting the data | splitAndCombineHiHMMStates.sh | |
| compare_hiHMMToMyChromHMM.sh | |||
| runningCompares.sh | |||
| plotting | jaccardOfChromatinStatePreds.R | ||
| F | Standard NGS plot code | See above | |
| G/H | See 2C/D | N/A | |
| S4 | A | See 2A | |
| B | Generating Null distribution | generatingAllDistalNegsOnScg3_withoutProms.sh | |
| plottingConservationScoresWithNulls.R | |||
| C | plottingConservationScoresVsHistoneMods_noNulls_medians.R | ||
| D | See examples.R | for general approach | |
| 3/S5 | all plots | enhsCands.R | |
| 4 | A | finding known motifs | findMotifsGenome_sizeGiven_narrowPeak_noL1sMetaPeakBackground_novelMotifsLO9.sh |
| B | Labeling peaks | makeEEL3Labels.sh | |
| Getting the counts of each motif in the peaks | getMotifCounts_EEvL3DeNovoToo.sh | ||
| Final prep of the input data | condenseCountsPerPeaks_withPreDoneNames.py | ||
| building then predicting from the model | predictingAccessibilityChangesWithHughesAndDeNovoMotifs_gbmMetricBalAcc_LO9Peaks_loopingOverInteractionDepth_noTSSDist.R | ||
| C | This isn't the exact code used, but this is a functio used in all predictors, so it's identical to what was then modified to make prettier for plotting | plotting_rel_importance.R | |
| D | Getting the data | getInsertSizesAndCountsInTfPeaksATAC100bpSummits_forAllStages.sh | |
| Processing the data | processingAtacInsertSizesAndEnrichmentInTfPeak100bpSummits.R | ||
| supporting the above | ProcessingAtacInsertSizesAndEnrichmentInTfPeaks_supportFunctions.r | ||
| Plotting | plottingATACSignalAsFunctionOfATACFragmentSize.R | ||
| E | just the plotting | plotting_eor1_insertSize_hist.R | |
| F | wrapper script for DANPOS | runningDanpos.sh | |
| called above | wigToBedGraph.py | ||
| Calculate the differences | nucDiffSignalInTF100bpSummits.sh | ||
| called above | getH3NucleosomeEEL3DiffCoverageInRegions.sh | ||
| plotting | plottingInferredNucEEL3DiffSignalInTF100bpSummits.R | ||
| called above | plottingAtacInsertSizesAndEnrichmentInTfPeaks_supportFunctions.R | ||
| S6 | A | See 4A for example | |
| B | See 4A for example | ||
| C | de novo motifs were called using standard homer settings. See methods for details | ||
| D | plotting accuracy of model | plottingBalancedAccuracy.R | |
| S7 | A | See S6C for example | |
| B | See 2A for example of approach | ||
| C/D | Plotting boxplots for each factor | plottingATACSignalAndFragmentSizeInTFChIPPeaks_v2.R | |
| E | See 4E | ||
| F | Generating the data | bedtoolsFisherTestOneVsManyWrapper.sh | |
| Plotting | plot_fishers_test_ORs_L3inL3.R | ||
| Sup Table 3 | Mostly copy and paste of how I pieced together the annotation files | annotating_consensus_atac_peaks.sh | |
| used in the above to rename some things | combiner.py |