study , contains a total of 32 ngs samples , divided in three series : gse71175 , containing 6 chip - seq samples ( 5 chip against p53 and one input ) ; gse71176 , containing 24 rna - seq samples ( 4 conditions with 2 replicates each for the p53 ko cells , 4 conditions with 4 replicates each for the c57/bl6 cells ) ; gse71177 , containing a dnase - seq sample and the corresponding input .
these samples allowed studying the genomic occupancy and the transcriptional changes induced by p53 activation in b and non - b cells in vivo , following dna damage produced by ionizing radiation .
we complement the methods of the original publication and the instructions deposited in the geo archive with the source code used to produce the figures from the next - generation sequencing ( ngs ) data files . under the accession number gse71180 , we provided the raw data files ( sequencing reads , in fastq format ) , plus a series of processed data files : for the chip - seq and dnase - seq samples ( excluding the inputs ) , we supplied the locations of the bound genomic regions in bed format , as obtained with the macs   peak caller ( v. 2.0.9 ) , while for the rna - seq samples , we provided the quantification of the expression of each gene , i.e. the number of reads assigned to every gene , normalized to the gene length and to the total number of reads aligned on any exon of any gene .
we called this quantification exonic rpkm , or erpkm , to distinguish it for the conventional normalization of read counts to the total number of aligned reads ( anywhere on the genome ) .
most information needed to produce the figures is already available in the processed data , with the exception of four fields for the chip - seq peaks : 1 ) annotation , 2 ) enrichment , 3 ) summit and 4 ) motif annotation . here
, we provide the complete resources needed to reproduce the figures of the main paper , and the instructions to generate the missing information .
finally , the genomic regions associated with previously published histone modifications ,   are also attached for convenience .
data analysis was entirely performed in r , the widely - used open - source environment for statistical computing and data analysis .
the main package used for the analysis is compepitools v1.2.6 , which is part of the bioconductor project   and it can be installed from the url http://www.bioconductor.org/packages/release/bioc/html/compepitools.html .
compepitools is a flexible and user - friendly package to perform basic analyses of ngs data .
the source code tonellietal2015_sourcecode.zip is composed of 5 files and 2 directories , described below:filemapping_geo.r this file contains the links between the r objects used to produce the figures and the files deposited on the geo archive , listed in table 1 .
in particular , chip - seq bed files are converted to granges and gene expression quantifications are organized in a data frame .
this code also arranges in a list chip - seq alignment ( bam ) files , which must be obtained from the raw sequencing files ( fastq ) following the instructions deposited on the geo archive.analysisenvironment.r analysisenvironment.r this r script loads all the data files needed to produce the figures of the main paper , and contains all the libraries and functions invoked in the r scripts contained in the file tonellietal2015_figures.r . in particular , the environment requires the following libraries : compepitools , gplots , venndiagram , lattice , flashclust , txdb.mmusculus.ucsc.mm9.knowngene , and org.mm.eg.db ( see sessioninfo.log).tonellietal2015_figures.r tonellietal2015_figures.r this r script sources the instructions contained in the file analysisenvironment.r to load the pre - generated data objects associated with the main paper , and contains the code used to produce all the figures referring to the computational analyses of ngs data . occasionally , some figures require computing tag density on genomic intervals , and therefore require alignment ( bam ) files : in these cases , a pre - computed table was used.preparedatasets.r this collection of r scripts allows complementing the processed chip - seq files available on geo with the extra fields required for the generation of the final figures .
the output of these scripts is contained in the chippeaks.rds datafile in the data directory , under the form of a list of genomic ranges , which is automatically loaded in the analysisenvironment.r script . the scripts contained in preparedatasets.r use several external tools ( meme , tomtom   fimo )
, the mm9 reference genome , available in bioconductor in the library bsgenome.mmusculus.ucsc.mm9 ( v. 1.4.0 ) , and may require a consistent amount of time ( 624  h ) to complete , depending on the platform used . in order to execute the scripts
, the processed chip - seq files should first be downloaded from geo and organized according to the instructions contained in the filemapping.r file .
subsequently , alignment ( bam ) files must be generated from the raw fastq files deposited in geo , following the instructions on the archive .
the extra fields consist in:1)the genomic annotation of the p53 chip - seq peaks : a peak overlapping with a [   5  kb , +  2  kb ] window around a standard promoter is considered  promoter  , those overlapping with an h3k4me1 peak ,  enhancers 
, otherwise they are classified as  distal;2)the enrichment of the peak : computed with the grenrichment function in the compepitools suite;3)the summit of the peak : computed with the grcoveragesummit function in the compepitools suite;4)the motif annotation of the peak , which is obtained through five main steps : i ) the generation of a fasta file containing the sequences of the top 1000 enriched genomic regions spanned by the peaks of the p53.wt.bcells.ir sample ; ii ) the estimation of the unspaced p53 motif from these sequences using meme   ( we verify that the estimated motif coincides with the p53 canonical motif contained in the jaspar core vertebrata database   using tomtom ) ; iii ) the creation of the motifs with spacers , obtained by inserting sequences with constant probability over the 4 nucleotides ( spacers ) between the two half decameric sites ; iv ) the scoring of these motifs against the mouse genome with fimo ; v ) the assignment of the motifs to the chip - seq peaks.sessioninfo.log the genomic annotation of the p53 chip - seq peaks : a peak overlapping with a [   5  kb , +  2  kb ] window around a standard promoter is considered  promoter  , those overlapping with an h3k4me1 peak ,  enhancers  , otherwise they are classified as  distal  ; the enrichment of the peak : computed with the grenrichment function in the compepitools suite ; the summit of the peak : computed with the grcoveragesummit function in the compepitools suite ; the motif annotation of the peak , which is obtained through five main steps : i ) the generation of a fasta file containing the sequences of the top 1000 enriched genomic regions spanned by the peaks of the p53.wt.bcells.ir sample ; ii ) the estimation of the unspaced p53 motif from these sequences using meme   ( we verify that the estimated motif coincides with the p53 canonical motif contained in the jaspar core vertebrata database   using tomtom ) ; iii ) the creation of the motifs with spacers , obtained by inserting sequences with constant probability over the 4 nucleotides ( spacers ) between the two half decameric sites ; iv ) the scoring of these motifs against the mouse genome with fimo ; v ) the assignment of the motifs to the chip - seq peaks . a log file containing the output of the r sessioninfo ( ) command , specifying all the versions of all the libraries used in the analysis environment.data folder this folder contains all the r objects needed to produce the figures of the main paper.figures folder this folder contains all the figures of the main paper , in pdf format , obtained by running the scripts contained in tonellietal2015_figures.r .
data analysis was entirely performed in r , the widely - used open - source environment for statistical computing and data analysis .
the main package used for the analysis is compepitools v1.2.6 , which is part of the bioconductor project   and it can be installed from the url http://www.bioconductor.org/packages/release/bioc/html/compepitools.html .
compepitools is a flexible and user - friendly package to perform basic analyses of ngs data .
the source code tonellietal2015_sourcecode.zip is composed of 5 files and 2 directories , described below:filemapping_geo.r this file contains the links between the r objects used to produce the figures and the files deposited on the geo archive , listed in table 1 .
in particular , chip - seq bed files are converted to granges and gene expression quantifications are organized in a data frame .
this code also arranges in a list chip - seq alignment ( bam ) files , which must be obtained from the raw sequencing files ( fastq ) following the instructions deposited on the geo archive.analysisenvironment.r analysisenvironment.r this r script loads all the data files needed to produce the figures of the main paper , and contains all the libraries and functions invoked in the r scripts contained in the file tonellietal2015_figures.r . in particular , the environment requires the following libraries : compepitools , gplots , venndiagram , lattice , flashclust , txdb.mmusculus.ucsc.mm9.knowngene , and org.mm.eg.db ( see sessioninfo.log).tonellietal2015_figures.r tonellietal2015_figures.r this r script sources the instructions contained in the file analysisenvironment.r to load the pre - generated data objects associated with the main paper , and contains the code used to produce all the figures referring to the computational analyses of ngs data . occasionally , some figures require computing tag density on genomic intervals , and therefore require alignment ( bam ) files : in these cases , a pre - computed table was used.preparedatasets.r this collection of r scripts allows complementing the processed chip - seq files available on geo with the extra fields required for the generation of the final figures .
the output of these scripts is contained in the chippeaks.rds datafile in the data directory , under the form of a list of genomic ranges , which is automatically loaded in the analysisenvironment.r script .
the scripts contained in preparedatasets.r use several external tools ( meme , tomtom   fimo ) , the mm9 reference genome , available in bioconductor in the library bsgenome.mmusculus.ucsc.mm9 ( v. 1.4.0 ) , and may require a consistent amount of time ( 624  h ) to complete , depending on the platform used . in order to execute the scripts , the processed chip - seq files should first be downloaded from geo and organized according to the instructions contained in the filemapping.r file .
subsequently , alignment ( bam ) files must be generated from the raw fastq files deposited in geo , following the instructions on the archive .
the extra fields consist in:1)the genomic annotation of the p53 chip - seq peaks : a peak overlapping with a [   5  kb , +  2  kb ] window around a standard promoter is considered  promoter  , those overlapping with an h3k4me1 peak ,  enhancers 
, otherwise they are classified as  distal;2)the enrichment of the peak : computed with the grenrichment function in the compepitools suite;3)the summit of the peak : computed with the grcoveragesummit function in the compepitools suite;4)the motif annotation of the peak , which is obtained through five main steps : i ) the generation of a fasta file containing the sequences of the top 1000 enriched genomic regions spanned by the peaks of the p53.wt.bcells.ir sample ; ii ) the estimation of the unspaced p53 motif from these sequences using meme   ( we verify that the estimated motif coincides with the p53 canonical motif contained in the jaspar core vertebrata database   using tomtom ) ; iii ) the creation of the motifs with spacers , obtained by inserting sequences with constant probability over the 4 nucleotides ( spacers ) between the two half decameric sites ; iv ) the scoring of these motifs against the mouse genome with fimo ; v ) the assignment of the motifs to the chip - seq peaks.sessioninfo.log the genomic annotation of the p53 chip - seq peaks : a peak overlapping with a [   5  kb , +  2  kb ] window around a standard promoter is considered  promoter  , those overlapping with an h3k4me1 peak ,  enhancers  , otherwise they are classified as  distal  ; the enrichment of the peak : computed with the grenrichment function in the compepitools suite ; the summit of the peak : computed with the grcoveragesummit function in the compepitools suite ; the motif annotation of the peak , which is obtained through five main steps : i ) the generation of a fasta file containing the sequences of the top 1000 enriched genomic regions spanned by the peaks of the p53.wt.bcells.ir sample ; ii ) the estimation of the unspaced p53 motif from these sequences using meme   ( we verify that the estimated motif coincides with the p53 canonical motif contained in the jaspar core vertebrata database   using tomtom ) ; iii ) the creation of the motifs with spacers , obtained by inserting sequences with constant probability over the 4 nucleotides ( spacers ) between the two half decameric sites ; iv ) the scoring of these motifs against the mouse genome with fimo ; v ) the assignment of the motifs to the chip - seq peaks . a log file containing the output of the r sessioninfo ( ) command , specifying all the versions of all the libraries used in the analysis environment.data folder this folder contains all the r objects needed to produce the figures of the main paper.figures folder this folder contains all the figures of the main paper , in pdf format , obtained by running the scripts contained in tonellietal2015_figures.r .
supplementary material contains the source codes and the datafiles needed to reproduce all the figures in the main paper obtained with ngs data , together with a sample of the figures themselves .
supplementary material contains the source codes and the datafiles needed to reproduce all the figures in the main paper obtained with ngs data , together with a sample of the figures themselves .
 the transcriptional programs activated by p53 in b cells in vivo following exposure to ionizing radiation were studied through the integrated analysis of various types of next - generation sequencing data : genome - wide profiling of p53 binding sites , mapping of histone marks and open chromatin regions and quantification of gene expression . moreover 
 , the binding of p53 was associated to a series of specific motifs on the dna , which were directly inferred from the data . here , we describe in detail the computational analysis of the datasets associated with our study ( tonelli et al . , 
 oncotarget 6 ( 2015 ) , 24611 - 26 ) , deposited in the geo archive ( accession code gse71180 ) , and we provide the r scripts needed to generated the figures of the paper . 
Direct link to deposited data
Experimental design, materials and methods
Data analysis
Analysis environment
Description of the source files
Supplementary data