DEEP: Data Standards and Policies
Introduction
The German Epigenome Program DEEP is a consortium targeted at interdisciplinary
epigenome research in Germany associated with IHEC initiative. It comprises 31
research centers, currently. The aim of the project is to generate and analyze more
than 70 human epigenomes of 13 tissue types in the context of metabolic,
inflammatory and neurodegenerative diseases. ChIP-seq, DNase-seq, RNA-seq and
bisulfite sequencing data will be processed along with genomic sequencing. Among
those experiments, due to high sequencing depth (more than 30X), processing of
bisulfite sequencing data will be particularly demanding with respect to resources.
The substantial volume and diversity of generated data requires adoption of
standardized data formats and experiment descriptions. Consistent quality standards
are crucial for downstream data integration.
This document describes data standards and policies used by the DEEP consortium to
exchange data between producers and the Data Coordination Center (DCC) and make
the data available to the whole consortium. We will propose both detailed data
exchange policies and standard data formats for data distribution.
Data flow and distribution
The six data providers in the DEEP project will submit the raw sequence data to the
DCC at the DKFZ. Metadata describing sequencing experiments as well as quality
values will be transferred to the DCC together with sequencing results. The DCC at
the DKFZ will provide access to this data for the whole consortium. The sequencing
data will be transferred (on request/automatically) to the Data Analysis Center (DAC)
in Saarbrücken via an Aspera interface. Metadata describing biological samples will
be transferred from the sample providers to the DCC by data producer in parallel with
or previous to sequencing results.
Primary Analysis results will be accessible from the DCC, if feasible. Initially, this
will be restricted to the Consortium members. Public access will be provided
according to the IHEC policies (http://ihec-epigenomes.net/about/policies-andguidelines/).
We plan to follow BLUEPRINT recommendation and make our high level analysis
results (the ChIP-seq, DNAse-seq, methylation signals and RNA-seq expression
analysis) visible via a common web portal. The Biomart approach already being
realized for ICGC was given a first attempt by the Spanish colleagues of
BLUEPRINT (groups of Ivo Gut from Barcelona Supercomputing Center (BSC) and
Alfonso Valencia from Spanish National Cancer research Center (CNIO)). Biomart
failed as it did not scale up to epigenetics approaches. Consequently, the
BLUEPRINT colleagues are currently developing a new database using MongoDB (a
NoSQL technology). There was agreement that DEEP will wait for the availability of
the MongoDB realization provided by BLUEPRINT’s Spanish colleagues.
Afterwards, we will continue the discussion with the BLUEPRINT team to harmonize
the web presentation of high level analysis results between DEEP and BLUEPRINT.
For access to the raw data from outside the DEEP consortium, the sequencing data
will be additionally transferred (automated, on request?) to the European Genomephenome Archive (EGA). The EGA will provide access to the wider public under the
review of DEEP Data Access Compliance Office (DACO) (which still has to be
established). Data access rules will follow the general guidelines from ICGC and
IHEC.
Sample
laboratory
Sample meta-data
Sequencing
groups
Reads
Sequencing meta data
Consortium
members
Data Coordination Center
(DCC)
Data Analysis Center
(DAC)
Reads
EGA meta data
Approved
researchers
DEEP
DACO
Data Access
Compliance
Office
EGA
Sample meta-data
Reads
Sequencing meta-data
Biomart
RNA-seq
analysis
(Kiel)
Figure 1 The Data Coordination Center has a central role in the project, providing a platform for
storage and reception of raw data
Data Formats
Large-scale projects like DEEP need to specify their data formats and data standards
to ensure interoperability and maximum usability of the data. DEEP is supporting
community driven data standards for all its data types.
Each kind of data transfer (each vertical arrow in figure 1) requires a clearly defined
data format specification.

Metadata for sequencing raw data (transfer from sequencing groups to DCC):
o Format will be used according to ICGC specifications.

Metadata for sequencing experimental data (transfer from sequencing groups
to DCC):
o Format will be used according to IHEC recommendations (NIH
Roadmap).

Metadata for primary cell lines (transfer from sample provider to DCC):
o Have to be determined by sample provider.
o Format will be used according to NIH roadmap for IHEC specification.
o In agreement with BLUEPRINT we propose to use EBI or NIH
Biosample database as a guideline.
 http://www.ncbi.nlm.nih.gov/biosample
 http://www.ebi.ac.uk/biosamples/index.html

Metadata for patient information (transfer from sample provider to DCC):
o Have to be defined by sample provider.
o Sample naming will be defined together by the DAC and the DCC.

Metadata for raw data to EGA (for submission from DCC to EGA at EBI):
o According to EGA requirements. It’s similar to ICGC operation.
o A Data Access Compliance Office (DACO) is required

Data exchange from DCC to DAC
o DAC obtains access to all the data gathered by DCC
Data (high-level analysis) exchange from DAC to DCC
o Based on BLUEPRINT precedent.
High level analysis results (for submission from DCC to the BLUEPRINT
MongoDB):
o Based on BLUEPRINT precedent.
o Naming conventions for high-level analysis files will be defined
together by DCC and DAC.


Primary data formats
The primary data formats produced by DEEP are described in Table 1
Experiment Type
Data Type
All
Alignment
RNASeq
Expression Levels
BiSulphite, DNase and Regions
ChipSeq
BiSulphite, DNase and Signal
ChipSeq
Data Format
Sam/Bam
GTF
BED
WIG
Table 1: File Formats used to store different data types produced by DEEP
Quality Metrics
The DCC will record some standard metrics to assist with quality control of both the
incoming data and primary analysis results.
All DEEP data will be submitted to three main types of quality control. First, all
sequence data will be checked for quality and contamination. Second, all alignments
and corresponding quality statistics will be calculated and distributed by DCC
together with certain quality statistics and distributed for QC purposes. Finally, there
will be data type specific metrics for RNA-Seq, Chip-Seq, DNase-Seq and BS-Seq
sequencing which are also described here.
It is expected that each sequencing center will perform internal quality control prior to
submission to the DCC. Once sequence reads are stored by the DCC group, they are
assessed for quality with specialized software.
Sequence Quality and Contamination
FastQC assesses multiple aspects of sequence quality and library diversity. These
include the per-base quality across reads and the degree of sequence duplication and
overrepresented sequence.
(http://www.bioinformatics.babraham.ac.uk/projects/fastq_screen)
FastQ Screen is used to check the data for contamination and ensure that the reads
map to the expected genome. Reads are mapped against the following:
 Human genome & transcriptome
 E. coli and yeast genomes
 UniVec (http://www.ncbi.nlm.nih.gov/VecScreen/UniVec.html)
 Common contaminants & PhiX
(http://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
The output from both programs is available via DCC web portal, allowing transparent
comparison of sequencing quality by all interested consortium members.
Alignment Quality Metrics
All our alignment metrics are collected using programs from the Picard toolkit
(http://picard.sourceforge.net/) for BAM files. The alignment quality metrics are
collected using AlignmentSummaryMetrics and DuplicationSummaryMetrics. These
provide statistics like the number of reads mapped, mismatch rates, read aligned in
pairs and duplication rates. (Table 2a and 2b) These statistics will be presented in a
tab-delimited format DCC web page alongside any defined data freezes.
RNA-Seq Quality Metrics
Picard also has an RNA-Seq statistics utility called RnaSeqMetrics which also
collects information such as Number of Ribosomal Bases, Number of coding bases
and other coverage statistics for the RNA-Seq data set against a specific
transcriptome (Table 3).
ChIP-Seq and DNase-Seq Quality Metrics
For these data sets measures including: percentage of reads mapped to enriched
regions, mean and median region length and region length variance will be calculated.
The ENCODE Project also defined a number of ChIP-Seq metrics that we will also
measure like the Irreproducible Discovery Rate (IDR), Normalised Strand Crosscorrelation coefficient (NSC) and Uniquely Mappable Reads. These statistics will be
distributed in tab-delimited files alongside the region and signal files.
(http://genome.ucsc.edu/ENCODE/qualityMetrics.html#chipSeq) (Table 4)
BS-Seq Quality Metrics
The primary metric measured for BS-Seq is the conversion efficient. This is estimated
using reads that uniquely map to the phage lambda reference; these should be derived
from the spiked in unmethylated lambda DNA. Conversion efficiency λ is estimated
from the formula: λ=1-(nC1+nG2)/(nG1+nC2), where nXy is the number of observed X
bases in read y. This metric will be distributed along the BS-Seq data.
A
Metric
#Reads
#Noise Reads
#Reads Aligned
#Aligned_Bases
#High-Quality
Aligned Reads
#High-Quality
Aligned Bases
#High-Quality
Aligned Q20 Bases
Median Mismatches
Mismatch Rate
In-del Rate
Mean Read Length
Reads Aligned in
Pairs
%Reads Aligned in
Pairs
#Bad Cycles
Strand Balance
%Chimeras
%Adapter
Description
Total Number of Reads
Number of Noise Reads
Number of Reads Aligned
Number of Aligned Bases
Number of Aligned Reads with Mapping Quality of 20 or higher
Number of Aligned Bases with Mapping Quality of 20 or higher
Number of High Quality Aligned bases with Base Quality of 20
of higher
Median Number of Mismatch High Quality Reads
Rate of Mismatches in all reads
Number of Indels per 100bp
Average Read Length
Number of Read Pairs where both reads aligned to the
reference
Percentage of Read Pairs where both reads aligned
Number of Cycles where 80% of the bases were no calls
Ratio of Number of Reads Aligned to the Positive Strand to the
Number of Reads Aligned to the Whole Genome
Number of Read Pairs where the reads map outside the
maximum insert size or on different chromosomes
Number of Reads which failed to map to the genome but do
map to a known adaptor sequence
B
Metric
#Unpaired_Reads
#Read Pairs
#Unmapped Reads
#Unpaired Read Duplicates
#Read Pair Duplicates
#Read Pair Optical Duplicates
%Duplication
Estimated Library Size
Description
Total Number of unpaired Reads
Total Number of Read Pairs
Total Number of Unmapped Reads
Number of Unpaired Reads which are
Duplicated
Number of Read Pairs which are Duplicated
Number of Read Pairs which are Optical
Duplicates
Percentage of Duplication
Estimated Number of Unique Molecules in the
Library
Table 2 Alignment Quality Metrics Collected by Picard AlignmentSummary and
DuplicationSummary Metrics
Metric
#Bases
#Aligned Bases
#Ribosomal Bases
#Coding Bases
#UTR Bases
#Intronic Bases
#Intergenic Bases
%Ribosomal Bases
%Coding Bases
%UTR Bases
%Intronic Bases
%Intergenic Bases
%mRna Bases (#UTR Bases + #Coding bases / #Aligned Bases)
Description
Number of Bases
Number of Aligned Bases
Number of Ribosomal Bases
Number of Coding Bases
Number of UTR Bases
Number of Intronic Bases
Number of Intergenic Bases
Percentage of Ribosomal Bases
Percentage of Coding Bases
Percentage of UTR Bases
Percentage of Intronic Bases
Percentage of Intergenic Bases
Percentage of mRNA Bases
%Usable Bases (#UTR Bases + #Coding bases / #Bases)
Percentage of Useable Bases
Median CV Coverage (Median Coefficient of Variation of Coefficient of Variation
coverage of the 1000 most highly expressed transcripts)
Coverage
Median 5' Bias
Median 5' bias
Median 3' Bias
Median 3' Bias
Median 5' to 3' Bias
Median 5' to 3' Bias
Table 3 RNA-Seq Quality Metrics Collected by Picard RNASeqMetrics
Metric
Region Enrichment
Median Region Length
Mean Region Length
Region Length Variance
Region Length Standard Deviation
Description
Percentage of reads maapping to enriched regions
Median Region Length
Mean Region Length
Table 4 ChIP-Seq and DNase-Seq Quality Metrics
of
DACO (Data Access Compliance Office): Public dissemination
For users to access the raw data files the correct authentication and authorization is
required. The EGA does not grant access to the data; data access must be applied for
from the IHEC Data Access Compliance Office (DEEP-DACO). The EGA provides a
personal account with access permissions for each successful applicant. Applicants
must also sign the Data Access Agreement with the DEEP-DACO that provides
instruction on how data can be stored, used and transferred once it has been download
from our system.
Conclusions
This report describes coherent set of standards and policies for the DEEP consortium
to follow. As the project progresses and data types evolve, the DCC will seek to find
appropriate new measures to ensure the data DEEP produces continues to represent a
high quality epigenomic resource.
Scheduling issues

Metadata for sequencing raw data (transfer from sequencing groups to DCC):
o First test submission of raw data from sequencing centers to DCC will
be initiated
Time: after the first runs have been performed (expected in summer
2013)

Metadata for sequencing experimental data (transfer from sequencing groups
to DCC):
o The extract of the current BLUEPRINT metadata is available and will
be distributed by the DAC to the sequencing centers and sample labs.
Time: will be discussed at the Saarbrücken conference (28.6.)

Metadata for patient information (transfer from sample provider to DCC):
o Has been initiated by the DAC.
o Time: waiting for answer from sample provider

Metadata for raw data to EGA (for submission from DCC to EGA at EBI):
o According to EGA requirements. It’s similar to ICGC operation.
o A Data Access Compliance Office (DACO) is required. Initiation will
be done by DEEP coordinator.
o Time: waiting for answer of coordinator