An overview of the assays performed and distributed by the ENCODE DCC as well as a summary of the uniform processing pipelines that are being implemented by the ENCODE Consortium. Here, we talk about the impact using GPUs has on speed of running the ChIP-seq pipeline.

1. The ENCODE DCC
https://www.encodeproject.org/
Eurie L. Hong, Ph.D. • Project Manager, ENCODE DCC
PI: J. Michael Cherry, Ph.D.
Department of Genetics • Stanford University School of Medicine

2. What is the ENCODE Consortium?
Image credit: NHGRI

3. Role of the Data Coordination Center
Production labs
Analysis groups
ENCODE portal
(DCC)
Data files
Metadata DDCCCC Integrative
Role: Data generation Data organization Data access
Genome Browser
Tasks: Perform assays Define submission process Web-based searches
Perform analyses Data processing & validation Data downloads
Validate data Data file storage
Submit data files Metadata curation
Submit metadata
websites
Scientific
community

4. DCC goals for implementation
Transparency of methods
• How was the experiment performed?
• What software was used to analyze the data?
Reproducibility of results
• What files were used?
• What software and parameters were used for the pipelines?
Interoperability with other genomic projects
• Can the pipeline software we use be used by other projects?
• Can the metadata allow easy integration with other data?

5. Data volume: diversity of assays
Modified from PLoS Biol 9-e1001046,2011
(M. Pazin)
Approximately ~30 different assays

6. Data volume: number of assays
(includes mouse & human, from https://www.encodeproject.org/, 10/12/2014)

7. Transparency & reproducibility:
Capture the experimental design
Biological
replicate 1
Technical
replicate 1
Biological
replicate 2
Raw data
file (fastq)
Processed
file (bam)
Experiment
Software &
pipelines
Technical
replicate 1
Raw data
file (fastq)
Processed
file (bam)
Software &
pipelines
Biological
replicate 1
Technical
replicate 1
Raw data
file (fastq)
Processed
file (bam)
Control
experiment
Software &
pipelines
Processed file
(peak calls)
Software &
pipelines

8. Data interoperability:
uniform processing pipelines
(includes mouse & human, from https://www.encodeproject.org/, 10/12/2014)

9. Processing of TF ChIP-seq assays
FASTQ (SE/PE)
Replicates
Controls
Map Reads
Filter
Pool
Subsample
Pseudoreplicates Call Peaks
IDR
BAM
Replicates
Pooled Reps
Controls
Signal Tracks
BAM
2 Pseudoreplicates
per replicate
2 Pseudoreplicates
per pool
peak
Replicates
Pseudoreplicates
Pools
peak
IDR-thresholded
Peak Calls
bigWig
Replicates
Pooled Replicates
Specification document (Anshul Kundaje):
https://docs.google.com/document/d/1lG_Rd7fnYgRpSIqrIfuVlAz2dW1VaSQThzk836Db99c/edit?usp=sharing

10. Relative CPU time for ChIP-seq (original)
Map
Signal Tracks
Subsample
Call Peaks
IDR
IDR
Peak
Calling
Relative CPU time per step for a typical transcription factor ChIP-seq experiment
IDR can take much longer if there are many regions, as in a typical histone ChIP
Nikhil Podduturi

11. Data volume: TF ChIp-seq
(includes mouse & human)

12. Performance Comparison:
IDR analysis CPU (re-engineered) vs GPU
1 10 100 1000 10000
NVIDIA GPU
CPU
Clock Time (Seconds) Log10 scale
~120x Speed Increase
60 min
30 sec
Nikhil Podduturi

13. Impact on use for data processing
Re-engineered
• improved stability
• tests!
• ability to run on CPU or GPU
Faster processing
• recalculation of entire data corpus against new genome build
• allow determination of data-based thresholds and cut-offs
Public availability
• Can be run on GPU instances available at AWS
• GPU implementation of IDR: https://github.com/ENCODE-DCC/idr-GPU
• TF ChIP-seq: https://github.com/ENCODE-DCC/tf_chipseq
• Others available: https://github.com/ENCODE-DCC

14. Next Steps
Data validation
• GPU vs CPU results
Pipeline release
• Integration into ChIP-seq pipeline
• Deployment via AWS instances and at DNAnexus
Adapt additional software components
• SPP: https://github.com/nikhilRP/spp-GPU
• Hotspots: https://github.com/nikhilRP/hotspot-GPU

15. 15
ENCODE DCC
Eurie Hong, Mike Cherry (PI), Jim Kent (co-PI), Ben Hitz
Esther Chan, Jean Davidson, Venkat Malladi, Cricket Sloan, Seth Strattan
Nikhil Podduturi, Laurence Rowe, Forrest Tanaka
Brian Lee, Stuart Miyasato, Matt Simison, Zhenhua Wang, Marcus Ho
Data Wranglers
Software
engineers
QA, sysadmins,
admin, biocurator
assistant
@encodedcc encode-help@lists.stanford.edu
https://github.com/ENCODE-DCC/
The ENCODE DCC is funded by NHGRI Grant U41HG006992

16. ENCODE Uniform Processing Pipeline Work
Ben Hitz, Seth Strattan, Nikhil Podduturi
ChIP-seq against transcription factors: Anshul Kundaje
ChIP-seq against histone marks: Anshul Kundaje
RNA-seq: ENCODE RNA working group
Whole genome bisulfite sequencing: Junko Tsuji, ZhipingWeng
DNAse-seq: Alvin Qin, Shirley Liu
DNAnexus (PaaS): Brett Hannigan, Andrey Kislyuk, Mike Lin, Singer Ma, Ohad Rodeh
NVIDIA Corporation: NVIDIA Academic Hardware donation program
donation of two Kepler K40 GPU; NVIDIA’s NVBIO framework