Green Center Computational Core - UT Southwestern...Filename HF_K9_GATCAG_L005_R1_001.fastq.gz File type Conventional base calls Encoding Sanger / Illumina 1.9 Total Sequences 22571166

Green Center Computational Core ChIP-‐Seq Pipeline, Just a Click Away

Venkat Malladi Computational Biologist Computational Core Cecil H. and Ida Green Center for Reproductive Biology Science

Green Center for Reproduc/ve Biology Science


Introduc<on to the Green Center

●  Basic research in female reproductive biology, with a focus on signaling, gene regulation, and genome function. ◦  pregnancy ◦  parturition ◦  stem cells ◦  oncology ◦  inflammation

●  Key areas: ◦  Chromatin structure and gene regulation ◦  Epigenetics ◦  Nuclear endpoints of cellular signaling pathways ◦  Genome organization and evolution ◦  DNA replication and repair


Who is in the Green Center?

W. Lee Kraus, Ph.D., Director of the Green Center.

● Associated with the Department of

Obstetrics and Gynecology

● Consists of 9 main faculty/labs

● 20 associated faculty/labs

● Computational Core

● Consists of 4 Computational Biologists ● Analysis of Genomic Sequencing Data ● Responsibilities

◦  Data Quality assurance

◦  Perform basic analyses

◦  Work with investigator to perform integrative analyses

Green Center Computation Team

Anusha Nagari Tulip Nandu Venkat Malladi Aishwarya Gogate

Role of the Computa<onal Core

Modified from PLoS Biol 9-‐e1001046,2011 (M. Pazin) Green Center for Reproduc/ve Biology Science

ATAC-seq RNA-seq GRO-seq

Challenge: Variety of Assays Supported?

Assay for transposase-accessible chromatin using Sequencing (ATAC-Seq): Genomic method that captures open chromatin sites.

What is ATAC-‐seq?

Buenrostro et al. ( 2013) Nature Methods

RNA Sequencing (RNA-Seq) : RNA-seq measures RNA abundance of mature RNA species in the cell. These experiments contribute to the understanding of how RNA-based mechanisms impact gene regulation.

● Types: ● Total RNA ● polyA mRNA (Long and short) ● shRNA ● small RNA ● microRNA ● polyA depleted RNA

What is RNA-‐Seq?


Global Run On Sequencing (GRO-Seq) : This is a genomic method that maps the position and orientation of all actively transcribing RNA polymerases.

● Transcription from all three RNA Polymerases is captured providing transcriptional profiles including: ● protein coding mRNA ●  long non-coding RNAs (lncRNAs) ● enhancer RNAs (eRNAs) ● divergent transcription ● antisense transcription ●  intergenic transcription in both annotated and unannotated regions of the genome.

What is GRO-‐Seq?

Annotated

Divergent

Intergenic

Antisense

ERα Enhancer Annotated

Other Genic

Green Center for Reproduc/ve Biology Science Hah et al. ( 2011) Cell

Chromatin immunoprecipitation followed by Sequencing (ChIP-Seq): Identify the binding sites of chromatin-associated proteins.

● Categories: •  Transcription factor ChIP-Seq: proteins

that associate with specific DNA sequences to influence the rate of transcription

•  Histone ChIP-Seq: measure histone content of chromatin, specifically to the incorporation of particular post-translational histone modifications in chromatin

What is ChIP-‐Seq?

Green Center for Reproduc/ve Biology Science Park ( 2009) Nature Reviews

Considera<on of making a Pipeline

1.  Who are the users

2.  Define what the pipeline should deliver

3.  Identify all input and output files

4.  What QA/QC metrics should be available for users

5.  Identify all software used in pipeline

6.  Breakdown pipeline into discrete steps (based on deliverable files and metrics)


Users and Goals


● Users:

● Wet lab scientists (Grad Students/Post Docs)

● Computational Biologists in the Green Center

● Goals:

● Allow wet lab scientists to quickly assess the quality and explore

their data

● Allow for easily reproducible analysis within the Green Center

Schema: ChIP-‐seq Pipeline

FASTQ (SE/PE)

Map bowtie2

Quality fastqc

BAM

QA Metrics

Remove Duplicates

picard

QA Metrics

BAM Cross-correlation

tagAlign Fragment

size

Call Peaks macs2

bigWig

narrowPeak

QA Metrics


FASTQ: Quality Metrics 3/13/13 10:44 AMHF_K9_GATCAG_L005_R1_001.fastq.gz FastQC Report

Page 1 of 15file:///Users/anushanagari/Desktop/TMP/HectorGROseq/HF_K9_GATCAG_L005_R1_001_fastqc/fastqc_report.html

FastQC Report Tue 19 Feb 2013HF_K9_GATCAG_L005_R1_001.fastq.gz

Summary

Basic Statistics

Per base sequence quality

Per sequence quality scores

Per base sequence content

Per base GC content

Per sequence GC content

Per base N content

Sequence Length Distribution

Sequence Duplication Levels

Overrepresented sequences

Kmer Content

Basic StatisticsMeasure Value

Filename HF_K9_GATCAG_L005_R1_001.fastq.gz

File type Conventional base calls

Encoding Sanger / Illumina 1.9

Total Sequences 22571166

Filtered Sequences 0

Sequence length 50

%GC 42

3/13/13 10:44 AMHF_K9_GATCAG_L005_R1_001.fastq.gz FastQC Report

Page 1 of 15file:///Users/anushanagari/Desktop/TMP/HectorGROseq/HF_K9_GATCAG_L005_R1_001_fastqc/fastqc_report.html

FastQC Report Tue 19 Feb 2013HF_K9_GATCAG_L005_R1_001.fastq.gz

Summary

Basic Statistics

Per base sequence quality

Per sequence quality scores

Per base sequence content

Per base GC content

Per sequence GC content

Per base N content

Sequence Length Distribution

Sequence Duplication Levels

Overrepresented sequences

Kmer Content

Basic StatisticsMeasure Value

Filename HF_K9_GATCAG_L005_R1_001.fastq.gz

File type Conventional base calls

Encoding Sanger / Illumina 1.9

Total Sequences 22571166

Filtered Sequences 0

Sequence length 50

%GC 42

Per Base Sequence Quality

Good quality calls

Reasonable quality calls

Poor quality calls


Alignment: Quality Metrics

FASTQ File:

DNA sequence

Aligned File:

DNA sequence +

Genomic localization

Alignment % = No. of aligned reads Total no. of raw reads

* 100


Uniquely Mapped Reads: Quality Metrics

● Depth ● Number of uniquely mapping reads

● Library Complexity ● Non-Redundant Fraction (NRF) - Number of distinct uniquely mapping reads

(i.e. after removing duplicates) / Total number of reads.

● PCR Bottlenecking Coefficient 1 (PBC1) ◦ PBC1=M1/M_DISTINCT where

M1: number of genomic locations where exactly one read maps uniquely M_DISTINCT: number of distinct genomic locations to which some read maps uniquely

● PCR Bottlenecking Coefficient 2 (PBC2) ◦ PBC2= M1/M2 where

M1: number of genomic locations where only one read maps uniquely M2: number of genomic locations where two reads map uniquely

Green Center for Reproduc/ve Biology Science ENCODE Standards hPps://www.encodeproject.org/data-‐standards/chip-‐seq/

Uniquely Mapped Reads: Quality Metrics (cont.)

NRF Guidelines PBC1 Guidelines

PBC2 Guidelines

ENCODE Standards hPps://www.encodeproject.org/data-‐standards/chip-‐seq/


Alignment: Quality Metrics Report

Sample Information Raw reads Alignment %Control Replicate 1 28,259,069 96.30%Control Replicate 2 28,892,302 96.00%Sample 2 Replicate 1 23,239,486 96.10%Sample 2 Replicate 2 25,637,094 96.90%Sample 3 Replicate 1 22,713,054 96.60%Sample 3 Replicate 2 20,419,272 95.90%Sample 4 Replicate 1 22,617,154 96.60%Sample 4 Replicate 2 20,068,460 96.00%

Sample Information Raw reads Alignment % Control Replicate 1 28,259,069 96.30% Control Replicate 2 28,892,302 96.00% Sample 2 Replicate 1 23,239,486 96.10% Sample 2 Replicate 2 25,637,094 96.90% Sample 3 Replicate 1 22,713,054 96.60% Sample 3 Replicate 2 20,419,272 95.90% Sample 4 Replicate 1 22,617,154 96.60% Sample 4 Replicate 2 20,068,460 96.00%


Cross-‐correla<on: Quality Metrics Report

Sample Information Raw reads Alignment % Control Replicate 1 28,259,069 96.30% Control Replicate 2 28,892,302 96.00% Sample 2 Replicate 1 23,239,486 96.10% Sample 2 Replicate 2 25,637,094 96.90% Sample 3 Replicate 1 22,713,054 96.60% Sample 3 Replicate 2 20,419,272 95.90% Sample 4 Replicate 1 22,617,154 96.60% Sample 4 Replicate 2 20,068,460 96.00%


Sample 1 Sample 2

R=0.99 R=0.99

R: Pearson correlation coefficient

Call Peaks: Quality Metrics Report


1.  Peak calls for individual replicates

2.  Overlapping peaks between the pooled pseudo replicates

3.  Bigwig files (UCSC Genome Browser, IGV…)

Call Peaks: Quality Metrics Report


Visualizing signal tracks (Bigwig files) in UCSC Genome Browser:

Franco et al (2015)

Working With BioHPC and Astrocyte


Crea<ng a Project


Create New Project to run analysis

Adding Data


Select “Add Data to this Project” ...

ChIP-‐Seq Workflow


ChIP-Input fastq files

ChIP TF or Histone fastq files

Sequence format

Assembly

Run Time of ChIP-‐Seq Pipeline

Thank you !

𐀌𐀧𐀻𐀾𐁎𐁏𐁞𐁟𐁠𐁡𐁢𐁣𐁤𐁥𐁦𐁧𐁨𐁩𐁪𐁫𐁬𐁭𐁮𐁯𐁰𐁱𐁲𐁳𐁴𐁵𐁶𐁷𐁸𐁹𐁺𐁻𐁼𐁽𐁾𐁿𐃻𐃼𐃽𐃾𐃿𐄃𐄄𐄅𐄆𐄴𐄵𐄶𐆋𐆌𐆍𐆎𐆏𐆜𐆝𐆞𐆟𐆠𐆡𐆢𐆣𐆤𐆥𐆦𐆧𐆨𐆩𐆪𐆫𐆬𐆭𐆮𐆯𐆰𐆱𐆲𐆳𐆴𐆵𐆶𐆷𐆸𐆹𐆺𐆻𐆼𐆽𐆾𐆿𐇀𐇁𐇂𐇃𐇄𐇅𐇆𐇇𐇈𐇉𐇊𐇋𐇌𐇍𐇎𐇏𐇾𐇿𐈀𐈁𐈂𐈃𐈄𐈅𐈆𐈇𐈈𐈉𐈊𐈋𐈌𐈍𐈎𐈏𐈐

Questions?

Documents

Green Center Computational Core - UT Southwestern...Filename HF_K9_GATCAG_L005_R1_001.fastq.gz File type Conventional base calls Encoding Sanger / Illumina 1.9 Total Sequences 22571166