Transcript ChIP-seq Methods & Analysis_pt1

ChIP-seq Methods & Analysis
Gavin Schnitzler
Asst. Prof. Medicine TUSM, Investigator at MCRI, TMC
[email protected]
617-636-0615
What is CBI?
• The Computational Biology Initiative
(CBI) is a forum for Tufts researchers to
collaborate and develop competitive
grants in ‘omics research.
• Beta Site: sites.tufts.edu/cbi
Founding CBI Members:
Lax Iyer, Tufts/TMC,Please
Peter Castaldi,
Larry
RegisterTMC,
at the
webParnell,
site: HNRCA, Gordon
Huggins, TMC, Gavin
Schnitzler, TMC, Joshua Ainsley, Tufts, Lionel Zupan,
http://tinyurl.com/CBISymposium
Tufts
For more information contact: [email protected]
CBI Partners:
TUSM Provost’s office: Tufts Collaborates! and Tufts Innovates! Grant Awards
What does CBI do?
• Bring together experts in computational
biology, genetics & genomics across all Tufts
campuses.
• Help create & maintain computational
biology resources
• Raise awareness and
educate researchers in
genomics
How can we work together?
• Discuss your research projects with us
• Attend Symposia/talks/courses & send
ideas for new ones
• Contribute to our website: Your ideas,
protocols/how-tos
• Attend an open meeting of CBI to discuss
how we could work together and what needs
to be done!
• Easiest way to contact: [email protected]
ChIP-seq COURSE OUTLINE
• Day 1: ChIP techniques, library
production, USCS browser tracks
• Day 2: QC on reads, Mapping binding site
peaks, examining read density maps.
• Day 3: Analyzing peaks in relation to
genomic feature, etc.
• Day 4: Analyzing peaks for transcription
factor binding site consensus sequences.
• Day 5: Variants & advanced approaches.
ChIP-seq big picture
• Combine “Next-Generation” sequencing with
Chromatin Immunoprecipitation to identify
genomewide chromatin binding sites.
• Select (and identify) fragments of DNA that interact
with specific proteins such as:
– Transcription factors
– Modified histones
– RNA Polymerase (survey actively transcribe portions of
the genome)
– DNA polymerase (investigate DNA replication)
– DNA repair enzymes
– Or fragments of DNA that are modified: e.g. CpG
methylation
DAY 1 LECTURE OUTLINE
• ChIP method, validating your antibody.
• Next Generation Sequencing technology
• Preparing a ChIP-seq library
• Choosing options for sequencing
• Looking at published ChIP-seq results
-Exercise: Tracks on the UCSC browser
Getting ready for a ChIP experiment
First, validate your antibody for ChIP
Step 1: Show reactivity on a Western (denatured epitope).
Requirements: Rough quantification, true band should be
>50% of combined signal from other bands (ENCODE
guidelines)
Step 2: Show IP ability. Perform IP, run Western and reprobe with same Ab (IP-Western)
Show that a control antibody, non-immune IgG or to an
irrelevant protein does not IP your protein in IP-Western.
Chromatin Immunoprecipitation
(ChIP) Step 1: Cross-linking
• Treat cells with formaldehyde:
protein-protein and protein-DNA
cross-links stop transcription
factors in their tracks (DAY 0)
Cross-linking can be done on:
•Suspension cells
•Adherent Cells
•Tissues
•Anatomical structures
Adapted from ChIP Workshop by Charlie Nicolet, Heather N. Witt & Peggy Farnham
Formaldehyde Crosslinking
DNA-DNA
Protein-Protein
Other Options
Adapted from ChIP Workshop by Charlie Nicolet, Heather N. Witt & Peggy Farnham
ChIP protocol--Day 1
•Fix cells or tissues immediately (formaldehyde most
frequently used) to ‘freeze’ protein:DNA interactions in
place.
•Stop the cross-linking reaction (glycine, for formaldehyde).
•[if using whole tissues, will want to grind them up with a Tissue
Disruptor, or similar small stick blender, & pellet the resulting
tissue fragments]
•Solubilize cells or tissue fragments in buffer with detergents
(NP40, TX100, Tween and/or SDS). Should also contain
protease inhibitors.
•Sonicate to break up chromatin into manageable sizes (for
some applications you might use micrococcal nuclease
digestion).
Probe sonicators vs. Bath sonicators
+ Every department has one.
-Requires practice to use
-Hard to prevent frothing
-Variable results (day to day or
experimenter to experimenter)
+ Easy to use
+ No frothing
+ Consistent results.
-Rare. Not easy to get access to one.
-Very expensive ($40k-$50k for a
“Bioruptor”)
Adapted from ChIP Workshop by Charlie Nicolet, Heather N. Witt & Peggy Farnham
Check Sonication
31 pulses
21 pulses
11 pulses
6 pulses
Sonication results in many different sized fragments
and should be optimized for your system
10 kbp
3 kbp
500 bp
100 bp
Ideal size range will generally have peak EtBr intensity
between ~300 and ~500 bp.
Can do quick check with un-crosslinked chromatin, but for
accurate size range need to reverse cross-links first.
Adapted from ChIP Workshop by Charlie Nicolet, Heather N. Witt & Peggy Farnham
Antibodies & Resins for ChIP
One Step
• 1º Ab to protein of interest
& control Ab
• Recover complexes on
protein A or protein G
beads (overnight)
(A+G OK for antibodies from mouse,
A for antibodies from rabbit, check
specificity for your Ab before
beginning).
• Wash to remove unbound
protein
Two steps
(if your Ab binds weakly to protein
A/G)
• 1º Ab & control (first
incubation)
• 2º Ab (e.g. rabbit antigoat)
• Recover complexes on
protein A beads
• Wash to remove unbound
protein
ChIP reactions
Positive Antibody: e.g. mouse antiPolII primary with rabbit antimouse IgG secondary
Negative Control: Nonspecific
Rabbit IgG with rabbit antimouse IgG secondary
2
°
1°
2
°
1°
ctrl
•
Adapted from ChIP Workshop by Charlie Nicolet, Heather N. Witt & Peggy Farnham
Wash with buffer containing nonionic detergent (DAY 2)
Washes
•Warning! Washes of Protein A or Protein G agarose beads by centrifugation results
in great loss.
•Protein A or G attached to magnetic beads much better.
Adapted from ChIP Workshop by Charlie Nicolet, Heather N. Witt & Peggy Farnham
Elution & Crosslink Reversal
1) Addition of NaHCO3 causes antibodies to release
from their target proteins, and incubation at 65º for 6
hours or more reverses crosslinks. DNA fragments
are now free in solution .
2) [Can treat with proteinase K (to remove any remaining
protein & RNase A to remove any residual RNA - not
always necessary]
3) Column-based PCR Purification kit to purify DNA.
[Read product literature & be mindful of kit limitations, e.g.
standard Qiagen kit is poor at recovering short (<100 bp)
or long (>2kb) fragments. This is actually good for some
applications, like ChIP-seq, since it removes small
fragments that may contribute the majority of ends to your
libraries, but could cause problems for others]
A standard ChIP protocol from cultured cells:
Chromatin immunoprecipitation (ChIP).
Carey MF, Peterson CL, Smale ST.
Cold Spring Harb Protoc. 2009 Sep;2009(9):pdb.prot5279. doi:
10.1101/pdb.prot5279. PMID:20147264
Target Primers
1)
Ratio of: (PolII
ChIP)/(input) with Target
Gene Primers, should be
much greater than (PolII
ChIP)/(input) with Control
Region Primers (shows
enrichment at target site).
2)
Ratio of (PolII ChIP)/(IgG
ChIP) for Target Gene
Primers should be much
greater than (PolII
ChIP)/(IgG ChIP) for
control region primers
(controls for non-specific
pull down).
MW
1 ul 0.2% Input
1 ul 0.02% Input
1 ul 0.004% Input
1 ul IgG ChIP
1 ul polII ChIP
1 ul 0.2% Input
1 ul 0.02% Input
1 ul 0.004% Input
1 ul IgG ChIP
1 ul polII ChIP
MW
Old school PCR to Verify ChIP
Control Region Primers
10% Input diluted into 50 ml =
0.2% Input/ml
•
Adapted from ChIP Workshop by Charlie Nicolet, Heather N. Witt & Peggy Farnham
Quantitative PCR to Verify ChIP
1) Convert Ct values into ‘approx. relative template
concentration’ values by taking 1.9^-Ct.
2) [IP@target]/[input@target] /
[IP@control]/[input@/control], gives fold-enrichment at
target locus vs. control *
3) [IP@target]/[Ctrl_Ab@target] /
[IP@control]/[Ctrl_Ab@conrol], gives indication of
antibody specificity and effectiveness of washes.
4) Both should be high before proceeding w/ ChIP-seq.
* If you don’t already know positive control & negative control target loci, for your Ab & your
cells then:
a) Comb through the literature for prior studies using antibodies to your protein of interest &
similar systems. Design a few primer sets, because transcription factor binding sites can
differ greatly between cell types, but a few are likley to be the same.
b) Alternatively, use mRNA expression data, etc. to identify candidate loci at which the FOI
might bind & try a bunch, along with predicted negative controls (e.g. 20kb down from the
end of the candidate gene). A high # in comparisons 2 & 3 above will confirm your ChIP
effectiveness & identify + & - control regions to use in later ChIPs. For this sort of search a
larger sonication size (~700 bp)
Cluster Account Test Break
• Find Putty.exe on the
desktop & launch
• Set up connection to
cluster.uit.tufts.edu
• Login w/ tufts UserID &
password.
Accessing the cluster from your
own computer
For Windows machines, get Putty at: www.putty.org
For MACs, open the “terminal” utility & type:
ssh [email protected]
DAY 1 LECTURE OUTLINE
• ChIP method, validating your antibody.
• Next Generation Sequencing
technology
• Preparing a ChIP-seq library
• Choosing options for sequencing
• Looking at published ChIP-seq results
-Exercise: Tracks on the UCSC browser
ChIP-chip (ChIP2)
“the pre-sequencing technology”
-Limited to organisms with available genomic
microarrays (or you’ll need to make your own)
-Microarrays with oligos covering whole
mammalian genomes are very expensive (many
arrays per sample)
ChIP
Input
WGA
-Can be economical for model organisms with
small genomes & commercially available arrays
(or for limited analysis: e.g. promoter regions).
Whole genome amplification (WGA) allows good
probe signal from small starting samples.
-Subject to hybridization curve limitations & hyb.
artifacts
Label w/ Cy Dyes
Apply to microarray
Compare Red/Green signal
intensities to identify binding sites
Adapted from ChIP Workshop by Charlie Nicolet,
Heather N. Witt & Peggy Farnham
ChIP-seq
Immunoprecipitate
POI= protein
of interest
High-throughput
sequencing of DNA ends
Map
sequence tags
to genome
& identify
peaks
Prepare
sequencing
library
Release DNA
Adapted from slide set by: Stuart M. Brown, Ph.D.,
Center for Health Informatics & Bioinformatics, NYU School of Medicine
ChIP-Seq advantages
• Doesn’t require a specially-constructed microarray
• Works with any sequenced genome (better if it’s also well
annotated)
• Can be economical:
• At ~160 Million reads, one lane can give you all the binding
sites in the genome
•Multiplexing can allow multiple samples per lane
Limitations:
• Like arrays, can’t make sense of repeat regions.
• Always genomewide:
…which is great for transcription factor binding sites & some
histone modifications (where only a few places in the genome
have many reads, over a low reads/kb background)
… can be problematic for very common events like
nucleosome positions & CpG methylation (where most places
in genome have roughly equal reads/kb, thus 160M reads still
gives read # at any one locus that is too low to quantitate).
What is next generation sequencing?
Next generation = the stuff that came after standard
one template per reaction Sanger sequencing
Next generation sequencing(NGS) =
High throughput sequencing(HTS) =
Deep sequencing
It’s all just massively parallel sequencing.
HTS Commonalities (so far)
 Fragmentation of starting DNA
 Ligation with custom adapters
 Library amplification on a solid surface (either
bead or glass)
 Direct detection of each incorporated nucleotide
 Hundreds of thousands to billions of reactions
 Shorter read lengths than capillary sequencers
 Count based data for quantitation
 Sampling both ends of every fragment sequenced
(paired end reads)
Sequencing Platforms
Company
Platform
Amplification
Roche
emPCR
Illumina
454
Illumina/
Solexa
Bridge PCR
Life
SOLiD
emPCR
Sequencing
Synthesis,
Pyrosequencing
Synthesis,
Fluorescence
Ligation,
Fluorescence
emPCR
Synthesis,
H+ detection
Life
Ion Torrent
PacBio
RS
None
Synthesis,
ZMW fluorescence
Oxford Nanopore
ION
None?
Nanopore current flow
To learn all about these cool upcoming technologies, check out Josh
Ainsley’s 1st day RNA seq course slides at:
http://sites.tufts.edu/cbi/resources/rna-seq-course/lectures/
Illumina Sequencing
(the current mostused standard)
Sequencing-bysynthesis
Separate fluorescent
tags on each nucleotide
Reversible terminators
Library preparation steps
Fragmentation
End repair and A-tailing
Adapter ligation
Amplification gives distinct ends
Getting your library on a flowcell
Cluster generation –
Bridge amplification
Cluster generation –
Bridge amplification
Sequencing
Single end – sequence one end
Paired end – sequence both ends
(separate runs w/ primer for orange end, & then w/ primer for green)
TruSeq
Adapters
Forked adapters
Not complete
without PCR
Indexes/ barcodes
allow for multiplex
sequencing using a
third sequencing
read (currently up
to 24)
In-line multiplex adapters
Index/barcode is in the first
4-8 bases of the
sequencing read
Indexes/barcodes allow for
multiplex sequencing
Up to 24 separate libraries on the
same Illumina HiSeq lane.
After sequencing, the 1st 8 bases of
read identifies which sample it came
from.
Some Introductions:
What do you want to learn from
this course?
What do you already know
about RNA-Seq?
DAY 1 LECTURE OUTLINE
• ChIP method, validating your antibody.
• Next Generation Sequencing technology
• Preparing a ChIP-seq library
• Choosing options for sequencing
• Looking at published ChIP-seq results
-Exercise: Tracks on the UCSC browser
Preparing ChIP-seq Libraries
Step 1: Quantitate your ChIP recovery
Typical ChIP recoveries are very low:
… on the order of 2 to 10 ng of total recovered DNA from 100
microliters of tissue or packed cells (about what you’d get from a 10
cm tissue culture plate at confluence).
Need a method to quantitate your recovery that is sensitive
down to ~0.1 ng/microliter. UV, ethidium bromide & pico green
are not good enough.
I use InVitrogen’s Quant-IT dsDNA HS Assay Kit. Requires a
flourescence microplate reader.
If your recovery is >3ng you should be able to make a library easily
enough.
If it is <2 ng you can try to make the library & if it passes QC it might
be fine, or you may want to scale up your ChIP.
Preparing ChIP-seq Libraries
Steps 2 through 4: End repair & Adapter Ligation
Sonicated fragments (from ChIP & equal weight of input control DNA)
End repair (3’ overhang
exonucleases + 5’ overhang fill-in)
A-tailing (Taq polymerase adds
terminal A)
For ChIP2 or ChIP-seq, it is
preferable to use purified
input chromatin fragments
for control library
construction.
The IgG negative is less
useful for ChIP-seq (but is
important to use in initial
ChIP experiments to
establish the effectiveness
& specificity of your
antibodies!).
Adapter ligation (Illumina
adapters & T4 DNA ligase)
Ethanol precipitate with ultrapure glycogen carrier, to reduce
volume & get into buffer that won’t interfere with agarose gel run
Preparing ChIP-seq Libraries
Step 5: First Size Selection (removing unligated adapters)
Electrophoresis on agarose gel works fine.
You won’t see a band (you
started with only 3 ng of DNA!),
so you’ll have to choose a
range to cut with reference to
MW markers.
[you may see some signal for
the adapters running at ~90 bp]
•Flexible, easy to do w/ standard lab supplies
•Prone to contamination: so run samples with spacer lane
between them.
•Qiagen gel extraction kit recovery is usually acceptable
Other methods of size selection:
Invitrogen E-gel System
(agarose separation w/ collection window)
•Can be faster than normal agarose gel but…
•Collection in narrow size range (~10-20bp) & hard to
collect in larger range
•Recoveries not much better than agarose
Closed cell systems w/ dynamic collection
Pippin Prep
Caliper LabChip XT
Great if you can afford them (very expensive!)
…fortunately, in my experience, Pippin Prep versus Qiagen
gel extraction kit showed comparable recoveries.
Step 6: Limited Amplification
•A tail establishes
orientation for primer.
5’
3’
5’
3’
•1st: primer matching
p7 creates complete
duplex.
•Next: P7 & P5 primers
amplify it.
Optimize your PCR cycles
Goal is to minimize PCR bias (some fragments amplifying
more than others)
Perform 9 cycles of PCR
remove aliquot
Perform 3 cycles of PCR
remove aliquot
…
Repeat for 18 total cycles
->Agarose gel
Find cycle number that gives
strong product with few primer
dimers, do multiple reactions w/
that cycle number & pool.
EtOH ppt. w/ glycogen carrier.
Specific
product
(should be
fragsize+~90bp
for adapters)
Primer
dimers, etc.
(<200bp,
often <100)
Step 7: Final gel purification
Isolate band & purify
(~20-50 bp range best, but
can take larger if you need
more recovery)
Quantitate recovery
(can use DNA HS
flourescence kit, but, ideally
you’ll have enough recovery
to measure even by
spectrophotometer).
Need ~10 microliters of
10 ng/microliter sample
for sequencing
(a bit less might be OK…
contact your core for their
requirements & opinions).
Gel should be new w/ clean buffer
& lanes separating samples.
Biological Repeats
•NGS technology is very robust & less prone to the
day to day or array to array variability that plagues
microarrays.
•Your major sources of variability will be:
1) -Biological variability (best to have 3, or at
least 2, biological replicates).
2) -Library preparation (need to be very careful to
prepare libraries the same way, ideally on the
same day).
Minimizing technical variation &
contamination
•Order all reagents needed for the experiment to ensure
consistency (don’t use old lab stocks)
•Make all solutions fresh (from 1st fixation step before ChIP
onwards)
•Use low retention, filter tip pipettes at every step
•Ideally, perform the library prep for all samples
simultaneously
•Follow exact same protocol, especially including size
ranges isolated at each gel purification.
DAY 1 LECTURE OUTLINE
• ChIP method, validating your antibody.
• Next Generation Sequencing technology
• Preparing a ChIP-seq library
• Choosing options for sequencing
• Looking at published ChIP-seq results
-Exercise: Tracks on the UCSC browser
How many reads do I need?
Minimum for ChIP-seq of a transcription factor with <
~30,000 binding sites in a mammalian genome:
• 2 replicates per condition
• 20+ million reads per sample (>40M per condition,
proportionately less for smaller genomes & fewer binding peaks)
• One HiSeq lane gives ~150 million reads
…& can multiplex ~4 samples (2 exp + 2 input / lane)
• Single end 50 bp reads almost always good
(unlike RNA-seq where longer and/or paired end reads are required
for many downstream questions).
For some applications need many more reads (e.g. mapping
nucleosome positions need >400 M). Make your best estimate. If you
have too few you can re-sequence the same samples or add
additional samples. Reads from all runs can be pooled in the end.
How much will it cost?
Multiplexing 4 samples per lane
Library prep costs ($~200 per sample)
Current Tufts Genomics Core
Probably around $2000 total per 2 biological
replicates of one condition.
Can save by using one input sample as background for several
replicates or conditions, but this assumes fragmentation was
virtually identical across all samples (not recommended unless
you’re really confident in your technique).
The TUCF Genomics Core:
http://genomics.med.tufts.edu
Sign up for an account. Login & click “create new order”
For questions about sample preparation and Illumina protocols, please
contact [email protected].
For all other questions about the service, including scheduling and
consulting, please contact:
Albert Tai
Genomics Core Manager
Tufts University School of Medicine
150 Harrison Ave, Jaharis 523A
Boston, MA 02111
617-636-3992
[email protected]
DAY 1 LECTURE OUTLINE
• ChIP method, validating your antibody.
• Next Generation Sequencing technology
• Preparing a ChIP-seq library
• Choosing options for sequencing
• Looking at published ChIP-seq results
-Exercise: Tracks on the UCSC browser
ChIP-seq for histone modifications
Method:
•Mouse ES cells vs ES-derived primary neural progenitor cells (NPCs).
•Prepare chromatin & ChIP with antibodies to specific histone modifications.
•H3K4 methylation marks active genes, H3K27 marks repressed genes, both marks together in ES
cells mark “poised” genes that will become activated in certain developmental lineages.
Meissner et al. 2008, Nature 454:766.
The ENCODE Project
Dozens of labs did ChIP-seq, under rigorous quality
guidelines, for over 100 transcription factors and
histone modifications, plus related assays for DNA
methylation, chromatin accessibility etc.
Major paper (many others provide additional details):
Encode Project Consortium (over 100 authors) An integrated encyclopedia
of DNA elements in the human genome. Nature. 2012 Sep 6;489(7414):5774. doi: 10.1038/nature11247.
Some ways to access this data:
Nature.com/encode (Nature’s summary & links to all related papers)
factorbook.org (a way to explore the data in a wiki format)
UCSC genome browser (hands on examples next)
sra (short read archive, repository for raw data, more on this later!)
Sample of Encode Data
Accessing Encode ChIP-seq data
Start up your browser: Go to: http://genome.ucsc.edu
Click on “genomes” tab on top left
Select Human and hg19
Hit [submit] button
Look around & familiarize yourself with the controls:
Click & drag on line with bp numbers-> zoom selection, 1x, 3x, 10x zoom
options, control click on track gives options: keep refseq genes track &
“hide” others
Scroll down to blue bar that says “regulation”
Click ENC histone (for Encode histone modification tracks) box to “show”
Then click on “Enc histone” blue underlined name-> opens controls.
Check “Broad histone” and set to “full”, & click on its name -> opens
specific controls.
Columns are ChIP-seq with different antibodies, rows are different cell
lines. Check “peaks” dense & “signal” full.
Uncheck any pre-checked boxes & then check, H3K4me3, H3K27ac &
H3K27me3 in cell lines H1-hESC (embyronic stem cells), HepG2 (liver) &
Osteoblasts.
Then go back to top & hit [submit]
At box on top type in “mtrf1l” & hit return, choose the top match.
Click zoom out 3x, & then zoom out 10x (on top right)
Example of ChIP-seq data tracks on UCSC browser
Peak calls:
regions of
significant
enrichment over
background
Processed read
density, read as #
of reads
overlapping a
given BP position,
data (used to
make peak calls)
H3K4me3 & H3K27acet are marks of active promoters (e.g. MTRF1L)
H3K27me2 is a mark of repressed promoters
Genes in ESCs that are required for differentiation are often “poised” and bear K4me3 & K27me3
“bivalent mark” (e.g. SYNE1)
Differentiation resolves bivalent mark to all activating marks (Osteoblasts) or all repressive marks
(HepG2)
Downloading data from UCSC browser
Try zooming in (you can go all the way to base pair resolution)
Want to learn more about a gene? Control click on it’s ideogram & select
“open details page in new window”
What if you want to use this data somewhere else?
Select Tools->Table Browser
Select Group: Regulation, Track: Broad Histone
Table: H1-hESC H3K4me3 … Pk (for the peaks data, the signal file will
be huge)
In output format, select “all fields from selected table”
--Note that you could have selected “sequence”… if you had you’d get the
actual DNA sequence for each one of these peaks. We’ll use this later.
Check “Galaxy” next to send output to:
--Note that you could have selected send to file, we’ll use this later as well.
Click “Get output” & then click “send query to galaxy”
Introduction to Galaxy Tools
Galaxy is a web platform providing a lot of basic tools for manipulating
genomics data.
On the right are input & analysis options.
On the left is your history of uploaded files & analyses.
You’ll have one item in process, which will finish soon & turn green.
Click on the title to get a sample of what the data looks like.
Click on the eye to see the data in the central panel.
Each entry has a chromosome#, BP for start & BP for end & some
other values (signal value=enrichment over background, p.value=log(base10)of p. value, so, for p=.0001 this would be 4)
Click on the pencil to look at and edit the name & other attributes of
any item.
We’ll look more at Galaxy tools later…
What about data that’s not on the UCSC Browser?
The ENCODE project was UNUSUALLY considerate when compared to most other
researchers who generate genomics data.
Even though ENCODE is huge, it’s probably <10% of published NGS data.
To publish, researchers must make their data accessible, but they will very rarely provide a
link to a UCSC browser track. If you’re lucky, they will have put processed data up
somewhere: generally on GEO…
The GENE EXPRESSION OMNIBUS (GEO) Key repository for microarray and genomics data.
Open a new browser tab (ctrl-T) & go to:
http://www.ncbi.nlm.nih.gov/geo/
Search for “encode h3k4me3 h1-hesc”. You’ll see several entries. The first few are larger
datasets that include this specific data. The one at the bottom is just the data for this track.
First note the “accession number GSM733657” - often publications will give this accession
number, providing an easy way to get directly to the right place.
---> Now, click on the title for this entry “Bernstein…”
Scroll down the next page: There’s lots of info about this experiment with links for more
information.
At the bottom are the processed data files:
They’ve been nice & offer us a “BROADPEAK” file (the same as what we just uploaded to
Galaxy), a “BAM” file for each experimental replicate (which has the genomic coordinates for
each read), and a “BIGWIG” file (the filetype for the “signal” track on the UCSC browser)
What if the data I’m interested in isn’t in GEO?
Authors are almost always required to make their NGS data accessible in order to
publish….
…but they’re often not required to make it easy!
Many times the only thing that’s available is the raw data, stored in…
The Sequence Read (SRA) - Repository for
data.
Open a new browser tab (ctrl-T) & go to:
raw NGS
http://www.ncbi.nlm.nih.gov/sra/
Search again for “encode h3k4me3 h1-hesc”.
A single record will be called up…
Clicking on link 1 or 2 under “run” will give you information about that particular biological
replicate sample.
Clicking on the link 1 or 2 under ‘size’, will take you to a page with a single linked file with a
“.sra” extension.
Note how big this file is… just a few of these would rapidly fill up a PC hard drive!
So what is a .sra file & what can you do with it? Don’t even try to download & open it…
besides being huge, it’s not even normal text.
In the next few lectures we’ll find out how to handle this sort of raw data.
SEQanswers
Answers to your questions about NGS applications
Forums (ask Q’s &/or search for A’s)
Wiki – Find NGS software
Instrument Map
ChIP-seq COURSE OUTLINE
• Day 1: ChIP techniques, library
production, USCS browser tracks
• Day 2: QC on reads, Mapping binding site
peaks, examining read density maps.
• Day 3: Analyzing peaks in relation to
genomic feature, etc.
• Day 4: Analyzing peaks for transcription
factor binding site consensus sequences.
• Day 5: Variants & advanced approaches.
ChIP-seq for
TF
(SISSRS software)
Jothi, et al. Genome-wide
identification of in vivo protein–
DNA binding sites from ChIP-Seq
data. NAR (2008), 36: 5221-31
Adapted from slide set by: Stuart M. Brown, Ph.D.,
Center for Health Informatics & Bioinformatics, NYU School of Medicine