Spatial structure of chromatin:
introduction to Hi-C

Aleksandra Galitsyna

“Analysis of omics data” course
Skoltech Term 4
24 April 2020

This presentation can be found at https://slides.com/agalicina/epigenetics-practice3-2020

Chromatin spatial structure

Ulianov et al. Genome Biology 2016

Methods to study chromatin structure

Microscopy:

Microscopy with fluorescing marks:
(FISH)

For 2 marks:

For multiple marks:

Chromatin conformation capture

3C: Dekker et al. Science 2002

Hi-C

Viewpoint

High-throughput chromosomes conformation capture:

DNA interactome map:

Lieberman-Aiden et al. Science 2009

Variety of C-based techniques

Adopted from Schmitt Nature Reviews 2016

Chromatin spatial structure

Adopted from Imakaev et al. Nature Methods 2012

Chromosome territories

Bonev et al. Nature Reviews 2016

TADs

Bonev et al. Nature Reviews 2016

Loops of chromatin

Bonev et al. Nature Reviews 2016

The importance of TADs and loops

Lupiáñez et al., Cell, 2015

Compartments of chromatin

Bonev et al. Nature Reviews 2016

Hi-C data processing outline

1. Reads mapping

File with mapping position of pairs

2. Contacts filtration

Removal of PCR duplicates
Filtering by restriction fragments

3. Contacts map retrieval

Binning

4. Balancing and map normalization
5. Features calling (TADs, compartments, loops)

Chromatin spatial structure

Adopted from Lajoie et al., The Hitchhiker's guide to Hi-C analysis: Practical guidelines.

Methods 2015

Iterative mapping or mapping allowing chimeric reads (split read alignment, e.g. bwa mem)

Chromatin spatial structure

Imakaev et al. Nature Methods 2012

Chromatin spatial structure

Hi-C restriction fragments are assigned to bins (sequential same size genomic windows) and aggregated by taking the sum:

Hi-C data correction

Two major types of approaches:

Adopted from Schmitt et al. Nature Reviews 2016

Hi-C data correction

Schmitt et al. Nature Reviews 2016

Iterative correction

Imakaev et al. Nature Methods 2012

Hi-C data mapping

Forcato et al. Nature Methods 2017

There are various tools for Hi-C data processing:

Example of Hi-C data processing

1. Mapping

Tools : bwa, samtools
Output: bam file

2. Filtering, sorting, creation of the contacts list

Tools: pairtools
Output: pairs file

3. Binning and normalization

Tools: cooler
Output: cool file

4. Aggregation

Tools: cooler

Output: multires.cool (mcool) file

Hi-C data processing

All of the processing steps are accessible in the form of pipelines,
e.g. distiller-nf:

https://github.com/mirnylab/distiller-nf

Practice:
Mapping Hi-C data

Practice results

As a result of this practice I expect a small report on the results (free form, PDF format).
The questions that should be covered in the report are highlighted in red.
Your mark for the report cannot exceed 10 points.
The deadline is in a week (next Friday).

Adjust the environment

1. Login to the server via terminal:

or use Putty on Windows.
(example instructions https://www.ssh.com/ssh/putty/windows/#sec-Configuration-options-and-saved-profiles)

2. Create the folder with your project:

3. Activate prepared environment:
(if you have problems with it, please, contact the TA)

4. Check your environment:

ssh username@servername

mkdir EpiPract3
cd EpiPract3

export PATH="/home/galitsyna/anaconda3/bin:$PATH"
conda activate hic

ls # Should list all the filesin the directory
pwd # Should result in your currentl location
conda list # Should list all the programs installed, if conda imported successfully.

Get the data

Copy to the local folder:
Check the content and file sizes:

$ cp /home/galitsyna/EpiPract3/fastq/your_file.fa.gz ~/EpiPract3/
$ mkdir ~/EpiPract3/genome/
$ cp /home/galitsyna/EpiPract3/genome/* ~/EpiPract3/genome/

$ ls -hts 
$ ls -hts genome/

Name	Files for EpiPract3
Anastasia Pivnyuk	nuclear_cycle_12_repl_1_run1_1.fastq.gz	nuclear_cycle_12_repl_1_run1_2.fastq.gz
Nikita Sharaev	nuclear_cycle_12_repl_1_run2_1.fastq.gz	nuclear_cycle_12_repl_1_run2_2.fastq.gz
Artemy Shumskiy	nuclear_cycle_12_repl_1_run3_1.fastq.gz	nuclear_cycle_12_repl_1_run3_2.fastq.gz
Dmitrii Kriukov	nuclear_cycle_12_repl_1_run4_1.fastq.gz	nuclear_cycle_12_repl_1_run4_2.fastq.gz
Pletenev Ilya	nuclear_cycle_12_repl_2_run1_1.fastq.gz	nuclear_cycle_12_repl_2_run1_2.fastq.gz
Konstantin Chernyshov	nuclear_cycle_12_repl_2_run2_1.fastq.gz	nuclear_cycle_12_repl_2_run2_2.fastq.gz
Ivan Kuznetsov	nuclear_cycle_13_repl_1_run1_1.fastq.gz	nuclear_cycle_13_repl_1_run1_2.fastq.gz
Anna Kalinina	nuclear_cycle_13_repl_1_run2_1.fastq.gz	nuclear_cycle_13_repl_1_run2_2.fastq.gz
Sofya Kasatskaya	nuclear_cycle_13_repl_1_run3_1.fastq.gz	nuclear_cycle_13_repl_1_run3_2.fastq.gz
Julia Bocharkina	nuclear_cycle_12_repl_2_run1_1.fastq.gz	nuclear_cycle_12_repl_2_run1_2.fastq.gz
Vasily Borodin	nuclear_cycle_14_repl_1_run1_1.fastq.gz	nuclear_cycle_14_repl_1_run1_2.fastq.gz
Sofia Kamalyan	nuclear_cycle_14_repl_1_run2_1.fastq.gz	nuclear_cycle_14_repl_1_run2_2.fastq.gz
Anna Krasivskaya	nuclear_cycle_14_repl_1_run3_1.fastq.gz	nuclear_cycle_14_repl_1_run3_2.fastq.gz
Aleksandra Ozerova	nuclear_cycle_14_repl_1_run4_1.fastq.gz	nuclear_cycle_14_repl_1_run4_2.fastq.gz
Victoria Kobets	3-4h_repl_1_run1_1.fastq.gz	3-4h_repl_1_run1_2.fastq.gz
Slesareva Anastasiia	3-4h_repl_1_run2_1.fastq.gz	3-4h_repl_1_run2_2.fastq.gz
Mikhail Moldovan	3-4h_repl_1_run3_1.fastq.gz	3-4h_repl_1_run3_2.fastq.gz
Viktor Mamontov	3-4h_repl_1_run4_1.fastq.gz	3-4h_repl_1_run4_2.fastq.gz
Evgeniia Alekseeva	3-4h_repl_2_run1_1.fastq.gz	3-4h_repl_2_run1_2.fastq.gz
Trofimova Anna	3-4h_repl_2_run2_1.fastq.gz	3-4h_repl_2_run2_2.fastq.gz

The files that should be in the genome folder:

dm3.fa.gz - File with Drosophila genome sequence, dm3 assembly
dm3.fa.gz.amb
dm3.fa.gz.ann
dm3.fa.gz.bwt
dm3.fa.gz.pac
dm3.fa.gz.sa
dm3.reduced.chrom.sizes - File with chromosome sizes for Drosophila genome

The files that should be in you working directory:

<Your unique name>_1.fastq.gz
<Your unique name>_2.fastq.gz

What's in the data?

We will analyse the data from the paper on Drosophila melanogaster embryogenesis:
Hug et al. Cell 2017
We have several data points and Hi-C for them (you all have different replicates of them):
I suggest you not to look to the results of the paper until you don't get the pictures of your maps at 20 Kb resolution.

Map Hi-C data

Map FASTQ -> BAM:

$ bwa mem -t 1 -v 3 -SP ${genome_file.fa.gz} ${fastq_file1} ${fastq_file2} > ${output.sam}
$ samtools view -bS ${output.sam} > ${output.bam}

$ pairtools parse --walks-policy 5unique -c ${chromosome_sizes} ${input.bam} -o ${output.pair}
$ pairtools dedup --output-stats ${output.stats} ${output.pair} -o ${output.nodup.pair}

$ cooler cload pairs -c1 2 -c2 4 -p1 3 -p2 5 ${chromsizes}:20000 ${input.nodup.pair} ${output.cool}

Task 1 (1 point): Report the number of uniquely mapped pairs, number and fraction removed after deduplication.

Task 2 (2 points): Visualize the data with cooler show in png format for any genomic region of 2 Mb size before and after correction. Report the resulting figures and your observations.

Parse BAM -> PAIR file and remove PCR duplicates:

Load cooler map (at 20 Kb resolution, note the ":2000"):

$ cooler balance ${input.cool}

Balance it:

Scaling plots

Lieberman-Aiden, 2009

Scaling plots

Task 3 (optional*): Convert your cool file to h5 with HiCExplorer command hicConvertFormat. Run hicInfo on the resulting file. What are the differences from cooler info?

Task 4 (optional*): Create the scaling plot for your dataset with HiCPlotDistVsCounts. What are your observations?

We'll try to plot our own scaling plots on the data with HiCExplorer.

Expected results of the report

5 points in total (rescaled proportionally to 10 in Canvas):

1 point for the running the initial steps of the script
2 points for finishing the pipeline and visualizing the result with and without iterative correction.
2 points for the part on genome reassembly (not included in this presentation).

Optional tasks (about HiCExplorer can substitute the points that you might miss in the required part).

Epigenetics Practice 3 2020

By agalicina

Epigenetics Practice 3 2020

Welcome to the 3rd practice on epigenetics of "Analysis of omics data" Skoltech course. Today we will talk about chromatin architecture and Hi-C. During the practice, we will go from raw Hi-C reads to the heat maps of interactions.

Spatial structure of chromatin: introduction to Hi-C

Aleksandra Galitsyna

Chromatin spatial structure

Methods to study chromatin structure

Chromatin conformation capture

Hi-C

Variety of C-based techniques

Chromatin spatial structure

Chromosome territories

TADs

Loops of chromatin

The importance of TADs and loops

Compartments of chromatin

Hi-C data processing outline

Chromatin spatial structure

Chromatin spatial structure

Chromatin spatial structure

Hi-C data correction

Hi-C data correction

Iterative correction

Hi-C data mapping

Example of Hi-C data processing

Hi-C data processing

Practice: Mapping Hi-C data

Practice results

Adjust the environment

Get the data

What's in the data?

Map Hi-C data

Scaling plots

Scaling plots

Expected results of the report

Epigenetics Practice 3 2020​

More from agalicina

Spatial structure of chromatin:
introduction to Hi-C

Practice:
Mapping Hi-C data

Epigenetics Practice 3 2020