# 3.2 Higer order data analysis

## Computational Approaches for Assessing Higher-order Chromatin

1. Analytical pipeline<br>

   &#x20;1.1. Alignment<br>

   &#x20;1.2. Binning and Generating Contact Matrices<br>

   &#x20;1.3. Normalization (Balancing)<br>

   &#x20;1.4. Identification of interactions<br>

   &#x20;1.5. Visualization<br>
2. Analytical tools<br>
3. TAD calling tools and algorithms<br>

### 3.2.1 Analytical pipeline:

* Raw reads&#x20;
* Pre-processing&#x20;
* Alignment (Full read or chimeric alignment)
* Binning & contact matrixing
* Normalization
* Detection (Intra/Inter-chr interactions)
* Visualization
* Analysis and Interpretation

#### 3.2.1.1 Alignment

Hi-C data produced by deep sequencing is no different than other genome-wide deep sequencing datasets. The data starts out as genomic reads in the traditional FASTQ file format.

\
Reads are filtered to remove duplicates, PCR artifact. Sequencing adaptors can also be removed prior to alignment.

\
The goal is to simply find a unique alignment for each read. The insert size of the Hi-C ligation product can vary between 1bp to hundreds of megabases (in terms of linear genome distance), it is difficult to use most paired-end alignment modes as is. One straightforward solution is to map each side of the paired end read separately/independently using a standard alignment procedure.

* Full-read alignment first ---- Bowtie2, BWA
* Unmapped reads: chimeric alignment ---- read splitting [\[1\]](https://doi.org/10.1101/gr.161620.113), iterative mapping [\[2\]](https://doi.org/10.1038/nmeth.2148)
* Average sufficient reads depth, sufficient mappable reads:4C (1– 2 million), 5C (25 million) and Hi-C (8.4 to 100 million) [\[3\]](https://doi.org/10.1038/nrg3642)

A detailed description of mapping analysis is covered in **Read mapping consideration** chapter.

#### 3.2.1.2 Binning and Generating Contact Matrices

**what is bin?**

A bin is a fixed, non-overlapping geno-mic span into which reads are grouped to increase the signal of the interaction frequency. The interactions between bins are simply summed up to aggregate the signals.

**why we use bin?**

* Using a 6-bp cutting restriction enzyme, there are almost $$10^6$$ restriction fragments, leading to an interaction space on the order of $$10^{12}$$ possible pairwise interactions. Thus, achieving sufficient coverage to support maximal resolution is a significant challenge. **It's critical to set goals (what resolution you desire) to choose the proper bin size**.
* To overcome the limitations that the signal-to-noise ratio decreases with increased distance between two target loci.

**when we use bin?**

If the goal is to measure large scale structures, such as genomic compartments, then a lower resolution will often suffice (1MB-10MB), then we'll choose a proper bin size. However if the goal is to measure specific interactions of a small region, e.g. promoter-enhancer looping, then one should choose to use a restriction enzyme that cuts more frequently (e.g. 4bp) and a method that does not measure the entire genome, but instead focuses on exploring only a subset of the genome (i.e. 3C/4C/5C).

**how to choose bin size**

Smaller bins usually are used for more frequent intra-chromosomal interactions, and larger bins are for less frequent inter-chromosomal interactions. selected bin size should be inversely proportional to the expected number of interactions in a region.

**what decide the Hi-C resolution**

* Sequencing coverage, more reads will cover more of the interaction space and thus improve the resolution.
* Library complexity -- the total number of unique chimeric molecules that exist in a Hi-C library, a library with a low complexity level will saturate quickly with increasing sequencing depth

**bin-level filtering**

Prior to matrix balancing, it is advised to remove any bins (rows/columns) from the dataset that have either very noisy or too low of a signal. These bins are normally found in genomic regions with low mappability or high repeat content, such as around telomeres and centromeres.

#### 3.2.1.3 Normalization (Balancing)

The goal of normalization is to reduce biases during the experiment as well as a better comparison between different experiment results (reduce batch effect).

**Two types of normalization**

* Explicit normalization:
  * known bias factors [\[4\]](https://www.ncbi.nlm.nih.gov/pubmed/22001755)
    * Distance between restriction enzyme cut sites (eg, for hi-c)
    * GC content of trimmed ligation junction
    * uniqueness of sequence reads
  * correction: integrate prior probabilistic model. <br>
* Implicit normalization:
  * Iterative correction [\[5\]](https://doi.org/10.1038/nmeth.2148) based on the assumption that all loci should have equal visibility since we are detecting the entire genome in an unbiased manner (By equalizing the sum of every row/column in the matrix). Faster and preferred.&#x20;

#### 3.2.1.4 Identification of interactions

* TAD calling (Details see 3.2.3)
  * Tools for calling TADs&#x20;
  * algorithms and principle&#x20;
  * GPU accelerating&#x20;
* Separating active/repressive compartments A/B&#x20;
* Identifying chromatin loops

#### 3.2.1.5 Visualization

* See Chapter4&#x20;

### 3.2.2 Analytical tools:

| Techniques | Tools                                                                                     | Description                                                                                                   |
| ---------- | ----------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
| 4C         | [FourCSeq](http://refhub.elsevier.com/S2001-0370\(17\)30093-4/rf9035)                     | Uses R to detect specific interactions between DNA elements and identify differential                          |
| 5C         | [HiFive](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0806-y)      | Python package for normalization and analysis of chromatin structural data produced using either              |
| Hi-C       | [Fit-Hi-C](https://genome.cshlp.org/content/24/6/999)                                     | Assigns statistical confidence to mid-range cis-chromosomal contacts                                          |
|            | [HOMER](http://homer.ucsd.edu/homer/)                                                     | Designed for high-resolution Hi-C data                                                                        |
|            | [HIPPIE](https://www.nature.com/articles/ng.947)                                          | Identifies chromatin interactions in a genome                                                                  |
|            | [HiCCUPS](https://www.sciencedirect.com/science/article/pii/S0092867414014974?via%3Dihub) | Detect sub-TAD chromatin interactions (cis)                                                                   |
|            | [Juicer](https://www.sciencedirect.com/science/article/pii/S2405471216302198?via%3Dihub)  | Aligns, filters and normalizes, identifies and compares TADs, loops and compartments and display using Juicebox |
|            | [HiC-Pro](https://www.sciencedirect.com/science/article/pii/S2405471216302198?via%3Dihub) | Aligns, quality control, inter-intra contact maps, fast iterative correction, allele specific contact maps    |

&#x20;Comprehensive tools list for hi-c data analysis can be found [here](https://omictools.com/3c-4c-5c-hi-c-chia-pet-category). Next we'll use Hi-C pro as a showcase to see hi-c data analysis workflow (See **Hi-C Pro Pipeline** chapter).&#x20;

### 3.2.3 TAD calling tools and algorithms Brief view of different tools.

| Tools                                                                                                                            | Description                                                               | Language |
| -------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- | -------- |
| [TADbit](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005665)                                          | TADbit includes quality control module, and aligns reads to the reference | Python   |
| [TADtree](https://app.gitbook.com/s/-LHa3gDECHFHyklI99-v/chapter2-computational-analysis/compbio.cs.brown.edu/projects/tadtree/) | Identifies hierarchical topological domains                               | Python   |
| [Armatus](https://github.com/kingsfordgroup/armatus)                                                                             | Uses dynamic programming to call TADs in different resolutions            | C++      |
| [Arrowhead](https://github.com/theaidenlab/juicer/wiki/Arrowhead)                                                                | Arrowhead is an algorithm for finding contact domains                     | Java     |

Performances comparison for different tools are surveyed [here](http://dx.doi.org/10.1038/nmeth.4325).

### Reference:

\[1] Computational Methods for Assessing Chromatin Hierarchy\
\[2] The Hitchhiker's Guide to Hi-C Analysis: Practical guidelines\
\[3] Comparison of computational methods for Hi-C data analysis<br>


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