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Human cells contain approximately three billion bases of DNA, which would measure three meters in length if laid in a line [14]. As these cells are only on the order of 10 µm in size, significant folding and compaction of the DNA is necessary. There are many levels of DNA organization, ranging from unorganized open regions called euchromatin, to compact heterocrhomatin sections wrapped around histones, all the way up to the most condensed chromosomal form [14]. A common misconception is that DNA exists in tight x-shaped chromosomes throughout the entirety of the cell cycle, when these bundles are actually only formed during cell division. In most of a cell’s life its DNA is in a semi-compacted state within the nucleus. Some regions are tightly wound and inaccessible, while others are open. Perhaps even more surprising is the dynamic nature of DNA, where the accessibility of different portions is constantly changing depending on the state of the cell [6].

DNA compaction serves as a mechanism to both address space limitation, and also to regulate which genes are turned off and on. Genes within tight heterochromatin are inaccessible to RNA polymerase and transcription factors, and cannot be turned into an active protein. Euchromatin, on the other hand, is more actively expressed. Adding additional complexity, certain DNA elements called enhancers can regulate transcription of genes millions of bases away on the same or different chromosomes. In order for a cell to control which genes it is expressing at a certain time, it must therefore carefully fold it’s entire chromosome to ensure appropriate interactions occur. Consequently, researchers have long desired to start with chromosome conformation data and work backwards to understand the state of a cell. Understanding the folding interactions of chromosomes is problematic because of the shear length of DNA and difficulty to capture these folds in physiological conditions. Some of the first approaches to investigate DNA structure were based on microscopy and molecular probes [13]. Examples of such methods are Fluorescent In Situ Hybridization (FISH) and Fluorescence Resonance Energy Transfer (FRET). While these techniques are useful, they can only be used to observe a few specific loci at a time, and cannot easily be scaled up. With the increasing ease of sequencing, however, methods were produced to yield full chromosomal conformation maps.

As stated above, the question of how DNA folds from invisible meter length strands to tight observable chromosomes, has long been of interest. Chromatin conformation capture (3C) is a relatively new technique used to describe the shape of compact DNA with massive throughput. The original high-throughput method was published by Dekker et. al in a highly-cited 2002 paper [3]. The essence of the technique is that a population of cells are suddenly fixed with formaldehyde or an alternate cross linker, effectively taking a snapshot of which pieces of chromosome are neighbors [6]. Then through the clever use of restriction enzymes, the pieces of nearby DNA are joined into a single strand that is able to be sequenced. If two sequences not normally adjacent are read out, then a count is added to the interaction matrix between these two regions. Note that 3C requires that the chromosome sequence is known, and the technique cannot be used in areas of highly repetitive DNA. It is not possible to confidently map where the interaction took place in such regions. Additional extensions to 3C, such as 4C, 5C, HiC, and ChIA-PET have since been produced and have varying strengths and weaknesses [5]. The applications of these tools have helped describe the biology of chromatin conformation on incredible scales [9][12].

As with all large-data producing techniques, the analysis of 3C results is often difficult and conclusions can vary with different approaches [7]. Furthermore, quite a bit of human inter- 1 action with the data is still necessary to find even simple patterns, such as domains, with high confidence [4]. In this work we apply machine learning as a conducive method towards identifying previously unstudied patterns in chromosome interaction data sets. We first use supervised learning to show that patterns identified by a user can be learned by tensor flow models, and then transition into unsupervised methods to delve even more deeply into the possibilities of discovery without human intervention.

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