After cloning the repo locally, with Anaconda/Miniconda previously installed, the Conda environment 'meet-u' can be created with the following command:
conda env create --file BananaTAD_linux.yamlThis command saves the list of all consensus TADs of the chromosome chr1 (from cell type GM12878) at 100kb resolution as tuples (start, end).
python BananaTAD.py --folder data/example --file chr1_100kb.RAWobserved --cell_type GM12878 --resolution 100000 --chrom 1arrowhead topdom tadtree ontad tadbit
This GitHub page is a project for the Meet-EU course at Sorbonne University on the creation and improvement of algorithms and methods for TAD detection (explained in more detail below).
The main page (which includes a detailed description, overview by teachers and organisers) for the project can be found here.
Input and validation data for the project can be found here.
A brief intro to TADs by Leopold Carron can be found here (in French).
DNA is a greatly complex and important molecule that is often referred to as the building block of life. The entire human genome is able to fit within a single nucleus, and this is in part thanks to how efficiently DNA is able to pack and compress itself while still retaining high levels of necessary interactions for gene expression. One such unit necessary for this function is known as a Topologically Associating Domain (TAD) – a genomic region capable of high self-interaction through the presence of chromatin and loop formation. TADs are a discovery made within the last decade after the advent of chromosome conformation techniques such as Hi-C sequencing.
The recency in TADs’ discoveries has come with many questions and challenges associated to it. TADs are important because their presence is often associated to the regulation of gene expression, and disruption in TADs has been linked to oncogenic onset, for example. However, TADs are not trivial to detect. There have been many algorithms and methods such as Topdom or Arrowhead, to cite the two most known, but because of a general lack of consensus on what exactly a TAD is, this field still faces many challenges in the vital aspect of “calling” (or identifying) a TAD from raw sequencing data.
Our method, Banana-TAD, seeks to help solve this challenge by the re-implementation of four existing algorithms: Topdom, TADTree, OnTAD as well as TADBit, and scoring them based on a set of data provided to us as ground truth (as lists of TADs called by Arrowhead on 5kb data). After scoring them, we then obtained a list of consensus TADs, which we believe to be a strong middle ground obtained from the combination of multiple recognized TAD calling methods.
Reimplementing various TAD detection algorithms such as TopDom, TAD tree, OnTAD and TADBit under Python, these algorithms are used to find said TADs by analysing HiC map files. They provide us with the location of the start and end of the TADs.
Comparing the output TADs between them and with other ones such as TADs produced by the Arrow-Head algorithm to create consensus TADs. Consensus TADs are a mean to avoid redundancy in detected TADs. Sometimes, when we use different methods or resolutions, we might detect TADs that are very close in proximity, but that are detected as two different ones. They still might be the same TAD in reality and that's why we need a consensus method to determine if it is the case or not.
Using a scoring system that aims to make the whole process more precise.
Developing a TAD length analysis method to help with the scoring system and maybe try to predict the distribution of TAD lengths later on (This part is very experimental and we hope more qualified researchers can use it as first step for a new method).
Tuning every functions to secure the best performances based on defined metrics.
Icons embedded from icons8, all rights belonging to their respective owners.