https://github.com/adamtaranto/te-dispersion-metric

Proposed metric for measuring the clustering of transposons within a genome
https://github.com/adamtaranto/te-dispersion-metric

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Proposed metric for measuring the clustering of transposons within a genome

• Host: GitHub
• URL: https://github.com/adamtaranto/te-dispersion-metric
• Owner: Adamtaranto
• License: mit
• Created: 2017-09-14T03:05:54.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-09-19T04:56:09.000Z (over 7 years ago)
• Last Synced: 2024-11-21T02:33:38.087Z (2 months ago)
• Language: Python
• Size: 5.86 KB
• Stars: 1
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# TE-dispersion-metric

**Aim:**

To measure the degree to which repetitive elements are clustered or dispersed 

within a chromosome. It has been widely observed that large genomic islands of 

repetitive sequences (transposons etc) comprise isolate specific regions in 

fungal genomes and often contain virulence determinants. Similarly, many fungal

pathogens possess accessory chromosomes which are predominantly composed of 

repetitive elements.  

Currently, transposon content is crudely summarised as a proportion of the 

chromosome or genome space. This measure is agnostic to the distribution of 

repetitive sequences across the total genome space and is

therefore unable to differentiate between TE-poor genomes with a small number 

of repeat-islands or accessory chromosomes and genomes with a diffuse 

distribution of small repeats and lacking any isolated clusters.  

**Proposed metric:**

Within a chromosome - the mean distance from each repeat to all other repeats 

weighted by target feature's contribution to the total feature space.

```

## Chrom length

S = Total sequence length

## Distance between features scaled to chromosome length

d = distance between features / S

## Total space within S occupied by repeat features

T = Sum length of all transposons in S

## Weight of repeat feature as proportion of total repeat space T

w = feature-length / T

## Count of non-overlapping features in Chrom

n = number of discrete features

```

Mean weighted distance from transposon i to all other features j

Mi = sum(d(i,j) * jw) / n - 1 

Mean weighted distance across all features i

sum(Mi) / n

## Example implementation

Requirements: 

  - biopython

  - pybedtools

```bash

./TEDcalc.py --gff repeats.gff3 --genome genome.fa

```