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A tool for analyzing SSRs in genomic data

Project description

Linux Python Made with love in India

Crossroad fastAPI and CLI

A comprehensive tool for analyzing Simple Sequence Repeats (SSRs) in genomic data,

Features

  • SSR comparative analysis of genomic data
  • Relative abundance and Relative density
  • Conserved, shared, and unique SSR motifs
  • Gene-based SSR analysis
  • Mutational hotspot detection
  • Evolutionary analysis:SSR length variation with respect to time point
  • Both Reference free and Reference-based comparison
  • REST API support

Installation

Using pip

Contributors

  • Dr. Preeti Agarwal (PhD & Senior Research Fellow)
  • Dr. Jitendra Narayan (Principal Investigator)
  • Pranjal Pruthi (Project Scientist, CSIR IGIB)

Institution

CSIR-Institute of Genomics and Integrative Biology Lab of Bioinformatics and Big Data analysis Mall Road, New Delhi - 110007, India

License

MIT License

Citation

If you use Crossroad in your research, please cite: [Citation information coming soon]

Output File With Reference ID Without Reference ID
hssr_data.csv 1. find_different_repeats() finds differences from reference
2. group_ssr_records_from_excluded() groups these differences
3. filter_hotspot_records() filters for variations
4. process_hssr_data() creates final HSSR data
1. group_ssr_records() groups all SSRs
2. filter_hotspot_records() filters for variations
3. process_hssr_data() creates final HSSR data
ref_ssr_genecombo.csv
(excluded_repeats)
1. find_different_repeats() identifies differences from reference
2. Saves directly to file
3. Only created when reference ID is given
Not created
all_variations.csv 1. find_different_repeats() finds differences
2. group_ssr_records_from_excluded() processes these
3. Saves to file before filtering
1. group_ssr_records() groups all SSRs
2. Saves to file before filtering
mutational_hotspots.csv 1. Uses grouped excluded repeats
2. filter_hotspot_records() finds variations
3. Applies min_repeat_count and min_genome_count filters
1. Uses grouped all SSRs
2. filter_hotspot_records() finds variations
3. Applies min_repeat_count and min_genome_count filters

Each file in simple terms:

hssr_data.csv:
  • Final output containing hotspot SSRs
  • With reference: Only includes variations different from reference
  • Without reference: Includes all variations meeting criteria
ref_ssr_genecombo.csv (excluded_repeats):
  • Only created when using reference ID
  • Lists all SSRs that are different from reference genome
  • Raw differences before processing
all_variations.csv:
  • With reference: All variations of different SSRs from reference
  • Without reference: All variations of all SSRs
  • Intermediate file before filtering
mutational_hotspots.csv:
  • Contains filtered hotspots meeting criteria

  • Uses min_repeat_count and min_genome_count

  • With reference: Only from differences

  • Without reference: From all SSRs

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