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Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Protein Organization

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Many proteins can be classified into two distinct subtypes - globular or fibrous. These two types differ in their shapes and solubilities.
Globular proteins are also known as spheroproteins and typically are approximately round in shape. They contain a mix of amino acid types and contain differing sequences in their primary structures. Globular proteins have many different functions, such as enzymes, cellular messengers, and molecular transporters. These roles often require the proteins to be...

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Abstractions, algorithms and data structures for structural bioinformatics in PyCogent.

Marcin Cieślik, Zygmunt S Derewenda, Cameron Mura

Journal of Applied Crystallography

|April 6, 2012

Summary

New PyCogent modules enhance structural bioinformatics by adding tools for 3D structure processing and analysis. This integration allows sequence-based and structure-based analyses to leverage each other

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Area of Science:

Bioinformatics
Structural Biology
Computational Biology

Background:

PyCogent is a popular framework for sequence-based bioinformatics.
It previously lacked robust tools for structural and coordinate-based data analysis.

Purpose of the Study:

Introduce new functionality for 3D structure processing and analysis into PyCogent.
Enable flexible and efficient structural bioinformatics analyses.
Facilitate symbiotic integration between sequence-based and structure-based analyses.

Main Methods:

Developed extensible Python modules with object-oriented abstractions for macromolecules.
Implemented efficient data structures like kD-trees.
Integrated fast algorithms for surface-area calculations and wrappers for external tools like Stride.
Added read/write support for Protein Data Bank (PDB) file formats.

Main Results:

PyCogent now offers powerful tools for handling structural and coordinate-based data.
The new modules provide efficient data structures and algorithms for structural analysis.
Seamless integration allows sequence data to benefit from structure-derived information and vice versa.

Conclusions:

The enhanced PyCogent framework supports comprehensive structural bioinformatics analyses.
This integration benefits both sequence-based and structure-based research.
The new functionality improves the efficiency and flexibility of macromolecular analyses.