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Related Experiment Video

Updated: Mar 12, 2026

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Published on: June 24, 2019

NullSeq: A Tool for Generating Random Coding Sequences with Desired Amino Acid and GC Contents.

Sophia S Liu¹, Adam J Hockenberry^1,2, Andrea Lancichinetti¹

¹Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America.

Plos Computational Biology

|November 12, 2016

Summary

Researchers can now generate unbiased random protein-coding sequences with specific amino acid and GC content. This tool aids in creating accurate null models for identifying genomic sequence motifs and understanding evolutionary pressures.

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Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

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Last Updated: Mar 12, 2026

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

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2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

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Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Published on: May 9, 2011

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genomes contain sequence motifs indicating evolutionary selection on biological processes.
Accurate null models are crucial for identifying these motifs and genome-scale patterns.
Existing tools for random sequences do not adequately address the constraints of protein-coding sequences.

Purpose of the Study:

To develop a method for generating unbiased random protein-coding sequences with specified amino acid composition and GC content.
To provide a tool for hypothesis testing in genomic sequence analysis.
To enable the creation of more accurate null models for motif discovery.

Main Methods:

Developed a method based on the principle of maximum entropy.
Implemented the method into a Python package for user accessibility.
Ensured the generated sequences maintain pre-specified amino acid and GC content.

Main Results:

Created a novel method for generating maximally unbiased random coding sequences.
The method accounts for GC usage and primary amino acid sequence constraints.
The approach is extensible to incorporate additional constraints like nucleotide or di-nucleotide frequencies.

Conclusions:

The developed method offers the simplest way to obtain unbiased random sequences with specified constraints.
Accurate null models are essential for precise motif identification, advancing biological understanding.
This tool facilitates more effective engineering of biological systems through better sequence analysis.