The NeoDarwinian Codon Adaptation Index (CAI) falls to Cas9 Crisper

Note GC bias in left column codons

Codon bias is the non-uniform usage of synonymous codons (codons that encode the same amino acid) in a genome. This bias can be observed at the level of individual genes, or across an entire genome. The amino acid leucine can be encoded by six different codons: CUU, CUC, CUA, CUG, UUA, and UUG. However, in the human genome, the codon CUU is used much more frequently than the other codons. This is an example of codon bias.

Codon bias was first discovered in the early 1970s by scientists studying the genomes of bacteria and viruses. They found that certain codons encoding the same amino acid were used more frequently than others. This was surprising because, according to the genetic code, all synonymous codons should be used equally under NeoDarwinism.

Over the years, scientists have learned that codon bias is a common phenomenon in all living organisms. It is now understood that codon bias can be influenced by a number of factors, including AT Mutational bias and gBGC GB bias. Mutations in the DNA are mostly biased and occur at some sites more than others due to non-random cellular mechanisms, not Darwinian random ones. This leads to bias in the distribution of codons over time.

Neo Darwinism is a waning  theory of evolution that combines Darwin's ideas about natural selection with modern understanding of genetics. One of the key tenets of neo darwinism is that evolution is driven by random mutations that are acted upon by natural selection.

Codon bias challenges neo darwinism in a number of ways. First, it shows that substitutions are biased. Some substitutes are more likely to be beneficial than others because they lead to the use of more efficient codons. This suggests that evolution is not random; rather change over time is guided and biased.

Second, codon bias can act on silent substitutions. Silent substitutions are changes that do not change the amino acid sequence of a protein, but they can change the codons that are used. 

Finally, codon bias shows that the genetic code is not just a random table of codons as with Darwin. 

Instead, the genetic code is optimized for efficiency and accuracy. Due to its improbable optimation  intelligent design theorists suggest that the genetic code may have been designed by some intelligent force.

Overall, codon bias is a complex phenomenon that has a number of implications for our understanding of change over time. 

In order to quantify codon bias these authors proposed the CAI: "The codon adaptation index: a measure of directional synonymous codon usage bias, and its potential applications", by Paul Sharp (Nucleic Acids Research, 1986)

The article discusses what codon usage bias is and why it is important. It also details a method for measuring codon usage bias called the codon adaptation index (CAI). The CAI is a measure of how well the codons in a gene are adapted to the codons that are most frequently used by the tRNAs in a cell. 

The article also discusses some potential applications of the CAI, such as predicting gene expression levels and assessing the adaptation of genes to their hosts.

The CAI is a measure of how well the codons in a gene are adapted to the codons that are most frequently used by the tRNAs in a cell.

Researchers have used the CAI to study the evolution of codon usage in viruses and other microorganisms.

Cas9-CRISPR DMS maps are replacing CAI (Codon Adaptation Index) in a number of ways, including:

• More accurate prediction of gene expression. CAI is a measure of how well a gene's codons are adapted to the tRNA pool of a particular organism. However, it has been shown to be inaccurate in predicting gene expression in some cases. Cas9-CRISPR DMS maps, on the other hand, are directly generated from gene expression data, making them more accurate for predicting gene expression levels.

• Ability to predict gene expression in different organisms. CAI is specific to a particular organism, while Cas9-CRISPR DMS maps can be used to predict gene expression in any organism with a sequenced genome. This makes them more versatile and useful for a wider range of applications.

• Ability to predict gene expression in different cell types and conditions. Cas9-CRISPR DMS maps can be generated from gene expression data from different cell types and conditions. This allows scientists to predict how gene expression will change in response to different stimuli or treatments.

Here are some specific examples of how Cas9-CRISPR DMS maps are being used to replace CAI:

• In synthetic biology, Cas9-CRISPR DMS maps are being used to design synthetic genes that are more efficiently expressed in the target organism. In gene therapy, 

• Cas9-CRISPR DMS maps are being used to identify and edit genes that are associated with disease. This could lead to new and more effective treatments for a variety of genetic disorders.

• In agriculture, Cas9-CRISPR DMS maps are being used to develop crops with improved traits, such as resistance to pests and diseases or increased yields.

Overall, Cas9-CRISPR DMS maps are a more powerful and versatile tool for predicting and manipulating gene expression than CAI. They are likely to replace CAI in many applications in the near future.

Cas9 CRISPR DMS maps are a tool for detecting nonneutral synonymous substitutions (NSS). NSS are synonymous substitutions that result in a change in the amino acid sequence of a protein. Cas9 CRISPR DMS maps work by first using Cas9 to create a double-stranded break in the DNA at a specific location. Then, dimethyl sulfate (DMS) is used to methylate the adenine bases in the single-stranded DNA. The methylated DNA is then sequenced, and the sequence is compared to the sequence of the unmethylated DNA. Any differences in sequence indicate that an NSS has occurred.

CAI is a relatively simple measure of codon usage, but it does not take into account all of the factors that can affect translation efficiency. 

Cas9 CRISPR DMS maps are a more sophisticated tool for detecting NSS, but they are also more complex and expensive to use. 

Cas9 CRISPR DMS maps have the potential to become a powerful tool for studying NSS in a wide range of organisms.

Cas9-CRISPR DMS mapping is a technique that can be used to measure the fitness effects of synonymous substitutions. If the synonymous substitution has a fitness (not natural selection) effect, it will affect how often the gene is transcribed. For example, a synonymous substitution that disrupts an important regulatory element may reduce the expression of the gene. This would lead to a decrease in the methylation of the DNA downstream of the regulatory element.

By comparing the methylation patterns of cells with and without the synonymous substitution, the researchers can determine whether the substitution has a fitness effect.

The Cas9-CRISPR DMS mapping technique is a more direct and accurate way to measure the fitness effects of synonymous substitutions than traditional methods such as Ka/Ks calculations. Ka/Ks calculations compare the rate of non-synonymous substitutions to the rate of synonymous substitutions in a gene. However, Ka/Ks calculations can be unreliable because they are based on the assumption that synonymous substitutions are neutral, meaning that they do not have a fitness effect.

60 years and tens of thousands of studies using Ka/Ks (or similar ratios) are incorrect in their claim that natural selection caused the changes they were studying.

The Cas9-CRISPR DMS mapping technique does not rely on this assumption, making it a more reliable way to measure the real fitness effects (again not natural selection) of synonymous substitutions.

Overall, Cas9-CRISPR DMS mapping is a promising new technique for studying the fitness effects of synonymous substitutions. It has the potential to provide more accurate and reliable results than traditional methods such as Ka/Ks calculations and determine at the nucleotide level what is the real fitness effect as it relates to adaptation.