A degeneration-reducing criterion for optimal digital mapping of genetic codes

Abstract

Bioinformatics may seem to be a scientific field processing primarily large string datasets, as nucleotides and amino acids are represented with dedicated characters. On the other hand, many computational tasks that bioinformatics challenges are mathematical problems understandable as operations with digits. In fact, many computational tasks are solved this way in the background. One of the most widely used digital representations is mapping of nucleotides and amino acids with integers 0–3 and 0–20, respectively. The limitation of this mapping occurs when the digital signal of nucleotides has to be translated into a digital signal of amino acids as the genetic code is degenerated. This causes non-monotonies in a mapping function. Although map for reducing this undesirable effect has already been proposed, it is defined theoretically and for standard genetic codes only. In this study, we derived a novel optimal criterion for reducing the influence of degeneration by utilizing a large dataset of real sequences with various genetic codes. As a result, we proposed a new robust global optimal map suitable for any genetic code as well as specialized optimal maps for particular genetic codes.

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

“…The evaluation criteria of all recurrent neural networks were defined as accuracy, precision, recall, f1-score, and AUC scores. The calculation formula of these metrics can be seen in [32,39,40]. Classification of protein families was performed with four different application scenarios.…”

“…Yet, considering all the results, EIIP, and hydrophobicity method cannot be said to very successful. The reason for this may be that these methods have become degenerated [20,39], since different amino acids can be mapped to same numbers (Q, and N in hydrophobicity, I, and L in EIIP). Further, the performance of the random encoded method depends on the number generated.…”

The metazoan landscape of mitochondrial DNA gene order and content is shaped by selection and affects mitochondrial transcription

“…Reading frames were validated by the presence of a starting codon on the 5′-end and a STOP codon on the 3′. The mitochondrial codons were translated based on taxon-specific mitochondrial translation tables (see NCBI Genetic Codes) 82 . In some organisms, the STOP codon was missing from certain mtDNA encoded sequences, as it is known to be added post-transcriptionally 83 .…”