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

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

Mitochondrial DNA (mtDNA) harbors essential genes in most metazoans, yet the regulatory impact of the multiple evolutionary mtDNA rearrangements has been overlooked. Here, by analyzing mtDNAs from ~8000 metazoans we found high gene content conservation (especially of protein and rRNA genes), and codon preferences for mtDNA-encoded tRNAs across most metazoans. In contrast, mtDNA gene order (MGO) was selectively constrained within but not between phyla, yet certain gene stretches (ATP8-ATP6, ND4-ND4L) were highly conserved across metazoans. Since certain metazoans with different MGOs diverge in mtDNA transcription, we hypothesized that evolutionary mtDNA rearrangements affected mtDNA transcriptional patterns. As a first step to test this hypothesis, we analyzed available RNA-seq data from 53 metazoans. Since polycistron mtDNA transcripts constitute a small fraction of the steady-state RNA, we enriched for polycistronic boundaries by calculating RNA-seq read densities across junctions between gene couples encoded either by the same strand (SSJ) or by different strands (DSJ). We found that organisms whose mtDNA is organized in alternating reverse-strand/forward-strand gene blocks (mostly arthropods), displayed significantly reduced DSJ read counts, in contrast to organisms whose mtDNA genes are preferentially encoded by one strand (all chordates). Our findings suggest that mtDNA rearrangements are selectively constrained and likely impact mtDNA regulation.

Mapping sequence to feature vector using numerical representation of codons targeted to amino acids for alignment-free sequence analysis

The phylogenetic analysis based on sequence similarity targeted to real biological taxa is one of the major challenging tasks. In this paper, we propose a novel alignment-free method, CoFASA (Codon Feature based Amino acid Sequence Analyser), for similarity analysis of nucleotide sequences. At first, we assign numerical weights to the four nucleotides. We then calculate a score of each codon based on the numerical value of the constituent nucleotides, termed as degree of codons. Accordingly, we obtain the degree of each amino acid based on the degree of codons targeted towards a specific amino acid. Utilizing the degree of twenty amino acids and their relative abundance within a given sequence, we generate 20-dimensional features for every coding DNA sequence or protein sequence. We use the features for performing phylogenetic analysis of the set of candidate sequences. We use multiple protein sequences derived from Beta-globin (BG), NADH dehydrogenase subunit 5 (ND5), Transferrins (TFs), Xylanases, low identity (<40%) and high identity (⩾40%) protein sequences (encompassing 533 and 1064 protein families) for experimental assessments. We compare our results with sixteen (16) well-known methods, including both alignment-based and alignment-free methods. Various assessment indices are used, such as the Pearson correlation coefficient, RF (Robinson-Foulds) distance and ROC score for performance analysis. While comparing the performance of CoFASA with alignment-based methods (ClustalW, ClustalΩ, MAFFT, and MUSCLE), it shows very similar results. Further, CoFASA shows better performance in comparison to well-known alignment-free methods, including LZW-Kernal, jD2Stat, FFP, spaced, and AFKS-D2s in predicting taxonomic relationship among candidate taxa. Overall, we observe that the features derived by CoFASA are very much useful in isolating the sequences according to their taxonomic labels. While our method is cost-effective, at the same time, produces consistent and satisfactory outcomes.

p-Adic hierarchical properties of the genetic code

In this article, we consider p-adic modeling of the standard genetic code and the vertebrate mitochondrial one. To this end, we use 5-adic and 2-adic distance as a mathematical tool to describe closeness (nearness, similarity) between codons as elements of a bioinformation space. Codons which are simultaneously at the smallest 5-adic and 2-adic distance code the same (or similar) amino acid or stop signal. The set of codons is presented as an ultrametric tree as well as a fractal and p-adic network. It is shown that genetic code can be treated as sequential translation between genetic languages. This p-adic approach gives possibility to be applied to sequences of nucleotides of an arbitrary finite length. We present an overview of published and some new results on various p-adic properties of the genetic code.