GC skew and mitochondrial origins of replication

Long

et al. 2019

Genes

A complete mitogenome of Trachys auricollis is reported, and a mitogenome-based phylogenetic tree of Elateriformia with all protein-coding genes (PCGs), rRNAs, and tRNAs is presented for the first time. The complete mitochondrial genome of T. auricollis is 16,429 bp in size and contains 13 PCGs, two rRNA genes, 22 tRNA genes, and an A + T-rich region. The A + T content of the entire genome is approximately 71.1%, and the AT skew and GC skew are 0.10 and −0.20, respectively. According to the the nonsynonymous substitution rate to synonymous substitution rates (Ka/Ks) of all PCGs, the highest and lowest evolutionary rates were observed for atp8 and cox1, respectively, which is a common finding among animals. The start codons of all PCGs are the typical ATN. Ten PCGs have complete stop codons, but three have incomplete stop codons with T or TA. As calculated based on the relative synonymous codon usage (RSCU) values, UUA(L) is the codon with the highest frequency. Except for trnS1, all 22 tRNA genes exhibit typical cloverleaf structures. The A + T-rich region of T. auricollis is located between rrnS and the trnI-trnG-trnM gene cluster, with six 72-bp tandem repeats. Both maximum likelihood (ML) and Bayesian (BI) trees suggest that Buprestoidea is close to Byrrhoidea and that Buprestoidea and Byrrhoidea are sister groups of Elateroidea, but the position of Psephenidae is undetermined. The inclusion of tRNAs might help to resolve the phylogeny of Coleoptera.

Section: Genome Organization and Base Compositionmentioning

confidence: 99%

Complete Mitogenome of a Leaf-Mining Buprestid Beetle, Trachys auricollis, and Its Phylogenetic Implications

Xiao

Long

et al. 2019

Genes

“…Cumulative skew plots have been used to indirectly infer ORI events. Most vertebrate mitogenomes have two origins of replication (OR): the strand uncoupling begins at the Hstrand OR (O H ), located within the control region (D-loop or CR), and proceeds unidirectionally until the L-strand OR (O L ) in the WANCY tRNA cluster is exposed (Sahyoun et al 2014). As a result, the skew magnitude exhibits a gradient corresponding to the duration of time that the H-strand spends in the mutagenic single-stranded state: the genes closest to the O L in the direction of L-strand replication exhibit the lowest skews, and the genes closest to O H exhibit the highest skews (Reyes et al 1998a;Faith and Pollock 2003).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary history of inversions in the direction of architecture-driven mutational pressures in crustacean mitochondrial genomes

Zou

et al. 2020

Preprint

Inversions of the origin of replication (ORI) of mitochondrial genomes produce asymmetrical mutational pressures that can cause artefactual clustering in phylogenetic analyses. It is therefore an absolute prerequisite for all molecular evolution studies that use mitochondrial data to account for ORI events in the evolutionary history of their dataset. The number of ORI events in crustaceans remains unknown; several studies reported ORI events in some crustacean lineages on the basis of fully inversed (e.g. negative vs. positive) GC skew patterns, but studies of isolated lineages could have easily overlooked ORI events that produced merely a reduction in the skew magnitude. In this study, we used a comprehensive taxonomic approach to systematically study the evolutionary history of ORI events in crustaceans using all available mitogenomes and combining signals from lineage-specific skew magnitude and direction (+ or -), cumulative skew diagrams, and gene rearrangements.We inferred 24 putative ORI events (14 of which have not been proposed before): 17 with relative confidence, and 7 speculative. Most of these were located at lower taxonomic levels, but there are indications of ORIs that occurred at or above the order-level: Copepoda, Isopoda, and putatively in Branchiopoda and Poecilostomatida+Cyclopoida. Several putative ORI events did not result in fully inversed skews. In many lineages skew plots were not informative for the prediction of replication origin and direction of mutational pressures, but inversions of the mitogenome fragment comprising the ancestral CR (rrnS-CR-trnI) were rather good predictors of skew inversions. We also found that skew plots can be a useful tool to indirectly infer the relative strengths of mutational/purifying pressures in some crustacean lineages: when purifying pressures outweigh mutational, GC skew plots are strongly affected by the strand distribution of genes, and when mutational > purifying, GC skew plots can be even completely (apparently) unaffected by the strand distribution of genes. This observation has very important repercussions for phylogenetic and evolutionary studies, as it implies that not only the relatively rare ORI events, but also much more common gene strand switches and same-strand rearrangements can produce mutational bursts, which in turn affect phylogenetic and evolutionary analyses. We argue that such compositional biases may produce misleading signals not only in phylogenetic but also in other types of evolutionary analyses (dN/dS ratios, codon usage bias, base composition, branch length comparison, etc.), and discuss several such examples. Therefore, all studies aiming to study the evolution of mtDNA sequences should pay close attention to architectural rearrangements.

“…Given that the rho-independent terminators are rich in GC base pairs, we extracted a set of features and collectively referred to as Base-content[15, 16]. Specifically, we mainly obtained the content features of the single nucleotide(A, C, G, T) in each DNA sequence[17, 18].…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, we mainly obtained the content features of the single nucleotide(A, C, G, T) in each DNA sequence[17, 18]. In this paper, 5 kinds of base content features(atContent, gcContent, gcSkew, atSkew, atgcRatio)[15, 16, 19-21] were took into account. where m G i, m C i are the contents of G and C in the i -th sequence, respectively.…”

Section: Methodsmentioning

confidence: 99%

iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC

Fan

Wang

Zhu

2020

Preprint

23Terminator is a DNA sequence that give the RNA polymerase the transcriptional 24 termination signal. Identifying terminators correctly can optimize the genome 25 annotation, more importantly, it has considerable application value in disease diagnosis 26 and therapies. However, accurate prediction methods are deficient and in urgent need.27 Therefore, we proposed a prediction method "iterb-PPse" for terminators by 28 incorporating 47 nucleotide properties into PseKNC-Ⅰ and PseKNC-Ⅱ and utilizing 29 Extreme Gradient Boosting to predict terminators based on Escherichia coli and 30 Bacillus subtilis. Combing with the preceding methods, we employed three new feature 31 extraction methods K-pwm, Base-content, Nucleotidepro to formulate raw samples. 32 The two-step method was applied to select features. When identifying terminators 33 based on optimized features, we compared five single models as well as 16 ensemble 34 models. As a result, the accuracy of our method on benchmark dataset achieved 35 99.88%, higher than the existing state-of-the-art predictor iTerm-PseKNC in 100 times 36 five-fold cross-validation test. It's prediction accuracy for two independent datasets 37 reached 94.24% and 99.45% respectively. For the convenience of users, a software was 38 developed with the same name on the basis of "iterb-PPse". The open software and 39 source code of "iterb-PPse" are available at https://github.com/Sarahyouzi/iterb-PPse. 3 40 1 Introduction 41 DNA transcription is an important step in the inheritance of genetic information 42 and terminators control the termination of transcription which exists in sequences that 43 have been transcribed. When transcription, the terminator will give the RNA 44 polymerase the transcriptional termination signal. Identifying terminators accurately 45 can optimize the genome annotation, more importantly, it has great application value 46 in disease diagnosis and therapies, so it is crucial to identify terminators. Whereas, 47 using traditional biological experiments to identify terminators is extremely time 48 consuming and labor intensive. Therefore, a more effective and convenient began to be 49 applied in researches, that is, adopting machine learning to identify gene sequences. 50 Previous research found there are two types of terminators in prokaryotes, namely 51 Rho-dependent and Rho-independent[1], as shown in Fig 1. Although there have been 52 a lot of studies on the prediction of terminators, most of them only focused on one kind 53 of them. In 2004, Wan XF, Xu D et al. proposed a prediction method for Rho-54 independent terminators with an accuracy of 92.25%. In 2005, Michiel J. L. de Hoon 55 et al. studied the sequence of Rho-independent terminators in B. subtilis[2], and the 56 final prediction accuracy was 94%. In 2011, Magali Naville et al. conducted a research 57 on Rho-dependent transcriptional terminators[3]. They used two published algorithms, 58 Erpin and RNA motif, to predict terminators. The specificity and sensitivity of the final 59 results were 95.3% and 87.8...