Skip to content

Two mitochondrial genomes and the phylogenetic relationships of Silpha (Coleoptera: Staphylinidae: Silphinae)

Sep 2026 · Zootaxa · 0 citations
Genomics and Phylogenetic Studies

Abstract

Silpha carinata (Herbst, 1783), and Silpha perforata (Gebler, 1832) (Coleoptera: Staphylinidae) belong to the tribe Silphini, which holds forensic significance due to its species’ rapid colonization of cadavers and relatively long developmental duration. In this study, we reported the complete mitochondrial genomes of S. carinata and S. perforata, representing the first fully sequenced mitochondrial genomes for the genus Silpha. These genomes were acquired via next-generation sequencing and then thoroughly characterized. We examined their sequence structure, base composition, codon usage bias, and the secondary structures of tRNA genes. Phylogenetic trees were generated using Bayesian inference (BI) and maximum likelihood (ML) methods. The results revealed that the mitochondrial genomes (mtDNA) of the two species are 16,322 bp (S. carinata) and 15,469 bp (S. perforata) in length, respectively, containing 13 protein-coding genes (PCGs), 22 transfer RNA (tRNA) genes, and two ribosomal RNA (rRNA) genes. Their gene arrangement is highly conserved compared to other Coleoptera species. The mtDNA exhibit a significant AT bias, with A+T contents of 74.6% (S. carinata) and 74.4% (S. perforata). Among the 22 tRNA gene secondary structures, the tRNA-Phe and tRNA-His genes of both S. carinata and S. perforata lack the TψC arm. In contrast, all other tRNA genes can fold into the canonical cloverleaf secondary structure. Phylogenetic analyses support the monophyly of the genus Silpha, with S. carinata and S. perforata showing close evolutionary relationships. This research enhances our understanding of mitochondrial genomics and the evolutionary relationships within Silphinae.

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

Google DeepMind Blog Nov 25, 2025

AlphaFold: Five years of impact

Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.