Next-generation sequencing has revolutionized entomological study, rendering it possible to analyze the genomes and transcriptomes of non-model insects. However, use of this technology is often limited to obtaining the nucleotide sequences of target or related genes, with many of the acquired sequences remaining unused because other available sequences are not sufficiently annotated. To address this issue, we have developed a functional annotation workflow for transcriptome-sequenced insects to determine transcript descriptions, which represents a significant improvement over the previous method (functional annotation pipeline for insects). The developed workflow attempts to annotate not only the protein sequences obtained from transcriptome analysis but also the ncRNA sequences obtained simultaneously. In addition, the workflow integrates the expression-level information obtained from transcriptome sequencing for application as functional annotation information. Using the workflow, functional annotation was performed on the sequences obtained from transcriptome sequencing of the stick insect () and silkworm (), yielding richer functional annotation information than that obtained in our previous study. The improved workflow allows the more comprehensive exploitation of transcriptome data and is applicable to other insects because the workflow has been openly developed on GitHub.
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http://dx.doi.org/10.3390/insects13070586 | DOI Listing |
Lett Appl Microbiol
January 2025
Clinical Laboratory, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University.
MRSA's resistance poses a global health challenge. This study investigates lysine succinylation in MRSA using proteomics and bioinformatics approaches to uncover metabolic and virulence mechanisms, with the goal of identifying novel therapeutic targets. Mass spectrometry and bioinformatics analyses mapped the MRSA succinylome, identifying 8 048 succinylation sites on 1 210 proteins.
View Article and Find Full Text PDFHum Mol Genet
January 2025
Laboratory Medicine and Pathology, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN, 55455, USA.
Background: Individuals with cystic fibrosis (CF; a recessive disorder) have an increased risk of colorectal cancer (CRC). Evidence suggests individuals with a single CFTR variant may also have increased CRC risk.
Methods: Using population-based studies (GECCO, CORECT, CCFR, and ARIC; 53 785 CRC cases and 58 010 controls), we tested for an association between the most common CFTR variant (Phe508del) and CRC risk.
BMC Genom Data
January 2025
Department of Applied Biosciences, College of Agriculture and Life Sciences, Kyungpook National University, Daegu, 41566, Republic of Korea.
Objectives: The data were collected to obtain the complete genome sequence of Pseudarthrobacter sp. NIBRBAC000502770, isolated from the rhizosphere of Sasamorpha in a heavy metal-contaminated coal mine in Hongcheon, Republic of Korea. The objective was to explore the strain's genetic potential for plant growth promotion and heavy metal resistance, particularly arsenate and copper.
View Article and Find Full Text PDFSci Data
January 2025
Department of Biotechnology, University of the Western Cape, Bellville, South Africa.
Drought and heat stress significantly limit crop growth and productivity. Their simultaneous occurrence, as often observed in summer crops, leads to larger yield losses. Sorghum is well adapted to dry and hot conditions.
View Article and Find Full Text PDFNat Commun
January 2025
National-Local Joint Engineering Laboratory of Druggability and New Drug Evaluation, National Engineering Research Center for New Drug and Druggability (cultivation), Guangdong Province Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006, China.
Epitranscriptomic modifications, particularly N6-methyladenosine (mA), are crucial regulators of gene expression, influencing processes such as RNA stability, splicing, and translation. Traditional computational methods for detecting mA from Nanopore direct RNA sequencing (DRS) data are constrained by their reliance on experimentally validated labels, often resulting in the underestimation of modification sites. Here, we introduce pum6a, an innovative attention-based framework that integrates positive and unlabeled multi-instance learning (MIL) to address the challenges of incomplete labeling and missing read-level annotations.
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