Coffee, an important agricultural product for tropical producing countries, is facing challenges due to climate change, including periods of drought, irregular rain distribution, and high temperatures. These changes result in plant water stress, leading to significant losses in coffee productivity and quality. Understanding the processes that affect coffee flowering is crucial for improving productivity and quality. In this chapter, we describe a protocol for transcriptome analysis using available Internet software, mainly in the Galaxy Platform, using RNA-Seq data from flowers collected from different parts of the coffee tree. The methods presented in this chapter provide a comprehensive protocol for transcriptome analysis of differentially expressed genes from flowers of coffee plant. This knowledge can be utilized in coffee genetic improvement programs, particularly in the selection of cultivars that are tolerant to water deficit.
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http://dx.doi.org/10.1007/978-1-0716-3778-4_15 | DOI Listing |
J Vis Exp
January 2025
Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota;
Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the characterization of microbial proteins present in low abundance relative to host proteins is difficult.
View Article and Find Full Text PDFGenes (Basel)
January 2025
Laboratório de Citogenética de Insetos, Departamento de Biologia Geral, Universidade Federal de Viçosa, Campus Universitário, Viçosa 36570-900, Minas Gerais, Brazil.
Background/objectives: A striking feature of the karyotypes of stingless bees is the large amount of heterochromatin present in most species. Cytogenomic studies performed in some Meliponini species have suggested that evolutionary events related to the diversification and amplification of satellite DNA families in the heterochromatin may reflect the structuring of phylogenetic clades in this tribe. In this study, we performed a genomic analysis in to characterize different satDNA families in its genome.
View Article and Find Full Text PDFBraz J Microbiol
January 2025
Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo (USP), São Paulo, SP, 05508-900, Brazil.
Despite meticulous precautions, contamination of genomic DNA samples is not uncommon, which can significantly compromise the analysis of microorganisms' whole-genome sequencing data, thus affecting all subsequent analyses. Thanks to advancements in software and bioinformatics techniques, it is now possible to address this issue and prevent the loss of the entire dataset obtained in a contaminated whole-genome sequencing, where the DNA of another bacterium is present. In this study, it was observed that the sequencing reads from Streptomyces sp.
View Article and Find Full Text PDFBMC Genomics
January 2025
Department of Medical Microbiology and Infectious Diseases, Erasmus University Medical Centre (Erasmus MC), Rotterdam, The Netherlands.
Background: The Joint Programming Initiative on Antimicrobial Resistance (JPIAMR) networks 'Seq4AMR' and 'B2B2B AMR Dx' were established to promote collaboration between microbial whole genome sequencing (WGS) and antimicrobial resistance (AMR) stakeholders. A key topic discussed was the frequent variability in results obtained between different microbial WGS-related AMR gene prediction workflows. Further, comparative benchmarking studies are difficult to perform due to differences in AMR gene prediction accuracy and a lack of agreement in the naming of AMR genes (semantic conformity) for the results obtained.
View Article and Find Full Text PDFBMC Genomics
January 2025
Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium.
The influx of whole genome sequencing (WGS) data in the public health and clinical diagnostic sectors has created a need for data analysis methods and bioinformatics expertise, which can be a bottleneck for many laboratories. At Sciensano, the Belgian national public health institute, an intuitive and user-friendly bioinformatics tool portal was implemented using Galaxy, an open-source platform for data analysis and workflow creation. The Galaxy @Sciensano instance is available to both internal and external scientists and offers a wide range of tools provided by the community, complemented by over 50 custom tools and pipelines developed in-house.
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