Browsing by Author "De Moro, Gianluca"
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- A genome-wide transcriptome map of pistachio (Pistacia vera L.) provides novel insights into salinity-related genes and marker discoveryPublication . Moazzzam Jazi, Maryam; Seyedi, Seyed M.; Ebrahimie, Esmaeil; Ebrahimi, Mansour; De Moro, Gianluca; Botanga, ChristopherBackground Pistachio (Pistacia vera L.) is one of the most important commercial nut crops worldwide. It is a salt-tolerant and long-lived tree, with the largest cultivation area in Iran. Climate change and subsequent increased soil salt content have adversely affected the pistachio yield in recent years. However, the lack of genomic/global transcriptomic sequences on P. vera impedes comprehensive researches at the molecular level. Hence, whole transcriptome sequencing is required to gain insight into functional genes and pathways in response to salt stress. Results RNA sequencing of a pooled sample representing 24 different tissues of two pistachio cultivars with contrasting salinity tolerance under control and salt treatment by Illumina Hiseq 2000 platform resulted in 368,953,262 clean 100 bp paired-ends reads (90 Gb). Following creating several assemblies and assessing their quality from multiple perspectives, we found that using the annotation-based metrics together with the length-based parameters allows an improved assessment of the transcriptome assembly quality, compared to the solely use of the length-based parameters. The generated assembly by Trinity was adopted for functional annotation and subsequent analyses. In total, 29,119 contigs annotated against all of five public databases, including NR, UniProt, TAIR10, KOG and InterProScan. Among 279 KEGG pathways supported by our assembly, we further examined the pathways involved in the plant hormone biosynthesis and signaling as well as those to be contributed to secondary metabolite biosynthesis due to their importance under salinity stress. In total, 11,337 SSRs were also identified, which the most abundant being dinucleotide repeats. Besides, 13,097 transcripts as candidate stress-responsive genes were identified. Expression of some of these genes experimentally validated through quantitative real-time PCR (qRT-PCR) that further confirmed the accuracy of the assembly. From this analysis, the contrasting expression pattern of NCED3 and SOS1 genes were observed between salt-sensitive and salt-tolerant cultivars. Conclusion This study, as the first report on the whole transcriptome survey of P. vera, provides important resources and paves the way for functional and comparative genomic studies on this major tree to discover the salinity tolerance-related markers and stress response mechanisms for breeding of new pistachio cultivars with more salinity tolerance.
- metaGOflow: a workflow for the analysis of marine genomic observatories shotgun metagenomics dataPublication . Zafeiropoulos, Haris; Beracochea, Martin; Ninidakis, Stelios; Exter, Katrina; Potirakis, Antonis; De Moro, Gianluca; Richardson, Lorna; Corre, Erwan; Machado, João Paulo; Pafilis, Evangelos; Kotoulas, Georgios; Santi, Ioulia; Finn, Robert D; J. Cox, Cymon; Pavloudi, ChristinaBackground: Genomic Observatories (GOs) are sites of long-term scientific study that undertake regular assessments of the genomic biodiversity. The European Marine Omics Biodiversity Observation Network (EMO BON) is a network of GOs that conduct regular biological community samplings to generate environmental and metagenomic data of microbial communities from designated marine stations around Europe. The development of an effective workflow is essential for the analysis of the EMO BON metagenomic data in a timely and reproducible manner. Findings: Based on the established MGnify resource, we developed metaGOflow. meta GOflow supports the fast inference of taxonomic profiles from GO-derived data based on ribosomal RNA genes and their functional annotation using the raw reads. Thanks to the Research Object Crate packaging, relevant metadata about the sample under study, and the details of the bioinformatics analysis it has been subjected to, are inherited to the data product while its modular implementation allows running the workflow partially. The analysis of 2 EMO BON samples and 1 Tara Oceans sample was performed as a use case. Conclusions: metaGOflow is an efficient and robust workflow that scales to the needs of projects producing big metagenomic data such as EMO BON. It highlights how containerization technologies along with modern workflow languages and metadata package approaches can support the needs of researchers when dealing with ever-increasing volumes of biological data. Despite being initially oriented to address the needs of EMO BON, metaGOflowis a flexible and easy-to-use workflow that can be broadly used for one-sample-at-a-time analysis of shotgun metagenomics data.