tspex: a tissue-specificity calculator for gene expression data

Camargo, Antônio Pedro; Vasconcelos, Adrielle Ayumi de; Fiamenghi, Mateus Bernabe; Pereira, Gonçalo Amarante Guimarães; Carazzolle, Marcelo F.

doi:10.21203/rs.3.rs-51998/v1

Cited by 30 publications

(28 citation statements)

References 15 publications

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“…All-versus-all pairwise comparisons between proteins in each family were performed using Orthofinder v2.5.2 [ 40 ], clustering the proteins into orthogroups (orthologous gene cluster), and generating count matrices with the number of genes within each orthogroup per genome. To identify orthogroups that are exclusive to the oil reservoir MAGs (environment-specific orthogroups), tspex v0.6.2 [ 41 ] was used to calculate the specificity measure (SPM) of each orthogroup from the matrices. Only orthogroups with at least three genes were used.…”

Section: Methodsmentioning

confidence: 99%

Genome-Resolved Meta-Analysis of the Microbiome in Oil Reservoirs Worldwide

et al. 2021

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Microorganisms inhabiting subsurface petroleum reservoirs are key players in biochemical transformations. The interactions of microbial communities in these environments are highly complex and still poorly understood. This work aimed to assess publicly available metagenomes from oil reservoirs and implement a robust pipeline of genome-resolved metagenomics to decipher metabolic and taxonomic profiles of petroleum reservoirs worldwide. Analysis of 301.2 Gb of metagenomic information derived from heavily flooded petroleum reservoirs in China and Alaska to non-flooded petroleum reservoirs in Brazil enabled us to reconstruct 148 metagenome-assembled genomes (MAGs) of high and medium quality. At the phylum level, 74% of MAGs belonged to bacteria and 26% to archaea. The profiles of these MAGs were related to the physicochemical parameters and recovery management applied. The analysis of the potential functional core in the reservoirs showed that the microbiota was specialized for each site, with 31.7% of the total KEGG orthologies annotated as functions (1690 genes) common to all oil fields, while 18% of the functions were site-specific, i.e., present only in one of the oil fields. The oil reservoirs with a lower level of intervention were the most similar to the potential functional core, while the oil fields with a long history of water injection had greater variation in functional profile. These results show how key microorganisms and their functions respond to the distinct physicochemical parameters and interventions of the oil field operations such as water injection and expand the knowledge of biogeochemical transformations in these ecosystems.

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Section: Methodsmentioning

confidence: 99%

Genome-Resolved Meta-Analysis of the Microbiome in Oil Reservoirs Worldwide

et al. 2021

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“…For all further analysis, the mean expression value from the three replicates was used. For each transcriptome, we performed tissue specificity analysis using the tspex program ( Camargo et al, 2020 ) considering a threshold of SPM > 0.95 to identify tissue-specific transcripts. Annotation of the whole transcriptome was performed with PANNZER2 ( Törönen, Medlar & Holm, 2018 ) and remaining unannotated sequences were submitted to a BLASTp against Uniref90 ( E -value 1e−5).…”

Section: Methodsmentioning

confidence: 99%

Fungal communities represent the majority of root-specific transcripts in the transcriptomes of Agave plants grown in semiarid regions

Marone

Campanari

Raya

et al. 2022

PeerJ

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Agave plants present drought resistance mechanisms, commercial applications, and potential for bioenergy production. Currently, Agave species are used to produce alcoholic beverages and sisal fibers in semi-arid regions, mainly in Mexico and Brazil. Because of their high productivities, low lignin content, and high shoot-to-root ratio, agaves show potential as biomass feedstock to bioenergy production in marginal areas. Plants host many microorganisms and understanding their metabolism can inform biotechnological purposes. Here, we identify and characterize fungal transcripts found in three fiber-producing agave cultivars (Agave fourcroydes, A. sisalana, and hybrid 11648). We used leaf, stem, and root samples collected from the agave germplasm bank located in the state of Paraiba, in the Brazilian semiarid region, which has faced irregular precipitation periods. We used data from a de novo assembled transcriptome assembly (all tissues together). Regardless of the cultivar, around 10% of the transcripts mapped to fungi. Surprisingly, most root-specific transcripts were fungal (58%); of these around 64% were identified as Ascomycota and 28% as Basidiomycota in the three communities. Transcripts that code for heat shock proteins (HSPs) and enzymes involved in transport across the membrane in Ascomycota and Basidiomycota, abounded in libraries generated from the three cultivars. Indeed, among the most expressed transcripts, many were annotated as HSPs, which appear involved in abiotic stress resistance. Most HSPs expressed by Ascomycota are small HSPs, highly related to dealing with temperature stresses. Also, some KEGG pathways suggest interaction with the roots, related to transport to outside the cell, such as exosome (present in the three Ascomycota communities) and membrane trafficking, which were further investigated. We also found chitinases among secreted CAZymes, that can be related to pathogen control. We anticipate that our results can provide a starting point to the study of the potential uses of agaves’ fungi as biotechnological tools.

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“…Z-scores and Jensen-Shannon scores (JS) were calculated based on the mean FPKM values from each tissue using the tspex Python script ( , accessed on 30 March 2021) [ 54 ]. Heatmap plots and a density plot were drawn using the ggplot2 R package.…”

Section: Methodsmentioning

confidence: 99%

“…Distribution of lncRNAs on the barley genome was visualized using the chromPlot R package [53] 4.4. Evaluation of lncRNA Tissue Specificity Z-scores and Jensen-Shannon scores (JS) were calculated based on the mean FPKM values from each tissue using the tspex Python script (https://github.com/apcamargo/ tspex/, accessed on 30 March 2021) [54]. Heatmap plots and a density plot were drawn using the ggplot2 R package.…”

Section: Identification Of Lncrna Transcriptsmentioning

confidence: 99%

Genome-Wide Identification of Barley Long Noncoding RNAs and Analysis of Their Regulatory Interactions during Shoot and Grain Development

Gasparis

Przyborowski

Nadolska‐Orczyk

2021

IJMS

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Long noncoding RNAs (lncRNAs) are a class of RNA molecules with gene regulatory functions in plant development and the stress response. Although the number of lncRNAs identified in plants is rapidly increasing, very little is known about their role in barley development. In this study, we performed global identification of barley lncRNAs based on 53 RNAseq libraries derived from nine different barley tissues and organs. In total, 17,250 lncRNAs derived from 10,883 loci were identified, including 8954 novel lncRNAs. Differential expression of lncRNAs was observed in the developing shoot apices and grains, the two organs that have a direct influence on the final yield. The regulatory interaction of differentially expressed lncRNAs with the potential target genes was evaluated. We identified 176 cis-acting lncRNAs in shoot apices and 424 in grains, while the number of trans-acting lncRNAs in these organs was 1736 and 540, respectively. The potential target protein-coding genes were identified, and their biological function was annotated using MapMan ontology. This is the first insight into the roles of lncRNAs in barley development on the genome-wide scale, and our results provide a solid background for future functional studies.

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tspex: a tissue-specificity calculator for gene expression data

Cited by 30 publications

References 15 publications

Genome-Resolved Meta-Analysis of the Microbiome in Oil Reservoirs Worldwide

Genome-Resolved Meta-Analysis of the Microbiome in Oil Reservoirs Worldwide

Fungal communities represent the majority of root-specific transcripts in the transcriptomes of Agave plants grown in semiarid regions

Genome-Wide Identification of Barley Long Noncoding RNAs and Analysis of Their Regulatory Interactions during Shoot and Grain Development

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