Protist.guru: A Comparative Transcriptomics Database for Protists

Villanueva, Erielle Marie Fajardo; Lim, Peng Ken; Lim, Jolyn Jia Jia; Lim, Shan Chun; Lau, Pei Yi; Koh, Kenny Ting Sween; Tan, Emmanuel; Kairon, Ryanjit Singh; See, Wei An; Liao, Jian Xiang; Hee, Ker Min; Vijay, Varsheni; Maitra, Ishani; Boon, Chong Jun; Fo, Kevin; Wang, Yee Tat; Jaya, Ryan; Hew, Li Anne; Lim, Yong Yee; Lee, Wei Quan; Lee, Zhi Qi; Foo, Herman; Santos, Adriana Lopes dos; Mutwil, Marek

doi:10.1016/j.jmb.2022.167502

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…Accessions of these RNA-seq variations are not useful for de novo transcriptome assembly and it is important for the pipeline to identify these undesirable RNA-seq accessions. Previous approaches removed undesirable accessions based on how well their reads map to their transcriptome references during pseudoalignment (Lim, Davey, et al, 2022; Tan & Mutwil, 2020; Villanueva et al, 2022). Although high-quality transcriptome references are not available for species without genomes, we believe that pseudoalignment of accessions against Draft CDSs can similarly be used to identify undesirable accessions.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…For a fair comparison of clusters obtained from the draft and reference CDS, we limit embedding comparisons to RNA-seq accession selected from the draft CDS. The similarity of draft and CDS reference clusters was evaluated with the V-measure metric (Villanueva et al, 2022), and the significance of the similarity was obtained with a Monte Carlo-based hypothesis testing (see supplementary methods S6). For every species, we observed statistically significant similarities (P= 0.00001; Figure 5) between draft (DES) and reference (RES) CDS clusterings with observed similarity (in descending order) of 0.855 in C. albicans, 0.812 in C. papaya, 0.766 in A. thaliana and 0.705 in M. musculus (Figure 5).…”

Section: Sample Selection With Unsupervised Clusteringmentioning

confidence: 99%

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LSTrAP-denovo: Automated Generation of Transcriptome Atlases for Eukaryotic Species Without Genomes

Lim

Mutwil

2023

Preprint

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Structured AbstractMotivationDespite the abundance of species with transcriptomic data, a significant number of the species still lack genomes, making it difficult to study gene function and expression in these organisms. Whilede novotranscriptome assembly can be used to assemble protein-coding transcripts from RNA-sequencing (RNA-seq) data, the datasets used often only feature samples of arbitrarily-selected or similar experimental conditions which might fail to capture condition-specific transcripts.ResultsWe developed the Large-Scale Transcriptome Assembly Pipeline forde novoassembled transcripts (LSTrAP-denovo) to automatically generate transcriptome atlases of eukaryotic species. Specifically, given an NCBI TaxID, LSTrAP-denovocan (1) filter undesirable RNA-seq accessions based on read data, (2) select RNA-seq accessions via unsupervised machine learning to construct a sample-balanced dataset for download, (3) assemble transcripts via over-assembly, (4) functionally annotate coding sequences (CDS) from assembled transcripts and (5) generate transcriptome atlases in the form of expression matrices for downstream transcriptomic analyses.Availability and ImplementationLSTrAP-denovois easy to implement, written in python, and is freely available athttps://github.com/pengkenlim/LSTrAP-denovo/.Supplementary InformationSupplementary data are available in the forms of supplementary figures, supplementary tables, and supplementary methods.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Sample Selection With Unsupervised Clusteringmentioning

confidence: 99%

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LSTrAP-denovo: Automated Generation of Transcriptome Atlases for Eukaryotic Species Without Genomes

Lim

Mutwil

2023

Preprint

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“…Protist.guru is a database for the comparative transcriptomics of protists, which can be used for identifying different co-expressed genes, gene families, and co-expression clusters and finding functionally enriched co-expression clusters [ 73 ]. There were 2342 transcriptomes and gene expression data from 17 protist species in Protist.guru, which was validated in the metabolites prediction of secondary carotenoids in Haematococcus lacustris .…”

Section: Geo2rmentioning

confidence: 99%

Multi-omic analysis tools for microbial metabolites prediction

Wu,

Zhou,

Chen

et al. 2024

Briefings in Bioinformatics

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How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

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“…In the view of this, we are set to construct a co-expression analysis tool that can effectively mine predictive information on gene function and regulation from transcriptomic data. To this end, we utilize CoNekT (Co-expression Network Toolkit) (Proost and Mutwil, 2018), a popular framework that has been used to construct comparative transcriptomic databases for plants and species from other kingdoms (Ng et al, 2020; Tan and Mutwil, 2020; Lim et al, 2020; Lim et al, 2022; Villanueva et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Comparative transcriptome database forCamellia sinensisreveals genes important for flavonoid synthesis in tea plants

Zheng,

Mohd Ali,

Lim

et al. 2024

Preprint

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Tea, as one of the most popular beverages in the world, possesses a plethora of secondary metabolites that are beneficial to human health. Tea plants (Camellia sinensis) exhibit rich genetic diversity, where different cultivars can vary significantly in terms of yield, adaptability, morphology, and composition of secondary metabolites. Many tea cultivars have been the subject of much research interest, which have led to the accumulation of publicly available RNA-seq data. As such, it has become possible to systematically summarize the characteristics of different cultivars at the transcriptomic level, identify valuable functional genes, and infer gene functions through co-expression analysis. Here, the transcriptomes of 9 cultivars of Camellia sinensis were assembled and combined with the coding sequences of 13 cultivars of Camellia sinensis to study the differences and similarities of gene expression and biological functions among cultivars. To give access to this data, we present TeaNekT (https://teanekt.sbs.ntu.edu.sg/), a web resource that provides user-friendly tools and interactive visualizations that facilitates the prediction of gene functions of various tea cultivars. We used TeaNekT to perform cross-cultivar comparison of co-expressed gene neighborhoods, clusters, and tissue-specific gene expression. We show that the members of the chalcone synthase (CHS) gene family, important for flavonoid synthesis, exhibit the highest expression variability, specific expression in leaves and buds, and significant modulation by methyl jasmonate (MeJA) treatment. By using comparative co-expression tools of TeaNekT, we identified multiple conserved genes involved in flavonoid biosynthesis among cultivars that have not been previously studied, warranting further research.

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Protist.guru: A Comparative Transcriptomics Database for Protists

Cited by 10 publications

References 23 publications

LSTrAP-denovo: Automated Generation of Transcriptome Atlases for Eukaryotic Species Without Genomes

LSTrAP-denovo: Automated Generation of Transcriptome Atlases for Eukaryotic Species Without Genomes

Multi-omic analysis tools for microbial metabolites prediction

Comparative transcriptome database forCamellia sinensisreveals genes important for flavonoid synthesis in tea plants

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