MyCLADE: a multi-source domain annotation server for sequence functional exploration.
Ontology highlight
ABSTRACT:
SUBMITTER: Vicedomini R
PROVIDER: S-EPMC8262732 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism and one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires the reconstruction of biochemical and regulatory pathways at the community level in a given environment. In order to understand how environmental factors affect the genetic material and the dynamics of the expression from one environment to another, we want to evaluate the quantity of gene protein sequences or transcripts associated to a given pathway by precisely estimating the abundance of protein domains, their weak presence or absence in environmental samples. RESULTS:MetaCLADE is a novel profile-based domain annotation pipeline based on a multi-source domain annotation strategy. It applies directly to reads and improves identification of the catalog of functions in microbiomes. MetaCLADE is applied to simulated data and to more than ten metagenomic and metatranscriptomic datasets from different environments where it outperforms InterProScan in the number of annotated domains. It is compared to the state-of-the-art non-profile-based and profile-based methods, UProC and HMM-GRASPx, showing complementary predictions to UProC. A combination of MetaCLADE and UProC improves even further the functional annotation of environmental samples. CONCLUSIONS:Learning about the functional activity of environmental microbial communities is a crucial step to understand microbial interactions and large-scale environmental impact. MetaCLADE has been explicitly designed for metagenomic and metatranscriptomic data and allows for the discovery of patterns in divergent sequences, thanks to its multi-source strategy. MetaCLADE highly improves current domain annotation methods and reaches a fine degree of accuracy in annotation of very different environments such as soil and marine ecosystems, ancient metagenomes and human tissues.
| S-EPMC6114274 | biostudies-literature
Project description:SNPnexus is a web-based annotation tool for the analysis and interpretation of both known and novel sequencing variations. Since its last release, SNPnexus has received continual updates to expand the range and depth of annotations provided. SNPnexus has undergone a complete overhaul of the underlying infrastructure to accommodate faster computational times. The scope for data annotation has been substantially expanded to enhance biological interpretations of queried variants. This includes the addition of pathway analysis for the identification of enriched biological pathways and molecular processes. We have further expanded the range of user directed annotation fields available for the study of cancer sequencing data. These new additions facilitate investigations into cancer driver variants and targetable molecular alterations within input datasets. New user directed filtering options have been coupled with the addition of interactive graphical and visualization tools. These improvements streamline the analysis of variants derived from large sequencing datasets for the identification of biologically and clinically significant subsets in the data. SNPnexus is the most comprehensible web-based application currently available and these new set of updates ensures that it remains a state-of-the-art tool for researchers. SNPnexus is freely available at https://www.snp-nexus.org.
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Project description:The unprecedented growth of high-throughput sequencing has led to an ever-widening annotation gap in protein databases. While computational prediction methods are available to make up the shortfall, a majority of public web servers are hindered by practical limitations and poor performance. Here, we introduce PANNZER2 (Protein ANNotation with Z-scoRE), a fast functional annotation web server that provides both Gene Ontology (GO) annotations and free text description predictions. PANNZER2 uses SANSparallel to perform high-performance homology searches, making bulk annotation based on sequence similarity practical. PANNZER2 can output GO annotations from multiple scoring functions, enabling users to see which predictions are robust across predictors. Finally, PANNZER2 predictions scored within the top 10 methods for molecular function and biological process in the CAFA2 NK-full benchmark. The PANNZER2 web server is updated on a monthly schedule and is accessible at http://ekhidna2.biocenter.helsinki.fi/sanspanz/. The source code is available under the GNU Public Licence v3.
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Project description:BACKGROUND: In the post-genomic era, transcriptomics and proteomics provide important information to understand the genomes. With fast development of high-throughput technology, more and more transcriptomics and proteomics data are generated at an unprecedented rate. Therefore, requirement of software to annotate those omics data and explore their biological nature arises. In the past decade, some pioneer works were presented to address this issue, but limitations still exist. Fox example, some of these tools offer command line only, which is not suitable for those users with little or no experience in programming. Besides, some tools don't support large scale gene and protein analysis. RESULTS: To overcome these limitations, an integrated gene and protein annotation server named iGepros has been developed. The server provides user-friendly interfaces and detailed on-line examples, so most researchers even those with little or no programming experience can use it smoothly. Moreover, the server provides many functionalities to compare transcriptomics and proteomics data. Especially, the server is constructed under a model-view-control framework, which makes it easy to incorporate more functions to the server in the future. CONCLUSIONS: In this paper, we present a server with powerful capability not only for gene and protein functional annotation, but also for transcriptomics and proteomics data comparison. Researchers can survey biological characters behind gene and protein datasets and accelerate their investigation of transcriptome and proteome by applying the server. The server is publicly available at http://www.biosino.org/iGepros/.
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Project description:: The integrative analysis of multiple high-throughput data sources that are available for a common sample set is an increasingly common goal in biomedical research. Joint and individual variation explained (JIVE) is a tool for exploratory dimension reduction that decomposes a multi-source dataset into three terms: a low-rank approximation capturing joint variation across sources, low-rank approximations for structured variation individual to each source and residual noise. JIVE has been used to explore multi-source data for a variety of application areas but its accessibility was previously limited. We introduce R.JIVE, an intuitive R package to perform JIVE and visualize the results. We discuss several improvements and extensions of the JIVE methodology that are included. We illustrate the package with an application to multi-source breast tumor data from The Cancer Genome Atlas.R.JIVE is available via the Comprehensive R Archive Network (CRAN) under the GPLv3 license: https://cran.r-project.org/web/packages/r.jive/elock@umn.eduSupplementary data are available at Bioinformatics online.
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Project description:Known as the "fifth RNA nucleotide", pseudouridine (? or psi) is the first-discovered and most abundant RNA modification occurring at the Uridine site, and it plays a prominent role in a number of biological processes. Thousands of ? sites have been identified within different biological contexts thanks to the advancement in high-throughput sequencing technology; nevertheless, the transcriptome-wide distribution, biomolecular functions, regulatory mechanisms, and disease relevance of pseudouridylation are largely elusive. We report here a web server-PIANO-for pseudouridine site (?) identification and functional annotation. PIANO was built upon a high-accuracy predictor that takes advantage of both conventional sequence features and 42 additional genomic features. When tested on six independent datasets generated from four independent ?-profiling technologies (?-seq, RBS-seq, Pseudo-seq, and CeU-seq) as benchmarks, PIANO achieved an average AUC of 0.955 and 0.838 under the full transcript and mature mRNA models, respectively, marking a substantial improvement in accuracy compared to the existing in silico ?-site prediction methods, i.e., PPUS (0.713 and 0.707), iRNA-PseU (0.713 and 0.712), and PseUI (0.634 and 0.652). Besides, PIANO web server systematically annotates the predicted ? sites with post-transcriptional regulatory mechanisms (miRNA-targets, RBP-binding regions, and splicing sites) in its prediction report to help the users explore potential machinery of ?. Moreover, a concise query interface was also built for 4,303 known ? sites, which is currently the largest collection of experimentally validated human ? sites. The PIANO website is freely accessible at: http://piano.rnamd.com.
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Project description:MicroRNA (miRNA) is an important endogenous post-transcriptional regulator, while lettuce (Lactuca sativa) is a leafy vegetable of global economic significance. However, there are few studies on miRNAs in lettuce, and research on miRNA regulatory network in lettuce is absent. In this study, through deep sequencing of small RNAs in different tissues, together with a reference genome, 157 high-confidence miRNA loci in lettuce were comprehensively identified, and their expression patterns were determined. Using a combination of computational prediction and high-throughput experimental verification, a set of reliable lettuce miRNA targets were obtained. Furthermore, through RNA-Seq, the expression profiles of these targets and a comprehensive view of the negative regulatory relationship between miRNAs and their targets was acquired based on a correlation analysis. To further understand miRNA functions, a miRNA regulatory network was constructed, with miRNAs at the core and combining transcription factors and miRNA target genes. This regulatory network, mainly composed of feed forward loop motifs, greatly increases understanding of the potential functions of miRNAs, and many unknown potential regulatory links were discovered. Finally, considering its specific expression pattern, Lsa-MIR408 as a hub gene was employed to illustrate the function of the regulatory network, and genetic experiments revealed its ability to increase the fresh weight and achene size of lettuce. In short, this work lays a solid foundation for the study of miRNA functions and regulatory networks in lettuce.
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Project description:Given trained models from multiple source domains, how can we predict the labels of unlabeled data in a target domain? Unsupervised multi-source domain adaptation (UMDA) aims for predicting the labels of unlabeled target data by transferring the knowledge of multiple source domains. UMDA is a crucial problem in many real-world scenarios where no labeled target data are available. Previous approaches in UMDA assume that data are observable over all domains. However, source data are not easily accessible due to privacy or confidentiality issues in a lot of practical scenarios, although classifiers learned in source domains are readily available. In this work, we target data-free UMDA where source data are not observable at all, a novel problem that has not been studied before despite being very realistic and crucial. To solve data-free UMDA, we propose DEMS (Data-free Exploitation of Multiple Sources), a novel architecture that adapts target data to source domains without exploiting any source data, and estimates the target labels by exploiting pre-trained source classifiers. Extensive experiments for data-free UMDA on real-world datasets show that DEMS provides the state-of-the-art accuracy which is up to 27.5% point higher than that of the best baseline.
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Project description:The web server XSuLT, an enhanced version of the protein alignment annotation program JoY, formats a submitted multiple-sequence alignment using three-dimensional (3D) structural information in order to assist in the comparative analysis of protein evolution and in the optimization of alignments for comparative modelling and construct design. In addition to the features analysed by JoY, which include secondary structure, solvent accessibility and sidechain hydrogen bonds, XSuLT annotates each amino acid residue with residue depth, chain and ligand interactions, inter-residue contacts, sequence entropy, root mean square deviation and secondary structure and disorder prediction. It is also now integrated with built-in 3D visualization which interacts with the formatted alignment to facilitate inspection and understanding. Results can be downloaded as stand-alone HTML for the formatted alignment and as XML with the underlying annotation data. XSuLT is freely available at http://structure.bioc.cam.ac.uk/xsult/.
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