Cell BLAST: Searching large-scale scRNA-seq databases via unbiased cell embedding

AbstractAn effective and efficient cell-querying method is critical for integrating existing scRNA-seq data and annotating new data. Herein, we present Cell BLAST, an accurate and robust cell-querying method. Powered by a well-curated reference database and a user-friendly Web server, Cell BLAST (http://cblast.gao-lab.org) provides a one-stop solution for real-world scRNA-seq cell querying and annotation.

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CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk

10.1101/102848 ◽

2017 ◽

Author(s):

Venkata Manem ◽

George Adam ◽

Tina Gruosso ◽

Mathieu Gigoux ◽

Nicholas Bertos ◽

...

Keyword(s):

Stromal Cells ◽

Large Scale ◽

Network Visualization ◽

Visualization Tool ◽

Biological Processes ◽

Web Based ◽

Link Type ◽

A Cell ◽

Large Scale Networks ◽

User Friendly

ABSTRACTBackground:Over the last several years, we have witnessed the metamorphosis of network biology from being a mere representation of molecular interactions to models enabling inference of complex biological processes. Networks provide promising tools to elucidate intercellular interactions that contribute to the functioning of key biological pathways in a cell. However, the exploration of these large-scale networks remains a challenge due to their high-dimensionality.Results:CrosstalkNet is a user friendly, web-based network visualization tool to retrieve and mine interactions in large-scale bipartite co-expression networks. In this study, we discuss the use of gene co-expression networks to explore the rewiring of interactions between tumor epithelial and stromal cells. We show how CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks representing the crosstalk occurring between the tumour and its microenvironment.Conclusion:CrosstalkNet serves as a tool to assist biologists and clinicians in exploring complex, large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. A comprehensive tutorial along with case studies are provided with the application.Availability:The web-based application is available at the following location: http://epistroma.pmgenomics.ca/app/. The code is open-source and freely available from http://github.com/bhklab/EpiStroma-webapp.Contact:[email protected]

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JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison

Bioinformatics ◽

10.1093/bioinformatics/btv681 ◽

2015 ◽

Vol 32 (6) ◽

pp. 929-931 ◽

Cited By ~ 775

Author(s):

Michael Richter ◽

Ramon Rosselló-Móra ◽

Frank Oliver Glöckner ◽

Jörg Peplies

Keyword(s):

Draft Genome ◽

Web Server ◽

Supplementary Information ◽

Genome Comparison ◽

Reference Database ◽

Online Service ◽

Genome Sequences ◽

Species Circumscription ◽

Correlation Search ◽

User Friendly

Abstract Summary: JSpecies Web Server (JSpeciesWS) is a user-friendly online service for in silico calculating the extent of identity between two genomes, a parameter routinely used in the process of polyphasic microbial species circumscription. The service measures the average nucleotide identity (ANI) based on BLAST+ (ANIb) and MUMmer (ANIm), as well as correlation indexes of tetra-nucleotide signatures (Tetra). In addition, it provides a Tetra Correlation Search function, which allows to rapidly compare selected genomes against a continuously updated reference database with currently about 32 000 published whole and draft genome sequences. For comparison, own genomes can be uploaded and references can be selected from the JSpeciesWS reference database. The service indicates whether two genomes share genomic identities above or below the species embracing thresholds, and serves as a fast way to allocate unknown genomes in the frame of the hitherto sequenced species. Availability and implementation: JSpeciesWS is available at http://jspecies.ribohost.com/jspeciesws. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: [email protected]

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BatchServer: a web server for batch effect evaluation, visualization and correction

10.1101/2020.03.23.996264 ◽

2020 ◽

Author(s):

Tiansheng Zhu ◽

Guo-Bo Chen ◽

Chunhui Yuan ◽

Rui Sun ◽

Fangfei Zhang ◽

...

Keyword(s):

Web Server ◽

Batch Effect ◽

Variance Component Analysis ◽

Data Sets ◽

Batch Effects ◽

Source Codes ◽

Link Type ◽

R Shiny ◽

User Friendly ◽

Web Platform

AbstractBatch effects are unwanted data variations that may obscure biological signals, leading to bias or errors in subsequent data analyses. Effective evaluation and elimination of batch effects are necessary for omics data analysis. In order to facilitate the evaluation and correction of batch effects, here we present BatchSever, an open-source R/Shiny based user-friendly interactive graphical web platform for batch effects analysis. In BatchServer we introduced autoComBat, a modified version of ComBat, which is the most widely adopted tool for batch effect correction. BatchServer uses PVCA (Principal Variance Component Analysis) and UMAP (Manifold Approximation and Projection) for evaluation and visualizion of batch effects. We demonstate its application in multiple proteomics and transcriptomic data sets. BatchServer is provided at https://lifeinfo.shinyapps.io/batchserver/ as a web server. The source codes are freely available at https://github.com/guomics-lab/batch_server.

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DOGMA: a web server for proteome and transcriptome quality assessment

Nucleic Acids Research ◽

10.1093/nar/gkz366 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W507-W510 ◽

Cited By ~ 3

Author(s):

Carsten Kemena ◽

Elias Dohmen ◽

Erich Bornberg-Bauer

Keyword(s):

Next Generation Sequencing ◽

Quality Assessment ◽

Large Scale ◽

Graphical Representation ◽

Web Server ◽

Crucial Step ◽

Core Set ◽

Core Sets ◽

User Friendly ◽

Generation Sequencing

Abstract Even in the era of next generation sequencing, in which bioinformatics tools abound, annotating transcriptomes and proteomes remains a challenge. This can have major implications for the reliability of studies based on these datasets. Therefore, quality assessment represents a crucial step prior to downstream analyses on novel transcriptomes and proteomes. DOGMA allows such a quality assessment to be carried out. The data of interest are evaluated based on a comparison with a core set of conserved protein domains and domain arrangements. Depending on the studied species, DOGMA offers precomputed core sets for different phylogenetic clades. We now developed a web server for the DOGMA software, offering a user-friendly, simple to use interface. Additionally, the server provides a graphical representation of the analysis results and their placement in comparison to publicly available data. The server is freely available under https://domainworld-services.uni-muenster.de/dogma/. Additionally, for large scale analyses the software can be downloaded free of charge from https://domainworld.uni-muenster.de.

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DAnIEL: A User-Friendly Web Server for Fungal ITS Amplicon Sequencing Data

10.1101/2021.04.12.437814 ◽

2021 ◽

Author(s):

Daniel Loos ◽

Lu Zhang ◽

Christine Beemelmanns ◽

Oliver Kurzai ◽

Gianni Panagiotou

Keyword(s):

Large Scale ◽

Web Server ◽

High Standard ◽

Amplicon Sequencing ◽

Sequencing Data ◽

Human Gut ◽

Online Tools ◽

Data Tables ◽

User Friendly ◽

Host Development

AbstractTrillions of microbes representing all kingdoms of life are resident in, and on, humans holding essential roles for host development and physiology. The last decade over a dozen online tools and servers, accessible via public domain, have been developed for the analysis of bacterial sequences, however, the analysis of fungi is still in its infancy. Here we present a web server dedicated to the comprehensive analysis of the human mycobiome for (i) translating raw sequencing reads to data tables and high-standard figures; (ii) integrating statistical analysis and machine learning with a manually curated relational database; (iii) comparing the user’s uploaded datasets with publicly available from the Sequence Read Archive. Using 2,048 publicly available ITS samples, we demonstrated the utility of DAnIEL web server on large scale datasets and show the differences in fungal communities between human gut, skin, nasopharynx, and oral body sites.

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DAnIEL: A User-Friendly Web Server for Fungal ITS Amplicon Sequencing Data

Frontiers in Microbiology ◽

10.3389/fmicb.2021.720513 ◽

2021 ◽

Vol 12 ◽

Author(s):

Daniel Loos ◽

Lu Zhang ◽

Christine Beemelmanns ◽

Oliver Kurzai ◽

Gianni Panagiotou

Keyword(s):

Large Scale ◽

Web Server ◽

High Standard ◽

Amplicon Sequencing ◽

Internal Transcribed Spacers ◽

Sequencing Data ◽

Online Tools ◽

Data Tables ◽

User Friendly ◽

Host Development

Trillions of microbes representing all kingdoms of life are resident in, and on, humans holding essential roles for the host development and physiology. The last decade over a dozen online tools and servers, accessible via public domain, have been developed for the analysis of bacterial sequences; however, the analysis of fungi is still in its infancy. Here, we present a web server dedicated to the comprehensive analysis of the human mycobiome for (i) translating raw sequencing reads to data tables and high-standard figures, (ii) integrating statistical analysis and machine learning with a manually curated relational database and (iii) comparing the user’s uploaded datasets with publicly available from the Sequence Read Archive. Using 1,266 publicly available Internal transcribed spacers (ITS) samples, we demonstrated the utility of DAnIEL web server on large scale datasets and show the differences in fungal communities between human skin and soil sites.

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Productive visualization of high-throughput sequencing data using the SeqCode open portable platform

Scientific Reports ◽

10.1038/s41598-021-98889-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Enrique Blanco ◽

Mar González-Ramírez ◽

Luciano Di Croce

Keyword(s):

High Throughput ◽

Large Scale ◽

High Throughput Sequencing ◽

Graphical Analysis ◽

Sequencing Data ◽

Efficient Manner ◽

Link Type ◽

High Throughput Sequencing Data ◽

Almost All ◽

User Friendly

AbstractLarge-scale sequencing techniques to chart genomes are entirely consolidated. Stable computational methods to perform primary tasks such as quality control, read mapping, peak calling, and counting are likewise available. However, there is a lack of uniform standards for graphical data mining, which is also of central importance. To fill this gap, we developed SeqCode, an open suite of applications that analyzes sequencing data in an elegant but efficient manner. Our software is a portable resource written in ANSI C that can be expected to work for almost all genomes in any computational configuration. Furthermore, we offer a user-friendly front-end web server that integrates SeqCode functions with other graphical analysis tools. Our analysis and visualization toolkit represents a significant improvement in terms of performance and usability as compare to other existing programs. Thus, SeqCode has the potential to become a key multipurpose instrument for high-throughput professional analysis; further, it provides an extremely useful open educational platform for the world-wide scientific community. SeqCode website is hosted at http://ldicrocelab.crg.eu, and the source code is freely distributed at https://github.com/eblancoga/seqcode.

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CancerMIRNome: a web server for interactive analysis and visualization of cancer miRNome data

10.1101/2020.10.04.325670 ◽

2020 ◽

Author(s):

Ruidong Li ◽

Han Qu ◽

Shibo Wang ◽

Xuesong Wang ◽

Yanru Cui ◽

...

Keyword(s):

Regulatory Networks ◽

Large Scale ◽

Web Server ◽

The Cancer Genome Atlas ◽

Multiple Cancer ◽

Circulating Mirna ◽

Interactive Analysis ◽

Mirna Expression Data ◽

Stable Forms ◽

User Friendly

ABSTRACTMicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising biomarkers for a variety of human diseases, including cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive diagnostic biomarkers for early cancer detection. The vast amount of miRNA expression data from tens of thousands of samples in various types of cancers generated by The Cancer Genome Atlas (TCGA) and circulating miRNA data produced by many large-scale circulating miRNA profiling studies provide extraordinary opportunities for the discovery and validation of miRNA signatures in cancer. Novel and user-friendly tools are desperately needed to facilitate the data mining of such valuable cancer miRNome datasets. To fill this void, we developed CancerMIRNome, a web server for interactive analysis and visualization of cancer miRNome data based on TCGA and public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and functions have been packaged in CancerMIRNome, allowing for a pan-cancer analysis of a miRNA of interest across multiple cancer types and a comprehensive analysis of cancer miRNome at the dataset level. The CancerMIRNome web server is freely available at http://bioinfo.jialab-ucr.org/CancerMIRNome.

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