scholarly journals ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 103
Author(s):  
Subina Mehta ◽  
Marie Crane ◽  
Emma Leith ◽  
Bérénice Batut ◽  
Saskia Hiltemann ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Genetic Material ◽  
Taxonomic Composition ◽  
Training Material ◽  
Sequencing Technologies ◽  
The Galaxy ◽  
Ngs Data Analysis ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

scholarly journals ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 103 ◽  
Author(s):  
Subina Mehta ◽  
Marie Crane ◽  
Emma Leith ◽  
Bérénice Batut ◽  
Saskia Hiltemann ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Human Health ◽  
Genetic Material ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Taxonomic Composition ◽  
Sequencing Technologies ◽  
The Galaxy ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

scholarly journals CSI NGS Portal: An Online Platform for Automated NGS Data Analysis and Sharing

2020 ◽  
Vol 21 (11) ◽  
pp. 3828
Author(s):  
Omer An ◽  
Kar-Tong Tan ◽  
Ying Li ◽  
Jia Li ◽  
Chan-Shuo Wu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Final Report ◽  
Online Platform ◽  
Fastq Format ◽  
Health And Disease ◽  
Ngs Data Analysis ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Ngs Data

Next-generation sequencing (NGS) has been a widely-used technology in biomedical research for understanding the role of molecular genetics of cells in health and disease. A variety of computational tools have been developed to analyse the vastly growing NGS data, which often require bioinformatics skills, tedious work and a significant amount of time. To facilitate data processing steps minding the gap between biologists and bioinformaticians, we developed CSI NGS Portal, an online platform which gathers established bioinformatics pipelines to provide fully automated NGS data analysis and sharing in a user-friendly website. The portal currently provides 16 standard pipelines for analysing data from DNA, RNA, smallRNA, ChIP, RIP, 4C, SHAPE, circRNA, eCLIP, Bisulfite and scRNA sequencing, and is flexible to expand with new pipelines. The users can upload raw data in FASTQ format and submit jobs in a few clicks, and the results will be self-accessible via the portal to view/download/share in real-time. The output can be readily used as the final report or as input for other tools depending on the pipeline. Overall, CSI NGS Portal helps researchers rapidly analyse their NGS data and share results with colleagues without the aid of a bioinformatician. The portal is freely available at: https://csibioinfo.nus.edu.sg/csingsportal.

scholarly journals CSI NGS Portal: An Online Platform for Automated NGS Data Analysis and Sharing

2020 ◽  
Author(s):  
Ömer An ◽  
Kar-Tong Tan ◽  
Ying Li ◽  
Jia Li ◽  
Chan-Shuo Wu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Final Report ◽  
Online Platform ◽  
Fastq Format ◽  
Health And Disease ◽  
Ngs Data Analysis ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Ngs Data

Next-generation sequencing (NGS) has been a widely-used technology in biomedical research for understanding the role of molecular genetics of cells in health and disease. A variety of computational tools have been developed to analyse the vastly growing NGS data, which often require bioinformatics skills, tedious work and significant amount of time. To facilitate data processing steps minding the gap between biologists and bioinformaticians, we developed CSI NGS Portal, an online platform which gathers established bioinformatics pipelines to provide fully automated NGS data analysis and sharing in a user-friendly website. The portal currently provides 16 standard pipelines for analysing data from DNA, RNA, smallRNA, ChIP, RIP, 4C, SHAPE, circRNA, eCLIP, Bisulfite and scRNA sequencing, and is flexible to expand with new pipelines. The users can upload raw data in fastq format and submit jobs in a few clicks, and the results will be self-accessible via the portal to view/download/share in real-time. The output can be readily used as the final report or as input for other tools depending on the pipeline. Overall, CSI NGS Portal helps researchers rapidly analyse their NGS data and share results with colleagues without the aid of a bioinformatician. The portal is freely available at: https://csibioinfo.nus.edu.sg/csingsportal

scholarly journals Patient Derived Xenografts for Genome-Driven Therapy of Osteosarcoma

Cells ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 416
Author(s):  
Lorena Landuzzi ◽  
Maria Cristina Manara ◽  
Pier-Luigi Lollini ◽  
Katia Scotlandi
Keyword(s):  
Clinical Trials ◽  
Tumor Heterogeneity ◽  
Functional Studies ◽  
Ngs Data Analysis ◽  
The Many ◽  
Orthotopic Xenografts ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing ◽  
Somatic Copy Number Alterations

Osteosarcoma (OS) is a rare malignant primary tumor of mesenchymal origin affecting bone. It is characterized by a complex genotype, mainly due to the high frequency of chromothripsis, which leads to multiple somatic copy number alterations and structural rearrangements. Any effort to design genome-driven therapies must therefore consider such high inter- and intra-tumor heterogeneity. Therefore, many laboratories and international networks are developing and sharing OS patient-derived xenografts (OS PDX) to broaden the availability of models that reproduce OS complex clinical heterogeneity. OS PDXs, and new cell lines derived from PDXs, faithfully preserve tumor heterogeneity, genetic, and epigenetic features and are thus valuable tools for predicting drug responses. Here, we review recent achievements concerning OS PDXs, summarizing the methods used to obtain ectopic and orthotopic xenografts and to fully characterize these models. The availability of OS PDXs across the many international PDX platforms and their possible use in PDX clinical trials are also described. We recommend the coupling of next-generation sequencing (NGS) data analysis with functional studies in OS PDXs, as well as the setup of OS PDX clinical trials and co-clinical trials, to enhance the predictive power of experimental evidence and to accelerate the clinical translation of effective genome-guided therapies for this aggressive disease.

scholarly journals Bio-Docklets: Virtualization Containers for Single-Step Execution of NGS Pipelines

2017 ◽  
Author(s):  
Baekdoo Kim ◽  
Thahmina Ali ◽  
Carlos Lijeron ◽  
Enis Afgan ◽  
Konstantinos Krampis
Keyword(s):  
Data Analysis ◽  
Cloud Service ◽  
Application Programming Interface ◽  
Single Step ◽  
Easy Access ◽  
Complex Data ◽  
The Galaxy ◽  
Ngs Data Analysis ◽  
Single Data ◽  
Ngs Data

ABSTRACTBackgroundProcessing of Next-Generation Sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized post-analysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers, towards seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform.FindingsWe present an approach for abstracting the complex data operations of multi-step, bioinformatics pipelines for NGS data analysis. As examples, we have deployed two pipelines for RNAseq and CHIPseq, pre-configured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines is as simple as running a single bioinformatics tool. This is achieved through a “meta-script” that automatically starts the Bio-Docklets, and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface (API). The pipelne output is post-processed using the Visual Omics Explorer (VOE) framework, providing interactive data visualizations that users can access through a web browser.ConclusionsThe goal of our approach is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts, on any computing environment whether a laboratory workstation, university computer cluster, or a cloud service provider,. Besides end-users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.

scholarly journals FastqCleaner: an interactive Bioconductor application for quality-control, filtering and trimming of FASTQ files

2018 ◽  
Author(s):  
Leandro Gabriel Roser ◽  
Fernán Agüero ◽  
Daniel Oscar Sánchez
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
The Novel ◽  
Web Environment ◽  
Ngs Data Analysis ◽  
Novel Concept ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Ngs Data ◽  
Analysis Platform

AbstractBackgroundExploration and processing of FASTQ files are the first steps in state-of-the-art data analysis workflows of Next Generation Sequencing (NGS) platforms. The large amount of data generated by these technologies has put a challenge in terms of rapid analysis and visualization of sequencing information. Recent integration of the R data analysis platform with web visual frameworks has stimulated the development of user-friendly, powerful, and dynamic NGS data analysis applications.ResultsThis paper presents FastqCleaner, a Bioconductor visual application for both quality-control (QC) and pre-processing of FASTQ files. The interface shows diagnostic information for the input and output data and allows to select a series of filtering and trimming operations in an interactive framework. FastqCleaner combines the technology of Bioconductor for NGS data analysis with the data visualization advantages of a web environment.ConclusionsFastqCleaner is an user-friendly, offline-capable tool that enables access to advanced Bioconductor infrastructure. The novel concept of a Bioconductor interactive application that can be used without the need for programming skills, makes FastqCleaner a valuable resource for NGS data analysis.

scholarly journals Microbial communities in Antarctic lakes: Entirely new perspectives from metagenomics and metaproteomics

2011 ◽  
Vol 32 (4) ◽  
pp. 157 ◽  
Author(s):  
Sheree Yau ◽  
Ricardo Cavicchioli
Keyword(s):  
Dna Sequencing ◽  
Microbial Diversity ◽  
Microbial Communities ◽  
Ecosystem Function ◽  
Exponential Growth ◽  
Genetic Material ◽  
Microbial Evolution ◽  
Antarctic Lakes ◽  
Sequencing Technologies ◽  
Level Of Understanding

Driven by advances in DNA sequencing technologies, an astounding amount of data is being generated from genetic material sourced directly from the environment, and this exponential growth of data is set to continue. By surmounting the challenges of working with such vast datasets, a whole new level of understanding is being gained about microbial diversity, microbial evolution and whole ecosystem function. For precious, pristine and logistically difficult to obtain Antarctic samples, metagenomic and metaproteomic approaches are providing the basis for fundamental new discoveries about how Antarctic systems function.

scholarly journals The Practical Application of Emerging Technologies Influencing the Diagnosis and Care of Patients With Primary Brain Tumors

2020 ◽  
pp. e35-e46 ◽  
Author(s):  
Leland S. Hu ◽  
Daniel J. Brat ◽  
Orin Bloch ◽  
Shakti Ramkissoon ◽  
Glenn J. Lesser
Keyword(s):  
Brain Tumors ◽  
Surgical Navigation ◽  
Tumor Biology ◽  
Primary Brain Tumors ◽  
Tumor Treating Fields ◽  
Histopathologic Diagnosis ◽  
Therapeutic Decision Making ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

Over the past decade, a variety of new and innovative technologies has led to important advances in the diagnosis and management of patients with primary malignant brain tumors. New approaches to surgical navigation and tumor localization, advanced imaging to define tumor biology and treatment response, and the widespread adoption of a molecularly defined integrated diagnostic paradigm that complements traditional histopathologic diagnosis continue to impact the day-to-day care of these patients. In the neuro-oncology clinic, discussions with patients about the role of tumor treating fields (TTFields) and the incorporation of next-generation sequencing (NGS) data into therapeutic decision-making are now a standard practice. This article summarizes newer applications of technology influencing the pathologic, neuroimaging, neurosurgical, and medical management of patients with malignant primary brain tumors.

scholarly journals PostSV: A Post–Processing Approach for Filtering Structural Variations

2020 ◽  
Vol 14 ◽  
pp. 117793221989295 ◽  
Author(s):  
Eman Alzaid ◽  
Achraf El Allali
Keyword(s):  
State Of The Art ◽  
Post Processing ◽  
Structural Variations ◽  
Sequencing Technologies ◽  
Structural Differences ◽  
Overall Performance ◽  
Next Generation Sequencing Ngs ◽  
Low Coverage ◽  
Ngs Data ◽  
Generation Sequencing

Genomic structural variations are significant causes of genome diversity and complex diseases. With advances in sequencing technologies, many algorithms have been designed to identify structural differences using next-generation sequencing (NGS) data. Due to repetitions in the human genome and the short reads produced by NGS, the discovery of structural variants (SVs) by state-of-the-art SV callers is not always accurate. To improve performance, multiple SV callers are often used to detect variants. However, most SV callers suffer from high false-positive rates, which diminishes the overall performance, especially in low-coverage genomes. In this article, we propose a post-processing classification–based algorithm that can be used to filter structural variation predictions produced by SV callers. Novel features are defined from putative SV predictions using reads at the local regions around the breakpoints. Several classifiers are employed to classify the candidate predictions and remove false positives. We test our classifier models on simulated and real genomes and show that the proposed approach improves the performance of state-of-the-art algorithms.

Sign in / Sign up

Export Citation Format

Share Document