scholarly journals HOME-BIO (sHOtgun MEtagenomic analysis of BIOlogical entities): a specific and comprehensive pipeline for metagenomic shotgun sequencing data analysis

2021 ◽  
Vol 22 (S7) ◽  
Author(s):  
Carlo Ferravante ◽  
Domenico Memoli ◽  
Domenico Palumbo ◽  
Paolo Ciaramella ◽  
Antonio Di Loria ◽  
...  
Keyword(s):  
Data Analysis ◽  
Low Complexity ◽  
Metagenomic Analysis ◽  
Sequencing Data ◽  
Metagenomics Data ◽  
Speed Up ◽  
Inclusive Analysis ◽  
Biological Entities ◽  
Next Generation Sequencing Ngs ◽  
User Friendly

Abstract Background Next-Generation-Sequencing (NGS) enables detection of microorganisms present in biological and other matrices of various origin and nature, allowing not only the identification of known phyla and strains but also the discovery of novel ones. The large amount of metagenomic shotgun data produced by NGS require comprehensive and user-friendly pipelines for data analysis, that speed up the bioinformatics steps, relieving the users from the need to manually perform complex and time-consuming tasks. Results We describe here HOME-BIO (sHOtgun MEtagenomic analysis of BIOlogical entities), an exhaustive pipeline for metagenomics data analysis, comprising three independent analytical modules designed for an inclusive analysis of large NGS datasets. Conclusions HOME-BIO is a powerful and easy-to-use tool that can be run also by users with limited computational expertise. It allows in-depth analyses by removing low-complexity/ problematic reads, integrating the analytical steps that lead to a comprehensive taxonomy profile of each sample by querying different source databases, and it is customizable according to specific users’ needs.

scholarly journals Automated processing of NGS data from raw sequencing files to ready-to-use information tables for genome modeling

2018 ◽  
Vol 4 (2) ◽  
pp. 100042
Author(s):  
Robert Deelen ◽  
Martin Wieland ◽  
Susanne Gerber ◽  
David Fournier
Keyword(s):  
Regulation Of Gene Expression ◽  
Sequencing Data ◽  
Processing Power ◽  
Automated Processing ◽  
Speed Up ◽  
Data Files ◽  
On Chip ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Ngs Data

Epigenetic features such as histone and DNA modifications are important mechanisms for the regulation of gene expression and for cell and tissue development. As a result, extensive efforts are currently undertaken using next-generation sequencing (NGS) to generate vast amounts of data regarding the epigenetic regulation of genomes. Several tools and frameworks for the processing of these NGS data have been developed in the last decade. Nevertheless, each user still bares the challenge to integrate all these tasks to perform the analysis. This procedure is not only tedious but also resource-intensive due to the putative large processing power involved. To automate, standardize and speed up the handling of NGS data, with focus on ChIP-seq data, we present a user-friendly pipeline that automatically processes a list of sequencing data files and returns a ready-to-use purified table for subsequent modelling or analysis attempts.

scholarly journals CoMA – an intuitive and user-friendly pipeline for amplicon-sequencing data analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243241
Author(s):  
Sebastian Hupfauf ◽  
Mohammad Etemadi ◽  
Marina Fernández-Delgado Juárez ◽  
María Gómez-Brandón ◽  
Heribert Insam ◽  
...  
Keyword(s):  
Operating System ◽  
Data Analysis ◽  
Amplicon Sequencing ◽  
Sequencing Data ◽  
Taxonomic Assignment ◽  
Benchmark Test ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Ngs Data ◽  
Mock Communities

In recent years, there has been a veritable boost in next-generation sequencing (NGS) of gene amplicons in biological and medical studies. Huge amounts of data are produced and need to be analyzed adequately. Various online and offline analysis tools are available; however, most of them require extensive expertise in computer science or bioinformatics, and often a Linux-based operating system. Here, we introduce “CoMA–Comparative Microbiome Analysis” as a free and intuitive analysis pipeline for amplicon-sequencing data, compatible with any common operating system. Moreover, the tool offers various useful services including data pre-processing, quality checking, clustering to operational taxonomic units (OTUs), taxonomic assignment, data post-processing, data visualization, and statistical appraisal. The workflow results in highly esthetic and publication-ready graphics, as well as output files in standardized formats (e.g. tab-delimited OTU-table, BIOM, NEWICK tree) that can be used for more sophisticated analyses. The CoMA output was validated by a benchmark test, using three mock communities with different sample characteristics (primer set, amplicon length, diversity). The performance was compared with that of Mothur, QIIME and QIIME2-DADA2, popular packages for NGS data analysis. Furthermore, the functionality of CoMA is demonstrated on a practical example, investigating microbial communities from three different soils (grassland, forest, swamp). All tools performed well in the benchmark test and were able to reveal the majority of all genera in the mock communities. Also for the soil samples, the results of CoMA were congruent to those of the other pipelines, in particular when looking at the key microbial players.

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 Blacklisting variants common in private cohorts but not in public databases optimizes human exome analysis

2018 ◽  
Vol 116 (3) ◽  
pp. 950-959 ◽  
Author(s):  
Patrick Maffucci ◽  
Benedetta Bigio ◽  
Franck Rapaport ◽  
Aurélie Cobat ◽  
Alessandro Borghesi ◽  
...  
Keyword(s):  
Reference Genome ◽  
Low Complexity ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Human Patient ◽  
Reference Genome Assembly ◽  
Exome Analysis ◽  
User Friendly ◽  
Computational Analyses ◽  
Generation Sequencing

Computational analyses of human patient exomes aim to filter out as many nonpathogenic genetic variants (NPVs) as possible, without removing the true disease-causing mutations. This involves comparing the patient’s exome with public databases to remove reported variants inconsistent with disease prevalence, mode of inheritance, or clinical penetrance. However, variants frequent in a given exome cohort, but absent or rare in public databases, have also been reported and treated as NPVs, without rigorous exploration. We report the generation of a blacklist of variants frequent within an in-house cohort of 3,104 exomes. This blacklist did not remove known pathogenic mutations from the exomes of 129 patients and decreased the number of NPVs remaining in the 3,104 individual exomes by a median of 62%. We validated this approach by testing three other independent cohorts of 400, 902, and 3,869 exomes. The blacklist generated from any given cohort removed a substantial proportion of NPVs (11–65%). We analyzed the blacklisted variants computationally and experimentally. Most of the blacklisted variants corresponded to false signals generated by incomplete reference genome assembly, location in low-complexity regions, bioinformatic misprocessing, or limitations inherent to cohort-specific private alleles (e.g., due to sequencing kits, and genetic ancestries). Finally, we provide our precalculated blacklists, together with ReFiNE, a program for generating customized blacklists from any medium-sized or large in-house cohort of exome (or other next-generation sequencing) data via a user-friendly public web server. This work demonstrates the power of extracting variant blacklists from private databases as a specific in-house but broadly applicable tool for optimizing exome analysis.

scholarly journals GOTHiC, a simple probabilistic model to resolve complex biases and to identify real interactions in Hi-C data

2015 ◽  
Author(s):  
Borbala Mifsud ◽  
Inigo Martincorena ◽  
Elodie Darbo ◽  
Robert Sugar ◽  
Stefan Schoenfelder ◽  
...  
Keyword(s):  
Data Analysis ◽  
Probabilistic Model ◽  
Unknown Origin ◽  
P Value ◽  
Bioconductor Package ◽  
Sequencing Data ◽  
Significance Threshold ◽  
Statistical Framework ◽  
Complex Models ◽  
User Friendly

Hi-C is one of the main methods for investigating spatial co-localisation of DNA in the nucleus. However, the raw sequencing data obtained from Hi-C experiments suffer from large biases and spurious contacts, making it difficult to identify true interactions. Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. Here we introduce a simple binomial probabilistic model that resolves complex biases and distinguishes between true and false interactions. The model corrects biases of known and unknown origin and yields a p-value for each interaction, providing a reliable threshold based on significance. We demonstrate this experimentally by testing the method against a random ligation dataset. Our method outperforms previous methods and provides a statistical framework for further data analysis, such as comparisons of Hi-C interactions between different conditions. GOTHiC is available as a user-friendly BioConductor package (http://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html).

scholarly journals miCloud: a plug and play, on-premises bioinformatics cloud, providing seamless integration with Illumina genome sequencers

2017 ◽  
Author(s):  
Baekdoo Kim ◽  
Thahmina Ali ◽  
Konstantinos Krampis ◽  
Changsu Dong ◽  
Bobby Laungani ◽  
...  
Keyword(s):  
Data Analysis ◽  
Low Cost ◽  
Illumina Miseq ◽  
Computer Hardware ◽  
Laboratory Equipment ◽  
Seamless Integration ◽  
Technical Data ◽  
Amazon Web Services ◽  
Next Generation Sequencing Ngs ◽  
User Friendly

Benchtop genome sequencers such as the Illumina MiSeq or MiniSeq [1], [2] are revolutionizing genomics research for smaller, independent laboratories, by enabling access to low-cost Next Generation Sequencing (NGS) technology in-house. These benchtop genome sequencing instruments require only standard laboratory equipment, in addition to minimal time for sample preparation. However, post-sequencing bioinformatics data analysis still presents a significant bottleneck, for research laboratories lacking specialized software and technical data analysis skills on their teams. While bioinformatics computes clouds providing solutions following a Software as a Service (SaaS) are available ([3]–[6], review in [7]), currently, there are only a few options which are user-friendly for non-experts while at the same time are also low-cost or free. One primary example is Illumina BaseSpace [8] that is very easy to access by non-experts, and also offers an integrated solution where data are streamed directly from the MiSeq sequencing instrument to the cloud. Once the data is on the BaseSpace cloud, users can access a range of bioinformatics applications with pre-installed algorithms through an intuitive web interface. Nonetheless, BaseSpace can be a costly solution as a yearly subscription depending on whether the user is associated with an academic or private institution, ranges in price from $999 - $4,999. Additional “iCredits” [9] might need to be purchased for frequent users that exhaust the base credit allowance as part of the subscription. Considering the reduction of computer hardware cost in recent years, a multi-core Intel Xeon server with 64 GigaByte (GB) of memory and multiple TeraByte (TB) of storage is priced less than the yearly subscription to Basespace [10], and similarly when compared to renting compute cycles from providers such as Amazon Web Services (AWS) [11]. Furthermore, the current generation of laptops usually come with 6–10 GigaBytes (GB) of memory and 1 TeraByte (TB) of storage, providing enough computational capacity to analyze data from small NGS experiments [12] that include only a few samples.

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 iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data

2021 ◽  
Author(s):  
Anjana Anilkumar Sithara ◽  
Devi Priyanka Maripuri ◽  
Keerthika Moorthy ◽  
Sai Sruthi Amirtha Ganesh ◽  
Philge Philip ◽  
...  
Keyword(s):  
Data Analysis ◽  
Workflow Management ◽  
Human Monocyte ◽  
Complex Data ◽  
Omics Data ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Sequencing Technologies ◽  
Fastq Format ◽  
User Friendly

Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics on the nature of the data. Our iCOMIC toolkit pipeline can analyze whole-genome and transcriptome data and is embedded in the popular Snakemake workflow management system. iCOMIC is characterized by a user-friendly GUI that offers several advantages, including executing analyses with minimal steps, eliminating the need for complex command-line arguments. The toolkit features many independent core workflows for both whole genomic and transcriptomic data analysis. Even though all the necessary, well-established tools are integrated into the pipeline to enable "out-of-the-box" analysis, we provide the user with the means to replace modules or alter the pipeline as needed. Notably, we have integrated algorithms developed in-house for predicting driver and passenger mutations based on mutational context and tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle (GIAB) benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM - GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of r=0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, with minimal steps, significantly ameliorating complex data analysis pipelines. Availability: https://github.com/RamanLab/iCOMIC

scholarly journals MetaWRAP - a flexible pipeline for genome-resolved metagenomic data analysis

2018 ◽  
Author(s):  
Gherman V Uritskiy ◽  
Jocelyne DiRuggiero ◽  
James Taylor
Keyword(s):  
Data Analysis ◽  
Shotgun Sequencing ◽  
Metagenomic Analysis ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
High Quality ◽  
Single Genome ◽  
Significant Burden ◽  
Versatile Tool ◽  
Genome Level

AbstractBackground:The study of microbiomes using whole-metagenome shotgun sequencing enables the analysis of uncultivated microbial populations that may have important roles in their environments. Extracting individual draft genomes (bins) facilitates metagenomic analysis at the single genome level. Software and pipelines for such analysis have become diverse and sophisticated, resulting in a significant burden for biologists to access and use them. Furthermore, while bin extraction algorithms are rapidly improving, there is still a lack of tools for their evaluation and visualization.Results:To address these challenges, we present metaWRAP, a modular pipeline software for shotgun metagenomic data analysis. MetaWRAP deploys state-of-the-art software to handle metagenomic data processing starting from raw sequencing reads and ending in metagenomic bins and their analysis. MetaWRAP is flexible enough to give investigators control over the analysis, while still being easy-to-install and easy-to-use. It includes hybrid algorithms that leverage the strengths of a variety of software to extract and refine high-quality bins from metagenomic data through bin consolidation and reassembly. MetaWRAP’s hybrid bin extraction algorithm outperforms individual binning approaches and other bin consolidation programs in both synthetic and real datasets. Finally, metaWRAP comes with numerous modules for the analysis of metagenomic bins, including taxonomy assignment, abundance estimation, functional annotation, and visualization.Conclusions:MetaWRAP is an easy-to-use modular pipeline that automates the core tasks in metagenomic analysis, while contributing significant improvements to the extraction and interpretation of high-quality metagenomic bins. The bin refinement and reassembly modules of metaWRAP consistently outperform other binning approaches. Each module of metaWRAP is also a standalone component, making it a flexible and versatile tool for tackling metagenomic shotgun sequencing data. MetaWRAP is open-source software available at https://github.com/bxlab/metaWRAP.

scholarly journals DNAseq Workflow in a Diagnostic Context and an Example of a User Friendly Implementation

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Beat Wolf ◽  
Pierre Kuonen ◽  
Thomas Dandekar ◽  
David Atlan
Keyword(s):  
Clinical Settings ◽  
Data Quality Control ◽  
Software Environment ◽  
Sequence Alignments ◽  
Online Databases ◽  
Speed Up ◽  
Gene Panels ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Ngs Data

Over recent years next generation sequencing (NGS) technologies evolved from costly tools used by very few, to a much more accessible and economically viable technology. Through this recently gained popularity, its use-cases expanded from research environments into clinical settings. But the technical know-how and infrastructure required to analyze the data remain an obstacle for a wider adoption of this technology, especially in smaller laboratories. We present GensearchNGS, a commercial DNAseq software suite distributed by Phenosystems SA. The focus of GensearchNGS is the optimal usage of already existing infrastructure, while keeping its use simple. This is achieved through the integration of existing tools in a comprehensive software environment, as well as custom algorithms developed with the restrictions of limited infrastructures in mind. This includes the possibility to connect multiple computers to speed up computing intensive parts of the analysis such as sequence alignments. We present a typical DNAseq workflow for NGS data analysis and the approach GensearchNGS takes to implement it. The presented workflow goes from raw data quality control to the final variant report. This includes features such as gene panels and the integration of online databases, like Ensembl for annotations or Cafe Variome for variant sharing.

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