CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data

Norouzi‐Beirami, Mohammad Hossein; Marashi, Sayed-Amir; Banaei-Moghaddam, Ali Mohammad; Kavousi, Kaveh

doi:10.1093/nargab/lqaa107

Cited by 5 publications

(3 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The pipeline was used to check the taxonomic profile of gut microbiota from colorectal adenoma and colorectal carcinoma individuals. The result predicted a significantly changed gut species ratio to 2.67% of the total 374 species [ 44 ]. ezTree, a computational pipeline, is developed to automatically identify single-copy marker genes for a group of genomes and build phylogenetic trees from the marker genes.…”

Section: Road Map To Metagenomics Studymentioning

confidence: 99%

Analysis and Interpretation of metagenomics data: an approach

et al. 2022

View full text Add to dashboard Cite

Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8–10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.

show abstract

Section: Road Map To Metagenomics Studymentioning

confidence: 99%

Analysis and Interpretation of metagenomics data: an approach

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Compositional Analysis of Single-Cell RNA-seq Data So far, compositional analysis has been an active and ongoing area in metagenomic data (Norouzi-Beirami et al, 2021) and microbiome research (Chen and Li, 2013;Bian et al, 2017;Rivera-Pinto et al, 2018), due to the compositional nature of metagenomic and microbiome data. This is also opens a new perspective on the analysis of single cell RNAseq data.…”

Section: Recover Dropout Events In Single-cell Transcriptome Profilesmentioning

confidence: 99%

Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

Fan

Zhao

et al. 2021

Front. Genet.

View full text Add to dashboard Cite

The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable insights for clinical and translational research. In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis. We will start with the pre-processing of single-cell RNA sequencing (scRNA-seq) data, including data imputation, cross-platform batch effect removal, and cell cycle and cell-type identification. Next, we will introduce advanced data analysis tools and methods used for copy number variance estimate, single-cell pseudo-time trajectory analysis, phylogenetic tree inference, cell–cell interaction, regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data. Finally, we will present the latest analyzing challenges, such as multi-omics integration and integrated analysis of scRNA-seq data.

show abstract

“…To our knowledge, there are few broadly accepted standard methods for RNA-seq data generation, processing, and analysis [ 25 , 39 , 40 ]. The most used analysis methods or pipelines can fall into three main categories: (i) mapping sequence reads directly to reference genomes from known pathogens such as Pathoscope [ 41 ] and CAMAMED [ 42 ]; (ii) assembling sequence reads and annotating contigs such as VirFind [ 43 ], VSD toolkit [ 44 ], VirusDetect [ 45 ], and Virtool [ 37 ]; and (iii) read-based taxonomic assignments such as Kaiju [ 46 ], Kraken2 [ 47 ], and Kodoja [ 48 ]. However, these methods or pipelines do not offer an integrated sequence read quality control, read assembly, pathogen reference mapping, and read classification to identify known pathogens and discover novel species, which is a common occurrence during plant virus detection.…”

Section: Introductionmentioning

confidence: 99%

PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data

Hu,

Hurtado-Gonzales,

Adhikari

et al. 2023

BMC Bioinformatics

View full text Add to dashboard Cite

Background Detection of exotic plant pathogens and preventing their entry and establishment are critical for the protection of agricultural systems while securing the global trading of agricultural commodities. High-throughput sequencing (HTS) has been applied successfully for plant pathogen discovery, leading to its current application in routine pathogen detection. However, the analysis of massive amounts of HTS data has become one of the major challenges for the use of HTS more broadly as a rapid diagnostics tool. Several bioinformatics pipelines have been developed to handle HTS data with a focus on plant virus and viroid detection. However, there is a need for an integrative tool that can simultaneously detect a wider range of other plant pathogens in HTS data, such as bacteria (including phytoplasmas), fungi, and oomycetes, and this tool should also be capable of generating a comprehensive report on the phytosanitary status of the diagnosed specimen. Results We have developed an open-source bioinformatics pipeline called PhytoPipe (Phytosanitary Pipeline) to provide the plant pathology diagnostician community with a user-friendly tool that integrates analysis and visualization of HTS RNA-seq data. PhytoPipe includes quality control of reads, read classification, assembly-based annotation, and reference-based mapping. The final product of the analysis is a comprehensive report for easy interpretation of not only viruses and viroids but also bacteria (including phytoplasma), fungi, and oomycetes. PhytoPipe is implemented in Snakemake workflow with Python 3 and bash scripts in a Linux environment. The source code for PhytoPipe is freely available and distributed under a BSD-3 license. Conclusions PhytoPipe provides an integrative bioinformatics pipeline that can be used for the analysis of HTS RNA-seq data. PhytoPipe is easily installed on a Linux or Mac system and can be conveniently used with a Docker image, which includes all dependent packages and software related to analyses. It is publicly available on GitHub at https://github.com/healthyPlant/PhytoPipe and on Docker Hub at https://hub.docker.com/r/healthyplant/phytopipe.

show abstract

CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data

Cited by 5 publications

References 48 publications

Analysis and Interpretation of metagenomics data: an approach

Analysis and Interpretation of metagenomics data: an approach

Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data

Contact Info

Product

Resources

About