Academic literature on the topic 'QIIME 2'

The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2’s unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.

The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2’s unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.

ObjectiveColorectal cancer (CRC) is the second leading cause of cancer-associated mortality in the USA. The faecal microbiome may provide non-invasive biomarkers of CRC and indicate transition in the adenoma–carcinoma sequence. Re-analysing raw sequence and metadata from several studies uniformly, we sought to identify a composite and generalisable microbial marker for CRC.DesignRaw 16S rRNA gene sequence data sets from nine studies were processed with two pipelines, (1) QIIME closed reference (QIIME-CR) or (2) a strain-specific method herein termed SS-UP (Strain Select, UPARSE bioinformatics pipeline). A total of 509 samples (79 colorectal adenoma, 195 CRC and 235 controls) were analysed. Differential abundance, meta-analysis random effects regression and machine learning analyses were carried out to determine the consistency and diagnostic capabilities of potential microbial biomarkers.ResultsDefinitive taxa, including Parvimonas micra ATCC 33270, Streptococcus anginosus and yet-to-be-cultured members of Proteobacteria, were frequently and significantly increased in stools from patients with CRC compared with controls across studies and had high discriminatory capacity in diagnostic classification. Microbiome-based CRC versus control classification produced an area under receiver operator characteristic (AUROC) curve of 76.6% in QIIME-CR and 80.3% in SS-UP. Combining clinical and microbiome markers gave a diagnostic AUROC of 83.3% for QIIME-CR and 91.3% for SS-UP.ConclusionsDespite technological differences across studies and methods, key microbial markers emerged as important in classifying CRC cases and such could be used in a universal diagnostic for the disease. The choice of bioinformatics pipeline influenced accuracy of classification. Strain-resolved microbial markers might prove crucial in providing a microbial diagnostic for CRC.

Abstract Objectives The objective was to evaluate the prebiotic effects of a milled whole cranberry beverage on modulating the gut microbiota in young adults. Methods Adults (n = 17; ages 18–42 y; BMI 30.5 ± 3.1 kg/m2) were enrolled in a 60-d, two-period, randomized, placebo-controlled, crossover clinical study. Throughout the study, participants were fed a standardized 10-d cycle menu on site. During each 20-d treatment period, participants consumed twice daily a whole cranberry or placebo beverage (240 mL per serving). Treatment periods were separated by an 11-wk washout period and preceded by 10-d run-in periods on the controlled study diet. Fecal samples were collected before and after the dietary intervention for bacterial compositional analysis and short-chain fatty acid analysis by LC-MS/MS. The V5-V6 region of the 16S rRNA gene in fecal DNA was amplified and sequenced. Taxonomy was assigned using the q2-feature-classifier in QIIME2 and matched against the Greengenes 13_8 database. Differential abundance was analyzed using ANCOM2 in R. Alpha-diversity was assessed using Faith's PD, Shannon diversity, and observed OTU richness generated by QIIME 2 and compared between treatments using Mann-Whitney U test. Beta-diversity was compared between treatments using PERMANOVA of the weighted and unweighted UniFrac distances between samples generated by QIIME 2. Results Coriobacteriaceae was significantly more abundant after participants consumed the cranberry as compared with the placebo beverage (ANCOM W > 0.7). The clinically-important pathogen Clostridium perfringens was present after consumption of the placebo beverage, but was a structural zero (not present) after consumption of the cranberry beverage. Alpha-diversity, beta-diversity, and fecal short-chain fatty acid concentrations did not differ between treatments. Conclusions Daily consumption of a whole cranberry beverage resulted in favorable change in the composition of the gut microbiota and thus showed prebiotic potential. Funding Sources Ocean Spray Cranberries, Inc.

The internally transcribed spacer (ITS) region between the small subunit ribosomal RNA gene and large subunit ribosomal RNA gene is a widely used phylogenetic marker for fungi and other taxa. The eukaryotic ITS contains the conserved 5.8S rRNA and is divided into the ITS1 and ITS2 hypervariable regions. These regions are variable in length and are amplified using primers complementary to the conserved regions of their flanking genes. Previous work has shown that removing the conserved regions results in more accurate taxonomic classification. An existing software program, ITSx, is capable of trimming FASTA sequences by matching hidden Markov model profiles to the ends of the conserved genes using the software suite HMMER. ITSxpress was developed to extend this technique from marker gene studies using Operational Taxonomic Units (OTU’s) to studies using exact sequence variants; a method used by the software packages Dada2, Deblur, QIIME 2, and Unoise. The sequence variant approach uses the quality scores of each read to identify sequences that are statistically likely to represent real sequences. ITSxpress enables this by processing FASTQ rather than FASTA files. The software also speeds up the trimming of reads by a factor of 14-23 times on a 4-core computer by temporarily clustering highly similar sequences that are common in amplicon data and utilizing optimized parameters for Hmmsearch. ITSxpress is available as a QIIME 2 plugin and a stand-alone application installable from the Python package index, Bioconda, and Github.

Studies have documented dysbiosis in the gut mycobiome in people with Type 2 diabetes mellitus (T2DM). However, it is not known whether dysbiosis in the gut mycobiome of T2DM patients would be reflected in people with diabetic retinopathy (DR) and if so, is the observed mycobiome dysbiosis similar in people with T2DM and DR. Gut mycobiomes were generated from healthy controls (HC), people with T2DM and people with DR through Illumina sequencing of ITS2 region. Data were analysed using QIIME and R software. Dysbiotic changes were observed in people with T2DM and DR compared to HC at the phyla and genera level. Mycobiomes of HC, T2DM and DR could be discriminated by heat map analysis, Beta diversity analysis and LEfSE analysis. Spearman correlation of fungal genera indicated more negative correlation in HC compared to T2DM and DR mycobiomes. This study demonstrates dysbiosis in the gut mycobiomes in people with T2DM and DR compared to HC. These differences were significant both at the phyla and genera level between people with T2DM and DR as well. Such studies on mycobiomes may provide new insights and directions to identification of specific fungi associated with T2DM and DR and help developing novel therapies for Diabetes Mellitus and DR.

Background The gut microbiota plays an important role in shaping the immune system and may be closely connected to the development of allergic diseases. Objective This study aimed to determine the gut microbiota composition in Chinese allergic rhinitis (AR) patients as compared with healthy controls (HCs). Methods We collected stool samples from 93 AR patients and 72 age- and sex-matched HCs. Gut microbiota composition was analyzed using QIIME targeting the 16S rRNA gene. Functional pathways were predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States. Statistical analysis was performed using the R program, linear discriminant analysis effect size (LefSe), analysis of QIIME, and statistical analysis of metagenomic profiles, among other tests. Results Compared with HCs, AR patients had significantly lower gut-microbiota α-diversity ( P < .001). The gut microbiota composition significantly differed between the 2 study groups. At the phylum level, the relative abundance of Bacteroidetes was higher while those of Actinobacteria and Proteobacteria were lower in the AR group than in the HC group ( P < .001, q < 0.001). At the genus level, Escherichia-Shigella, Prevotella, and Parabacteroides ( P < .001, q < 0.001) had significantly higher relative abundances in the AR group than in the HC group. LefSe analysis indicated that Escherichia-Shigella, Lachnoclostridium, Parabacteroides, and Dialister were potential biomarkers for AR. In addition, predictive metagenome functional analysis showed that pyruvate, porphyrin, chlorophyll, purine metabolism, and peptidoglycan biosynthesis significantly differed between the AR and HC groups. Conclusion A comparison of the gut microbiota of AR patients and HCs suggested that dysbiosis of the fecal microbiota is involved in the development of AR. The present results may reveal key differences and identify targets for preventive or therapeutic intervention.

Background: Massive high-throughput sequencing of short, hypervariable segments of the 16S ribosomal RNA (rRNA) gene has transformed the methodological landscape describing microbial diversity within and across complex biomes. However, several studies have shown that the methodology rather than the biological variation is responsible for the observed sample composition and distribution. This compromises meta-analyses, although this fact is often disregarded. Results: To facilitate true meta-analysis of microbiome studies, we developed NG-Tax, a pipeline for 16S rRNA gene amplicon sequence analysis that was validated with different mock communities and benchmarked against QIIME as a frequently used pipeline. The microbial composition of 49 independently amplified mock samples was characterized by sequencing two variable 16S rRNA gene regions, V4 and V5-V6, in three separate sequencing runs on Illumina’s HiSeq2000 platform. This allowed for the evaluation of important causes of technical bias in taxonomic classification: 1) run-to-run sequencing variation, 2) PCR–error, and 3) region/primer specific amplification bias. Despite the short read length (~140 nt) and all technical biases, the average specificity of the taxonomic assignment for the phylotypes included in the mock communities was 97.78%. On average 99.95% and 88.43% of the reads could be assigned to at least family or genus level, respectively, while assignment to ‘spurious genera’ represented on average only 0.21% of the reads per sample. Analysis of α- and β-diversity confirmed conclusions guided by biology rather than the aforementioned methodological aspects, which was not achieved with QIIME. Conclusions: Different biological outcomes are commonly observed due to 16S rRNA region-specific performance. NG-Tax demonstrated high robustness against choice of region and other technical biases associated with 16S rRNA gene amplicon sequencing studies, diminishing their impact and providing accurate qualitative and quantitative representation of the true sample composition. This will improve comparability between studies and facilitate efforts towards standardization.

O presente estágio foi realizado no Instituto Gulbenkian de Ciência (IGC) no âmbito do mestrado em engenharia biológica e química. O estagiário esteve envolvido com termos e ferramentas usadas em bioinformática, metagenómica e NGS. A principal tarefa do estagiário focou-se na atualização de uma pipeline de análises genómicas feito pelo IGC designado por “GenoQual”. O principal objetivo do estágio do discente focou-se na atualização de uma pipeline de análises genómicas feito pelo IGC, há vários anos, designado por “GenoQual”. Desde a última atualização do GenoQual, tem havido uma evolução natural das ferramentas usadas em bioinformática, surgindo assim novas alternativas e melhorias nas ferramentas usadas pelo GenoQual. Uma das atualizações mais importantes foi o lançamento do QIIME 2 que trouxe melhorias e novas funcionalidades em relação ao QIIME ainda em utilização no GenoQual. A tarefa principal desta dissertação foi a atualização do código Python do pipeline de modo ser compatível com uma versão mais recente de Python e adicionar novas funcionalidades à pipeline, nomeadamente a compatibilidade com o QIIME 2 e Kraken2. O projeto foi organizado em duas etapas distintas, a primeira foi a atualização do código do GenoQual de Python 2.7 para o novo Python 3.x. A segunda etapa consistiu na atualização dos softwares utilizados pela versão original do GenoQual de modo garantir que a nova pipeline era compatível com as novas versões desses softwares para aproveitar as novas melhorias e funcionalidades provenientes das novas atualizações. O código do GenoQual foi sucessivamente atualizado de modo ser compatível com o Python 3.8 e foi proposto a adição da nova plataforma de bioinformáticas microbioma QIIME 2 e o classificador taxonómico Kraken 2 de modo poder realizar analises do tipo 16S e WGS.
The following internship was developed at the Instituto Gulbenkian de Ciência (IGC) in the scope of the master’s biological and chemical engineering degree. The intern dealt with bioinformatics, metagenomics and NGS related terms and tools and focused on the task of updating a pipeline of genomic analyses developed by IGC a few years ago designated as “GenoQual. Ever since GenoQual was last updated, there has been a natural evolution of the tools used in the bioinformatics field, appearing newer alternatives and updates to the tools used by GenoQual. One of the main updates that occurred was the release of QIIME 2 which brought newer upgrades and features in relation to QIIME 1 which GenoQual was still using at the time. The main objective of this internship was to update the Python code used by the pipeline so that it would become compatible with a more recent Python version as well as adding newer functionalities to the GenoQual pipeline, namely the compatibility with QIIME 2 and Kraken 2. The project was organized into two distinct stages; the first was the updating of GenoQual’s Python 2.7 code to the newer Python 3.x version. The second stage was the updating of the packages used by the original version of GenoQual to make sure that the pipeline was still compatible with the newer versions of those required packages, so that it could make use of their improvements and newer functionalities. GenoQual’s code was successively updated to be compatible with Python 3.8 and the addition of the new microbiome bioinformatics QIIME 2 platform and Kraken 2 taxonomic classifier were proposed as additions to the GenoQual pipeline so that it would be able to do both 16S and WGS type analyses.