scholarly journals CAMISIM: Simulating metagenomes and microbial communities

2018 ◽  
Author(s):  
Adrian Fritz ◽  
Peter Hofmann ◽  
Stephan Majda ◽  
Eik Dahms ◽  
Johannes Dröge ◽  
...  
Keyword(s):  
Microbial Communities ◽  
De Novo ◽  
Real Data ◽  
Small Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Taxonomic Profiling ◽  
Benchmark Data ◽  
Sequencing Technologies ◽  
Wide Range

Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. Here, we describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series and differential abundance studies, includes real and simulated strain-level diversity, and generates second and third generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT and metaSPAdes, on several thousand small data sets generated with CAMISIM. CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with truth standards for method evaluation. All data sets and the software are freely available at: https://github.com/CAMI-challenge/CAMISIM

scholarly journals Assessing 16S marker gene survey data analysis methods using mixtures of human stool sample DNA extracts

2018 ◽  
Author(s):  
Nathan D Olson ◽  
M. Senthil Kumar ◽  
Shan Li ◽  
Stephanie Hao ◽  
Winston Timp ◽  
...  
Keyword(s):  
16S Rrna ◽  
De Novo ◽  
Marker Gene ◽  
Real Data ◽  
Qualitative Assessment ◽  
Data Sets ◽  
Sequencing Data ◽  
Differential Abundance ◽  
Analysis Methods ◽  
Downstream Analysis

AbstractBackgroundAnalysis of 16S rRNA marker-gene surveys, used to characterize prokaryotic microbial communities, may be performed by numerous bioinformatic pipelines and downstream analysis methods. However, there is limited guidance on how to decide between methods, appropriate data sets and statistics for assessing these methods are needed. We developed a mixture dataset with real data complexity and an expected value for assessing 16S rRNA bioinformatic pipelines and downstream analysis methods. We generate an assessment dataset using a two-sample titration mixture design. The sequencing data were processed using multiple bioinformatic pipelines, i) DADA2 a sequence inference method, ii) Mothur a de novo clustering method, and iii) QIIME with open-reference clustering. The mixture dataset was used to qualitatively and quantitatively assess count tables generated using the pipelines.ResultsThe qualitative assessment was used to evalute features only present in unmixed samples and titrations. The abundance of Mothur and QIIME features specific to unmixed samples and titrations were explained by sampling alone. However, for DADA2 over a third of the unmixed sample and titration specific feature abundance could not be explained by sampling alone. The quantitative assessment evaluated pipeline performance by comparing observed to expected relative and differential abundance values. Overall the observed relative abundance and differential abundance values were consistent with the expected values. Though outlier features were observed across all pipelines.ConclusionsUsing a novel mixture dataset and assessment methods we quantitatively and qualitatively evaluated count tables generated using three bioinformatic pipelines. The dataset and methods developed for this study will serve as a valuable community resource for assessing 16S rRNA marker-gene survey bioinformatic methods.

scholarly journals CATCh, an Ensemble Classifier for Chimera Detection in 16S rRNA Sequencing Studies

2014 ◽  
Vol 81 (5) ◽  
pp. 1573-1584 ◽  
Author(s):  
Mohamed Mysara ◽  
Yvan Saeys ◽  
Natalie Leys ◽  
Jeroen Raes ◽  
Pieter Monsieurs
Keyword(s):  
16S Rrna ◽  
De Novo ◽  
Pcr Amplification ◽  
Illumina Miseq ◽  
Ensemble Classifier ◽  
Marker Genes ◽  
Data Sets ◽  
Sequencing Data ◽  
Wide Range ◽  
Sequencing Studies

ABSTRACTIn ecological studies, microbial diversity is nowadays mostly assessed via the detection of phylogenetic marker genes, such as 16S rRNA. However, PCR amplification of these marker genes produces a significant amount of artificial sequences, often referred to as chimeras. Different algorithms have been developed to remove these chimeras, but efforts to combine different methodologies are limited. Therefore, two machine learning classifiers (reference-based andde novoCATCh) were developed by integrating the output of existing chimera detection tools into a new, more powerful method. When comparing our classifiers with existing tools in either the reference-based orde novomode, a higher performance of our ensemble method was observed on a wide range of sequencing data, including simulated, 454 pyrosequencing, and Illumina MiSeq data sets. Since our algorithm combines the advantages of different individual chimera detection tools, our approach produces more robust results when challenged with chimeric sequences having a low parent divergence, short length of the chimeric range, and various numbers of parents. Additionally, it could be shown that integrating CATCh in the preprocessing pipeline has a beneficial effect on the quality of the clustering in operational taxonomic units.

scholarly journals Discovery of novel community-relevant small proteins in a simplified human intestinal microbiome

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Hannes Petruschke ◽  
Christian Schori ◽  
Sebastian Canzler ◽  
Sarah Riesbeck ◽  
Anja Poehlein ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Intestinal Microbiota ◽  
De Novo ◽  
Bacterial Species ◽  
Intestinal Microbiome ◽  
Single Strain ◽  
Small Proteins ◽  
Human Intestinal Microbiota ◽  
Wide Range

Abstract Background The intestinal microbiota plays a crucial role in protecting the host from pathogenic microbes, modulating immunity and regulating metabolic processes. We studied the simplified human intestinal microbiota (SIHUMIx) consisting of eight bacterial species with a particular focus on the discovery of novel small proteins with less than 100 amino acids (= sProteins), some of which may contribute to shape the simplified human intestinal microbiota. Although sProteins carry out a wide range of important functions, they are still often missed in genome annotations, and little is known about their structure and function in individual microbes and especially in microbial communities. Results We created a multi-species integrated proteogenomics search database (iPtgxDB) to enable a comprehensive identification of novel sProteins. Six of the eight SIHUMIx species, for which no complete genomes were available, were sequenced and de novo assembled. Several proteomics approaches including two earlier optimized sProtein enrichment strategies were applied to specifically increase the chances for novel sProtein discovery. The search of tandem mass spectrometry (MS/MS) data against the multi-species iPtgxDB enabled the identification of 31 novel sProteins, of which the expression of 30 was supported by metatranscriptomics data. Using synthetic peptides, we were able to validate the expression of 25 novel sProteins. The comparison of sProtein expression in each single strain versus a multi-species community cultivation showed that six of these sProteins were only identified in the SIHUMIx community indicating a potentially important role of sProteins in the organization of microbial communities. Two of these novel sProteins have a potential antimicrobial function. Metabolic modelling revealed that a third sProtein is located in a genomic region encoding several enzymes relevant for the community metabolism within SIHUMIx. Conclusions We outline an integrated experimental and bioinformatics workflow for the discovery of novel sProteins in a simplified intestinal model system that can be generically applied to other microbial communities. The further analysis of novel sProteins uniquely expressed in the SIHUMIx multi-species community is expected to enable new insights into the role of sProteins on the functionality of bacterial communities such as those of the human intestinal tract.

scholarly journals RECORD: Reference-Assisted Genome Assembly for Closely Related Genomes

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Krisztian Buza ◽  
Bartek Wilczynski ◽  
Norbert Dojer
Keyword(s):  
Reference Genome ◽  
De Novo ◽  
Real Data ◽  
Reference Sequence ◽  
Individual Genome ◽  
Single Experiment ◽  
Sequencing Technologies ◽  
Sequencing Cost ◽  
The Individual ◽  
Assembly Software

Background. Next-generation sequencing technologies are now producing multiple times the genome size in total reads from a single experiment. This is enough information to reconstruct at least some of the differences between the individual genome studied in the experiment and the reference genome of the species. However, in most typical protocols, this information is disregarded and the reference genome is used.Results. We provide a new approach that allows researchers to reconstruct genomes very closely related to the reference genome (e.g., mutants of the same species) directly from the reads used in the experiment. Our approach applies de novo assembly software to experimental reads and so-called pseudoreads and uses the resulting contigs to generate a modified reference sequence. In this way, it can very quickly, and at no additional sequencing cost, generate new, modified reference sequence that is closer to the actual sequenced genome and has a full coverage. In this paper, we describe our approach and test its implementation called RECORD. We evaluate RECORD on both simulated and real data. We made our software publicly available on sourceforge.Conclusion. Our tests show that on closely related sequences RECORD outperforms more general assisted-assembly software.

scholarly journals Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

2018 ◽  
Author(s):  
Arghavan Bahadorinejad ◽  
Ivan Ivanov ◽  
Johanna W Lampe ◽  
Meredith AJ Hullar ◽  
Robert S Chapkin ◽  
...  
Keyword(s):  
16S Rrna ◽  
Sample Size ◽  
Microbial Communities ◽  
State Of The Art ◽  
Metagenomic Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Sample Data

AbstractWe propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.Author summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

Improved Compression of DNA Sequencing Data with Cascading Bloom Filters

2018 ◽  
Vol 29 (08) ◽  
pp. 1249-1255
Author(s):  
Kamil Salikhov
Keyword(s):  
Dna Sequencing ◽  
Sequence Data ◽  
Real Data ◽  
Compression Algorithm ◽  
Computational Experiments ◽  
Bloom Filters ◽  
Dna Fragments ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Memory Reduction

Modern DNA sequencing technologies generate prodigious volumes of sequence data consisting of short DNA fragments (reads). Storing and transferring this data is often challenging. With this motivation, several specialized compression methods have been developed. In this paper, we present an improvement of the lossless reference-free compression algorithm, suggested by Rozov et al., based on the technique of cascading Bloom filters. Through computational experiments on real data, we demonstrate that our method results in a significant associated memory reduction in practice.

scholarly journals De Novo Sequencing and Hybrid Assembly of the Biofuel Crop Jatropha curcas L.: Identification of Quantitative Trait Loci for Geminivirus Resistance

Genes ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 69 ◽  
Author(s):  
Nagesh Kancharla ◽  
Saakshi Jalali ◽  
J. Narasimham ◽  
Vinod Nair ◽  
Vijay Yepuri ◽  
...  
Keyword(s):  
Ssr Markers ◽  
Genome Assembly ◽  
Jatropha Curcas ◽  
Quantitative Trait ◽  
De Novo ◽  
Mapping Population ◽  
Single Copy ◽  
Sequencing Data ◽  
De Novo Genome Assembly ◽  
Sequencing Technologies

Jatropha curcas is an important perennial, drought tolerant plant that has been identified as a potential biodiesel crop. We report here the hybrid de novo genome assembly of J. curcas generated using Illumina and PacBio sequencing technologies, and identification of quantitative loci for Jatropha Mosaic Virus (JMV) resistance. In this study, we generated scaffolds of 265.7 Mbp in length, which correspond to 84.8% of the gene space, using Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis. Additionally, 96.4% of predicted protein-coding genes were captured in RNA sequencing data, which reconfirms the accuracy of the assembled genome. The genome was utilized to identify 12,103 dinucleotide simple sequence repeat (SSR) markers, which were exploited in genetic diversity analysis to identify genetically distinct lines. A total of 207 polymorphic SSR markers were employed to construct a genetic linkage map for JMV resistance, using an interspecific F2 mapping population involving susceptible J. curcas and resistant Jatropha integerrima as parents. Quantitative trait locus (QTL) analysis led to the identification of three minor QTLs for JMV resistance, and the same has been validated in an alternate F2 mapping population. These validated QTLs were utilized in marker-assisted breeding for JMV resistance. Comparative genomics of oil-producing genes across selected oil producing species revealed 27 conserved genes and 2986 orthologous protein clusters in Jatropha. This reference genome assembly gives an insight into the understanding of the complex genetic structure of Jatropha, and serves as source for the development of agronomically improved virus-resistant and oil-producing lines.

Computational Approaches for Transcriptome Assembly Based on Sequencing Technologies

2020 ◽  
Vol 15 (1) ◽  
pp. 2-16
Author(s):  
Yuwen Luo ◽  
Xingyu Liao ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
De Novo ◽  
Transcriptome Assembly ◽  
Critical Role ◽  
High Sensitivity ◽  
Biological Properties ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Massive Sequencing ◽  
Generation Sequencing

Transcriptome assembly plays a critical role in studying biological properties and examining the expression levels of genomes in specific cells. It is also the basis of many downstream analyses. With the increase of speed and the decrease in cost, massive sequencing data continues to accumulate. A large number of assembly strategies based on different computational methods and experiments have been developed. How to efficiently perform transcriptome assembly with high sensitivity and accuracy becomes a key issue. In this work, the issues with transcriptome assembly are explored based on different sequencing technologies. Specifically, transcriptome assemblies with next-generation sequencing reads are divided into reference-based assemblies and de novo assemblies. The examples of different species are used to illustrate that long reads produced by the third-generation sequencing technologies can cover fulllength transcripts without assemblies. In addition, different transcriptome assemblies using the Hybrid-seq methods and other tools are also summarized. Finally, we discuss the future directions of transcriptome assemblies.

scholarly journals csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows

2015 ◽  
Vol 44 (5) ◽  
pp. e45-e45 ◽  
Author(s):  
Aaron T.L. Lun ◽  
Gordon K. Smyth
Keyword(s):  
De Novo ◽  
Massively Parallel Sequencing ◽  
Real Data ◽  
Sequencing Data ◽  
Scientific Application ◽  
Sliding Windows ◽  
Treatment Conditions ◽  
Bioconductor Project ◽  
Differential Binding ◽  
Genomic Regions

Abstract Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify binding sites for a target protein in the genome. An important scientific application is to identify changes in protein binding between different treatment conditions, i.e. to detect differential binding. This can reveal potential mechanisms through which changes in binding may contribute to the treatment effect. The csaw package provides a framework for the de novo detection of differentially bound genomic regions. It uses a window-based strategy to summarize read counts across the genome. It exploits existing statistical software to test for significant differences in each window. Finally, it clusters windows into regions for output and controls the false discovery rate properly over all detected regions. The csaw package can handle arbitrarily complex experimental designs involving biological replicates. It can be applied to both transcription factor and histone mark datasets, and, more generally, to any type of sequencing data measuring genomic coverage. csaw performs favorably against existing methods for de novo DB analyses on both simulated and real data. csaw is implemented as a R software package and is freely available from the open-source Bioconductor project.

scholarly journals eSCAN: Scan Regulatory Regions for Aggregate Association Testing using Whole Genome Sequencing Data

2020 ◽  
Author(s):  
Yingxi Yang ◽  
Yuchen Yang ◽  
Le Huang ◽  
Jai G. Broome ◽  
Adolfo Correa ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
New Technologies ◽  
Real Data ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Association Testing ◽  
Wide Range ◽  
Sequencing Studies

AbstractWith advances in whole genome sequencing (WGS) technology, multiple statistical methods for aggregate association testing have been developed. Many common approaches aggregate variants in a given genomic window of a fixed/varying size and are not reliant on existing knowledge to define appropriate test units, resulting in most identified regions not being clearly linked to genes, limiting biological understanding. Functional information from new technologies (such as Hi-C and its derivatives), which can help link enhancers to the genes they affect, can be leveraged to predefine variant sets for aggregate testing in WGS. Therefore, in this paper we propose the eSCAN (Scan the Enhancers) method for genome-wide assessment of enhancer regions in sequencing studies, combining the advantages of dynamic window selection in SCANG with the advantages of increased incorporation of genomic annotation. eSCAN searches biologically meaningful searching windows, increasing power and aiding biological interpretation, as demonstrated by simulation studies under a wide range of scenarios. We also apply eSCAN for association analysis of blood cell traits using TOPMed WGS data from Women’s Health Initiative (WHI) and Jackson Heart Study (JHS). Results from this real data example show that eSCAN is able to capture more significant signals, and these signals are of shorter length and drive association of larger regions detected by other methods.

Sign in / Sign up

Export Citation Format

Share Document