Alignment-free supervised classification of metagenomes by recursive SVM.

Here, we apply an alignment-free method (kPal) that leverages on k-mer frequencies to classify reads within a single long read metagenomic dataset. In addition to a series of simulated metagenomic datasets, we generated sequencing data from a bioreactor microbiome using the PacBio RSII single-molecule real-time sequencing platform. We show that distances obtained after the comparison of k-mer profiles can reveal relationships between reads within a single metagenome, leading to a clustering per species.

Determining abundances of microbial genomes in metagenomic samples is an important problem in analyzing metagenomic data. Although homology-based methods are popular, they have shown to be computationally expensive due to the alignment of tens of millions of reads from metagenomic samples to reference genomes of hundreds to thousands of environmental microbial species. We introduce an efficient alignment-free approach to estimate abundances of microbial genomes in metagenomic samples. The approach is based on solving linear and quadratic programs, which are represented by genome-specific markers (GSM). We compared our method against popular alignment-free and homology-based methods. Without contamination, our method was more accurate than other alignment-free methods while being much faster than a homology-based method. In more realistic settings where samples were contaminated with human DNA, our method was the most accurate method in predicting abundance at varying levels of contamination. We achieve higher accuracy than both alignment-free and homology-based methods.

Background There are only a few studies that have investigated the role of intestinal infections other than Clostridium difficile in Inflammatory Bowel Disease (IBD). Objectives: Our goal was to investigate the frequency of intestinal infections detected by rectal swabs in IBD patients during admission due to exacerbation of the disease. Methods We conducted a cross-sectional analysis of 299 patients who underwent microbiome sampling with the use of rectal swabs from January 1, 2017 to June 30, 2019, treated at a tertiary IBD centre in Poland. Our main goal was to assess the presence of any infection that could mimic or coexist with IBD exacerbation. Results A total of 299 patients were subjected to microbiological examination, including 61 patients with Crohn’s Disease (CD), 143 patients with Ulcerative Colitis (UC) and 95 patients with diarrhoea not related to IBD. Positive results of rectal swab culture were found in a total of 22 samples - in 17 patients with IBD and 5 patients without IBD. Among the IBD patients, positive rectal swab cultures were obtained in cases of UC - 13 samples, 76.5%, p <0.001, CD - 4 samples, 23.5% positive results, p = 0.001. In patients with IBD compared with patients without IBD, a higher incidence of intestinal infections was found (in patients with IBD 8.3%, p <0.001, in patients without IBD 5.2%, p < 0.001). Analysis of the results obtained showed seasonal variability in the number of positive microbiological tests found for cultures of rectal swabs in IBD patients. Positive results of rectal swabs from patients with IBD were significantly more frequent in the summer months - 12, 70.6%, p < 0.001 than in the winter months, when 5 positive test results were found (29.4%, p <0.001). Conclusion A retrospective analysis of rectal swabs for microbiome sampling collected from IBD patients during disease exacerbation treated in a tertiary IBD centre in Poland was performed. Intestinal infections, other than Clostridium difficile, were found in 17 patients with IBD symptoms. Positive results from rectal swabs of patients without symptoms of IBD were found in 5 cases. The evaluation of the obtained results shows that positive results of rectal swabs were found more often in samples taken from women (12 samples - 68.7%) than from men (5 samples - 29.3%).

Microorganisms are everywhere. Recent studies showed that the mixture of microbes or the microbiome on the human body plays important roles in human physiology and diseases. Metagenomic sequencing is a key technology for studying microbiomes. It produces massive amounts of data in the form of short sequencing reads. A single metagenomic sample can contain 10 7 to 10 8 reads of about 100-nucleotide (nt) length each in a typical shotgun metagenomic sequencing study. They contain rich information about microbiomes and their functions, but reading out those information from the huge highly fragmented data has multiple challenges for mathematical models, bioinformatics methods, and computer algorithms. In this paper, we review the basic bioinformatics tasks and existing methods in processing and analyzing metagenomic data, and discuss remaining open challenges and practical observations. The aim of the paper is to provide readers a whole picture of metagenomic data processing and analysis, and a reference and perspective to start with for computational scientists who are interested in this exciting field.

Microbiome association studies typically link host disease or other traits to summary statistics measured in metagenomics data, such as diversity or taxonomic composition. But identifying disease-associated species based on their relative abundance does not provide insight into why these microbes act as disease markers, and it overlooks cases where disease risk is related to specific strains with unique biological functions. To bridge this knowledge gap, we developed microSLAM, a mixed-effects model and an R package that performs association tests that connect host traits to the presence/absence of genes within each microbiome species, while accounting for strain genetic relatedness across hosts. Traits can be quantitative or binary (such as case/control). MicroSLAM is fit in three steps for each species. The first step estimates population structure across hosts. Step two calculates the association between population structure and the trait, enabling detection of species for which a subset of related strains confer risk. To identify specific genes whose presence/absence across diverse strains is associated with the trait, step three models the trait as a function of gene occurrence plus random effects estimated from step two. Applying microSLAM to 710 gut metagenomes from inflammatory bowel disease (IBD) samples, we discovered 56 species whose population structure correlates with IBD, meaning that different lineages are found in cases versus controls. After controlling for population structure, 20 species had genes significantly associated with IBD. Twenty-one of these genes were more common in IBD patients, while 32 genes were enriched in healthy controls, including a seven-gene operon in Faecalibacterium prausnitzii that is involved in utilization of fructoselysine from the gut environment. The vast majority of species detected by microSLAM were not significantly associated with IBD using standard relative abundance tests. These findings highlight the importance of accounting for within-species genetic variation in microbiome studies.

The recent developments performed at PRABI for NGS data analysis are led in three main directions: i) short reads clustering for metagenomic data; ii) Open Reading Frames (ORFs) detection in metagenomes; and iii) statistical detection of peaks applied to the identification of replication origins on the human genome and to chIP-Seq data. One of the problems frequently encountered with present day metagenomic data is the large amount of reads that have no significant homologs in the repository sequence data banks. In order to see if, at least, those “orphans” share some similarities among themselves, a lot of different clustering strategies have been developed. The strategy we have chosen to explore at PRABI is a distance-based one, as opposed to the model-based ones. More precisely, we have focused on the use of Correspondence Analysis (CA) and derived methods [1]. Due to its simplicity, this method is easy to use, very fast and efficient with large data sets containing hundreds of thousands of reads. On the other hand, its efficiency rapidly decreases when the number of different taxa present in the samples is high. The approach chosen for ORFs detection is also based on CA. In this case, the analysis is computed on the codon composition of the six possible reading frames of a sequence [2]. The main advantage of this method is that it does not require a training step (like in Glimmer), therefore it can be used on metagenomic data, even if the biodiversity expected in the samples is very high. Tests on simulated metagenomic data sets show that the sensitivity of the program is 59% while specificity is 89%. The low sensitivity is due to fact that the efficiency of the method is highly dependant on the intensity of the codon bias in the coding sequence. Therefore, weakly biased genes (such as lowly expressed genes when there is translational selection in the species considered) are often missed by the method. Lastly, for the detection of peaks in NGS data, the novelty is to develop a rigorous statistical framework to detect exceptional enrichment of reads using Poisson processes and scan statistics. It is a powerful framework that allows to define a proper P-value and FDR for the peaks, and our project is now to focus on the realistic modeling of the coverage function along the genome in order to adapt the significance of the peaks to a background noise that is highly dependent on the genomic context. As an extension and perspective, we plan to develop a statistical methodology to compare chIP-Seq data between conditions, and to assess the significance of differential peaks. This strategy will be applied also to the detection of differentially expressed small RNAs.

The metagenomic paradigm allows for an understanding of the metabolic and functional potential of microbes in a community via a study of their proteins. The substrate for protein identification is either the set of individual nucleotide reads generated from metagenomic samples or the set of contig sequences produced by assembling these reads. However, a read-based strategy using reads generated by next-generation sequencing (NGS) technologies, results in an overwhelming majority of partial-length protein predictions. A nucleotide assembly-based strategy does not fare much better, as metagenomic assemblies are typically fragmented and also leave a large fraction of reads unassembled. Here, we present a method for reconstructing complete protein sequences directly from NGS metagenomic data. Our framework is based on a novel short peptide assembler (SPA) that assembles protein sequences from their constituent peptide fragments identified on short reads. The SPA algorithm is based on informed traversals of a de Bruijn graph, defined on an amino acid alphabet, to identify probable paths that correspond to proteins. Using large simulated and real metagenomic data sets, we show that our method outperforms the alternate approach of identifying genes on nucleotide sequence assemblies and generates longer protein sequences that can be more effectively analysed.

Metagenomes present assembly challenges, when assembling multiple genomes from mixed reads of multiple species. An assembler for single genomes can’t adapt well when applied in this case. A metagenomic assembler, Genovo, is a de novo assembler for metagenomes under a generative probabilistic model. Genovo assembles all reads without discarding any reads in a preprocessing step, and is therefore able to extract more information from metagenomic data and, in principle, generate better assembly results. Paired end sequencing is currently widely-used yet Genovo was designed for 454 single end reads. In this research, we attempted to extend Genovo by incorporating paired-end information, named Xgenovo, so that it generates higher quality assemblies with paired end reads.First, we extended Genovo by adding a bonus parameter in the Chinese Restaurant Process used to get prior accounts for the unknown number of genomes in the sample. This bonus parameter intends for a pair of reads to be in the same contig and as an effort to solve chimera contig case. Second, we modified the sampling process of the location of a read in a contig. We used relative distance for the number of trials in the symmetric geometric distribution instead of using distance between the offset and the center of contig used in Genovo. Using this relative distance, a read sampled in the appropriate location has higher probability. Therefore a read will be mapped in the correct location.Results of extensive experiments on simulated metagenomic datasets from simple to complex with species coverage setting following uniform and lognormal distribution showed that Xgenovo can be superior to the original Genovo and the recently proposed metagenome assembler for 454 reads, MAP. Xgenovo successfully generated longer N50 than Genovo and MAP while maintaining the assembly quality even for very complex metagenomic datasets consisting of 115 species. Xgenovo also demonstrated the potential to decrease the computational cost. This means that our strategy worked well. The software and all simulated datasets are publicly available online at http://xgenovo.dna.bio.keio.ac.jp.

Discovery of disease biomarkers is a key step in translating advances in genomics into clinical practice. There is growing evidence that changes in gut microbial composition are associated with the onset and progression of Type 2 Diabetes (T2D), Obesity, and Inflammatory Bowel Disease (IBD). Reliable identification of the most informative features (i.e., microbes) for discriminating metagenomics samples from two or more groups (i.e., phenotypes) is a major challenge in computational metagenomics. We propose a Network-Based Biomarker Discovery (NBBD) framework for detecting disease biomarkers from metagenomics data. NBBD has two major customizable modules: i) A network inference module for inferring ecological networks from the abundances of microbial operational taxonomic units (OTUs); ii) A node importance scoring module for comparing the constructed networks for the chosen phenotypes and assigning a score to each node based on the degree to which the topological properties of the node differ across two networks. We empirically evaluated the proposed NBBD framework, using five network inference methods for inferring gut microbial networks combined with six node topological properties, on the identification of IBD biomarkers using a large dataset from a cohort of 657 and 316 IBD and healthy controls metagenomic biopsy samples, respectively. Our results show that NBBD is very competitive with some of the state-of-the-art feature selection methods including the widely used method based on random forest variable importance scores.

With the advance of new sequencing technologies producing massive short reads data, metagenomics is rapidly growing, especially in the fields of environmental biology and medical science. The metagenomic data are not only high dimensional with large number of features and limited number of samples but also complex with a large number of zeros and skewed distribution. Efficient computational and statistical tools are needed to deal with these unique characteristics of metagenomic sequencing data. In metagenomic studies, one main objective is to assess whether and how multiple microbial communities differ under various environmental conditions. We propose a two-stage statistical procedure for selecting informative features and identifying differentially abundant features between two or more groups of microbial communities. In the functional analysis of metagenomes, the features may refer to the pathways, subsystems, functional roles and so on. In the first stage of the proposed procedure, the informative features are selected using elastic net as reducing the dimension of metagenomic data. In the second stage, the differentially abundant features are detected using generalized linear models with a negative binomial distribution. Compared with other available methods, the proposed approach demonstrates better performance for most of the comprehensive simulation studies. The new method is also applied to two real metagenomic datasets related to human health. Our findings are consistent with those in previous reports. R code and two example datasets are available at http://cals.arizona.edu/∼anling/software.htm. Supplementary file is available at Bioinformatics online.

BackgroundNext-generation sequencing (NGS) has greatly facilitated metagenomic analysis but also raised new challenges for metagenomic DNA sequence assembly, owing to its high-throughput nature and extremely short reads generated by sequencers such as Illumina. To date, how to generate a high-quality draft assembly for metagenomic sequencing projects has not been fully addressed.ResultsWe conducted a comprehensive assessment on state-of-the-art de novo assemblers and revealed that the performance of each assembler depends critically on the sequencing depth. To address this problem, we developed a pipeline named InteMAP to integrate three assemblers, ABySS, IDBA-UD and CABOG, which were found to complement each other in assembling metagenomic sequences. Making a decision of which assembling approaches to use according to the sequencing coverage estimation algorithm for each short read, the pipeline presents an automatic platform suitable to assemble real metagenomic NGS data with uneven coverage distribution of sequencing depth. By comparing the performance of InteMAP with current assemblers on both synthetic and real NGS metagenomic data, we demonstrated that InteMAP achieves better performance with a longer total contig length and higher contiguity, and contains more genes than others.ConclusionsWe developed a de novo pipeline, named InteMAP, that integrates existing tools for metagenomics assembly. The pipeline outperforms previous assembly methods on metagenomic assembly by providing a longer total contig length, a higher contiguity and covering more genes. InteMAP, therefore, could potentially be a useful tool for the research community of metagenomics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0686-x) contains supplementary material, which is available to authorized users.

Phylogenetics and population genetics are central disciplines in evolutionary biology. Both are based on the comparison of single DNA sequences, or a concatenation of a number of these. However, with the advent of next-generation DNA sequencing technologies, the approaches that consider large genomic data sets are of growing importance for the elucidation of evolutionary relationships among species. Among these approaches, the assembly and alignment-free methods which allow an efficient distance computation and phylogeny reconstruction are of great importance. However, it is not yet clear under what quality conditions and abundance of genomic data such methods are able to infer phylogenies accurately. In the present study we assess the method originally proposed by Fan et al. for whole genome data, in the elucidation of Tomatoes' chloroplast phylogenetics using short read sequences. We find that this assembly and alignment-free method is capable of reproducing previous results under conditions of high coverage, given that low frequency k-mers (i.e. error prone data) are effectively filtered out. Finally, we present a complete chloroplast phylogeny for the best data quality candidates of the recently published 360 tomato genomes.

High-throughput sequencing technologies have greatly facilitated omics research in the past two decades. An enormous volume of omics data have been generated, with the potential to answer various key biological questions. Unsupervised learning technologies such as cluster analysis serve as a powerful tool to uncover hidden patterns and associations in omics data. However, considering the complex natural structure and special characteristics of omics data, it is usually inappropriate or inefficient to directly apply clustering methods designed for general purposes. To address these issues, we develop several clustering and biclustering algorithms specifically designed for omics data. %In this dissertation, we tried to address these concerns from multiple different aspects: The first project is about Poisson hurdle model-based clustering, which aims to deal with extra sparsity in microbiome count data. Extra sparsity is a huge problem when dealing with microbiome data and some other omics data. In this study, we present a novel model-based clustering framework based on Poisson hurdle models, designed specifically for the intricacies of sparse microbiome data. We describe an expectation-maximization algorithm and a modified version using simulated annealing to conduct the cluster analysis. Additionally, we offer algorithms for intuitive initialization and determining the optimal number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. A practical application of our method to a sorghum rhizosphere microbiome dataset yielded intriguing biological insights. The second project is a Dirichlet-Multinomial biclustering algorithm for compositional microbiome data. Similar to (one-way) cluster analysis, biclustering is a popular tool to study the interactions between microbial sub-communities and environmental samples. In this project, we introduce a Dirichlet-Multinomial (DM) model-based checkerboard biclustering technique tailored for the simultaneous clustering of microbiome features and samples. This approach assumes the presence of a DM distribution mixture within each microbiome sample, and utilizes a combination of Expectation-Maximization and coordinate descent algorithm to solve for optimal parameters. Compared to conventional, general-purpose biclustering algorithms, our method consistently demonstrated superior clustering accuracy and enriched biological interpretation across diverse simulation settings and multiple real datasets. The third project studied and compared the effect of several feature selection techniques implemented in the biclustering framework. When dealing with biological data, usually tens of thousands of features are present in a single dataset, but most of them are likely non-informative and do not benefit our analysis. Including them in the analysis can be computationally inefficient and lead to overfitting issues. This project gives a review of implementing penalization methods in the biclustering framework to do feature selection. Penalization/regularization are broadly used in regression analysis, and have also been applied to one-way clustering. This project focuses on extending and applying penalization methods to bicluster analysis of biological data. We show two different classes of penalization methods, one on the with-cluster sum of squares (WCSS) and the other on the feature weights. By introducing sparsity into the feature space, we are able to simultaneously find feature groups/sample subtypes and identify non-informative features. The fourth project focused on clustering analysis using an over-clustering and merging structure. This idea originates from a problem we encountered in the third project: if the true feature classes show a highly imbalanced structure, K-means type of clustering or biclustering methods usually lose power. This is due to the spherical nature of K-means clusters, which lets it perform poorly when the class sizes differ a lot. Motivated by this drawback of K-means, and also by the simple fact that K-means can only produce convex clusters, we developed a novel K-means merging algorithm \emph{CavMerge} that is suitable for producing general-sized and imbalanced clustering results. This method is intuitive, free of parameter tuning, and computationally efficient. It starts from an over-clustered K-means result, and utilizes a property called log-concavity to perform merging. Operating under minimal local distributional assumptions, our algorithm demonstrates strong consistency and guarantees rapid convergence. Empirical studies on various simulated and real datasets demonstrate that our method yields more reliable clusters in comparison to current state-of-the-art algorithms.

The emergence of next-generation sequencing (NGS) technology has greatly influenced microbiome research and led to the development of novel bioinformatics tools to deeply analyze metagenomics datasets. Identifying strain-level variations in microbial communities is important to understanding the onset and progression of diseases, host-pathogen interrelationships, and drug resistance, in addition to designing new therapeutic regimens. In this study, we developed a novel tool called StrainIQ (strain identification and quantification) based on a new n-gram-based (series of n number of adjacent nucleotides in the DNA sequence) algorithm for predicting and quantifying strain-level taxa from whole-genome metagenomic sequencing data. We thoroughly evaluated our method using simulated and mock metagenomic datasets and compared its performance with existing methods. On average, it showed 85.8% sensitivity and 78.2% specificity on simulated datasets. It also showed higher specificity and sensitivity using n-gram models built from reduced reference genomes and on models with lower coverage sequencing data. It outperforms alternative approaches in genus- and strain-level prediction and strain abundance estimation. Overall, the results show that StrainIQ achieves high accuracy by implementing customized model-building and is an efficient tool for site-specific microbial community profiling.

BackgroundEnormous volumes of short read data from next-generation sequencing (NGS) technologies have posed new challenges to the area of genomic sequence comparison. The multiple sequence alignment approach is hardly applicable to NGS data due to the challenging problem of short read assembly. Thus alignment-free methods are needed for the comparison of NGS samples of short reads.ResultsRecently several k-mer based distance measures such as CVTree, , and co-phylog have been proposed or enhanced to address this problem. However, how to choose an optimal k value for those distance measures is not trivial since it may depend on different aspects of the sequence data. In this paper, we considered an alternative parameter-free approach: compression-based distance measures. These measures have shown good performance for the comparison of long genomic sequences, but they have not yet been tested on NGS short reads. Hence, we performed extensive validation in this study and showed that the compression-based distances are highly consistent with those distances obtained from the k-mer based methods, from the multiple sequence alignment approach, and from existing benchmarks in the literature. Moreover, as the compression-based distance measures are parameter-free, no parameter optimization is required and these measures still perform consistently well on multiple types of sequence data, for different kinds of species and taxonomy levels.ConclusionsThe compression-based distance measures are assembly-free, alignment-free, parameter-free, and thus represent useful tools for the comparison of long genomic sequences as well as the comparison of NGS samples of short reads.