16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets

The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier.     

Binpairs: Utilization of Illumina Paired-End Information for Improving Efficiency of Taxonomic Binning of Metagenomic Sequences

Motivation

Paired-end sequencing protocols, offered by next generation sequencing (NGS) platforms like Illumia, generate a pair of reads for every DNA fragment in a sample. Although this protocol has been utilized for several metagenomics studies, most taxonomic binning approaches classify each of the reads (forming a pair), independently. The present work explores some simple but effective strategies of utilizing pairing-information of Illumina short reads for improving the accuracy of taxonomic binning of metagenomic datasets. The strategies proposed can be used in conjunction with all genres of existing binning methods.

Results

Validation results suggest that employment of these “Binpairs” strategies can provide significant improvements in the binning outcome. The quality of the taxonomic assignments thus obtained are often comparable to those that can only be achieved with relatively longer reads obtained using other NGS platforms (such as Roche).

Availability

An implementation of the proposed strategies of utilizing pairing information is freely available for academic users at https://metagenomics.atc.tcs.com/binning/binpairs.     

Probabilistic Inference of Biochemical Reactions in Microbial Communities from Metagenomic Sequences

Shotgun metagenomics has been applied to the studies of the functionality of various microbial communities. As a critical analysis step in these studies, biological pathways are reconstructed based on the genes predicted from metagenomic shotgun sequences. Pathway reconstruction provides insights into the functionality of a microbial community and can be used for comparing multiple microbial communities. The utilization of pathway reconstruction, however, can be jeopardized because of imperfect functional annotation of genes, and ambiguity in the assignment of predicted enzymes to biochemical reactions (e.g., some enzymes are involved in multiple biochemical reactions). Considering that metabolic functions in a microbial community are carried out by many enzymes in a collaborative manner, we present a probabilistic sampling approach to profiling functional content in a metagenomic dataset, by sampling functions of catalytically promiscuous enzymes within the context of the entire metabolic network defined by the annotated metagenome. We test our approach on metagenomic datasets from environmental and human-associated microbial communities. The results show that our approach provides a more accurate representation of the metabolic activities encoded in a metagenome, and thus improves the comparative analysis of multiple microbial communities. In addition, our approach reports likelihood scores of putative reactions, which can be used to identify important reactions and metabolic pathways that reflect the environmental adaptation of the microbial communities. Source code for sampling metabolic networks is available online at http://omics.informatics.indiana.edu/mg/MetaNetSam/.     

Evaluation of a Hybrid Approach Using UBLAST and BLASTX for Metagenomic Sequences Annotation of Specific Functional Genes

The fast development of next generation sequencing (NGS) has dramatically increased the application of metagenomics in various aspects. Functional annotation is a major step in the metagenomics studies. Fast annotation of functional genes has been a challenge because of the deluge of NGS data and expanding databases. A hybrid annotation pipeline proposed previously for taxonomic assignments was evaluated in this study for metagenomic sequences annotation of specific functional genes, such as antibiotic resistance genes, arsenic resistance genes and key genes in nitrogen metabolism. The hybrid approach using UBLAST and BLASTX is 44–177 times faster than direct BLASTX in the annotation using the small protein database for the specific functional genes, with the cost of missing a small portion (<1.8%) of target sequences compared with direct BLASTX hits. Different from direct BLASTX, the time required for specific functional genes annotation using the hybrid annotation pipeline depends on the abundance for the target genes. Thus this hybrid annotation pipeline is more suitable in specific functional genes annotation than in comprehensive functional genes annotation.     

一个识别蛋白质折叠模式的SVM分类器

蛋白质折叠模式识别是一种分析蛋白质结构的重要方法。以序列相似性较低的蛋白质为训练集,提取蛋白质序列信息频数及疏水性等信息作为折叠类型特征,从SCOP数据库中已分类蛋白质构建1 393种折叠模式的数据集,采用SVM预测蛋白质1 393种折叠模式。封闭测试准确率达99.612 2%,基于SCOP的开放测试准确率达79.632 9%。基于另一个权威测试集的开放测试折叠准确率达64.705 9%,SCOP类准确率达76.470 6%,可以有效地对蛋白质折叠模式进行预测,从而为蛋白质从头预测提供参考。     

Metagenomic mining for microbiologists

Microbial ecologists can now start digging into the accumulating mountains of metagenomic data to uncover the occurrence of functional genes and their correlations to microbial community members. Limitations and biases in DNA extraction and sequencing technologies impact sequence distributions, and therefore, have to be considered. However, when comparing metagenomes from widely differing environments, these fluctuations have a relatively minor role in microbial community discrimination. As a consequence, any functional gene or species distribution pattern can be compared among metagenomes originating from various environments and projects. In particular, global comparisons would help to define ecosystem specificities, such as involvement and response to climate change (for example, carbon and nitrogen cycle), human health risks (eg, presence of pathogen species, toxin genes and viruses) and biodegradation capacities. Although not all scientists have easy access to high-throughput sequencing technologies, they do have access to the sequences that have been deposited in databases, and therefore, can begin to intensively mine these metagenomic data to generate hypotheses that can be validated experimentally. Information about metabolic functions and microbial species compositions can already be compared among metagenomes from different ecosystems. These comparisons add to our understanding about microbial adaptation and the role of specific microbes in different ecosystems. Concurrent with the rapid growth of sequencing technologies, we have entered a new age of microbial ecology, which will enable researchers to experimentally confirm putative relationships between microbial functions and community structures.     

Metagenomic era for biocatalyst identification

Microbial enzymes have many known applications as biocatalysts. However, only a few of them are currently employed for biocatalysis even though an annotated collection of more than 190 billion bases is available in metagenome sequence databases from uncultured and highly diverse microbial populations. This review aims at providing conceptual and technical bases for the translation of metagenome data into both experimental and computational frameworks that facilitates a comprehensive analysis of the biocatalysts diversity space. We will also briefly present the status of the current capabilities that assess and predict catalytic potential of environmental sites and track its diversity and evolution in large-scale biocatalysis process resulting from studies applying metagenomics in association with gene fingerprinting, catabolic arrays and complementary '-omics'.     

A Metagenomic Framework for the Study of Airborne Microbial Communities

Understanding the microbial content of the air has important scientific, health, and economic implications. While studies have primarily characterized the taxonomic content of air samples by sequencing the 16S or 18S ribosomal RNA gene, direct analysis of the genomic content of airborne microorganisms has not been possible due to the extremely low density of biological material in airborne environments. We developed sampling and amplification methods to enable adequate DNA recovery to allow metagenomic profiling of air samples collected from indoor and outdoor environments. Air samples were collected from a large urban building, a medical center, a house, and a pier. Analyses of metagenomic data generated from these samples reveal airborne communities with a high degree of diversity and different genera abundance profiles. The identities of many of the taxonomic groups and protein families also allows for the identification of the likely sources of the sampled airborne bacteria.     

A Novel Cascade Classifier for Automatic Microcalcification Detection

In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC). Our framework comprises three classification stages: i) a random forest (RF) classifier for simple features capturing the second order local structure of individual μCs, where non-μC pixels in the target mammogram are efficiently eliminated; ii) a more complex discriminative restricted Boltzmann machine (DRBM) classifier for μC candidates determined in the RF stage, which automatically learns the detailed morphology of μC appearances for improved discriminative power; and iii) a detector to detect clusters of μCs from the individual μC detection results, using two different criteria. From the two-stage RF-DRBM classifier, we are able to distinguish μCs using explicitly computed features, as well as learn implicit features that are able to further discriminate between confusing cases. Experimental evaluation is conducted on the original Mammographic Image Analysis Society (MIAS) and mini-MIAS databases, as well as our own Seoul National University Bundang Hospital digital mammographic database. It is shown that the proposed method outperforms comparable methods in terms of receiver operating characteristic (ROC) and precision-recall curves for detection of individual μCs and free-response receiver operating characteristic (FROC) curve for detection of clustered μCs.     

dbCNS: A New Database for Conserved Noncoding Sequences

We developed dbCNS (http://yamasati.nig.ac.jp/dbcns), a new database for conserved noncoding sequences (CNSs). CNSs exist in many eukaryotes and are assumed to be involved in protein expression control. Version 1 of dbCNS, introduced here, includes a powerful and precise CNS identification pipeline for multiple vertebrate genomes. Mutations in CNSs may induce morphological changes and cause genetic diseases. For this reason, many vertebrate CNSs have been identified, with special reference to primate genomes. We integrated ∼6.9 million CNSs from many vertebrate genomes into dbCNS, which allows users to extract CNSs near genes of interest using keyword searches. In addition to CNSs, dbCNS contains published genome sequences of 161 species. With purposeful taxonomic sampling of genomes, users can employ CNSs as queries to reconstruct CNS alignments and phylogenetic trees, to evaluate CNS modifications, acquisitions, and losses, and to roughly identify species with CNSs having accelerated substitution rates. dbCNS also produces links to dbSNP for searching pathogenic single-nucleotide polymorphisms in human CNSs. Thus, dbCNS connects morphological changes with genetic diseases. A test analysis using 38 gnathostome genomes was accomplished within 30 s. dbCNS results can evaluate CNSs identified by other stand-alone programs using genome-scale data.     

Metagenomic Screening for Bleomycin Resistance Genes

A metagenomic library of activated sludge was screened for bleomycin resistance genes. Two genes were identified that differed greatly from each other, from the genes of bleomycin-producing actinomycetes, and from those of clinical isolates. Therefore, the nonclinical environment is a rich reservoir of new resistance elements, and metagenomics can be used to sample the resistome rapidly.     

A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads

The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial community. Multiple genomes contained in a metagenomic sample can be identified and quantitated through homology searches of sequence reads with known sequences catalogued in reference databases. Traditionally, reads with multiple genomic hits are assigned to non-specific or high ranks of the taxonomy tree, thereby impacting on accurate estimates of relative abundance of multiple genomes present in a sample. Instead of assigning reads one by one to the taxonomy tree as many existing methods do, we propose a statistical framework to model the identified candidate genomes to which sequence reads have hits. After obtaining the estimated proportion of reads generated by each genome, sequence reads are assigned to the candidate genomes and the taxonomy tree based on the estimated probability by taking into account both sequence alignment scores and estimated genome abundance. The proposed method is comprehensively tested on both simulated datasets and two real datasets. It assigns reads to the low taxonomic ranks very accurately. Our statistical approach of taxonomic assignment of metagenomic reads, TAMER, is implemented in R and available at http://faculty.wcas.northwestern.edu/hji403/MetaR.htm.     

The Metagenomic Telescope

Next generation sequencing technologies led to the discovery of numerous new microbe species in diverse environmental samples. Some of the new species contain genes never encountered before. Some of these genes encode proteins with novel functions, and some of these genes encode proteins that perform some well-known function in a novel way. A tool, named the Metagenomic Telescope, is described here that applies artificial intelligence methods, and seems to be capable of identifying new protein functions even in the well-studied model organisms. As a proof-of-principle demonstration of the Metagenomic Telescope, we considered DNA repair enzymes in the present work. First we identified proteins in DNA repair in well–known organisms (i.e., proteins in base excision repair, nucleotide excision repair, mismatch repair and DNA break repair); next we applied multiple alignments and then built hidden Markov profiles for each protein separately, across well–researched organisms; next, using public depositories of metagenomes, originating from extreme environments, we identified DNA repair genes in the samples. While the phylogenetic classification of the metagenomic samples are not typically available, we hypothesized that some very special DNA repair strategies need to be applied in bacteria and Archaea living in those extreme circumstances. It is a difficult task to evaluate the results obtained from mostly unknown species; therefore we applied again the hidden Markov profiling: for the identified DNA repair genes in the extreme metagenomes, we prepared new hidden Markov profiles (for each genes separately, subsequent to a cluster analysis); and we searched for similarities to those profiles in model organisms. We have found well known DNA repair proteins, numerous proteins with unknown functions, and also proteins with known, but different functions in the model organisms.     

A Simple Silica-based Method for Metagenomic DNA Extraction from Soil and Sediments

A new method is described for extraction of metagenomic DNA from soil and sediments which is based on DNA adsorption to silica without the use of phenol, ethanol precipitation or a cesium chloride gradient. High-quality DNA was obtained, and PCR inhibition was overcome by adding bovine serum albumin and adjusting magnesium concentration. By using PCR-DGGE with Firmicutes and lactic acid bacteria-specific primers the extracted metagenomic DNA was shown to contain a mixture of bacterial genomes. This method can be used for screening bacterial diversity in soil and sediment samples.