FragGeneScan: predicting genes in short and error-prone reads

The advances of next-generation sequencing technology have facilitated metagenomics research that attempts to determine directly the whole collection of genetic material within an environmental sample (i.e. the metagenome). Identification of genes directly from short reads has become an important yet challenging problem in annotating metagenomes, since the assembly of metagenomes is often not available. Gene predictors developed for whole genomes (e.g. Glimmer) and recently developed for metagenomic sequences (e.g. MetaGene) show a significant decrease in performance as the sequencing error rates increase, or as reads get shorter. We have developed a novel gene prediction method FragGeneScan, which combines sequencing error models and codon usages in a hidden Markov model to improve the prediction of protein-coding region in short reads. The performance of FragGeneScan was comparable to Glimmer and MetaGene for complete genomes. But for short reads, FragGeneScan consistently outperformed MetaGene (accuracy improved ∼62% for reads of 400 bases with 1% sequencing errors, and ∼18% for short reads of 100 bases that are error free). When applied to metagenomes, FragGeneScan recovered substantially more genes than MetaGene predicted (>90% of the genes identified by homology search), and many novel genes with no homologs in current protein sequence database.     

A Primer on Metagenomics

Metagenomics is a discipline that enables the genomic study of uncultured microorganisms. Faster, cheaper sequencing technologies and the ability to sequence uncultured microbes sampled directly from their habitats are expanding and transforming our view of the microbial world. Distilling meaningful information from the millions of new genomic sequences presents a serious challenge to bioinformaticians. In cultured microbes, the genomic data come from a single clone, making sequence assembly and annotation tractable. In metagenomics, the data come from heterogeneous microbial communities, sometimes containing more than 10,000 species, with the sequence data being noisy and partial. From sampling, to assembly, to gene calling and function prediction, bioinformatics faces new demands in interpreting voluminous, noisy, and often partial sequence data. Although metagenomics is a relative newcomer to science, the past few years have seen an explosion in computational methods applied to metagenomic-based research. It is therefore not within the scope of this article to provide an exhaustive review. Rather, we provide here a concise yet comprehensive introduction to the current computational requirements presented by metagenomics, and review the recent progress made. We also note whether there is software that implements any of the methods presented here, and briefly review its utility. Nevertheless, it would be useful if readers of this article would avail themselves of the comment section provided by this journal, and relate their own experiences. Finally, the last section of this article provides a few representative studies illustrating different facets of recent scientific discoveries made using metagenomics.     

MetCap: a bioinformatics probe design pipeline for large-scale targeted metagenomics

BackgroundMassive sequencing of genes from different environments has evolved metagenomics as central to enhancing the understanding of the wide diversity of micro-organisms and their roles in driving ecological processes. Reduced cost and high throughput sequencing has made large-scale projects achievable to a wider group of researchers, though complete metagenome sequencing is still a daunting task in terms of sequencing as well as the downstream bioinformatics analyses. Alternative approaches such as targeted amplicon sequencing requires custom PCR primer generation, and is not scalable to thousands of genes or gene families.ResultsIn this study, we are presenting a web-based tool called MetCap that circumvents the limitations of amplicon sequencing of multiple genes by designing probes that are suitable for large-scale targeted metagenomics sequencing studies. MetCap provides a novel approach to target thousands of genes and genomic regions that could be used in targeted metagenomics studies. Automatic analysis of user-defined sequences is performed, and probes specifically designed for metagenome studies are generated. To illustrate the advantage of a targeted metagenome approach, we have generated more than 300,000 probes that match more than 400,000 publicly available sequences related to carbon degradation, and used these probes for target sequencing in a soil metagenome study. The results show high enrichment of target genes and a successful capturing of the majority of gene families. MetCap is freely available to users from: http://soilecology.biol.lu.se/metcap/.ConclusionMetCap is facilitating probe-based target enrichment as an easy and efficient alternative tool compared to complex primer-based enrichment for large-scale investigations of metagenomes. Our results have shown efficient large-scale target enrichment through MetCap-designed probes for a soil metagenome. The web service is suitable for any targeted metagenomics project that aims to study several genes simultaneously. The novel bioinformatics approach taken by the web service will enable researchers in microbial ecology to tap into the vast diversity of microbial communities using targeted metagenomics as a cost-effective alternative to whole metagenome sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0501-8) contains supplementary material, which is available to authorized users.     

Sequence organization and matrix attachment regions of the human serine protease inhibitor gene cluster at 14q32.1

The human serine protease inhibitor (serpin) gene cluster at 14q32.1 is a useful model system for studying the regulation of gene activity and chromatin structure. We demonstrated previously that the six known serpin genes in this region were organized into two subclusters of three genes each that occupied ~370 kb of DNA. To more fully understand the genomic organization of this region, we annotated a 1-Mb sequence contig from data from the Genoscope sequencing consortium ( ). We report that 11 different serpin genes reside within the 14q32.1 cluster, including two novel 1-antiproteinase-like gene sequences, a kallistatin-like sequence, and two recently identified serpins that had not been mapped previously to 14q32.1. The genomic regions proximal and distal to the serpin cluster contain a variety of unrelated gene sequences of diverse function. To gain insight into the chromatin organization of the region, sequences with putative nuclear matrix-binding potential were identified by using the MAR-Wiz algorithm, and these MAR-Wiz candidate sequences were tested for nuclear matrix-binding activity in vitro. Several differences between the MAR-Wiz predictions and the results of biochemical tests were observed. The genomic organization of the serpin gene cluster is discussed. These authors (Stephanie J. Namciu and Richard D. Friedman) contributed equally to this work.     

A metagenomic assessment of microbial eukaryotic diversity in the global ocean

Surveying microbial diversity and function is accomplished by combining complementary molecular tools. Among them, metagenomics is a PCR free approach that contains all genetic information from microbial assemblages and is today performed at a relatively large scale and reasonable cost, mostly based on very short reads. Here, we investigated the potential of metagenomics to provide taxonomic reports of marine microbial eukaryotes. We prepared a curated database with reference sequences of the V4 region of 18S rDNA clustered at 97% similarity and used this database to extract and classify metagenomic reads. More than half of them were unambiguously affiliated to a unique reference whilst the rest could be assigned to a given taxonomic group. The overall diversity reported by metagenomics was similar to that obtained by amplicon sequencing of the V4 and V9 regions of the 18S rRNA gene, although either one or both of these amplicon surveys performed poorly for groups like Excavata, Amoebozoa, Fungi and Haptophyta. We then studied the diversity of picoeukaryotes and nanoeukaryotes using 91 metagenomes from surface down to bathypelagic layers in different oceans, unveiling a clear taxonomic separation between size fractions and depth layers. Finally, we retrieved long rDNA sequences from assembled metagenomes that improved phylogenetic reconstructions of particular groups. Overall, this study shows metagenomics as an excellent resource for taxonomic exploration of marine microbial eukaryotes.     

A robust PCR primer design platform applied to the detection of Acidobacteria Group 1 in soil

Environmental biosurveillance and microbial ecology studies use PCR-based assays to detect and quantify microbial taxa and gene sequences within a complex background of microorganisms. However, the fragmentary nature and growing quantity of DNA-sequence data make group-specific assay design challenging. We solved this problem by developing a software platform that enables PCR-assay design at an unprecedented scale. As a demonstration, we developed quantitative PCR assays for a globally widespread, ecologically important bacterial group in soil, Acidobacteria Group 1. A total of 33,684 Acidobacteria 16S rRNA gene sequences were used for assay design. Following 1 week of computation on a 376-core cluster, 83 assays were obtained. We validated the specificity of the top three assays, collectively predicted to detect 42% of the Acidobacteria Group 1 sequences, by PCR amplification and sequencing of DNA from soil. Based on previous analyses of 16S rRNA gene sequencing, Acidobacteria Group 1 species were expected to decrease in response to elevated atmospheric CO(2). Quantitative PCR results, using the Acidobacteria Group 1-specific PCR assays, confirmed the expected decrease and provided higher statistical confidence than the 16S rRNA gene-sequencing data. These results demonstrate a powerful capacity to address previously intractable assay design challenges.     

Prediction of functional tertiary interactions and intermolecular interfaces from primary sequence data

Given the availability of sequence information for many species, one can examine how the sequence of a gene varies among different organisms. This is accomplished by aligning the sequences and observing patterns of conservation, mutation and counter-mutation at different positions in the gene. Imbedded in these patterns is information on energetic coupling and macromolecular interactions, which can be deciphered by application of statistical algorithms. Here we report a robust approach for predicting interactions within (or between) any type of biopolymer, including proteins, RNAs and RNA-protein complexes. Rather than maximize the number of predictions, this approach is designed to detect a limited number of highly significant interactions, thereby providing accurate results from alignments that contain a modest number of sequences (20-60). The versatility and accuracy of the algorithm is demonstrated by the successful prediction of important intramolecular interactions within RNAs, modified RNAs, and proteins, as well as the prediction of RNA-protein and protein-protein interactions.     

Promoter prediction analysis on the whole human genome

Promoter prediction programs (PPPs) are important for in silico gene discovery without support from expressed sequence tag (EST)/cDNA/mRNA sequences, in the analysis of gene regulation and in genome annotation. Contrary to previous expectations, a comprehensive analysis of PPPs reveals that no program simultaneously achieves sensitivity and a positive predictive value >65%. PPP performances deduced from a limited number of chromosomes or smaller data sets do not hold when evaluated at the level of the whole genome, with serious inaccuracy of predictions for non-CpG-island-related promoters. Some PPPs even perform worse than, or close to, pure random guessing.     

Get the most out of your metagenome: computational analysis of environmental sequence data

New advances in sequencing technologies bring random shotgun sequencing of ecosystems within reach of smaller labs, but the complexity of metagenomics data can be overwhelming. Recently, many novel computational tools have been developed to unravel ecosystem properties starting from fragmented sequences. In addition, the so-called 'comparative metagenomics' approaches have allowed the discovery of specific genomic and community adaptations to environmental factors. However, many of the parameters extracted from these data to describe the environment at hand (e.g. genomic features, functional complement, phylogenetic composition) are interdependent and influenced by technical aspects of sample preparation and data treatment, leading to various pitfalls during analysis. To avoid this and complement existing initiatives in data standards, we propose a minimal standard for metagenomics data analysis ('MINIMESS') to be able to take full advantage of the power of comparative metagenomics in understanding microbial life on earth.     

Recent advances in gene structure prediction

De novo gene predictors are programs that predict the exon-intron structures of genes using the sequences of one or more genomes as their only input. In the past two years, dual-genome de novo predictors, which exploit local rates and patterns of mutation inferred from alignments between two genomes, have led to significant improvements in accuracy. Systems that exploit more than two genomes simultaneously have only recently begun to appear and are not yet competitive on practical tasks, but offer the greatest hope for near-term improvements. Dual-genome de novo prediction for compact eukaryotic genomes such as those of Arabidopsis thaliana and Caenorhabditis elegans is already quite accurate. Although mammalian gene prediction lags behind in accuracy, it is yielding ever more useful results. Coupled with significant improvements in pseudogene detection methods, which have eliminated many false positives, we have reached the point where de novo gene predictions are being used as hypotheses to drive experimental annotation via systematic RT-PCR and sequencing.     

Metaxa: a software tool for automated detection and discrimination among ribosomal small subunit (12S/16S/18S) sequences of archaea,bacteria, eukaryotes,mitochondria, and chloroplasts in metagenomes and environmental sequencing datasets

The ribosomal small subunit (SSU) rRNA gene has emerged as an important genetic marker for taxonomic identification in environmental sequencing datasets. In addition to being present in the nucleus of eukaryotes and the core genome of prokaryotes, the gene is also found in the mitochondria of eukaryotes and in the chloroplasts of photosynthetic eukaryotes. These three sets of genes are conceptually paralogous and should in most situations not be aligned and analyzed jointly. To identify the origin of SSU sequences in complex sequence datasets has hitherto been a time-consuming and largely manual undertaking. However, the present study introduces Metaxa (), an automated software tool to extract full-length and partial SSU sequences from larger sequence datasets and assign them to an archaeal, bacterial, nuclear eukaryote, mitochondrial, or chloroplast origin. Using data from reference databases and from full-length organelle and organism genomes, we show that Metaxa detects and scores SSU sequences for origin with very low proportions of false positives and negatives. We believe that this tool will be useful in microbial and evolutionary ecology as well as in metagenomics.     

Finding novel genes in bacterial communities isolated from the environment

MOTIVATION: Novel sequencing techniques can give access to organisms that are difficult to cultivate using conventional methods. When applied to environmental samples, the data generated has some drawbacks, e.g. short length of assembled contigs, in-frame stop codons and frame shifts. Unfortunately, current gene finders cannot circumvent these difficulties. At the same time, the automated prediction of genes is a prerequisite for the increasing amount of genomic sequences to ensure progress in metagenomics. RESULTS: We introduce a novel gene finding algorithm that incorporates features overcoming the short length of the assembled contigs from environmental data, in-frame stop codons as well as frame shifts contained in bacterial sequences. The results show that by searching for sequence similarities in an environmental sample our algorithm is capable of detecting a high fraction of its gene content, depending on the species composition and the overall size of the sample. The method is valuable for hunting novel unknown genes that may be specific for the habitat where the sample is taken. Finally, we show that our algorithm can even exploit the limited information contained in the short reads generated by 454 technology for the prediction of protein coding genes. AVAILABILITY: The program is freely available upon request.     

Captured metagenomics: large-scale targeting of genes based on ‘sequence capture’ reveals functional diversity in soils

Microbial enzyme diversity is a key to understand many ecosystem processes. Whole metagenome sequencing (WMG) obtains information on functional genes, but it is costly and inefficient due to large amount of sequencing that is required. In this study, we have applied a captured metagenomics technique for functional genes in soil microorganisms, as an alternative to WMG. Large-scale targeting of functional genes, coding for enzymes related to organic matter degradation, was applied to two agricultural soil communities through captured metagenomics. Captured metagenomics uses custom-designed, hybridization-based oligonucleotide probes that enrich functional genes of interest in metagenomic libraries where only probe-bound DNA fragments are sequenced. The captured metagenomes were highly enriched with targeted genes while maintaining their target diversity and their taxonomic distribution correlated well with the traditional ribosomal sequencing. The captured metagenomes were highly enriched with genes related to organic matter degradation; at least five times more than similar, publicly available soil WMG projects. This target enrichment technique also preserves the functional representation of the soils, thereby facilitating comparative metagenomics projects. Here, we present the first study that applies the captured metagenomics approach in large scale, and this novel method allows deep investigations of central ecosystem processes by studying functional gene abundances.     

Short-read reading-frame predictors are not created equal: sequence error causes loss of signal

ABSTRACT: BACKGROUND: Gene prediction algorithms (or gene callers) are an essential tool for analyzing shotgun nucleic acid sequence data. Gene prediction is a ubiquitous step in sequence analysis pipelines; it reduces the volume of data by identifying the most likely reading frame for a fragment, permitting the out-of-frame translations to be ignored. In this study we evaluate five widely used ab initio gene-calling algorithms--FragGeneScan, MetaGeneAnnotator, MetaGeneMark, Orphelia, and Prodigal--for accuracy on short (75-1000 bp) fragments containing sequence error from previously published artificial data and "real" metagenomic datasets. RESULTS: While gene prediction tools have similar accuracies predicting genes on error-free fragments, in the presence of sequencing errors considerable differences between tools become evident. For error-containing short reads, FragGeneScan finds more prokaryotic coding regions than does MetaGeneAnnotator, MetaGeneMark, Orphelia, or Prodigal. This improved detection of genes in error-containing fragments, however, comes at the cost of much lower (50%) specificity and overprediction of genes in noncoding regions. CONCLUSIONS: Ab initio gene callers offer a significant reduction in the computational burden of annotating individual nucleic acid reads and are used in many metagenomic annotation systems. For predicting reading frames on raw reads, we find the hidden Markov model approach in FragGeneScan is more sensitive than other gene prediction tools, while Prodigal, MGA, and MGM are better suited for higher-quality sequences such as assembled contigs.     

Integrating metagenomic and amplicon databases to resolve the phylogenetic and ecological diversity of the Chlamydiae

In the era of metagenomics and amplicon sequencing, comprehensive analyses of available sequence data remain a challenge. Here we describe an approach exploiting metagenomic and amplicon data sets from public databases to elucidate phylogenetic diversity of defined microbial taxa. We investigated the phylum Chlamydiae whose known members are obligate intracellular bacteria that represent important pathogens of humans and animals, as well as symbionts of protists. Despite their medical relevance, our knowledge about chlamydial diversity is still scarce. Most of the nine known families are represented by only a few isolates, while previous clone library-based surveys suggested the existence of yet uncharacterized members of this phylum. Here we identified more than 22 000 high quality, non-redundant chlamydial 16S rRNA gene sequences in diverse databases, as well as 1900 putative chlamydial protein-encoding genes. Even when applying the most conservative approach, clustering of chlamydial 16S rRNA gene sequences into operational taxonomic units revealed an unexpectedly high species, genus and family-level diversity within the Chlamydiae, including 181 putative families. These in silico findings were verified experimentally in one Antarctic sample, which contained a high diversity of novel Chlamydiae. In our analysis, the Rhabdochlamydiaceae, whose known members infect arthropods, represents the most diverse and species-rich chlamydial family, followed by the protist-associated Parachlamydiaceae, and a putative new family (PCF8) with unknown host specificity. Available information on the origin of metagenomic samples indicated that marine environments contain the majority of the newly discovered chlamydial lineages, highlighting this environment as an important chlamydial reservoir.     

Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies

The advent of next generation sequencing has coincided with a growth in interest in using these approaches to better understand the role of the structure and function of the microbial communities in human, animal, and environmental health. Yet, use of next generation sequencing to perform 16S rRNA gene sequence surveys has resulted in considerable controversy surrounding the effects of sequencing errors on downstream analyses. We analyzed 2.7×10(6) reads distributed among 90 identical mock community samples, which were collections of genomic DNA from 21 different species with known 16S rRNA gene sequences; we observed an average error rate of 0.0060. To improve this error rate, we evaluated numerous methods of identifying bad sequence reads, identifying regions within reads of poor quality, and correcting base calls and were able to reduce the overall error rate to 0.0002. Implementation of the PyroNoise algorithm provided the best combination of error rate, sequence length, and number of sequences. Perhaps more problematic than sequencing errors was the presence of chimeras generated during PCR. Because we knew the true sequences within the mock community and the chimeras they could form, we identified 8% of the raw sequence reads as chimeric. After quality filtering the raw sequences and using the Uchime chimera detection program, the overall chimera rate decreased to 1%. The chimeras that could not be detected were largely responsible for the identification of spurious operational taxonomic units (OTUs) and genus-level phylotypes. The number of spurious OTUs and phylotypes increased with sequencing effort indicating that comparison of communities should be made using an equal number of sequences. Finally, we applied our improved quality-filtering pipeline to several benchmarking studies and observed that even with our stringent data curation pipeline, biases in the data generation pipeline and batch effects were observed that could potentially confound the interpretation of microbial community data.     

The JCVI standard operating procedure for annotating prokaryotic metagenomic shotgun sequencing data

The JCVI metagenomics analysis pipeline provides for the efficient and consistent annotation of shotgun metagenomics sequencing data for sampling communities of prokaryotic organisms. The process can be equally applied to individual sequence reads from traditional Sanger capillary electrophoresis sequences, newer technologies such as 454 pyrosequencing, or sequence assemblies derived from one or more of these data types. It includes the analysis of both coding and non-coding genes, whether full-length or, as is often the case for shotgun metagenomics, fragmentary. The system is designed to provide the best-supported conservative functional annotation based on a combination of trusted homology-based scientific evidence and computational assertions and an annotation value hierarchy established through extensive manual curation. The functional annotation attributes assigned by this system include gene name, gene symbol, GO terms, EC numbers, and JCVI functional role categories.     

Evaluation of a transposase protocol for rapid generation of shotgun high-throughput sequencing libraries from nanogram quantities of DNA

Construction of DNA fragment libraries for next-generation sequencing can prove challenging, especially for samples with low DNA yield. Protocols devised to circumvent the problems associated with low starting quantities of DNA can result in amplification biases that skew the distribution of genomes in metagenomic data. Moreover, sample throughput can be slow, as current library construction techniques are time-consuming. This study evaluated Nextera, a new transposon-based method that is designed for quick production of DNA fragment libraries from a small quantity of DNA. The sequence read distribution across nine phage genomes in a mock viral assemblage met predictions for six of the least-abundant phages; however, the rank order of the most abundant phages differed slightly from predictions. De novo genome assemblies from Nextera libraries provided long contigs spanning over half of the phage genome; in four cases where full-length genome sequences were available for comparison, consensus sequences were found to match over 99% of the genome with near-perfect identity. Analysis of areas of low and high sequence coverage within phage genomes indicated that GC content may influence coverage of sequences from Nextera libraries. Comparisons of phage genomes prepared using both Nextera and a standard 454 FLX Titanium library preparation protocol suggested that the coverage biases according to GC content observed within the Nextera libraries were largely attributable to bias in the Nextera protocol rather than to the 454 sequencing technology. Nevertheless, given suitable sequence coverage, the Nextera protocol produced high-quality data for genomic studies. For metagenomics analyses, effects of GC amplification bias would need to be considered; however, the library preparation standardization that Nextera provides should benefit comparative metagenomic analyses.