Large Volumes Of Sequence Data Research Articles

MarFERReT, an open-source, version-controlled reference library of marine microbial eukaryote functional genes

Metatranscriptomics generates large volumes of sequence data about transcribed genes in natural environments. Taxonomic annotation of these datasets depends on availability of curated reference sequences. For marine microbial eukaryotes, current reference libraries are limited by gaps in sequenced organism diversity and barriers to updating libraries with new sequence data, resulting in taxonomic annotation of about half of eukaryotic environmental transcripts. Here, we introduce Marine Functional EukaRyotic Reference Taxa (MarFERReT), a marine microbial eukaryotic sequence library designed for use with taxonomic annotation of eukaryotic metatranscriptomes. We gathered 902 publicly accessible marine eukaryote genomes and transcriptomes and assessed their sequence quality and cross-contamination issues, selecting 800 validated entries for inclusion in MarFERReT. Version 1.1 of MarFERReT contains reference sequences from 800 marine eukaryotic genomes and transcriptomes, covering 453 species- and strain-level taxa, totaling nearly 28 million protein sequences with associated NCBI and PR2 Taxonomy identifiers and Pfam functional annotations. The MarFERReT project repository hosts containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT.

Inference of B cell clonal families using heavy/light chain pairing information.

Next generation sequencing of B cell receptor (BCR) repertoires has become a ubiquitous tool for understanding the antibody-mediated immune response: it is now common to have large volumes of sequence data coding for both the heavy and light chain subunits of the BCR. However, until the recent development of high throughput methods of preserving heavy/light chain pairing information, these samples contained no explicit information on which heavy chain sequence pairs with which light chain sequence. One of the first steps in analyzing such BCR repertoire samples is grouping sequences into clonally related families, where each stems from a single rearrangement event. Many methods of accomplishing this have been developed, however, none so far has taken full advantage of the newly-available pairing information. This information can dramatically improve clustering performance, especially for the light chain. The light chain has traditionally been challenging for clonal family inference because of its low diversity and consequent abundance of non-clonal families with indistinguishable naive rearrangements. Here we present a method of incorporating this pairing information into the clustering process in order to arrive at a more accurate partition of the data into clonally related families. We also demonstrate two methods of fixing imperfect pairing information, which may allow for simplified sample preparation and increased sequencing depth. Finally, we describe several other improvements to the partis software package.

Progress in Plant Genome Sequencing

The genome sequence of any organism is key to understanding the biology and utility of that organism. Plants have diverse, complex and sometimes very large nuclear genomes, mitochondrial genomes and much smaller and more highly conserved chloroplast genomes. Plant genome sequences underpin our understanding of plant biology and serve as a key platform for the genetic selection and improvement of crop plants to achieve food security. The development of technology that can capture large volumes of sequence data at low costs and with high accuracy has driven the acceleration of plant genome sequencing advancements. More recently, the development of long read sequencing technology has been a key advance for supporting the accurate sequencing and assembly of chromosome-level plant genomes. This review explored the progress in the sequencing and assembly of plant genomes and the outcomes of plant genome sequencing to date. The outcomes support the conservation of biodiversity, adaptations to climate change and improvements in the sustainability of agriculture, which support food and nutritional security.

Inference of B cell clonal families using heavy/light chain pairing information

Next generation sequencing of B cell receptor (BCR) repertoires has become a ubiquitous tool for understanding the antibody-mediated immune response: it is now common to have large volumes of sequence data coding for both the heavy and light chain subunits of the BCR. However, until the recent development of high throughput methods of preserving heavy/light chain pairing information, these samples contained no explicit information on which heavy chain sequence pairs with which light chain sequence. One of the first steps in analyzing such BCR repertoire samples is grouping sequences into clonally related families, where each stems from a single rearrangement event. Many methods of accomplishing this have been developed, however, none so far has taken full advantage of the newly-available pairing information. This information can dramatically improve clustering performance, especially for the case of the light chain; the light chain has traditionally been challenging for clonal family inference because of its low diversity and consequent abundance of non-clonal families with indistinguishable naive rearrangements. Here we present a method of incorporating this pairing information into the clustering process in order to arrive at a more accurate partition of the data into clonally related families. We also demonstrate two methods of fixing imperfect pairing information, which may allow for simplified sample preparation and increased sequencing depth. Finally, we describe several other improvements to the partis software package (https://github.com/psathyrella/partis).

Decentralized Investigation of Bacterial Outbreaks Based on Hashed cgMLST.

Whole-genome sequencing (WGS)-based outbreak investigation has proven to be a valuable method for the surveillance of bacterial pathogens. Its utility has been successfully demonstrated using both gene-by-gene (cgMLST or wgMLST) and single-nucleotide polymorphism (SNP)-based approaches. Among the obstacles of implementing a WGS-based routine surveillance is the need for an exchange of large volumes of sequencing data, as well as a widespread reluctance to share sequence and metadata in public repositories, together with a lacking standardization of suitable bioinformatic tools and workflows. To address these issues, we present chewieSnake, an intuitive and simple-to-use cgMLST workflow. ChewieSnake builds on the allele calling software chewBBACA and extends it by the concept of allele hashing. The resulting hashed allele profiles can be readily compared between laboratories without the need of a central allele nomenclature. The workflow fully automates the computation of the allele distance matrix, cluster membership, and phylogeny and summarizes all important findings in an interactive HTML report. Furthermore, chewieSnake can join allele profiles generated at different laboratories and identify shared clusters, including a stable and intercommunicable cluster nomenclature, thus facilitating a joint outbreak investigation. We demonstrate the feasibility of the proposed approach with a thorough method comparison using publically available sequencing data for Salmonella enterica. However, chewieSnake is readily applicable to all bacterial taxa, provided that a suitable cgMLST scheme is available. The workflow is freely available as an open-source tool and can be easily installed via conda or docker.

BASiNET—BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification

With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant information to understand the functioning of organisms. This work introduces BASiNET, an alignment-free tool for classifying biological sequences based on the feature extraction from complex network measurements. The method initially transform the sequences and represents them as complex networks. Then it extracts topological measures and constructs a feature vector that is used to classify the sequences. The method was evaluated in the classification of coding and non-coding RNAs of 13 species and compared to the CNCI, PLEK and CPC2 methods. BASiNET outperformed all compared methods in all adopted organisms and datasets. BASiNET have classified sequences in all organisms with high accuracy and low standard deviation, showing that the method is robust and non-biased by the organism. The proposed methodology is implemented in open source in R language and freely available for download at https://cran.r-project.org/package=BASiNET.

SAMSA2: a standalone metatranscriptome analysis pipeline

BackgroundComplex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms.ResultsSAMSA2 is an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for standalone use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution.ConclusionsSAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the needs of any experiment.

NtCard: a streaming algorithm for cardinality estimation in genomics data.

MotivationMany bioinformatics algorithms are designed for the analysis of sequences of some uniform length, conventionally referred to as k-mers. These include de Bruijn graph assembly methods and sequence alignment tools. An efficient algorithm to enumerate the number of unique k-mers, or even better, to build a histogram of k-mer frequencies would be desirable for these tools and their downstream analysis pipelines. Among other applications, estimated frequencies can be used to predict genome sizes, measure sequencing error rates, and tune runtime parameters for analysis tools. However, calculating a k-mer histogram from large volumes of sequencing data is a challenging task.ResultsHere, we present ntCard, a streaming algorithm for estimating the frequencies of k-mers in genomics datasets. At its core, ntCard uses the ntHash algorithm to efficiently compute hash values for streamed sequences. It then samples the calculated hash values to build a reduced representation multiplicity table describing the sample distribution. Finally, it uses a statistical model to reconstruct the population distribution from the sample distribution. We have compared the performance of ntCard and other cardinality estimation algorithms. We used three datasets of 480 GB, 500 GB and 2.4 TB in size, where the first two representing whole genome shotgun sequencing experiments on the human genome and the last one on the white spruce genome. Results show ntCard estimates k-mer coverage frequencies >15× faster than the state-of-the-art algorithms, using similar amount of memory, and with higher accuracy rates. Thus, our benchmarks demonstrate ntCard as a potentially enabling technology for large-scale genomics applications.Availability and ImplementationntCard is written in C ++ and is released under the GPL license. It is freely available at https://github.com/bcgsc/ntCard.Supplementary information Supplementary data are available at Bioinformatics online.

ExactSearch: a web-based plant motif search tool.

Background Plant biologists frequently need to examine if a sequence motif bound by a specific transcription or translation factor is present in the proximal promoters or 3′ untranslated regions (3′ UTR) of a set of plant genes of interest. To achieve such a task, plant biologists have to not only identify an appropriate algorithm for motif searching, but also manipulate the large volume of sequence data, making it burdensome to carry out or fulfill.Result In this study, we developed a web portal that enables plant molecular biologists to search for DNA motifs especially degenerate ones in custom sequences or the flanking regions of all genes in the 50 plant species whose genomes have been sequenced. A web tool like this is demanded to meet a variety of needs of plant biologists for identifying the potential gene regulatory relationships. We implemented a suffix tree algorithm to accelerate the searching process of a group of motifs in a multitude of target genes. The motifs to be searched can be in the degenerate bases in addition to adenine (A), cytosine (C), guanine (G), and thymine (T). The target sequences to be searched can be custom sequences or the selected proximal gene sequences from any one of the 50 sequenced plant species. The web portal also contains the functionality to facilitate the search of motifs that are represented by position probability matrix in above-mentioned species. Currently, the algorithm can accomplish an exhaust search of 100 motifs in 35,000 target sequences of 2 kb long in 4.2 min. However, the runtime may change in the future depending on the space availability, number of running jobs, network traffic, data loading, and output packing and delivery through electronic mailing.ConclusionA web portal was developed to facilitate searching of motifs presents in custom sequences or the proximal promoters or 3′ UTR of 50 plant species with the sequenced genomes. This web tool is accessible by using this URL: http://sys.bio.mtu.edu/motif/index.php.

WheatExp: an RNA-seq expression database for polyploid wheat

BackgroundFor functional genomics studies, it is important to understand the dynamic expression profiles of transcribed genes in different tissues, stages of development and in response to environmental stimuli. The proliferation in the use of next-generation sequencing technologies by the plant research community has led to the accumulation of large volumes of expression data. However, analysis of these datasets is complicated by the frequent occurrence of polyploidy among economically-important crop species. In addition, processing and analyzing such large volumes of sequence data is a technical and time-consuming task, limiting their application in functional genomics studies, particularly for smaller laboratories which lack access to high-powered computing infrastructure. Wheat is a good example of a young polyploid species with three similar genomes (97 % identical among homoeologous genes), rapidly accumulating RNA-seq datasets and a large research community.DescriptionWe present WheatExp, an expression database and visualization tool to analyze and compare homoeologue-specific transcript profiles across a broad range of tissues from different developmental stages in polyploid wheat. Beginning with publicly-available RNA-seq datasets, we developed a pipeline to distinguish between homoeologous transcripts from annotated genes in tetraploid and hexaploid wheat. Data from multiple studies is processed and compiled into a database which can be queried either by BLAST or by searching for a known gene of interest by name or functional domain. Expression data of multiple genes can be displayed side-by-side across all expression datasets providing immediate access to a comprehensive panel of expression data for specific subsets of wheat genes.ConclusionsThe development of a publicly accessible expression database hosted on the GrainGenes website - http://wheat.pw.usda.gov/WheatExp/ - coupled with a simple and readily-comparable visualization tool will empower the wheat research community to use RNA-seq data and to perform functional analyses of target genes. The presented expression data is homoeologue-specific allowing for the analysis of relative contributions from each genome to the overall expression of a gene, a critical consideration for breeding applications. Our approach can be expanded to other polyploid species by adjusting sequence mapping parameters according to the specific divergence of their genomes.

Accurate read-based metagenome characterization using a hierarchical suite of unique signatures.

A major challenge in the field of shotgun metagenomics is the accurate identification of organisms present within a microbial community, based on classification of short sequence reads. Though existing microbial community profiling methods have attempted to rapidly classify the millions of reads output from modern sequencers, the combination of incomplete databases, similarity among otherwise divergent genomes, errors and biases in sequencing technologies, and the large volumes of sequencing data required for metagenome sequencing has led to unacceptably high false discovery rates (FDR). Here, we present the application of a novel, gene-independent and signature-based metagenomic taxonomic profiling method with significantly and consistently smaller FDR than any other available method. Our algorithm circumvents false positives using a series of non-redundant signature databases and examines Genomic Origins Through Taxonomic CHAllenge (GOTTCHA). GOTTCHA was tested and validated on 20 synthetic and mock datasets ranging in community composition and complexity, was applied successfully to data generated from spiked environmental and clinical samples, and robustly demonstrates superior performance compared with other available tools.

Closing the Gaps in Rat Cytomegalovirus ALL-03 (Malaysian Strain) Genomic Scaffold

Next generation sequencing technologies has revolutionized genomic research by producing a large volume of sequence data and lowest per base cost compared to the traditional sanger method. Although this technology offers many advantages, gap occurrences are commonly found in draft assemblies. The same problem was observed with Rat Cytomegalovirus (RCMV) ALL-03 (Malaysia strain), where a complete genome sequence could not produce the complete genome due to the presence of gaps in the draft genome. This restrains our ability to take full advantage of genome data. This study aimed to identify the sequence data present in the gap regions and close these gaps in order to produce a complete genome sequence for RCMV ALL-03. Twenty sets of specific primers were designed between two adjacent contigs and PCR was carried out to obtain the appropriate sequences in respective gap regions. Sanger sequencing was employed in the PCR product to get the gap sequences. Out of the five identified gaps in the RCMV ALL-03 genome sequence, only three were confirmed to be true gaps, while the other two were due to sequence repeats. In conclusion, all the gaps were closed successfully and complete genome sequence of RCMV ALL-03 can now be explored in further studies.

Next-generation sequencing technologies: breaking the sound barrier of human genetics.

Demand for new technologies that deliver fast, inexpensive and accurate genome information has never been greater. This challenge has catalysed the rapid development of advances in next-generation sequencing (NGS). The generation of large volumes of sequence data and the speed of data acquisition are the primary advantages over previous, more standard methods. In 2013, the Food and Drug Administration granted marketing authorisation for the first high-throughput NG sequencer, Illumina’s MiSeqDx, which allowed the development and use of a large number of new genome-based tests. Here, we present a review of template preparation, nucleic acid sequencing and imaging, genome assembly and alignment approaches as well as recent advances in current and near-term commercially available NGS instruments. We also outline the broad range of applications for NGS technologies and provide guidelines for platform selection to best address biological questions of interest. DNA sequencing has revolutionised biological and medical research, and is poised to have a similar impact on the practice of medicine. This tool is but one of an increasing arsenal of developing tools that enhance our capabilities to identify, quantify and functionally characterise the components of biological networks that keep us healthy or make us sick. Despite advances in other ‘omic’ technologies, DNA sequencing and analysis, in many respects, have played the leading role to date. The new technologies provide a bridge between genotype and phenotype, both in man and model organisms, and have revolutionised how risk of developing a complex human disease may be assessed. The generation of large DNA sequence data sets is producing a wealth of medically relevant information on a large number of individuals and populations that will potentially form the basis of truly individualised medical care in the future.

Analysis of plant microbe interactions in the era of next generation sequencing technologies.

Next generation sequencing (NGS) technologies have impressively accelerated research in biological science during the last years by enabling the production of large volumes of sequence data to a drastically lower price per base, compared to traditional sequencing methods. The recent and ongoing developments in the field allow addressing research questions in plant-microbe biology that were not conceivable just a few years ago. The present review provides an overview of NGS technologies and their usefulness for the analysis of microorganisms that live in association with plants. Possible limitations of the different sequencing systems, in particular sources of errors and bias, are critically discussed and methods are disclosed that help to overcome these shortcomings. A focus will be on the application of NGS methods in metagenomic studies, including the analysis of microbial communities by amplicon sequencing, which can be considered as a targeted metagenomic approach. Different applications of NGS technologies are exemplified by selected research articles that address the biology of the plant associated microbiota to demonstrate the worth of the new methods.

Tackling soil diversity with the assembly of large, complex metagenomes

The large volumes of sequencing data required to sample deeply the microbial communities of complex environments pose new challenges to sequence analysis. De novo metagenomic assembly effectively reduces the total amount of data to be analyzed but requires substantial computational resources. We combine two preassembly filtering approaches--digital normalization and partitioning--to generate previously intractable large metagenome assemblies. Using a human-gut mock community dataset, we demonstrate that these methods result in assemblies nearly identical to assemblies from unprocessed data. We then assemble two large soil metagenomes totaling 398 billion bp (equivalent to 88,000 Escherichia coli genomes) from matched Iowa corn and native prairie soils. The resulting assembled contigs could be used to identify molecular interactions and reaction networks of known metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes Orthology database. Nonetheless, more than 60% of predicted proteins in assemblies could not be annotated against known databases. Many of these unknown proteins were abundant in both corn and prairie soils, highlighting the benefits of assembly for the discovery and characterization of novelty in soil biodiversity. Moreover, 80% of the sequencing data could not be assembled because of low coverage, suggesting that considerably more sequencing data are needed to characterize the functional content of soil.

Comparison of mapping algorithms used in high-throughput sequencing: application to Ion Torrent data

BackgroundThe rapid evolution in high-throughput sequencing (HTS) technologies has opened up new perspectives in several research fields and led to the production of large volumes of sequence data. A fundamental step in HTS data analysis is the mapping of reads onto reference sequences. Choosing a suitable mapper for a given technology and a given application is a subtle task because of the difficulty of evaluating mapping algorithms.ResultsIn this paper, we present a benchmark procedure to compare mapping algorithms used in HTS using both real and simulated datasets and considering four evaluation criteria: computational resource and time requirements, robustness of mapping, ability to report positions for reads in repetitive regions, and ability to retrieve true genetic variation positions. To measure robustness, we introduced a new definition for a correctly mapped read taking into account not only the expected start position of the read but also the end position and the number of indels and substitutions. We developed CuReSim, a new read simulator, that is able to generate customized benchmark data for any kind of HTS technology by adjusting parameters to the error types. CuReSim and CuReSimEval, a tool to evaluate the mapping quality of the CuReSim simulated reads, are freely available. We applied our benchmark procedure to evaluate 14 mappers in the context of whole genome sequencing of small genomes with Ion Torrent data for which such a comparison has not yet been established.ConclusionsA benchmark procedure to compare HTS data mappers is introduced with a new definition for the mapping correctness as well as tools to generate simulated reads and evaluate mapping quality. The application of this procedure to Ion Torrent data from the whole genome sequencing of small genomes has allowed us to validate our benchmark procedure and demonstrate that it is helpful for selecting a mapper based on the intended application, questions to be addressed, and the technology used. This benchmark procedure can be used to evaluate existing or in-development mappers as well as to optimize parameters of a chosen mapper for any application and any sequencing platform.

Sensitive Detection of Viral Transcripts in Human Tumor Transcriptomes

In excess of % of human cancer incidents have a viral cofactor. Epidemiological studies of idiopathic human cancers indicate that additional tumor viruses remain to be discovered. Recent advances in sequencing technology have enabled systematic screenings of human tumor transcriptomes for viral transcripts. However, technical problems such as low abundances of viral transcripts in large volumes of sequencing data, viral sequence divergence, and homology between viral and human factors significantly confound identification of tumor viruses. We have developed a novel computational approach for detecting viral transcripts in human cancers that takes the aforementioned confounding factors into account and is applicable to a wide variety of viruses and tumors. We apply the approach to conducting the first systematic search for viruses in neuroblastoma, the most common cancer in infancy. The diverse clinical progression of this disease as well as related epidemiological and virological findings are highly suggestive of a pathogenic cofactor. However, a viral etiology of neuroblastoma is currently contested. We mapped transcriptomes of neuroblastoma as well as positive and negative controls to the human and all known viral genomes in order to detect both known and unknown viruses. Analysis of controls, comparisons with related methods, and statistical estimates demonstrate the high sensitivity of our approach. Detailed investigation of putative viral transcripts within neuroblastoma samples did not provide evidence for the existence of any known human viruses. Likewise, de-novo assembly and analysis of chimeric transcripts did not result in expression signatures associated with novel human pathogens. While confounding factors such as sample dilution or viral clearance in progressed tumors may mask viral cofactors in the data, in principle, this is rendered less likely by the high sensitivity of our approach and the number of biological replicates analyzed. Therefore, our results suggest that frequent viral cofactors of metastatic neuroblastoma are unlikely.

Can long-range PCR be used to amplify genetically divergent mitochondrial genomes for comparative phylogenetics? A case study within spiders (Arthropoda: Araneae).

The development of second generation sequencing technology has resulted in the rapid production of large volumes of sequence data for relatively little cost, thereby substantially increasing the quantity of data available for phylogenetic studies. Despite these technological advances, assembling longer sequences, such as that of entire mitochondrial genomes, has not been straightforward. Existing studies have been limited to using only incomplete or nominally intra-specific datasets resulting in a bottleneck between mitogenome amplification and downstream high-throughput sequencing. Here we assess the effectiveness of a wide range of targeted long-range PCR strategies, encapsulating single and dual fragment primer design approaches to provide full mitogenomic coverage within the Araneae (Spiders). Despite extensive rounds of optimisation, full mitochondrial genome PCR amplifications were stochastic in most taxa, although 454 Roche sequencing confirmed the successful amplification of 10 mitochondrial genomes out of the 33 trialled species. The low success rates of amplification using long-Range PCR highlights the difficulties in consistently obtaining genomic amplifications using currently available DNA polymerases optimised for large genomic amplifications and suggests that there may be opportunities for the use of alternative amplification methods.

FAVR (Filtering and Annotation of Variants that are Rare): methods to facilitate the analysis of rare germline genetic variants from massively parallel sequencing datasets

BackgroundCharacterising genetic diversity through the analysis of massively parallel sequencing (MPS) data offers enormous potential to significantly improve our understanding of the genetic basis for observed phenotypes, including predisposition to and progression of complex human disease. Great challenges remain in resolving genetic variants that are genuine from the millions of artefactual signals.ResultsFAVR is a suite of new methods designed to work with commonly used MPS analysis pipelines to assist in the resolution of some of the issues related to the analysis of the vast amount of resulting data, with a focus on relatively rare genetic variants. To the best of our knowledge, no equivalent method has previously been described. The most important and novel aspect of FAVR is the use of signatures in comparator sequence alignment files during variant filtering, and annotation of variants potentially shared between individuals. The FAVR methods use these signatures to facilitate filtering of (i) platform and/or mapping-specific artefacts, (ii) common genetic variants, and, where relevant, (iii) artefacts derived from imbalanced paired-end sequencing, as well as annotation of genetic variants based on evidence of co-occurrence in individuals. We applied conventional variant calling applied to whole-exome sequencing datasets, produced using both SOLiD and TruSeq chemistries, with or without downstream processing by FAVR methods. We demonstrate a 3-fold smaller rare single nucleotide variant shortlist with no detected reduction in sensitivity. This analysis included Sanger sequencing of rare variant signals not evident in dbSNP131, assessment of known variant signal preservation, and comparison of observed and expected rare variant numbers across a range of first cousin pairs. The principles described herein were applied in our recent publication identifying XRCC2 as a new breast cancer risk gene and have been made publically available as a suite of software tools.ConclusionsFAVR is a platform-agnostic suite of methods that significantly enhances the analysis of large volumes of sequencing data for the study of rare genetic variants and their influence on phenotypes.

Answering biological questions by querying k‐mer databases

SUMMARYThis paper describes a k‐mer approach to analysing DNA data and quickly answering certain types of ad hoc biological questions. These k‐mers (short DNA strings) are stored in a conventional relational database and indexed to support efficient exact match operations. We show that k‐mers around 20–25 bases long have interesting and useful uniqueness properties that can be used to compute a ‘relatedness’ metric and also allow k‐mers to be used as ‘unique enough’ tags to identify organisms and genes. This relatedness metric is used in SQL queries that can directly answer questions such as how two related species differ, and what genes are unique to an organism. The k‐mer tags have proven useful in applications, largely metagenomic ones that can quickly process large volumes of sequencing data to say something about what organisms and genes might be present in an environmental sample. All of this work is based on simple and fast exact matches of k‐mer strings using a database, rather than conventional alignment based on inexact matches of much longer strings. These k‐mer tools provide ways of rapidly exploring large genome spaces and handling large volumes of sequence data, and complement rather than replace existing alignment and assembly tools. Copyright © 2012 John Wiley & Sons, Ltd.