Accurate Reads Research Articles

Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs.

Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large datasets or consider reads as mere suites of k-mers, without taking into account their full-length sequence information. We propose a new method to correct short reads using de Bruijn graphs and implement it as a tool called Bcool. As a first step, Bcool constructs a compacted de Bruijn graph from the reads. This graph is filtered on the basis of k-mer abundance then of unitig abundance, thereby removing most sequencing errors. The cleaned graph is then used as a reference on which the reads are mapped to correct them. We show that this approach yields more accurate reads than k-mer-spectrum correctors while being scalable to human-size genomic datasets and beyond. The implementation is open source, available at http://github.com/Malfoy/BCOOL under the Affero GPL license and as a Bioconda package. Supplementary data are available at Bioinformatics online.

Connecting genome structural variation with complex traits in crop plants.

Structural genome variation is a major determinant of useful trait diversity. We describe how genome analysis methods are enabling discovery of trait-associated structural variants and their potential impact on breeding. As our understanding of complex crop genomes continues to grow, there is growing evidence that structural genome variation plays a major role in determining traits important for breeding and agriculture. Identifying the extent and impact of structural variants in crop genomes is becoming increasingly feasible with ongoing advances in the sophistication of genome sequencing technologies, particularly as it becomes easier to generate accurate long sequence reads on a genome-wide scale. In this article, we discuss the origins of structural genome variation in crops from ancient and recent genome duplication and polyploidization events and review high-throughput methods to assay such variants in crop populations in order to find associations with phenotypic traits. There is increasing evidence from such studies that gene presence-absence and copy number variation resulting from segmental chromosome exchanges may be at the heart of adaptive variation of crops to counter abiotic and biotic stress factors. We present examples from major crops that demonstrate the potential of pangenomic diversity as a key resource for future plant breeding for resilience and sustainability.

Genetic repertoires of anaerobic microbiomes driving generation of biogas

BackgroundBiogas production is an attractive technology for a sustainable generation of renewable energy. Although the microbial community is fundamental for such production, the process control is still limited to technological and chemical parameters. Currently, most of the efforts on microbial management system (MiMaS) are focused on process-specific marker species and community dynamics, but a practical implementation is in its infancy. The high number of unknown and uncharacterized microorganisms in general is one of the reasons hindering further advancements.ResultsA Biogas Metagenomics Hybrid Assembly (BioMETHA) database, derived from microbiomes of biogas plants, was generated using a dedicated assembly strategy for different metagenomic datasets. Long reads from nanopore sequencing (MinION) were combined with short, more accurate second-generation sequencing reads (Illumina). The hybrid assembly resulted in 231 genomic bins each representing a taxonomic unit with an average completeness of 47%. Functional annotation identified 13,190 non-redundant genes covering roughly 207 k coding sequences. Mapping rates of metagenomics DNA derived from diverse biogas plants and laboratory reactors increased up to 73%. In addition, an EC (enzyme commission) reference sequence collection (ERSC) was generated whose genes are crucial for biogas-related processes, consisting of 235 unique EC numbers organized in 52 metabolic modules. Mapping rates of metatranscriptomic data to this ERSC revealed coverages of up to 93%. Process parameters and imbalances of laboratory reactors could be reconstructed by evaluating abundance of biogas-specific metabolic modules using metatranscriptomic data derived from various fermenter systems.ConclusionThis newly established metagenomic hybrid assembly in combination with an EC reference sequence collection might help to shed light on the microbial dark matter of biogas plants by contributing to the development of a reference for biogas plant microbiome-specific gene sequences. Considering a biogas microbiome as a complex meta-organism expressing a meta-transcriptome, the approach established here could lay the foundation for a function-based microbial management system.

Improving nanopore read accuracy with the R2C2 method enables the sequencing of highly multiplexed full-length single-cell cDNA

High-throughput short-read sequencing has revolutionized how transcriptomes are quantified and annotated. However, while Illumina short-read sequencers can be used to analyze entire transcriptomes down to the level of individual splicing events with great accuracy, they fall short of analyzing how these individual events are combined into complete RNA transcript isoforms. Because of this shortfall, long-distance information is required to complement short-read sequencing to analyze transcriptomes on the level of full-length RNA transcript isoforms. While long-read sequencing technology can provide this long-distance information, there are issues with both Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) long-read sequencing technologies that prevent their widespread adoption. Briefly, PacBio sequencers produce low numbers of reads with high accuracy, while ONT sequencers produce higher numbers of reads with lower accuracy. Here, we introduce and validate a long-read ONT-based sequencing method. At the same cost, our Rolling Circle Amplification to Concatemeric Consensus (R2C2) method generates more accurate reads of full-length RNA transcript isoforms than any other available long-read sequencing method. These reads can then be used to generate isoform-level transcriptomes for both genome annotation and differential expression analysis in bulk or single-cell samples.

Hercules: a profile HMM-based hybrid error correction algorithm for long reads.

Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several types of studies require long and accurate reads. In such cases researchers often combine both technologies and the erroneous long reads are corrected using the short reads. Current approaches rely on various graph or alignment based techniques and do not take the error profile of the underlying technology into account. Efficient machine learning algorithms that address these shortcomings have the potential to achieve more accurate integration of these two technologies. We propose Hercules, the first machine learning-based long read error correction algorithm. Hercules models every long read as a profile Hidden Markov Model with respect to the underlying platform’s error profile. The algorithm learns a posterior transition/emission probability distribution for each long read to correct errors in these reads. We show on two DNA-seq BAC clones (CH17-157L1 and CH17-227A2) that Hercules-corrected reads have the highest mapping rate among all competing algorithms and have the highest accuracy when the breadth of coverage is high. On a large human CHM1 cell line WGS data set, Hercules is one of the few scalable algorithms; and among those, it achieves the highest accuracy.

Abstract 3400: Characterizing genomic variation and tumor heterogeneity in cancer

Abstract Cancer genomes are highly unstable with new genetic variations emerging even within a single metastatic site, making it difficult to track the causal changes that drive metastasis and treatment resistance. Here we present a two-pronged approach for analyzing the full spectrum of genetic variations present in cancer samples. The first approach allows for comprehensive and high-resolution characterization of a broad range of variant types on bulk tumor sample. While the second approach characterizes structural variation at the level of the single cell, allowing for the exploration of tumor clonality and heterogeneity. At the core of our approaches is a microfluidics platform that enables the production of hundreds of thousands to millions of partitioned barcoded reactions. This platform can partition high-molecular weight DNA or single cells. Together, these complementary approaches provide a more complete picture of the genomic variation and clonal structure present in a tumor. For bulk tumor analysis, we obtained high molecular weight DNA from known cancer cell lines and used the 10x Chromium Genome solution to produce Illumina-ready sequencing libraries. In this workflow, partitioning of a limited amount of genomic DNA allows for haplotype-level dilution of genome equivalents, which are then barcoded to create a novel data type referred to as “Linked-Reads”. These molecular barcodes are used to identify reads originating from the same input molecule providing long range information on highly accurate short reads. In addition to highly accurate SNP calling, this further enables identification of complex structural rearrangements in tumor genomes. To gain insight into tumor heterogeneity and clonal structure, we performed single cell DNA sequencing and analysis using 10x Chromium scDNA solution. This platform integrates single cell encapsulation, cell lysis and DNA barcoding into a streamlined workflow. Molecular barcodes are used to associate reads with individual cells allowing for copy number variant (CNV) detection. We applied our scDNA sequencing method to a variety of cancer cell lines revealing their clonal structure, as identified by CNVs, with the capability to identify as few as 10 cells in a sample size of one thousand cells. Using cluster analyses we were able to detect 100kb scale events and by aggregating reads in large clones we were able to confidently identify smaller CNV events down to tens of kilobases. Using whole genome bulk sequencing we identified more than 500 large structural variants in HCC1954, including balanced and unbalanced events. In this presentation, we will integrate this Linked-Read data with single cell genome analysis on the same samples, and compare the genetic variation revealed by these two approaches. We will further explore the power of combining these data types for a more complete picture of tumor genome dynamic Citation Format: Claudia Catalanotti, Sarah Garcia, Kamila Belhocine, Vijay Kumar, Zeljko Dzakula, Andrew Price, Shamoni Maheshwar, Yifeng Yin, Michael Schnall-Levin, Rajiv Bharadwaj, Sara Agee Le, Deanna M. Church. Characterizing genomic variation and tumor heterogeneity in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3400.

Minimap2: pairwise alignment for nucleotide sequences.

Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms. Minimap2 is a general-purpose alignment program to map DNA or long mRNA sequences against a large reference database. It works with accurate short reads of ≥100 bp in length, ≥1 kb genomic reads at error rate ∼15%, full-length noisy Direct RNA or cDNA reads and assembly contigs or closely related full chromosomes of hundreds of megabases in length. Minimap2 does split-read alignment, employs concave gap cost for long insertions and deletions and introduces new heuristics to reduce spurious alignments. It is 3-4 times as fast as mainstream short-read mappers at comparable accuracy, and is ≥30 times faster than long-read genomic or cDNA mappers at higher accuracy, surpassing most aligners specialized in one type of alignment. https://github.com/lh3/minimap2. Supplementary data are available at Bioinformatics online.

Highly accurate and sensitive short read aligner

Highly accurate and sensitive short read aligner

CT colonography interpretation: how to maximize polyp detection and minimize overcalling

This article outlines how to achieve maximum accuracy in interpreting CT colonography (CTC) regarding colonic findings. Interpreting extracolonic findings seen on CTC is a separate diagnostic task and will not be addressed in this article. While many interpretive pitfalls are in fact related to CTC techniques, this article focuses on issues that are related to interpretive knowledge and skills, avoiding in-depth discussions on CTC techniques. Principal methods and further tips for detecting possible polyp candidates and for confirming true soft-tissue polyps will be discussed. Specific points about optimizing interpretation strategies for difficult flat polyps including sessile serrated polyp will be raised. There are numerous interpretive pitfalls regarding the colonic interpretation of CTC. Knowledge of these pitfalls will shorten the learning curve and help achieve accurate reads.

Progress in plant paleogenomics.

As a new branch of genomics, plant paleogenomics reconstructs ancestral genomes from actual modern species and infers palaeohistory, evolutionary and/or speciation events that have shaped the modern species. Advances in high-throughput sequencing technologies yield accurate long reads, promote the progress of plant genome sequence assembly, and thereby offer paleogenomics a large collection of valuable reference genomes from modern species. Whole-genome duplication (WGD) and polyploidization cause rapid genomic reorganization, massive gene losses and structural variations. WGD events are therefore central to plant evolution. In this review, we summarize recent progress in sequencing and assembly of plant genomes, principles of plant paleogenomics, WGD events in plant genomes, and the most likely evolutionary scenario in plants. Furthermore, we highlight some of the challenges as well as future directions.

MinION rapid sequencing: Review of potential applications in neurosurgery.

Background:Gene sequencing has played an integral role in the advancement and understanding of disease pathology and treatment. Although historically expensive and time consuming, new sequencing technologies improve our capability to obtain the genetic information in an accurate and timely manner. Within neurosurgery, gene sequencing is routinely used in the diagnosis and treatment of neurosurgical diseases, primarily for brain tumors. This paper reviews nanopore sequencing, an innovation utilized by MinION and outlines its potential use for neurosurgery.Methods:A literature search was conducted for publications containing the keywords of Oxford MinION, nanopore sequencing, brain tumor, glioma, whole genome sequencing (WGS), epigenomics, molecular neuropathology, and next-generation sequencing (NGS). In total, 64 articles were selected and used for this review.Results:The Oxford MinION nanopore sequencing technology has had successful applications within clinical microbiology, human genome sequencing, and cancer genotyping across multiple specialties. Technical details, methodology, and current use of MinION sequencing are discussed through the prism of potential applications to solve neurosurgery-related scientific and diagnostic questions. The MinION device has proven to provide rapid and accurate reads with longer read lengths when compared with NGS. For applications within neurosurgery, the MinION device is capable of providing critical diagnostic information for central nervous system (CNS) tumors within a single day.Conclusions:MinION provides rapid and accurate gene sequencing with better affordability and convenience compared with current NGS methods. Widespread success of the MinION nanopore sequencing technology in providing accurate, rapid, and convenient gene sequencing suggests a promising future within research laboratories and to improve care for neurosurgical patients.

ASElux: an ultra-fast and accurate allelic reads counter.

Mapping bias causes preferential alignment to the reference allele, forming a major obstacle in allele-specific expression (ASE) analysis. The existing methods, such as simulation and SNP-aware alignment, are either inaccurate or relatively slow. To fast and accurately count allelic reads for ASE analysis, we developed a novel approach, ASElux, which utilizes the personal SNP information and counts allelic reads directly from unmapped RNA-sequence (RNA-seq) data. ASElux significantly reduces runtime by disregarding reads outside single nucleotide polymorphisms (SNPs) during the alignment. When compared to other tools on simulated and experimental data, ASElux achieves a higher accuracy on ASE estimation than non-SNP-aware aligners and requires a much shorter time than the benchmark SNP-aware aligner, GSNAP with just a slight loss in performance. ASElux can process 40 million read-pairs from an RNA-sequence (RNA-seq) sample and count allelic reads within 10 min, which is comparable to directly counting the allelic reads from alignments based on other tools. Furthermore, processing an RNA-seq sample using ASElux in conjunction with a general aligner, such as STAR, is more accurate and still ∼4× faster than STAR + WASP, and ∼33× faster than the lead SNP-aware aligner, GSNAP, making ASElux ideal for ASE analysis of large-scale transcriptomic studies. We applied ASElux to 273 lung RNA-seq samples from GTEx and identified a splice-QTL rs11078928 in lung which explains the mechanism underlying an asthma GWAS SNP rs11078927. Thus, our analysis demonstrated ASE as a highly powerful complementary tool to cis-expression quantitative trait locus (eQTL) analysis. The software can be downloaded from https://github.com/abl0719/ASElux. zmiao@ucla.edu or a5ko@ucla.edu. Supplementary data are available at Bioinformatics online.

Comparative analysis of targeted long read sequencing approaches for characterization of a plant\u2019s immune receptor repertoire

BackgroundThe Oxford Nanopore Technologies MinION™ sequencer is a small, portable, low cost device that is accessible to labs of all sizes and attractive for in-the-field sequencing experiments. Selective breeding of crops has led to a reduction in genetic diversity, and wild relatives are a key source of new genetic resistance to pathogens, usually via NLR immune receptor-encoding genes. Recent studies have demonstrated how crop NLR repertoires can be targeted for sequencing on Illumina or PacBio (RenSeq) and the specific gene conveying pathogen resistance identified.ResultsSequence yields per MinION run are lower than Illumina, making targeted resequencing an efficient approach. While MinION generates long reads similar to PacBio it doesn’t generate the highly accurate multipass consensus reads, which presents downstream bioinformatics challenges. Here we demonstrate how MinION data can be used for RenSeq achieving similar results to the PacBio and how novel NLR gene fusions can be identified via a Nanopore RenSeq pipeline.ConclusionThe described library preparation and bioinformatics methods should be applicable to other gene families or any targeted long DNA fragment nanopore sequencing project.

Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads.

The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce complete genome assemblies, but the sequencing is more expensive and error-prone. There is significant interest in combining data from these complementary sequencing technologies to generate more accurate “hybrid” assemblies. However, few tools exist that truly leverage the benefits of both types of data, namely the accuracy of short reads and the structural resolving power of long reads. Here we present Unicycler, a new tool for assembling bacterial genomes from a combination of short and long reads, which produces assemblies that are accurate, complete and cost-effective. Unicycler builds an initial assembly graph from short reads using the de novo assembler SPAdes and then simplifies the graph using information from short and long reads. Unicycler uses a novel semi-global aligner to align long reads to the assembly graph. Tests on both synthetic and real reads show Unicycler can assemble larger contigs with fewer misassemblies than other hybrid assemblers, even when long-read depth and accuracy are low. Unicycler is open source (GPLv3) and available at github.com/rrwick/Unicycler.

Dynamic Data-Dependent Reference to Improve Sense Margin and Speed of Magnetoresistive Random Access Memory

Magnetoresistive random access memory (MRAM) suffers from low magnetoresistance ratio and serious variations in both low-resistance state (R P ) and high-resistance state (R AP ). The resulting narrow resistance window between R P and R AP makes it difficult to acquire a sufficient sense margin for accurate read operation. In this brief, a novel read circuit for MRAM is proposed with dynamic data-dependent reference current to improve the sense margin. Instead of using the average of read currents of a pair of dummy R P and R AP cells as reference, our reference current is generated in a data-dependent manner by subtracting the read current of the selected cell from the sum of read currents of a pair of dummy R P and R AP cells. Thus, a larger or smaller reference current can be obtained for the read of R AP or R P cell, respectively, helping in expanding the sense margins. The larger sense margin can further improve the sense speed and the read yield. Evaluation shows that 2× increase in typical sense margin, 60% reduction in sense time, and remarkable reduction in bit error rate are achieved compared with the conventional averaging reference scheme. The accompanying cost in power consumption is acceptable.

Hybrid assembly of the large and highly repetitive genome of Aegilops tauschii, a progenitor of bread wheat, with the MaSuRCA mega-reads algorithm.

Long sequencing reads generated by single-molecule sequencing technology offer the possibility of dramatically improving the contiguity of genome assemblies. The biggest challenge today is that long reads have relatively high error rates, currently around 15%. The high error rates make it difficult to use this data alone, particularly with highly repetitive plant genomes. Errors in the raw data can lead to insertion or deletion errors (indels) in the consensus genome sequence, which in turn create significant problems for downstream analysis; for example, a single indel may shift the reading frame and incorrectly truncate a protein sequence. Here, we describe an algorithm that solves the high error rate problem by combining long, high-error reads with shorter but much more accurate Illumina sequencing reads, whose error rates average <1%. Our hybrid assembly algorithm combines these two types of reads to construct mega-reads, which are both long and accurate, and then assembles the mega-reads using the CABOG assembler, which was designed for long reads. We apply this technique to a large data set of Illumina and PacBio sequences from the species Aegilops tauschii, a large and extremely repetitive plant genome that has resisted previous attempts at assembly. We show that the resulting assembled contigs are far larger than in any previous assembly, with an N50 contig size of 486,807 nucleotides. We compare the contigs to independently produced optical maps to evaluate their large-scale accuracy, and to a set of high-quality bacterial artificial chromosome (BAC)-based assemblies to evaluate base-level accuracy.

Accelerating metagenomic read classification on CUDA-enabled GPUs

BackgroundMetagenomic sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification; i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes software tools for fast and accurate metagenomic read classification are urgently needed.ResultsWe present cuCLARK, a read-level classifier for CUDA-enabled GPUs, based on the fast and accurate classification of metagenomic sequences using reduced k-mers (CLARK) method. Using the processing power of a single Titan X GPU, cuCLARK can reach classification speeds of up to 50 million reads per minute. Corresponding speedups for species- (genus-)level classification range between 3.2 and 6.6 (3.7 and 6.4) compared to multi-threaded CLARK executed on a 16-core Xeon CPU workstation.ConclusioncuCLARK can perform metagenomic read classification at superior speeds on CUDA-enabled GPUs. It is free software licensed under GPL and can be downloaded at https://github.com/funatiq/cuclark free of charge.

High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs.

Genetic variation at the Human Leucocyte Antigen (HLA) genes is associated with many autoimmune and infectious disease phenotypes, is an important element of the immunological distinction between self and non-self, and shapes immune epitope repertoires. Determining the allelic state of the HLA genes (HLA typing) as a by-product of standard whole-genome sequencing data would therefore be highly desirable and enable the immunogenetic characterization of samples in currently ongoing population sequencing projects. Extensive hyperpolymorphism and sequence similarity between the HLA genes, however, pose problems for accurate read mapping and make HLA type inference from whole-genome sequencing data a challenging problem. We describe how to address these challenges in a Population Reference Graph (PRG) framework. First, we construct a PRG for 46 (mostly HLA) genes and pseudogenes, their genomic context and their characterized sequence variants, integrating a database of over 10,000 known allele sequences. Second, we present a sequence-to-PRG paired-end read mapping algorithm that enables accurate read mapping for the HLA genes. Third, we infer the most likely pair of underlying alleles at G group resolution from the IMGT/HLA database at each locus, employing a simple likelihood framework. We show that HLA*PRG, our algorithm, outperforms existing methods by a wide margin. We evaluate HLA*PRG on six classical class I and class II HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1) and on a set of 14 samples (3 samples with 2 x 100bp, 11 samples with 2 x 250bp Illumina HiSeq data). Of 158 alleles tested, we correctly infer 157 alleles (99.4%). We also identify and re-type two erroneous alleles in the original validation data. We conclude that HLA*PRG for the first time achieves accuracies comparable to gold-standard reference methods from standard whole-genome sequencing data, though high computational demands (currently ~30–250 CPU hours per sample) remain a significant challenge to practical application.

OR45 RNA sequencing of two lymphoblastoid cell lines reveals novel microrna transcripts of the MHC

Aim MicroRNA (miRNA) are short (∼22 bp) single stranded RNA transcripts that bind to and regulate the posttranscriptional expression of targeted mRNA transcripts. There are 12 known miRNA transcribed from within the MHC, which have been previously identified by RNA sequencing of various primary cell types. This approach to identify novel miRNA is however inherently flawed since it relies upon significant sequence concordance between the reference genome and the generated reads, which is particularly problematic for transcripts originating from within the highly polymorphic MHC. To address this gap, we sequenced short RNA transcripts (less than 50 bp) from two homozygous cell lines with fully characterized MHC haplotypes, PGF and COX, in order to identify and characterize novel microRNA (miRNA) transcripts from within the MHC. Methods Total RNA was extracted from two lymphoblastoid cell lines, PGF and COX. Size selected transcripts (less thank 50 bp) were isolated for library construction and sequenced on an Illumina HiSeq 2500. Single-end reads from PGF and COX were mapped to their respective MHC haplotype reference sequence using NovoAlign. Novel miRNA were identified using a custom computational analysis pipeline and validated by qPCR. Results In total, our analysis has identified 67 and 47 high confidence, significantly expressed, novel miRNAs from RNA-Seq data generated from PGF and COX lymphoblastoid cell lines, respectively. Of these, 25 identified novel miRNA were found to be expressed in both PGF and COX cells, while 42 and 22 novel miRNA were found to exhibit haplotype specific expression patterns in PGF and COX, respectively. Conclusions The ability to de novo assemble the MHC is critical to ensure accurate RNA-Seq read mapping within the highly variable MHC. Our results demonstrate the utility of an accurate MHC reference assembly in combination with a custom developed computational analysis pipeline to identify and characterize novel miRNA transcripts from within the MHC of two homozygous cell lines with fully characterized MHC reference sequences. Our analysis revealed haplotype specific as well as commonly expressed novel miRNA transcripts, identifying novel regulatory elements encoded within the MHC.

MetaProb: accurate metagenomic reads binning based on probabilistic sequence signatures.

Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Taxonomic analysis of microbial communities, a process referred to as binning, is one of the most challenging tasks when analyzing metagenomic reads data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species and the limitations due to short read lengths and sequencing errors. MetaProb is a novel assembly-assisted tool for unsupervised metagenomic binning. The novelty of MetaProb derives from solving a few important problems: how to divide reads into groups of independent reads, so that k-mer frequencies are not overestimated; how to convert k-mer counts into probabilistic sequence signatures, that will correct for variable distribution of k-mers, and for unbalanced groups of reads, in order to produce better estimates of the underlying genome statistic; how to estimate the number of species in a dataset. We show that MetaProb is more accurate and efficient than other state-of-the-art tools in binning both short reads datasets (F-measure 0.87) and long reads datasets (F-measure 0.97) for various abundance ratios. Also, the estimation of the number of species is more accurate than MetaCluster. On a real human stool dataset MetaProb identifies the most predominant species, in line with previous human gut studies. https://bitbucket.org/samu661/metaprob cinzia.pizzi@dei.unipd.it or comin@dei.unipd.it Supplementary data are available at Bioinformatics online.