SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing

Assembling genomes from single-cell sequencing data is essential for single-cell studies. However, single-cell assemblies are challenging due to (i) the highly non-uniform read coverage and (ii) the elevated levels of sequencing errors and chimeric reads. Although several assemblers for single-cell data have been proposed in recent years, most of them fail to construct correct long contigs. In this study, we present a new framework called EPGA-SC for de novo assembly of single-cell sequencing reads. The EPGA assembler has designed strategies to solve the problems caused by sequencing errors, sequencing biases, and repetitive regions. However, the extremely unbalanced and richer error types prevent EPGA to achieve high performance in single-cell sequencing data. In this study, we designed EPGA-SC based on EPGA. The main innovations of EPGA-SC are as follows: (i) classifying reads to reduce the proportion of false reads; (ii) using multiple sets of high precision paired-end reads generated from the high precision assemblies produced by other assembler such as SPAdes to overcome the impact of sequencing biases and repetitive regions; and (iii) developing novel algorithms for removing chimeric errors and extending contigs. We test EPGA-SC with seven datasets. The experimental results show that EPGA-SC can generate better assemblies than most current tools in most time in term of MAX contig, N50, NG50, NA50, and NGA50.

Recent advances in single-cell genomics provide an alternative to gene-centric metagenomics studies, enabling whole genome sequencing of uncultivated bacteria. However, single-cell assembly projects are challenging due to (i) the highly non-uniform read coverage, and (ii) a greatly elevated number of chimeric reads and read pairs. While recently developed single-cell assemblers have addressed the former challenge, methods for assembling highly chimeric reads remain poorly explored. We present algorithms for identifying chimeric edges and resolving complex bulges in de Bruijn graphs, which significantly improve single-cell assemblies. We further describe applications of the single-cell assembler SPAdes to a new approach for capturing and sequencing “dark matter of life” that forms small pools of randomly selected single cells (called a mini-metagenome) and further sequences all genomes from the mini-metagenome at once. We demonstrate that SPAdes enables sequencing mini-metagenomes and benchmark it against various assemblers. On single-cell bacterial datasets, SPAdes improves on the recently developed E+V-SC and IDBA-UD assemblers specifically designed for single-cell sequencing. For standard (multicell) datasets, SPAdes also improves on A5, ABySS, CLC, EULER-SR, Ray, SOAPdenovo, and Velvet.

Recent advances in single-cell genomics provide an alternative to largely gene-centric metagenomics studies, enabling whole-genome sequencing of uncultivated bacteria. However, single-cell assembly projects are challenging due to (i) the highly nonuniform read coverage and (ii) a greatly elevated number of chimeric reads and read pairs. While recently developed single-cell assemblers have addressed the former challenge, methods for assembling highly chimeric reads remain poorly explored. We present algorithms for identifying chimeric edges and resolving complex bulges in de Bruijn graphs, which significantly improve single-cell assemblies. We further describe applications of the single-cell assembler SPAdes to a new approach for capturing and sequencing "microbial dark matter" that forms small pools of randomly selected single cells (called a mini-metagenome) and further sequences all genomes from the mini-metagenome at once. On single-cell bacterial datasets, SPAdes improves on the recently developed E+V-SC and IDBA-UD assemblers specifically designed for single-cell sequencing. For standard (cultivated monostrain) datasets, SPAdes also improves on A5, ABySS, CLC, EULER-SR, Ray, SOAPdenovo, and Velvet. Thus, recently developed single-cell assemblers not only enable single-cell sequencing, but also improve on conventional assemblers on their own turf. SPAdes is available for free online download under a GPLv2 license.

Single-cell analysis of monoallelic expression and genomic imprinting requires accurate genotype determination at the cellular level. However, genotype inference from single-cell RNA sequencing data is challenging due to technical noise, allelic dropout, and sparse gene expression patterns, particularly in genetically heterogeneous populations. Here, we present scGeno, a categorical Hidden Markov Model that infers chromosome-level genotype states in organisms with mixed genotypes by modeling sequential gene expression ratios from single-cell RNA sequencing data. Our method leverages the sequential continuity of the genotype states along chromosomes to overcome single-cell data limitations and generates chromosome-resolved, comprehensive genotype maps for individual samples. Our probabilistic framework accounts for technical noise while maintaining high accuracy in genotype assignment. Validation on experimental data demonstrates robust performance in determining clear genotypic states, thereby enabling systematic investigation of allele-specific expression patterns at single-cell resolution. scGeno is an open-source Python package under an MIT license. Source code, documentation, and installation instructions can be downloaded from GitHub (https://github.com/RosariaTornisiello/Genotype_HMM.git).

BackgroundVarious statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model.ResultsWe developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model.ConclusionA comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.

<h3>Abstract</h3> Identifying high-confidence cell-type specific open chromatin regions with coherent regulatory function from single-cell open chromatin data (scATAC-seq) is difficult due to the complexity of resolving cell types given the low coverage of reads per cell. In order to address this problem, we present Semi-Supervised Identification of Populations of cells in scATAC-seq data (SSIPs), a semi-supervised approach that integrates bulk and single-cell data through a generalizable network model featuring two types of nodes. Nodes of the first type represent cells from scATAC-seq with edges between them encoding information about cell similarity. A second set of nodes represents “supervising” datasets connected to cell nodes with edges that encode the similarity between that data and each cell. Via global calculations of network influence, this model allows us to quantify the influence of bulk data on scATAC-seq data and estimate the contributions of scATAC-seq cell populations to signals in bulk data. Using simulated data, we show that SSIPs successfully separates distinct cell types even when they differ in very few mapped scATAC-seq reads, with a significant improvement over unsupervised cell type identification. We apply SSIPs to scATAC-seq data from the developing human brain and show that supervising with just 25 differentially expressed genes from scRNA-seq enables the identification of two subtypes of interneurons not identifiable from scATAC-seq data alone. SSIPs opens the door to identifying high resolution cell types in single-cell open chromatin data, enabling the study of cell-type specific regulatory elements.

Sequencing error correction has become an important step in the analyses of next-generation sequencing (NGS) datasets in order to improve data quality for downstream applications. In this chapter, we discuss different formulations for sequencing read error corrections that are based on probabilistic models able to handle datasets with a nonuniform read coverage. Nonuniform coverage is common in several applications of NGS, including small RNA and messenger RNA sequencing, metagenomics, metatranscriptomics, and single-cell sequencing. Here, we review popular formulations based on the Hamming graph of k-mers found in sequencing reads and introduce a more complete formulation that can also handle insertion and deletion errors. as found in As the breadth of applications is steadily increasing to In this chapter, we will introduce different approaches to correct sequencing errors with probabilistic models. One common formulation is based on models over Hamming graphs. A particular focus will be on a more general formulation using hidden Markov models that can solve indel errors. These methods are suitable for the correction of reads from experiments with nonuniform coverage, like RNA-Seq, single-cell sequencing, or metagenomics, a topic of rising importance in the community.

Next-generation high-throughput sequencing has become a state-of-the-art technique in genome assembly. Scaffolding is one of the main stages of the assembly pipeline. During this stage, contigs assembled from the paired-end reads are merged into bigger chains called scaffolds. Because of a high level of statistical noise, chimeric reads, and genome repeats the problem of scaffolding is a challenging task. Current scaffolding software packages widely vary in their quality and are highly dependent on the read data quality and genome complexity. There are no clear winners and multiple opportunities for further improvements of the tools still exist. This article presents an efficient scaffolding algorithm ScaffMatch that is able to handle reads with both short (<600 bp) and long (>35 000 bp) insert sizes producing high-quality scaffolds. We evaluate our scaffolding tool with the F score and other metrics (N50, corrected N50) on eight datasets comparing it with the most available packages. Our experiments show that ScaffMatch is the tool of preference for the most datasets. The source code is available at http://alan.cs.gsu.edu/NGS/?q=content/scaffmatch. mandric@cs.gsu.edu Supplementary data are available at Bioinformatics online.

Transposons are genomic parasites, and their new insertions can cause instability and spur the evolution of their host genomes. Rapid accumulation of short-read whole-genome sequencing data provides a great opportunity for studying new transposon insertions and their impacts on the host genome. Although many algorithms are available for detecting transposon insertions, the task remains challenging and existing tools are not designed for identifying de novo insertions. Here, we present a new benchmark fly dataset based on PacBio long-read sequencing and a new method TEMP2 for detecting germline insertions and measuring de novo ‘singleton’ insertion frequencies in eukaryotic genomes. TEMP2 achieves high sensitivity and precision for detecting germline insertions when compared with existing tools using both simulated data in fly and experimental data in fly and human. Furthermore, TEMP2 can accurately assess the frequencies of de novo transposon insertions even with high levels of chimeric reads in simulated datasets; such chimeric reads often occur during the construction of short-read sequencing libraries. By applying TEMP2 to published data on hybrid dysgenic flies inflicted by de-repressed P-elements, we confirmed the continuous new insertions of P-elements in dysgenic offspring before they regain piRNAs for P-element repression. TEMP2 is freely available at Github: https://github.com/weng-lab/TEMP2.

The recent advances in sequencing throughput and genome assembly algorithms have established whole-genome shotgun (WGS) assemblies as the cornerstone of the genomic infrastructure for many species. WGS assemblies can be constructed with comparative ease and give a comprehensive representation of the gene space even of large and complex genomes. One major obstacle in utilizing WGS assemblies for important research applications such as gene isolation or comparative genomics has been the lack of chromosomal positioning and contextualization of short sequence contigs. Assigning chromosomal locations to sequence contigs required the construction and integration of genome-wide physical maps and dense genetic linkage maps as well as synteny to model species. Recently, methods to rapidly construct ultra-dense linkage maps encompassing millions of genetic markers from WGS sequencing data of segregating populations have made possible the direct assignment of genetic positions to short sequence contigs. Here, we review recent developments in the integration of WGS assemblies and sequence-based linkage maps, discuss challenges for further improvement of the methodology and outline possible applications building on genetically anchored WGS assemblies.

DNA signatures are nucleotide sequences that can be used to detect the presence of an organism and to distinguish that organism from all other species. Here we describe Insignia, a new, comprehensive system for the rapid identification of signatures in the genomes of bacteria and viruses. With the availability of hundreds of complete bacterial and viral genome sequences, it is now possible to use computational methods to identify signature sequences in all of these species, and to use these signatures as the basis for diagnostic assays to detect and genotype microbes in both environmental and clinical samples. The success of such assays critically depends on the methods used to identify signatures that properly differentiate between the target genomes and the sample background. We have used Insignia to compute accurate signatures for most bacterial genomes and made them available through our Web site. A sample of these signatures has been successfully tested on a set of 46 Vibrio cholerae strains, and the results indicate that the signatures are highly sensitive for detection as well as specific for discrimination between these strains and their near relatives. Our approach, whereby the entire genomic complement of organisms are compared to identify probe targets, is a promising method for diagnostic assay development, and it provides assay designers with the flexibility to choose probes from the most relevant genes or genomic regions. The Insignia system is freely accessible via a Web interface and has been released as open source software at: http://insignia.cbcb.umd.edu.

BackgroundA critical topic of insertional mutagenesis experiments performed on model organisms is mapping the hits of artificial transposons (ATs) at nucleotide level accuracy. Mapping errors may occur when sequencing artifacts or mutations as single nucleotide polymorphisms (SNPs) and small indels are present very close to the junction between a genomic sequence and a transposon inverted repeat (TIR). Another particular item of insertional mutagenesis is mapping of the transposon self-insertions and, to our best knowledge, there is no publicly available mapping tool designed to analyze such molecular events.ResultsWe developed Genome ARTIST, a pairwise gapped aligner tool which works out both issues by means of an original, robust mapping strategy. Genome ARTIST is not designed to use next-generation sequencing (NGS) data but to analyze ATs insertions obtained in small to medium-scale mutagenesis experiments. Genome ARTIST employs a heuristic approach to find DNA sequence similarities and harnesses a multi-step implementation of a Smith-Waterman adapted algorithm to compute the mapping alignments. The experience is enhanced by easily customizable parameters and a user-friendly interface that describes the genomic landscape surrounding the insertion. Genome ARTIST is functional with many genomes of bacteria and eukaryotes available in Ensembl and GenBank repositories. Our tool specifically harnesses the sequence annotation data provided by FlyBase for Drosophila melanogaster (the fruit fly), which enables mapping of insertions relative to various genomic features such as natural transposons. Genome ARTIST was tested against other alignment tools using relevant query sequences derived from the D. melanogaster and Mus musculus (mouse) genomes. Real and simulated query sequences were also comparatively inquired, revealing that Genome ARTIST is a very robust solution for mapping transposon insertions.ConclusionsGenome ARTIST is a stand-alone user-friendly application, designed for high-accuracy mapping of transposon insertions and self-insertions. The tool is also useful for routine aligning assessments like detection of SNPs or checking the specificity of primers and probes. Genome ARTIST is an open source software and is available for download at www.genomeartist.ro and at GitHub (https://github.com/genomeartist/genomeartist ).Electronic supplementary materialThe online version of this article (doi:10.1186/s13100-016-0061-0) contains supplementary material, which is available to authorized users.

Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve.

Chromatin 3D folding creates numerous DNA interactions, participating in gene expression regulation. Single-cell chromatin-accessibility assays now profile hundreds of thousands of cells, challenging existing methods for mapping cis-regulatory interactions. We present CIRCE, a fast and scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data. CIRCE re-implements the Cicero workflow to analyse single-cell atlases, cutting runtime and memory use by several orders of magnitude. We also provide new options to compute metacells, grouping similar cells to reduce data sparsity. We benchmarked CIRCE against Cicero on two datasets of different sizes and demonstrated the improvement from CIRCE's metacells' strategy with promoter capture Hi-C data. We also evaluated how DNA interaction predictions are impacted by different pre-processing. We observed a negative impact of Cicero's count normalization, and the best performance was obtained with the single-cell count matrix directly. Finally, we demonstrated the scalability of CIRCE by processing a dataset of more than 700 000 cells and 1 million DNA regions in less than an hour. CIRCE should greatly facilitate the prediction of DNA region interactions for scverse and Python users, while providing new and up-to-date pre-processing insights. CIRCE is released as an open-source software under the AGPL-3.0 licence. The package source code is available on GitHub at https://github.com/cantinilab/CIRCE, and its documentation is accessible at https://circe.readthedocs.io. The code to reproduce the presented results is available as a Snakemake pipeline at https://github.com/cantinilab/circe_reproducibility.s.

MotivationSingle-cell RNA sequencing allows us to simultaneously profile the transcriptomes of thousands of cells and to indulge in exploring cell diversity, development and discovery of new molecular mechanisms. Analysis of scRNA data involves a combination of non-trivial steps from statistics, data visualization, bioinformatics and machine learning. Training molecular biologists in single-cell data analysis and empowering them to review and analyze their data can be challenging, both because of the complexity of the methods and the steep learning curve.ResultsWe propose a workshop-style training in single-cell data analytics that relies on an explorative data analysis toolbox and a hands-on teaching style. The training relies on scOrange, a newly developed extension of a data mining framework that features workflow design through visual programming and interactive visualizations. Workshops with scOrange can proceed much faster than similar training methods that rely on computer programming and analysis through scripting in R or Python, allowing the trainer to cover more ground in the same time-frame. We here review the design principles of the scOrange toolbox that support such workshops and propose a syllabus for the course. We also provide examples of data analysis workflows that instructors can use during the training.Availability and implementationscOrange is an open-source software. The software, documentation and an emerging set of educational videos are available at http://singlecell.biolab.si.