Set Of Short Reads Research Articles

CloudrnaSPAdes: isoform assembly using bulk barcoded RNA sequencing data.

Recent advancements in long-read RNA sequencing have enabled the examination of full-length isoforms, previously uncaptured by short-read sequencing methods. An alternative powerful method for studying isoforms is through the use of barcoded short-read RNA reads, for which a barcode indicates whether two short-reads arise from the same molecule or not. Such techniques included the 10x Genomics linked-read based SParse Isoform Sequencing (SPIso-seq), as well as Loop-Seq, or Tell-Seq. Some applications, such as novel-isoform discovery, require very high coverage. Obtaining high coverage using long reads can be difficult, making barcoded RNA-seq data a valuable alternative for this task. However, most annotation pipelines are not able to work with a set of short reads instead of a single transcript, also not able to work with coverage gaps within a molecule if any. In order to overcome this challenge, we present an RNA-seq assembler that allows the determination of the expressed isoform per barcode. In this article, we present cloudrnaSPAdes, a tool for assembling full-length isoforms from barcoded RNA-seq linked-read data in a reference-free fashion. Evaluating it on simulated and real human data, we found that cloudrnaSPAdes accurately assembles isoforms, even for genes with high isoform diversity. cloudrnaSPAdes is a feature release of a SPAdes assembler and version used for this article is available at https://github.com/1dayac/cloudrnaSPAdes-release.

A Crowdsourced Gameplay for Whole-Genome Assembly via Short Reads

Next-generation sequencing has revolutionized the field of genomics by producing accurate, rapid and cost-effective genome analysis with the use of high throughput sequencing technologies. This has intensified the need for accurate and performance efficient genome assemblers to assemble a large set of short reads produced by next-generation sequencing technology. Genome assembly is an NP-hard problem that is computationally challenging. Therefore, the current methods that rely on heuristic and approximation algorithms to assemble genomes prevent them from arriving at the most accurate solution. This paper presents a novel approach by gamifying whole-genome shotgun assembly from next-generation sequencing data; we present "Geno", a human-computing game designed with the aim of improving the accuracy of whole-genome shotgun assembly. We evaluate the feasibility of crowdsourcing the problem of whole-genome shotgun assembly by breaking the problem into small subtasks. The evaluation results, for single-cell Escherichia coli K-12 substr. MG1655 with a read length of 25 bp that produced 144,867 game instances of mean 25 sequences per instance at 40x coverage indicate the feasibility of sub-tasking the problem of genome assembly to be solved using crowdsourcing.<br /><br />

ABayesQR: A Bayesian Method for Reconstruction of Viral Populations Characterized by Low Diversity.

RNA viruses replicate with high mutation rates, creating closely related viral populations. The heterogeneous virus populations, referred to as viral quasispecies, rapidly adapt to environmental changes thus adversely affecting efficiency of antiviral drugs and vaccines. Therefore, studying the underlying genetic heterogeneity of viral populations plays a significant role in the development of effective therapeutic treatments. Recent high-throughput sequencing technologies have provided invaluable opportunity for uncovering the structure of quasispecies populations. However, accurate reconstruction of viral quasispecies remains difficult due to limited read lengths and presence of sequencing errors. The problem is particularly challenging when the strains in a population are highly similar, that is, the sequences are characterized by low mutual genetic distances, and further exacerbated if some of those strains are relatively rare; this is the setting where state-of-the-art methods struggle. In this article, we present a novel viral quasispecies reconstruction algorithm, aBayesQR, that uses a maximum-likelihood framework to infer individual sequences in a mixture from high-throughput sequencing data. The search for the most likely quasispecies is conducted on long contigs that our method constructs from the set of short reads via agglomerative hierarchical clustering; operating on contigs rather than short reads enables identification of close strains in a population and provides computational tractability of the Bayesian method. Results on both simulated and real HIV-1 data demonstrate that the proposed algorithm generally outperforms state-of-the-art methods; aBayesQR particularly stands out when reconstructing a set of closely related viral strains (e.g., quasispecies characterized by low diversity).

Reference Transcriptomes and Detection of Duplicated Copies in Hexaploid and Allododecaploid Spartina Species (Poaceae).

In this study, we report the assembly and annotation of five reference transcriptomes for the European hexaploid Spartina species (S. maritima, S. alterniflora and their homoploid hybrids S. x townsendii and S. x neyrautii) and the allododecaploid invasive species S. anglica. These transcriptomes were constructed from various leaf and root cDNA libraries that were sequenced using both Roche-454 and Illumina technologies. Considering the high ploidy levels of the Spartina genomes under study, and considering the absence of diploid reference genome and the need of an appropriate analytical strategy, we developed generic bioinformatics tools to (1) detect different haplotypes of each gene within each species and (2) assign a parental origin to haplotypes detected in the hexaploid hybrids and the neo-allopolyploid. The approach described here allows the detection of putative homeologs from sets of short reads. Synonymous substitution rate (KS) comparisons between haplotypes from the hexaploid species revealed the presence of one KS peak (likely resulting from the tetraploid duplication event). The procedure developed in this study can be applied for future differential gene expression or genomics experiments to study the fate of duplicated genes in the invasive allododecaploid S. anglica.

RNA-CODE: a noncoding RNA classification tool for short reads in NGS data lacking reference genomes.

The number of transcriptomic sequencing projects of various non-model organisms is still accumulating rapidly. As non-coding RNAs (ncRNAs) are highly abundant in living organism and play important roles in many biological processes, identifying fragmentary members of ncRNAs in small RNA-seq data is an important step in post-NGS analysis. However, the state-of-the-art ncRNA search tools are not optimized for next-generation sequencing (NGS) data, especially for very short reads. In this work, we propose and implement a comprehensive ncRNA classification tool (RNA-CODE) for very short reads. RNA-CODE is specifically designed for ncRNA identification in NGS data that lack quality reference genomes. Given a set of short reads, our tool classifies the reads into different types of ncRNA families. The classification results can be used to quantify the expression levels of different types of ncRNAs in RNA-seq data and ncRNA composition profiles in metagenomic data, respectively. The experimental results of applying RNA-CODE to RNA-seq of Arabidopsis and a metagenomic data set sampled from human guts demonstrate that RNA-CODE competes favorably in both sensitivity and specificity with other tools. The source codes of RNA-CODE can be downloaded at http://www.cse.msu.edu/~chengy/RNA_CODE.

Short read sequence typing (SRST): multi-locus sequence types from short reads

BackgroundMulti-locus sequence typing (MLST) has become the gold standard for population analyses of bacterial pathogens. This method focuses on the sequences of a small number of loci (usually seven) to divide the population and is simple, robust and facilitates comparison of results between laboratories and over time. Over the last decade, researchers and population health specialists have invested substantial effort in building up public MLST databases for nearly 100 different bacterial species, and these databases contain a wealth of important information linked to MLST sequence types such as time and place of isolation, host or niche, serotype and even clinical or drug resistance profiles. Recent advances in sequencing technology mean it is increasingly feasible to perform bacterial population analysis at the whole genome level. This offers massive gains in resolving power and genetic profiling compared to MLST, and will eventually replace MLST for bacterial typing and population analysis. However given the wealth of data currently available in MLST databases, it is crucial to maintain backwards compatibility with MLST schemes so that new genome analyses can be understood in their proper historical context.ResultsWe present a software tool, SRST, for quick and accurate retrieval of sequence types from short read sets, using inputs easily downloaded from public databases. SRST uses read mapping and an allele assignment score incorporating sequence coverage and variability, to determine the most likely allele at each MLST locus. Analysis of over 3,500 loci in more than 500 publicly accessible Illumina read sets showed SRST to be highly accurate at allele assignment. SRST output is compatible with common analysis tools such as eBURST, Clonal Frame or PhyloViz, allowing easy comparison between novel genome data and MLST data. Alignment, fastq and pileup files can also be generated for novel alleles.ConclusionsSRST is a novel software tool for accurate assignment of sequence types using short read data. Several uses for the tool are demonstrated, including quality control for high-throughput sequencing projects, plasmid MLST and analysis of genomic data during outbreak investigation. SRST is open-source, requires Python, BWA and SamTools, and is available from http://srst.sourceforge.net.