miReader: Discovering Novel miRNAs in Species without Sequenced Genome

Along with computational approaches, NGS led technologies have caused a major impact upon the discoveries made in the area of miRNA biology, including novel miRNAs identification. However, to this date all microRNA discovery tools compulsorily depend upon the availability of reference or genomic sequences. Here, for the first time a novel approach, miReader, has been introduced which could discover novel miRNAs without any dependence upon genomic/reference sequences. The approach used NGS read data to build highly accurate miRNA models, molded through a Multi-boosting algorithm with Best-First Tree as its base classifier. It was comprehensively tested over large amount of experimental data from wide range of species including human, plants, nematode, zebrafish and fruit fly, performing consistently with >90% accuracy. Using the same tool over Illumina read data for Miscanthus, a plant whose genome is not sequenced; the study reported 21 novel mature miRNA duplex candidates. Considering the fact that miRNA discovery requires handling of high throughput data, the entire approach has been implemented in a standalone parallel architecture. This work is expected to cause a positive impact over the area of miRNA discovery in majority of species, where genomic sequence availability would not be a compulsion any more.     

Targeted assembly of short sequence reads

As next-generation sequence (NGS) production continues to increase, analysis is becoming a significant bottleneck. However, in situations where information is required only for specific sequence variants, it is not necessary to assemble or align whole genome data sets in their entirety. Rather, NGS data sets can be mined for the presence of sequence variants of interest by localized assembly, which is a faster, easier, and more accurate approach. We present TASR, a streamlined assembler that interrogates very large NGS data sets for the presence of specific variants by only considering reads within the sequence space of input target sequences provided by the user. The NGS data set is searched for reads with an exact match to all possible short words within the target sequence, and these reads are then assembled stringently to generate a consensus of the target and flanking sequence. Typically, variants of a particular locus are provided as different target sequences, and the presence of the variant in the data set being interrogated is revealed by a successful assembly outcome. However, TASR can also be used to find unknown sequences that flank a given target. We demonstrate that TASR has utility in finding or confirming genomic mutations, polymorphisms, fusions and integration events. Targeted assembly is a powerful method for interrogating large data sets for the presence of sequence variants of interest. TASR is a fast, flexible and easy to use tool for targeted assembly.     

Microsatellite markers from the Ion Torrent: a multi‐species contrast to 454 shotgun sequencing

The development and screening of microsatellite markers have been accelerated by next‐generation sequencing (NGS) technology and in particular GS‐FLX pyro‐sequencing (454). More recent platforms such as the PGM semiconductor sequencer (Ion Torrent) offer potential benefits such as dramatic reductions in cost, but to date have not been well utilized. Here, we critically compare the advantages and disadvantages of microsatellite development using PGM semiconductor sequencing and GS‐FLX pyro‐sequencing for two gymnosperm (a conifer and a cycad) and one angiosperm species. We show that these NGS platforms differ in the quantity of returned sequence data, unique microsatellite data and primer design opportunities, mostly consistent with the differences in read length. The strength of the PGM lies in the large amount of data generated at a comparatively lower cost and time. The strength of GS‐FLX lies in the return of longer average length sequences and therefore greater flexibility in producing markers with variable product length, due to longer flanking regions, which is ideal for capillary multiplexing. These differences need to be considered when choosing a NGS method for microsatellite discovery. However, the ongoing improvement in read lengths of the NGS platforms will reduce the disadvantage of the current short read lengths, particularly for the PGM platform, allowing greater flexibility in primer design coupled with the power of a larger number of sequences.     

Investigating Microbial Eukaryotic Diversity from a Global Census: Insights from a Comparison of Pyrotag and Full-Length Sequences of 18S rRNA Genes

Next-generation DNA sequencing (NGS) approaches are rapidly surpassing Sanger sequencing for characterizing the diversity of natural microbial communities. Despite this rapid transition, few comparisons exist between Sanger sequences and the generally much shorter reads of NGS. Operational taxonomic units (OTUs) derived from full-length (Sanger sequencing) and pyrotag (454 sequencing of the V9 hypervariable region) sequences of 18S rRNA genes from 10 global samples were analyzed in order to compare the resulting protistan community structures and species richness. Pyrotag OTUs called at 98% sequence similarity yielded numbers of OTUs that were similar overall to those for full-length sequences when the latter were called at 97% similarity. Singleton OTUs strongly influenced estimates of species richness but not the higher-level taxonomic composition of the community. The pyrotag and full-length sequence data sets had slightly different taxonomic compositions of rhizarians, stramenopiles, cryptophytes, and haptophytes, but the two data sets had similarly high compositions of alveolates. Pyrotag-based OTUs were often derived from sequences that mapped to multiple full-length OTUs at 100% similarity. Thus, pyrotags sequenced from a single hypervariable region might not be appropriate for establishing protistan species-level OTUs. However, nonmetric multidimensional scaling plots constructed with the two data sets yielded similar clusters, indicating that beta diversity analysis results were similar for the Sanger and NGS sequences. Short pyrotag sequences can provide holistic assessments of protistan communities, although care must be taken in interpreting the results. The longer reads (>500 bp) that are now becoming available through NGS should provide powerful tools for assessing the diversity of microbial eukaryotic assemblages.     

Assembly errors cause false tandem duplicate regions in the chicken (Gallus gallus) genome sequence

The complexity of eukaryote genomes makes assembly errors inevitable in the process of constructing reference genomes. Next-generation sequencing (NGS) could provide an efficient way to validate previously assembled genomes. Here, we exploited NGS data to interrogate the chicken reference genome and identified 35 pairs of nearly identical regions with >99.5 % sequence similarity and a median size of 109 kb. Several lines of evidence, including read depth, the composition of junction sequences, and sequence similarity, suggest that these regions present genome assembly errors and should be excluded from forthcoming genomic studies.     

DNA barcodes from century‐old type specimens using next‐generation sequencing

Type specimens have high scientific importance because they provide the only certain connection between the application of a Linnean name and a physical specimen. Many other individuals may have been identified as a particular species, but their linkage to the taxon concept is inferential. Because type specimens are often more than a century old and have experienced conditions unfavourable for DNA preservation, success in sequence recovery has been uncertain. This study addresses this challenge by employing next‐generation sequencing (NGS) to recover sequences for the barcode region of the cytochrome c oxidase 1 gene from small amounts of template DNA. DNA quality was first screened in more than 1800 century‐old type specimens of Lepidoptera by attempting to recover 164‐bp and 94‐bp reads via Sanger sequencing. This analysis permitted the assignment of each specimen to one of three DNA quality categories – high (164‐bp sequence), medium (94‐bp sequence) or low (no sequence). Ten specimens from each category were subsequently analysed via a PCR‐based NGS protocol requiring very little template DNA. It recovered sequence information from all specimens with average read lengths ranging from 458 bp to 610 bp for the three DNA categories. By sequencing ten specimens in each NGS run, costs were similar to Sanger analysis. Future increases in the number of specimens processed in each run promise substantial reductions in cost, making it possible to anticipate a future where barcode sequences are available from most type specimens.     

Stepwise Threshold Clustering: A New Method for Genotyping MHC Loci Using Next-Generation Sequencing Technology

Genes of the vertebrate major histocompatibility complex (MHC) are of great interest to biologists because of their important role in immunity and disease, and their extremely high levels of genetic diversity. Next generation sequencing (NGS) technologies are quickly becoming the method of choice for high-throughput genotyping of multi-locus templates like MHC in non-model organisms.Previous approaches to genotyping MHC genes using NGS technologies suffer from two problems:1) a “gray zone” where low frequency alleles and high frequency artifacts can be difficult to disentangle and 2) a similar sequence problem, where very similar alleles can be difficult to distinguish as two distinct alleles. Here were present a new method for genotyping MHC loci – Stepwise Threshold Clustering (STC) – that addresses these problems by taking full advantage of the increase in sequence data provided by NGS technologies. Unlike previous approaches for genotyping MHC with NGS data that attempt to classify individual sequences as alleles or artifacts, STC uses a quasi-Dirichlet clustering algorithm to cluster similar sequences at increasing levels of sequence similarity. By applying frequency and similarity based criteria to clusters rather than individual sequences, STC is able to successfully identify clusters of sequences that correspond to individual or similar alleles present in the genomes of individual samples. Furthermore, STC does not require duplicate runs of all samples, increasing the number of samples that can be genotyped in a given project. We show how the STC method works using a single sample library. We then apply STC to 295 threespine stickleback (Gasterosteus aculeatus) samples from four populations and show that neighboring populations differ significantly in MHC allele pools. We show that STC is a reliable, accurate, efficient, and flexible method for genotyping MHC that will be of use to biologists interested in a variety of downstream applications.     

BAC-Pool Sequencing and Analysis of Large Segments of A12 and D12 Homoeologous Chromosomes in Upland Cotton

Although new and emerging next-generation sequencing (NGS) technologies have reduced sequencing costs significantly, much work remains to implement them for de novo sequencing of complex and highly repetitive genomes such as the tetraploid genome of Upland cotton (Gossypium hirsutum L.). Herein we report the results from implementing a novel, hybrid Sanger/454-based BAC-pool sequencing strategy using minimum tiling path (MTP) BACs from Ctg-3301 and Ctg-465, two large genomic segments in A12 and D12 homoeologous chromosomes (Ctg). To enable generation of longer contig sequences in assembly, we implemented a hybrid assembly method to process ~35x data from 454 technology and 2.8-3x data from Sanger method. Hybrid assemblies offered higher sequence coverage and better sequence assemblies. Homology studies revealed the presence of retrotransposon regions like Copia and Gypsy elements in these contigs and also helped in identifying new genomic SSRs. Unigenes were anchored to the sequences in Ctg-3301 and Ctg-465 to support the physical map. Gene density, gene structure and protein sequence information derived from protein prediction programs were used to obtain the functional annotation of these genes. Comparative analysis of both contigs with Arabidopsis genome exhibited synteny and microcollinearity with a conserved gene order in both genomes. This study provides insight about use of MTP-based BAC-pool sequencing approach for sequencing complex polyploid genomes with limited constraints in generating better sequence assemblies to build reference scaffold sequences. Combining the utilities of MTP-based BAC-pool sequencing with current longer and short read NGS technologies in multiplexed format would provide a new direction to cost-effectively and precisely sequence complex plant genomes.     

QC-Chain: Fast and Holistic Quality Control Method for Next-Generation Sequencing Data

Next-generation sequencing (NGS) technologies have been widely used in life sciences. However, several kinds of sequencing artifacts, including low-quality reads and contaminating reads, were found to be quite common in raw sequencing data, which compromise downstream analysis. Therefore, quality control (QC) is essential for raw NGS data. However, although a few NGS data quality control tools are publicly available, there are two limitations: First, the processing speed could not cope with the rapid increase of large data volume. Second, with respect to removing the contaminating reads, none of them could identify contaminating sources de novo, and they rely heavily on prior information of the contaminating species, which is usually not available in advance. Here we report QC-Chain, a fast, accurate and holistic NGS data quality-control method. The tool synergeticly comprised of user-friendly tools for (1) quality assessment and trimming of raw reads using Parallel-QC, a fast read processing tool; (2) identification, quantification and filtration of unknown contamination to get high-quality clean reads. It was optimized based on parallel computation, so the processing speed is significantly higher than other QC methods. Experiments on simulated and real NGS data have shown that reads with low sequencing quality could be identified and filtered. Possible contaminating sources could be identified and quantified de novo, accurately and quickly. Comparison between raw reads and processed reads also showed that subsequent analyses (genome assembly, gene prediction, gene annotation, etc.) results based on processed reads improved significantly in completeness and accuracy. As regard to processing speed, QC-Chain achieves 7–8 time speed-up based on parallel computation as compared to traditional methods. Therefore, QC-Chain is a fast and useful quality control tool for read quality process and de novo contamination filtration of NGS reads, which could significantly facilitate downstream analysis. QC-Chain is publicly available at: http://www.computationalbioenergy.org/qc-chain.html.     

Light-weight reference-based compression of FASTQ data

Background

The exponential growth of next generation sequencing (NGS) data has posed big challenges to data storage, management and archive. Data compression is one of the effective solutions, where reference-based compression strategies can typically achieve superior compression ratios compared to the ones not relying on any reference.

Results

This paper presents a lossless light-weight reference-based compression algorithm namely LW-FQZip to compress FASTQ data. The three components of any given input, i.e., metadata, short reads and quality score strings, are first parsed into three data streams in which the redundancy information are identified and eliminated independently. Particularly, well-designed incremental and run-length-limited encoding schemes are utilized to compress the metadata and quality score streams, respectively. To handle the short reads, LW-FQZip uses a novel light-weight mapping model to fast map them against external reference sequence(s) and produce concise alignment results for storage. The three processed data streams are then packed together with some general purpose compression algorithms like LZMA. LW-FQZip was evaluated on eight real-world NGS data sets and achieved compression ratios in the range of 0.111-0.201. This is comparable or superior to other state-of-the-art lossless NGS data compression algorithms.

Conclusions

LW-FQZip is a program that enables efficient lossless FASTQ data compression. It contributes to the state of art applications for NGS data storage and transmission. LW-FQZip is freely available online at: http://csse.szu.edu.cn/staff/zhuzx/LWFQZip.     

VCF2CAPS–A high-throughput CAPS marker design from VCF files and its test-use on a genotyping-by-sequencing (GBS) dataset

Next-generation sequencing (NGS) is a powerful tool for massive detection of DNA sequence variants such as single nucleotide polymorphisms (SNPs), multi-nucleotide polymorphisms (MNPs) and insertions/deletions (indels). For routine screening of numerous samples, these variants are often converted into cleaved amplified polymorphic sequence (CAPS) markers which are based on the presence versus absence of restriction sites within PCR products. Current computational tools for SNP to CAPS conversion are limited and usually infeasible to use for large datasets as those generated with NGS. Moreover, there is no available tool for massive conversion of MNPs and indels into CAPS markers. Here, we present VCF2CAPS–a new software for identification of restriction endonucleases that recognize SNP/MNP/indel-containing sequences from NGS experiments. Additionally, the program contains filtration utilities not available in other SNP to CAPS converters–selection of markers with a single polymorphic cut site within a user-specified sequence length, and selection of markers that differentiate up to three user-defined groups of individuals from the analyzed population. Performance of VCF2CAPS was tested on a thoroughly analyzed dataset from a genotyping-by-sequencing (GBS) experiment. A selection of CAPS markers picked by the program was subjected to experimental verification. CAPS markers, also referred to as PCR-RFLPs, belong to basic tools exploited in plant, animal and human genetics. Our new software–VCF2CAPS–fills the gap in the current inventory of genetic software by high-throughput CAPS marker design from next-generation sequencing (NGS) data. The program should be of interest to geneticists involved in molecular diagnostics. In this paper we show a successful exemplary application of VCF2CAPS and we believe that its usefulness is guaranteed by the growing availability of NGS services.

This is a PLOS Computational Biology Software paper.

     

Intra‐individual polymorphism in chloroplasts from NGS data: where does it come from and how to handle it?

Next‐generation sequencing allows access to a large quantity of genomic data. In plants, several studies used whole chloroplast genome sequences for inferring phylogeography or phylogeny. Even though the chloroplast is a haploid organelle, NGS plastome data identified a nonnegligible number of intra‐individual polymorphic SNPs. Such observations could have several causes such as sequencing errors, the presence of heteroplasmy or transfer of chloroplast sequences in the nuclear and mitochondrial genomes. The occurrence of allelic diversity has practical important impacts on the identification of diversity, the analysis of the chloroplast data and beyond that, significant evolutionary questions. In this study, we show that the observed intra‐individual polymorphism of chloroplast sequence data is probably the result of plastid DNA transferred into the mitochondrial and/or the nuclear genomes. We further assess nine different bioinformatics pipelines’ error rates for SNP and genotypes calling using SNPs identified in Sanger sequencing. Specific pipelines are adequate to deal with this issue, optimizing both specificity and sensitivity. Our results will allow a proper use of whole chloroplast NGS sequence and will allow a better handling of NGS chloroplast sequence diversity.     

SAMMate: a GUI tool for processing short read alignments in SAM/BAM format

Background  

Next Generation Sequencing (NGS) technology generates tens of millions of short reads for each DNA/RNA sample. A key step in NGS data analysis is the short read alignment of the generated sequences to a reference genome. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing this information.     

Identification of Optimum Sequencing Depth Especially for De Novo Genome Assembly of Small Genomes Using Next Generation Sequencing Data

Next Generation Sequencing (NGS) is a disruptive technology that has found widespread acceptance in the life sciences research community. The high throughput and low cost of sequencing has encouraged researchers to undertake ambitious genomic projects, especially in de novo genome sequencing. Currently, NGS systems generate sequence data as short reads and de novo genome assembly using these short reads is computationally very intensive. Due to lower cost of sequencing and higher throughput, NGS systems now provide the ability to sequence genomes at high depth. However, currently no report is available highlighting the impact of high sequence depth on genome assembly using real data sets and multiple assembly algorithms. Recently, some studies have evaluated the impact of sequence coverage, error rate and average read length on genome assembly using multiple assembly algorithms, however, these evaluations were performed using simulated datasets. One limitation of using simulated datasets is that variables such as error rates, read length and coverage which are known to impact genome assembly are carefully controlled. Hence, this study was undertaken to identify the minimum depth of sequencing required for de novo assembly for different sized genomes using graph based assembly algorithms and real datasets. Illumina reads for E.coli (4.6 MB) S.kudriavzevii (11.18 MB) and C.elegans (100 MB) were assembled using SOAPdenovo, Velvet, ABySS, Meraculous and IDBA-UD. Our analysis shows that 50X is the optimum read depth for assembling these genomes using all assemblers except Meraculous which requires 100X read depth. Moreover, our analysis shows that de novo assembly from 50X read data requires only 6–40 GB RAM depending on the genome size and assembly algorithm used. We believe that this information can be extremely valuable for researchers in designing experiments and multiplexing which will enable optimum utilization of sequencing as well as analysis resources.     

A comparison of next-generation sequencing analysis methods for cancer xenograft samples

The application of next-generation sequencing (NGS) technology in cancer is influenced by the quality and purity of tissue samples. This issue is especially critical for patient-derived xenograft (PDX) models, which have proven to be by far the best preclinical tool for investigating human tumor biology, because the sensitivity and specificity of NGS analysis in xenograft samples would be compromised by the contamination of mouse DNA and RNA. This definitely affects downstream analyses by causing inaccurate mutation calling and gene expression estimates. The reliability of NGS data analysis for cancer xenograft samples is therefore highly dependent on whether the sequencing reads derived from the xenograft could be distinguished from those originated from the host. That is, each sequence read needs to be accurately assigned to its original species. Here, we review currently available methodologies in this field, including Xenome, Disambiguate, bamcmp and pdxBlacklist, and provide guidelines for users.     

amplisas: a web server for multilocus genotyping using next‐generation amplicon sequencing data

Next‐generation sequencing (NGS) technologies are revolutionizing the fields of biology and medicine as powerful tools for amplicon sequencing (AS). Using combinations of primers and barcodes, it is possible to sequence targeted genomic regions with deep coverage for hundreds, even thousands, of individuals in a single experiment. This is extremely valuable for the genotyping of gene families in which locus‐specific primers are often difficult to design, such as the major histocompatibility complex (MHC). The utility of AS is, however, limited by the high intrinsic sequencing error rates of NGS technologies and other sources of error such as polymerase amplification or chimera formation. Correcting these errors requires extensive bioinformatic post‐processing of NGS data. Amplicon Sequence Assignment (amplisas ) is a tool that performs analysis of AS results in a simple and efficient way, while offering customization options for advanced users. amplisas is designed as a three‐step pipeline consisting of (i) read demultiplexing, (ii) unique sequence clustering and (iii) erroneous sequence filtering. Allele sequences and frequencies are retrieved in excel spreadsheet format, making them easy to interpret. amplisas performance has been successfully benchmarked against previously published genotyped MHC data sets obtained with various NGS technologies.