Alignment-Free Sequence Comparison Based on Next-Generation Sequencing Reads

Next generation sequencing (NGS) technologies have generated enormous amount of shotgun read data and assembly of the reads can be challenging, especially for organisms without template sequences. We study the power of genome comparison based on shotgun read data without assembly using three alignment-free sequence comparison statistics, D2, D2*, and D2S, both theoretically and by simulations. Theoretical formulas for the power of detecting the relationship between two sequences related through a common motif model are derived. It is shown that both D2* and D2S outperform D2 for detecting the relationship between two sequences based on NGS data. We then study the effects of length of the tuple, read length, coverage, and sequencing error on the power of D2* and D2S. Finally, variations of these statistics, d2, d2* and d2S, respectively, are used to first cluster 5 mammalian species with known phylogenetic relationships and then cluster 13 tree species whose complete genome sequences are not available using NGS shotgun reads. The clustering results using d2S are consistent with biological knowledge for the 5 mammalian and 13 tree species, respectively. Thus, the statistic d2S provides a powerful alignment-free comparison tool to study the relationships among different organisms based on NGS read data without assembly.

P842: Unified comprehensive analysis of NGS and optical genome mapping data for constitutional applications using Bionano VIA software

Background: The structural annotation of genes serves as an important resource in the genomics era. Next-generation sequencing (NGS)-based RNA-seq has been used to reconstruct transcriptomes by assembling short reads; however, coupled alternative RNA processing events occurring over long distances cannot be captured. Although the more recently developed third-generation sequencing (TGS) technologies can generate longer reads, challenges exist in full-length transcriptome (FLT) reconstruction due to their higher sequencing error rate and relatively lower throughput. Results: Here, to address the above-stated technical challenges and provide practical FLT annotation in rat, we developed a hybrid-sequencing workflow for reconstructing FLT and applied it to rat hippocampus. Our workflow combines high sequencing accuracy from NGS and long read length from TGS, and has two main features: (1) cDNA normalization and size fractionation to facilitate the profiling of diverse transcripts at an affordable throughput; and (2) a computational approach to correct TGS sequencing errors using NGS reads. The error correction substantially improved the read accuracy with respect to gene expression levels. To determine high-confidence FLT, we used independent 5´ and 3´-end profiling data to validate and filter for a final FLT set in rat hippocampus, which contains 28,268 transcripts derived from 6,380 RefSeq gene loci and 849 unannotated loci. Comparing our FLT to RefSeq annotation, we found that our FLT displayed a higher diversity and more than 90% of our FLT isoforms were not annotated in RefSeq. Moreover, we discovered significant co-occurrence of alternative RNA processing events between transcript 5´-ends and 3´-ends, alternative 5´ and 3´ splice sites, and multiple retained intron. Integrating with polysome profiling and ribosome footprinting data, we predicted active or inactive translational status of transcript isoforms, and reconstructed an open reading frame (ORF)-eome in rat hippocampus, which was largely supported by mass spectrometry-based proteomics datasets. Compared to RefSeq-annotated ORFs, only less than one-third of our ORFome were annotated in RefSeq, whereas the majority were ORF variants, and about one-tenth were novel ORFs. Finally, we identify isoforms with specific subcellular localization patterns in neurons. Conclusions: Altogether, our data expand the current rat gene annotation and provide a rich resource for future functional studies. The developed hybrid-sequencing workflow and downstream analysis can be easily adapted to uncover the complex transcriptome/ORFeome landscapes expressed in other tissues from other organisms. Full article: PMID: 31676752 PMCID: PMC6825209 DOI: 10.1038/s41467-019-13037-0

Sensitive detection of internal tandem duplication (ITD) mutation of FLT3 is very important in acute myeloid leukemia. To increase detection sensitivity of FLT3-ITD, we developed new detection algorithm using next generation sequencing (NGS) data. We validated results using nested polymerase chain reaction (PCR) methods. We compared results of NGS data, nested PCR and conventional PCR methods. First, using whole exome sequencing data of 83 AML patients, we applied calling algorithm for FLT3-ITD. Briefly, to detect ITDs with NGS data, the reads are aligned to a reference sequence (UCSC hg19), with BWA which is a read aligner allowing soft-clipping. Some reads can be an indication of the occurrence of ITD and BWA aligns those reads as soft-clipped. Second, we deigned two types of primer for Nested PCR. The first primer was targeted wildly for between exon14 and exon15 of FLT3 gene. Nested PCR primer was deigned to target previously reported regions which are frequently occurred ITD mutation. PCR reactions of two steps were performed using the PCR primers sequentially. In these 83 patients, FLT3-ITD was positive only in 7 patients when tested by conventional PCR methods. When NGS detection method was applied, this resulted in positive FLT3-ITD in 11 patients (11/83, 13%). When validation was performed using nested PCR, FLT3-ITD was confirmed in all of 11 patients. Nested PCR detected additional 4 patient positive for FLT3-ITD in this population. For 68 patients, FLT3-ITD was negative by both NGS and nested PCR method. Overall, NGS method improved sensitivity of FLT3-ITD detection by 57% in this population. And the concordance rate of NGS method and nested PCR was 95.2% (79/83). Then we investigated clinical significance of sensitive FLT3-ITD detection. For this, we performed nested PCR and conventional PCR at the same time in 238 AML patients to detect FLT3-ITD. Positive rate for FLT3-ITD was 20% (48/238) and 10% (24/238) by nested PCR and conventional PCR respectively. When survival analysis was performed, among patients with negative FLT3-ITD result by conventional PCR, patients who showed positive for FLT3-ITD by nested PCR had shorter overall survival compared to those who showed negative for FLT3-ITD by nested PCR. (p = 0.03). This implies that sensitive FLT3-ITD detection using nested PCR is clinically meaningful. Diagnosis of FLT3-ITD is very important genetic factor, leading a therapeutic direction for AML patient. Here we report that we have developed alternative more sensitive detection methods for FLT3-ITD based on nested PCR and NGS. Sensitive detection of FLT3-ITD was clinically meaningful, suggesting that these methods should be incorporated in a future clinical practice. Also, we want to note that, NGS method is capable of quantifying FLT3-ITD size and amount in AML patients. Citation Format: Daeyoon Kim, Yoojin Hong, Youngil Koh, Sung-Soo Yoon, Choong-Hyun Sun, Kwang-Sung Ahn, Seungmook Lee, Hongseok Yun, Suyeon Lee. Improved sensitive detection method of FLT3 (FMS-like tyrosine kinase) internal tandem duplication (ITD) mutation using next-generation sequencing technology and nested PCR. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3186.

Next generation sequencing (NGS) technologies have enabled cheap, large-scale, and high-throughput production of short DNA sequence reads and thereby have promoted the explosive growth of data volume. Unfortunately, the produced reads are short and prone to contain errors that are incurred during sequencing cycles. Both large data volume and sequencing errors have complicated the mapping of NGS reads onto the reference genome and have motivated the development of various aligners for very short reads, typically less than 100 base pairs (bps) in length. As read length continues to increase, propelled by advances in NGS technologies, these longer reads tend to have higher sequencing error rates and more true mutations (including substitutions, insertions, or deletions) to the genome. Such new characteristics make inefficient the aligners, which are optimized for very short reads and support only ungapped alignments or gapped alignments with very limited number of gaps (typically one gap), and thereby call for new aligners with fully gapped alignment supported. In this chapter, we present the CUSHAW software suite for NGS read alignment, which is open-source and consists of three individual aligners: CUSHAW, CUSHAW2, and CUSHAW3. This suite offers parallel and efficient NGS read alignments to large genomes, such as the human genome, by harnessing multi-core CPUs or compute unified device architecture (CUDA)-enabled graphics processing units (GPUs). Moreover, it has the capability to align both base-space and color-space reads and is consistently shown to be one of the best alignment tools through our performance evaluations.

Towards standardization of the description and publication of next‐generation sequencing datasets of fungal communities

BackgroundWith the advent of Next-Generation Sequencing technologies (NGS), a large amount of short read data has been generated. If a reference genome is not available, the assembly of a template sequence is usually challenging because of repeats and the short length of reads. When NGS reads cannot be mapped onto a reference genome alignment-based methods are not applicable. However it is still possible to study the evolutionary relationship of unassembled genomes based on NGS data.ResultsWe present a parameter-free alignment-free method, called , based on variable-length patterns, for the direct comparison of sets of NGS reads. We define a similarity measure using variable-length patterns, as well as reverses and reverse-complements, along with their statistical and syntactical properties. We evaluate several alignment-free statistics on the comparison of NGS reads coming from simulated and real genomes. In almost all simulations our method outperforms all other statistics. The performance gain becomes more evident when real genomes are used.ConclusionThe new alignment-free statistic is highly successful in discriminating related genomes based on NGS reads data. In almost all experiments, it outperforms traditional alignment-free statistics that are based on fixed length patterns.

BackgroundThe mitochondrial genomes (mitogenomes) of metazoans generally include the same set of protein-coding genes, which ensures the homology of mitochondrial genes between species. The mitochondrial genes are often used as reference data for species identification based on genetic data (DNA barcoding). The need for such reference data has been increasing due to the application of environmental DNA (eDNA) analysis for environmental assessments. Recently, the number of publicly available sequence reads obtained with next-generation sequencing (NGS) has been increasing in the public database (the NCBI Sequence Read Archive, SRA). Such freely available NGS reads would be promising sources for assembling mitochondrial protein-coding genes (mPCGs) of organisms whose mitochondrial genes are not available in GenBank. The present study aimed to assemble annelid mPCGs from raw data deposited in the SRA.MethodsThe recent progress in the classification of Annelida was briefly introduced. In the present study, the mPCGs of 32 annelid species of 19 families in clitellates and allies in Sedentaria (echiurans and polychaetes) were newly assembled from the reads deposited in the SRA. Assembly was performed with a recently published pipeline mitoRNA, which includes cycles of Bowtie2 mapping and Trinity assembly. Assembled mPCGs were deposited in GenBank as Third Party Data (TPA) data. A phylogenetic tree was reconstructed with maximum likelihood (ML) analysis, together with other mPCGs deposited in GenBank.Results and DiscussionmPCG assembly was largely successful except for Travisia forbesii; only four genes were detected from the assembled contigs of the species probably due to the reads targeting its parasite. Most genes were largely successfully obtained, whereas atp8, nad2, and nad4l were only successful in 22–24 species. The high nucleotide substitution rates of these genes might be relevant to the failure in the assembly although nad6, which showed a similarly high substitution rate, was successfully assembled. Although the phylogenetic positions of several lineages were not resolved in the present study, the phylogenetic relationships of some polychaetes and leeches that were not inferred by transcriptomes were well resolved probably due to a more dense taxon sampling than previous phylogenetic analyses based on transcriptomes. Although NGS data are generally better sources for resolving phylogenetic relationships of both higher and lower classifications, there are ensuring needs for specific loci of the mitochondrial genes for analyses that do not require high resolutions, such as DNA barcoding, eDNA, and phylogenetic analysis among lower taxa. Assembly from publicly available NGS reads would help design specific primers for the mitochondrial gene sequences of species, whose mitochondrial genes are hard to amplify by Sanger sequencing using universal primers.

Collection and storage of HLA NGS genotyping data for the 17th International HLA and Immunogenetics Workshop

The next-generation sequencing (NGS) technology has revolutionized research in genetics and genomics, resulting in massive NGS data and opening more fronts to answer unresolved issues in genetics. NGS data are usually stored at three levels: image files, sequence tags, and alignment reads. The sizes of these types of data usually range from several hundreds of gigabytes to several terabytes. Biostatisticians and bioinformaticians are typically working with the aligned NGS read count data (hence the last level of NGS data) for data modeling and interpretation.To horn in on the use of NGS technology, researchers utilize it to profile the whole genome to study DNA copy number variations (CNVs) for an individual subject (or patient) as well as groups of subjects (or patients). The resulting aligned NGS read count data are then modeled by proper mathematical and statistical approaches so that the loci of CNVs can be accurately detected. In this book chapter, a summary of most popularly used statistical methods for detecting CNVs using NGS data is given. The goal is to provide readers with a comprehensive resource of available statistical approaches for inferring DNA copy number variations using NGS data.

The genomic era of bacteriology began in 1995 when the first bacterial genome (Haemophilus influenzae) was sequenced using the Sanger sequencing method (1). A decade later, the introduction of high-throughput or next-generation sequencing (NGS) technologies allowed sequencing of bacterial genomes to be performed in days rather than months or years (2). These newer technologies use different processes but rely on a combination of template preparation, sequencing and imaging, and genome alignment and assembly methods (3). The major advantages of NGS over Sanger sequencing are speed of sequencing combined with lower costs (4). The ability to readily sequence the whole genome of microorganisms has enabled the performance of large-scale comparative and evolutionary studies that were unimaginable even a few years ago (5–9). Furthermore, the development of rapid benchtop sequencing platforms (10) that are able to sequence a microbial genome in a day makes them increasingly appropriate for introduction into the diagnostic microbiology laboratory environment (11). This chapter will briefly review the currently available NGS technologies and platforms, followed by an in-depth review of the potential clinical applications of whole-genome sequencing (WGS) in the microbiology laboratory. We will also present the challenges for implementation of WGS in the clinical setting and consider some future directions.

BackgroundNext generation sequencing (NGS) is a state of the art technology for microRNA (miRNA) analysis. The quantitative interpretation of the primary output of NGS i.e. the read counts for a miRNA sequence that can vary by several orders of magnitude (1 to 107) remains incompletely understood.FindingsNGS (SOLiD 3 technology) was performed on biopsies from 6 Barrett’s esophagus (BE) and 5 Gastroesophageal Reflux Disease (GERD) patients. Read sequences were aligned to miRBase 18.0. Differential expression analysis was adjusted for false discovery rate of 5%. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed for 36 miRNA in a validation cohort of 47 patients (27 BE and 20 GERD). Correlation coefficients, accuracy, precision and recall of NGS compared to qRT-PCR were calculated. Increase in NGS reads was associated with progressively lower Cq values, p < 0.05. Although absolute quantification between NGS reads and Cq values correlated modestly: -0.38, p = 0.01 for BE and -0.32, p = 0.05 for GERD, relative quantification (fold changes) of miRNA expression between BE &GERD by NGS correlated highly with qRT-PCR 0.86, p = 2.45E-11. Fold change correlations were unaffected when different thresholds of NGS read counts were compared (>1000 vs. <1000, >500 vs. <500 and >100 vs. <100). The accuracy, precision and recall of NGS to label a miRNA as differentially expressed were 0.71, 0.88 and 0.74 respectively.ConclusionAbsolute NGS reads correlated modestly with qRT-PCR but fold changes correlated highly. NGS is robust at relative but not absolute quantification of miRNA levels and accurate for high-throughput identification of differentially expressed miRNA.

Computational analysis of next generation sequencing data and its applications in clinical oncology

The incidence rate of hepatitis C virus (HCV) infection in Pakistan is very high. In this study, we evaluated the genetic heterogeneity of HCV hypervariable region 1 (HVR1) from the HCV-infected Pakistani population and compare the isolated genotypes with representative sequences from internationally diverse geographic regions. We also investigated potential transmission events in non-high-risk HCV patients. Next-generation sequencing (NGS) data from the E1-HVR1 region from 30 HCV patients were used for phylogenetic analysis. Reference sequences were retrieved from the Los Alamos HCV and GenBank databases. NGS data were analyzed to examine HCV HVR1 sequence diversity and identify transmission links among HCV-infected individuals using Global Hepatitis Outbreak and Surveillance Technology (GHOST). Phylogenetic analysis showed the predominance of HCV genotype 3a (86.6%), followed by 1a (6.6%), 1b (3.3%), and 3b (3.3%). NGS of HVR1 displayed significant genetic heterogeneity of HCV populations within each patient. The average nucleotide sequence diversity for HVR1 was 0.055. JR781281 was found to be the most diverse (0.14) of the specimens. Phylogenetic analysis demonstrated that all HCV specimens sequenced in this study were more similar to each other and showed variations from the representative sequences. The GHOST results suggested genetic relatedness between two (6.6%) HCV cases, possibly defining an incipient outbreak in a non-high-risk population. We urge rigorous countrywide investigation of outbreaks to identify transmission clusters and their sources to incorporate preventive measures for disease control.

BackgroundInnumerable opportunities for new genomic research have been stimulated by advancement in high-throughput next-generation sequencing (NGS). However, the pitfall of NGS data abundance is the complication of distinction between true biological variants and sequence error alterations during downstream analysis. Many error correction methods have been developed to correct erroneous NGS reads before further analysis, but independent evaluation of the impact of such dataset features as read length, genome size, and coverage depth on their performance is lacking. This comparative study aims to investigate the strength and weakness as well as limitations of some newest k-spectrum-based methods and to provide recommendations for users in selecting suitable methods with respect to specific NGS datasets.MethodsSix k-spectrum-based methods, i.e., Reptile, Musket, Bless, Bloocoo, Lighter, and Trowel, were compared using six simulated sets of paired-end Illumina sequencing data. These NGS datasets varied in coverage depth (10× to 120×), read length (36 to 100 bp), and genome size (4.6 to 143 MB). Error Correction Evaluation Toolkit (ECET) was employed to derive a suite of metrics (i.e., true positives, false positive, false negative, recall, precision, gain, and F-score) for assessing the correction quality of each method.ResultsResults from computational experiments indicate that Musket had the best overall performance across the spectra of examined variants reflected in the six datasets. The lowest accuracy of Musket (F-score = 0.81) occurred to a dataset with a medium read length (56 bp), a medium coverage (50×), and a small-sized genome (5.4 MB). The other five methods underperformed (F-score < 0.80) and/or failed to process one or more datasets.ConclusionsThis study demonstrates that various factors such as coverage depth, read length, and genome size may influence performance of individual k-spectrum-based error correction methods. Thus, efforts have to be paid in choosing appropriate methods for error correction of specific NGS datasets. Based on our comparative study, we recommend Musket as the top choice because of its consistently superior performance across all six testing datasets. Further extensive studies are warranted to assess these methods using experimental datasets generated by NGS platforms (e.g., 454, SOLiD, and Ion Torrent) under more diversified parameter settings (k-mer values and edit distances) and to compare them against other non-k-spectrum-based classes of error correction methods.