Rapid and accurate alignment of nucleotide conversion sequencing reads with HISAT-3N.
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ABSTRACT:
SUBMITTER: Zhang Y
PROVIDER: S-EPMC8256862 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Structural variations (SVs) play a crucial role in genetic diversity. However, the alignments of reads near/across SVs are made inaccurate by the presence of polymorphisms. BatAlign is an algorithm that integrated two strategies called 'Reverse-Alignment' and 'Deep-Scan' to improve the accuracy of read-alignment. In our experiments, BatAlign was able to obtain the highest F-measures in read-alignments on mismatch-aberrant, indel-aberrant, concordantly/discordantly paired and SV-spanning data sets. On real data, the alignments of BatAlign were able to recover 4.3% more PCR-validated SVs with 73.3% less callings. These suggest BatAlign to be effective in detecting SVs and other polymorphic-variants accurately using high-throughput data. BatAlign is publicly available at https://goo.gl/a6phxB.
| S-EPMC4652746 | biostudies-literature
Project description:MotivationAligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit [Garrison et al. (Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018;36:875-9)] is a popular aligner of short reads, GraphAligner [Rautiainen and Marschall (GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol 2020;21:253-28)] is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds.ResultsWe present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. We run experiments aligning real and simulated PacBio CLR reads with average error rates 15% and 5%. Compared to GraphAligner, GraphChainer aligns 12-17% more reads, and 21-28% more total read length, on real PacBio CLR reads from human chromosomes 1, 22, and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph [Li et al. (The design and construction of reference pangenome graphs with minigraph. Genome Biol 2020;21:265-19.)] and minichain [Chandra and Jain (Sequence to graph alignment using gap-sensitive co-linear chaining. In: Proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2023). Springer, 2023, 58-73.)] obtain an accuracy of <60% on this setting.Availability and implementationGraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address.
| S-EPMC10423031 | biostudies-literature
Project description:BACKGROUND: Over the past few years, new massively parallel DNA sequencing technologies have emerged. These platforms generate massive amounts of data per run, greatly reducing the cost of DNA sequencing. However, these techniques also raise important computational difficulties mostly due to the huge volume of data produced, but also because of some of their specific characteristics such as read length and sequencing errors. Among the most critical problems is that of efficiently and accurately mapping reads to a reference genome in the context of re-sequencing projects. RESULTS: We present an efficient method for the local alignment of pyrosequencing reads produced by the GS FLX (454) system against a reference sequence. Our approach explores the characteristics of the data in these re-sequencing applications and uses state of the art indexing techniques combined with a flexible seed-based approach, leading to a fast and accurate algorithm which needs very little user parameterization. An evaluation performed using real and simulated data shows that our proposed method outperforms a number of mainstream tools on the quantity and quality of successful alignments, as well as on the execution time. CONCLUSIONS: The proposed methodology was implemented in a software tool called TAPyR--Tool for the Alignment of Pyrosequencing Reads--which is publicly available from http://www.tapyr.net.
| S-EPMC3118166 | biostudies-literature
Project description:BackgroundOut of the many pathogenic bacterial species that are known, only a fraction are readily identifiable directly from a complex microbial community using standard next generation DNA sequencing. Long-read sequencing offers the potential to identify a wider range of species and to differentiate between strains within a species, but attaining sufficient accuracy in complex metagenomes remains a challenge.MethodsHere, we describe and analytically validate LoopSeq, a commercially available synthetic long-read (SLR) sequencing technology that generates highly accurate long reads from standard short reads.ResultsLoopSeq reads are sufficiently long and accurate to identify microbial genes and species directly from complex samples. LoopSeq perfectly recovered the full diversity of 16S rRNA genes from known strains in a synthetic microbial community. Full-length LoopSeq reads had a per-base error rate of 0.005%, which exceeds the accuracy reported for other long-read sequencing technologies. 18S-ITS and genomic sequencing of fungal and bacterial isolates confirmed that LoopSeq sequencing maintains that accuracy for reads up to 6 kb in length. LoopSeq full-length 16S rRNA reads could accurately classify organisms down to the species level in rinsate from retail meat samples, and could differentiate strains within species identified by the CDC as potential foodborne pathogens.ConclusionsThe order-of-magnitude improvement in length and accuracy over standard Illumina amplicon sequencing achieved with LoopSeq enables accurate species-level and strain identification from complex- to low-biomass microbiome samples. The ability to generate accurate and long microbiome sequencing reads using standard short read sequencers will accelerate the building of quality microbial sequence databases and removes a significant hurdle on the path to precision microbial genomics. Video abstract.
| S-EPMC8179091 | biostudies-literature
Project description:BackgroundNanopore sequencing provides a rapid, cheap and portable real-time sequencing platform with the potential to revolutionize genomics. However, several applications are limited by relatively high single-read error rates (>10 %), including RNA-seq, haplotype sequencing and 16S sequencing.ResultsWe developed the Intramolecular-ligated Nanopore Consensus Sequencing (INC-Seq) as a strategy for obtaining long and accurate nanopore reads, starting with low input DNA. Applying INC-Seq for 16S rRNA-based bacterial profiling generated full-length amplicon sequences with a median accuracy >97 %.ConclusionsINC-Seq reads enabled accurate species-level classification, identification of species at 0.1 % abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system.
| S-EPMC4970289 | biostudies-literature
Project description:Next Generation Sequencing (NGS) machines extract from a biological sample a large number of short DNA fragments (reads). These reads are then used for several applications, e.g., sequence reconstruction, DNA assembly, gene expression profiling, mutation analysis.We propose a method to evaluate the similarity between reads. This method does not rely on the alignment of the reads and it is based on the distance between the frequencies of their substrings of fixed dimensions (k-mers). We compare this alignment-free distance with the similarity measures derived from two alignment methods: Needleman-Wunsch and Blast. The comparison is based on a simple assumption: the most correct distance is obtained by knowing in advance the reference sequence. Therefore, we first align the reads on the original DNA sequence, compute the overlap between the aligned reads, and use this overlap as an ideal distance. We then verify how the alignment-free and the alignment-based distances reproduce this ideal distance. The ability of correctly reproducing the ideal distance is evaluated over samples of read pairs from Saccharomyces cerevisiae, Escherichia coli, and Homo sapiens. The comparison is based on the correctness of threshold predictors cross-validated over different samples.We exhibit experimental evidence that the proposed alignment-free distance is a potentially useful read-to-read distance measure and performs better than the more time consuming distances based on alignment.Alignment-free distances may be used effectively for reads comparison, and may provide a significant speed-up in several processes based on NGS sequencing (e.g., DNA assembly, reads classification).
| S-EPMC4265526 | biostudies-literature
Project description:Next-generation sequencing (NGS) technologies have generated enormous amounts 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, D(2), D(*)(2) and D(s)(2), 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 D(*)(2) and D(s)(2), outperform D(2) 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 D(*)(2) and D(s)(2). Finally, variations of these statistics, d(2), d(*)(2) and d(s)(2), respectively, are used to first cluster five 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 d(s)(2) are consistent with biological knowledge for the 5 mammalian and 13 tree species, respectively. Thus, the statistic d(s)(2) provides a powerful alignment-free comparison tool to study the relationships among different organisms based on NGS read data without assembly.
| S-EPMC3581251 | biostudies-literature
Project description:The use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens.Here we present Clinical PathoScope, a pipeline to rapidly and accurately remove host contamination, isolate microbial reads, and identify potential disease-causing pathogens. We have accomplished three essential tasks in the development of Clinical PathoScope. First, we developed an optimized framework for pathogen identification using a computational subtraction methodology in concordance with read trimming and ambiguous read reassignment. Second, we have demonstrated the ability of our approach to identify multiple pathogens in a single clinical sample, accurately identify pathogens at the subspecies level, and determine the nearest phylogenetic neighbor of novel or highly mutated pathogens using real clinical sequencing data. Finally, we have shown that Clinical PathoScope outperforms previously published pathogen identification methods with regard to computational speed, sensitivity, and specificity.Clinical PathoScope is the only pathogen identification method currently available that can identify multiple pathogens from mixed samples and distinguish between very closely related species and strains in samples with very few reads per pathogen. Furthermore, Clinical PathoScope does not rely on genome assembly and thus can more rapidly complete the analysis of a clinical sample when compared with current assembly-based methods. Clinical PathoScope is freely available at: http://sourceforge.net/projects/pathoscope/.
| S-EPMC4131054 | biostudies-literature
Project description:BACKGROUND:As the cost of sequencing has declined, clinical diagnostics based on next generation sequencing (NGS) have become reality. Diagnostics based on sequencing will require rapid and precise mapping against redundant databases because some of the most important determinants, such as antimicrobial resistance and core genome multilocus sequence typing (MLST) alleles, are highly similar to one another. In order to facilitate this, a novel mapping method, KMA (k-mer alignment), was designed. KMA is able to map raw reads directly against redundant databases, it also scales well for large redundant databases. KMA uses k-mer seeding to speed up mapping and the Needleman-Wunsch algorithm to accurately align extensions from k-mer seeds. Multi-mapping reads are resolved using a novel sorting scheme (ConClave scheme), ensuring an accurate selection of templates. RESULTS:The functionality of KMA was compared with SRST2, MGmapper, BWA-MEM, Bowtie2, Minimap2 and Salmon, using both simulated data and a dataset of Escherichia coli mapped against resistance genes and core genome MLST alleles. KMA outperforms current methods with respect to both accuracy and speed, while using a comparable amount of memory. CONCLUSION:With KMA, it was possible map raw reads directly against redundant databases with high accuracy, speed and memory efficiency.
| S-EPMC6116485 | biostudies-literature
Project description:BackgroundSingle Molecule Sequencing (SMS) technology can produce longer reads with higher sequencing error rate. Mapping these reads to a reference genome is often the most fundamental and computing-intensive step for downstream analysis. Most existing mapping tools generally adopt the traditional seed-and-extend strategy, and the candidate aligned regions for each query read are selected either by counting the number of matched seeds or chaining a group of seeds. However, for all the existing mapping tools, the coverage ratio of the alignment region to the query read is lower, and the read alignment quality and efficiency need to be improved. Here, we introduce smsMap, a novel mapping tool that is specifically designed to map the long reads of SMS to a reference genome.ResultssmsMap was evaluated with other existing seven SMS mapping tools (e.g., BLASR, minimap2, and BWA-MEM) on both simulated and real-life SMS datasets. The experimental results show that smsMap can efficiently achieve higher aligned read coverage ratio and has higher sensitivity that can align more sequences and bases to the reference genome. Additionally, smsMap is more robust to sequencing errors.ConclusionssmsMap is computationally efficient to align SMS reads, especially for the larger size of the reference genome (e.g., H. sapiens genome with over 3 billion base pairs). The source code of smsMap can be freely downloaded from https://github.com/NWPU-903PR/smsMap .
| S-EPMC7430848 | biostudies-literature