NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks.
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ABSTRACT:
SUBMITTER: Ahsan MU
PROVIDER: S-EPMC8419925 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe rapid advancements in the field of genome sequencing are aiding our understanding on many biological systems. In the last five years, computational biologists and bioinformatics specialists have come up with newer, better and more efficient tools towards the discovery, analysis and interpretation of different genomic variants from high-throughput sequencing data. Availability of reliable simulated dataset is essential and is the first step towards testing any newly developed analytical tools for variant discovery. Although there are tools currently available that can simulate variants, none present the possibility of simulating all the three major types of variations (Single Nucleotide Polymorphisms, Insertions and Deletions and Copy Number Variations) and can generate reads taking a realistic error-model into consideration. Therefore, an efficient simulator and read generator is needed that can simulate variants taking the error rates of true biological samples into consideration.ResultsWe report SInC (Snp, Indel and Cnv) an open-source variant simulator and read generator capable of simulating all the three common types of biological variants taking into account a distribution of base quality score from a most commonly used next-generation sequencing instrument from Illumina. SInC is capable of generating single- and paired-end reads with user-defined insert size and with high efficiency compared to the other existing tools. SInC, due to its multi-threaded capability during read generation, has a low time footprint. SInC is currently optimised to work in limited infrastructure setup and can efficiently exploit the commonly used quad-core desktop architecture to simulate short sequence reads with deep coverage for large genomes.ConclusionsWe have come up with a user-friendly multi-variant simulator and read-generator tools called SInC. SInC can be downloaded from http://sourceforge.net/projects/sincsimulator.
| S-EPMC3926339 | biostudies-literature
Project description:SummaryThe PacBio High-Fidelity (HiFi) sequencing technology produces long reads of >99% in accuracy. It has enabled the development of a new generation of de novo sequence assemblers, which all have sequencing error correction (EC) as the first step. As HiFi is a new data type, this critical step has not been evaluated before. Here, we introduced hifieval, a new command-line tool for measuring over- and under-corrections produced by EC algorithms. We assessed the accuracy of the EC components of existing HiFi assemblers on the CHM13 and the HG002 datasets and further investigated the performance of EC methods in challenging regions such as homopolymer regions, centromeric regions, and segmental duplications. Hifieval will help HiFi assemblers to improve EC and assembly quality in the long run.Availability and implementationThe source code is available at https://github.com/magspho/hifieval.
| S-EPMC10612404 | biostudies-literature
Project description:The PacBio High-Fidelity (HiFi) sequencing technology produces long reads of >99% in accuracy. It has enabled the development of a new generation of de novo sequence assemblers, which all have sequencing error correction as the first step. As HiFi is a new data type, this critical step has not been evaluated before. Here, we introduced hifieval, a new command-line tool for measuring over- and under-corrections produced by error correction algorithms. We assessed the accuracy of the error correction components of existing HiFi assemblers on the CHM13 and the HG002 datasets and further investigated the performance of error correction methods in challenging regions such as homopolymer regions, centromeric regions, and segmental duplications. Hifieval will help HiFi assemblers to improve error correction and assembly quality in the long run.
| S-EPMC10274712 | biostudies-literature
Project description:Single-molecule sequencing technologies have recently been commercialized by Pacific Biosciences and Oxford Nanopore with the promise of sequencing long DNA fragments (kilobases to megabases order) and then, using efficient algorithms, provide high quality assemblies in terms of contiguity and completeness of repetitive regions. However, the error rate of long-read technologies is higher than that of short-read technologies. This has a direct consequence on the base quality of genome assemblies, particularly in coding regions where sequencing errors can disrupt the coding frame of genes. In the case of diploid genomes, the consensus of a given gene can be a mixture between the two haplotypes and can lead to premature stop codons. Several methods have been developed to polish genome assemblies using short reads and generally, they inspect the nucleotide one by one, and provide a correction for each nucleotide of the input assembly. As a result, these algorithms are not able to properly process diploid genomes and they typically switch from one haplotype to another. Herein we proposed Hapo-G (Haplotype-Aware Polishing Of Genomes), a new algorithm capable of incorporating phasing information from high-quality reads (short or long-reads) to polish genome assemblies and in particular assemblies of diploid and heterozygous genomes.
| S-EPMC8092372 | biostudies-literature
Project description:We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.
| S-EPMC2689609 | biostudies-literature
Project description:MotivationThe variation graph toolkit (VG) represents genetic variation as a graph. Although each path in the graph is a potential haplotype, most paths are non-biological, unlikely recombinations of true haplotypes.ResultsWe augment the VG model with haplotype information to identify which paths are more likely to exist in nature. For this purpose, we develop a scalable implementation of the graph extension of the positional Burrows-Wheeler transform. We demonstrate the scalability of the new implementation by building a whole-genome index of the 5008 haplotypes of the 1000 Genomes Project, and an index of all 108 070 Trans-Omics for Precision Medicine Freeze 5 chromosome 17 haplotypes. We also develop an algorithm for simplifying variation graphs for k-mer indexing without losing any k-mers in the haplotypes.Availability and implementationOur software is available at https://github.com/vgteam/vg, https://github.com/jltsiren/gbwt and https://github.com/jltsiren/gcsa2.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC7223266 | biostudies-literature
Project description:MotivationThe identification of short insertions and deletions (indels) and single nucleotide polymorphisms (SNPs) from Ion Torrent and 454 reads is a challenging problem, essentially because these techniques are prone to sequence erroneously at homopolymers and can, therefore, raise indels in reads. Most of the existing mapping programs do not model homopolymer errors when aligning reads against the reference. The resulting alignments will then contain various kinds of mismatches and indels that confound the accurate determination of variant loci and alleles.ResultsTo address these challenges, we realign reads against the reference using our previously proposed hidden Markov model that models homopolymer errors and then merges these pairwise alignments into a weighted alignment graph. Based on our weighted alignment graph and hidden Markov model, we develop a method called PyroHMMvar, which can simultaneously detect short indels and SNPs, as demonstrated in human resequencing data. Specifically, by applying our methods to simulated diploid datasets, we demonstrate that PyroHMMvar produces more accurate results than state-of-the-art methods, such as Samtools and GATK, and is less sensitive to mapping parameter settings than the other methods. We also apply PyroHMMvar to analyze one human whole genome resequencing dataset, and the results confirm that PyroHMMvar predicts SNPs and indels accurately.Availability and implementationSource code freely available at the following URL: https://code.google.com/p/pyrohmmvar/, implemented in C++ and supported on Linux. .
| S-EPMC3888126 | biostudies-literature
Project description:Next-generation DNA sequencing (NGS) in short-read mode has recently been used for genetic testing in various clinical settings. NGS data accuracy is crucial in clinical settings, and several reports regarding quality control of NGS data, primarily focusing on establishing NGS sequence read accuracy, have been published thus far. Variant calling is another critical source of NGS errors that remains unexplored at the single-nucleotide level despite its established significance. In this study, we used a machine-learning-based method to establish an exome-wide benchmark of difficult-to-sequence regions at the nucleotide-residue resolution using 10 genome sequence features based on real-world NGS data accumulated in The Genome Aggregation Database (gnomAD) of the human reference genome sequence (GRCh38/hg38). The newly acquired metric, designated the 'UNMET score,' along with additional lines of structural information from the human genome, allowed us to assess the sequencing challenges within the exonic region of interest using conventional short-read NGS. Thus, the UNMET score could provide a basis for addressing potential sequential errors in protein-coding exons of the human reference genome sequence GRCh38/hg38 in clinical sequencing.
| S-EPMC10783491 | biostudies-literature
Project description:MotivationMost modern seed-and-extend NGS read mappers employ a seeding scheme that requires extracting t non-overlapping seeds in each read in order to find all valid mappings under an edit distance threshold of t. As t grows, this seeding scheme forces mappers to use more and shorter seeds, which increases the seed hits (seed frequencies) and therefore reduces the efficiency of mappers.ResultsWe propose a novel seeding framework, context-aware seeds (CAS). CAS guarantees finding all valid mappings but uses fewer (and longer) seeds, which reduces seed frequencies and increases efficiency of mappers. CAS achieves this improvement by attaching a confidence radius to each seed in the reference. We prove that all valid mappings can be found if the sum of confidence radii of seeds are greater than t. CAS generalizes the existing pigeonhole-principle-based seeding scheme in which this confidence radius is implicitly always 1. Moreover, we design an efficient algorithm that constructs the confidence radius database in linear time. We experiment CAS with E. coli genome and show that CAS significantly reduces seed frequencies when compared with the state-of-the-art pigeonhole-principle-based seeding algorithm, the Optimal Seed Solver.Availabilityhttps://github.com/Kingsford-Group/CAS_code.
| S-EPMC7245042 | biostudies-literature
Project description:MotivationPrediction of functional variant consequences is an important part of sequencing pipelines, allowing the categorization and prioritization of genetic variants for follow up analysis. However, current predictors analyze variants as isolated events, which can lead to incorrect predictions when adjacent variants alter the same codon, or when a frame-shifting indel is followed by a frame-restoring indel. Exploiting known haplotype information when making consequence predictions can resolve these issues.ResultsBCFtools/csq is a fast program for haplotype-aware consequence calling which can take into account known phase. Consequence predictions are changed for 501 of 5019 compound variants found in the 81.7M variants in the 1000 Genomes Project data, with an average of 139 compound variants per haplotype. Predictions match existing tools when run in localized mode, but the program is an order of magnitude faster and requires an order of magnitude less memory.Availability and implementationThe program is freely available for commercial and non-commercial use in the BCFtools package which is available for download from http://samtools.github.io/bcftools .Contactpd3@sanger.ac.uk.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5870570 | biostudies-literature