Clair3-trio: high-performance Nanopore long-read variant calling in family trios with trio-to-trio deep neural networks.

Accurate identification of genetic variants from family child-mother-father trio sequencing data is important in genomics. However, state-of-the-art approaches treat variant calling from trios as three independent tasks, which limits their calling accuracy for Nanopore long-read sequencing data. For better trio variant calling, we introduce Clair3-Trio, the first variant caller tailored for family trio data from Nanopore long-reads. Clair3-Trio employs a Trio-to-Trio deep neural network model, which allows it to input the trio sequencing information and output all of the trio's predicted variants within a single model to improve variant calling. We also present MCVLoss, a novel loss function tailor-made for variant calling in trios, leveraging the explicit encoding of the Mendelian inheritance. Clair3-Trio showed comprehensive improvement in experiments. It predicted far fewer Mendelian inheritance violation variations than current state-of-the-art methods. We also demonstrated that our Trio-to-Trio model is more accurate than competing architectures. Clair3-Trio is accessible as a free, open-source project at https://github.com/HKU-BAL/Clair3-Trio.

Boosting variant-calling performance with multi-platform sequencing data using Clair3-MP.

BACKGROUND: With the continuous advances in third-generation sequencing technology and the increasing affordability of next-generation sequencing technology, sequencing data from different sequencing technology platforms is becoming more common. While numerous benchmarking studies have been conducted to compare variant-calling performance across different platforms and approaches, little attention has been paid to the potential of leveraging the strengths of different platforms to optimize overall performance, especially integrating Oxford Nanopore and Illumina sequencing data. RESULTS: We investigated the impact of multi-platform data on the performance of variant calling through carefully designed experiments with a deep learning-based variant caller named Clair3-MP (Multi-Platform). Through our research, we not only demonstrated the capability of ONT-Illumina data for improved variant calling, but also identified the optimal scenarios for utilizing ONT-Illumina data. In addition, we revealed that the improvement in variant calling using ONT-Illumina data comes from an improvement in difficult genomic regions, such as the large low-complexity regions and segmental and collapse duplication regions. Moreover, Clair3-MP can incorporate reference genome stratification information to achieve a small but measurable improvement in variant calling. Clair3-MP is accessible as an open-source project at: https://github.com/HKU-BAL/Clair3-MP . CONCLUSIONS: These insights have important implications for researchers and practitioners alike, providing valuable guidance for improving the reliability and efficiency of genomic analysis in diverse applications.

Duet: SNP-assisted structural variant calling and phasing using Oxford nanopore sequencing.

BACKGROUND: Whole genome sequencing using the long-read Oxford Nanopore Technologies (ONT) MinION sequencer provides a cost-effective option for structural variant (SV) detection in clinical applications. Despite the advantage of using long reads, however, accurate SV calling and phasing are still challenging. RESULTS: We introduce Duet, an SV detection tool optimized for SV calling and phasing using ONT data. The tool uses novel features integrated from both SV signatures and single-nucleotide polymorphism signatures, which can accurately distinguish SV haplotype from a false signal. Duet was benchmarked against state-of-the-art tools on multiple ONT sequencing datasets of sequencing coverage ranging from 8× to 40×. At low sequencing coverage of 8×, Duet performs better than all other tools in SV calling, SV genotyping and SV phasing. When the sequencing coverage is higher (20× to 40×), the F1-score for SV phasing is further improved in comparison to the performance of other tools, while its performance of SV genotyping and SV calling remains higher than other tools. CONCLUSION: Duet can perform accurate SV calling, SV genotyping and SV phasing using low-coverage ONT data, making it very useful for low-coverage genomes. It has great performance when scaled to high-coverage genomes, which is adaptable to various clinical applications. Duet is open source and is available at https://github.com/yekaizhou/duet .

MegaPath: sensitive and rapid pathogen detection using metagenomic NGS data.

BACKGROUND: Next-generation sequencing (NGS) enables unbiased detection of pathogens by mapping the sequencing reads of a patient sample to the known reference sequence of bacteria and viruses. However, for a new pathogen without a reference sequence of a close relative, or with a high load of mutations compared to its predecessors, read mapping fails due to a low similarity between the pathogen and reference sequence, which in turn leads to insensitive and inaccurate pathogen detection outcomes. RESULTS: We developed MegaPath, which runs fast and provides high sensitivity in detecting new pathogens. In MegaPath, we have implemented and tested a combination of polishing techniques to remove non-informative human reads and spurious alignments. MegaPath applies a global optimization to the read alignments and reassigns the reads incorrectly aligned to multiple species to a unique species. The reassignment not only significantly increased the number of reads aligned to distant pathogens, but also significantly reduced incorrect alignments. MegaPath implements an enhanced maximum-exact-match prefix seeding strategy and a SIMD-accelerated Smith-Waterman algorithm to run fast. CONCLUSIONS: In our benchmarks, MegaPath demonstrated superior sensitivity by detecting eight times more reads from a low-similarity pathogen than other tools. Meanwhile, MegaPath ran much faster than the other state-of-the-art alignment-based pathogen detection tools (and compariable with the less sensitivity profile-based pathogen detection tools). The running time of MegaPath is about 20 min on a typical 1 Gb dataset.

AC-DIAMOND v1: accelerating large-scale DNA-protein alignment.

Summary: AC-DIAMOND (v1) is a DNA-protein alignment tool designed to tackle the efficiency challenge of aligning large amount of reads or contigs to protein databases. When compared with the previously most efficient method DIAMOND, AC-DIAMOND gains a 6- to 7-fold speed-up, while retaining a similar degree of sensitivity. The improvement is rooted at two aspects: first, using a compressed index of seeds with adaptive-length to speed-up the matching between query and reference sequences; second, adopting a compact form of dynamic programing to fully utilize the parallelism of the SIMD capability. Availability and implementation: Software source codes and binaries available at https://github.com/Maihj/AC-DIAMOND/. Supplementary information: Supplementary data are available at Bioinformatics online.

MegaGTA: a sensitive and accurate metagenomic gene-targeted assembler using iterative de Bruijn graphs.

BACKGROUND: The recent release of the gene-targeted metagenomics assembler Xander has demonstrated that using the trained Hidden Markov Model (HMM) to guide the traversal of de Bruijn graph gives obvious advantage over other assembly methods. Xander, as a pilot study, indeed has a lot of room for improvement. Apart from its slow speed, Xander uses only 1 k-mer size for graph construction and whatever choice of k will compromise either sensitivity or accuracy. Xander uses a Bloom-filter representation of de Bruijn graph to achieve a lower memory footprint. Bloom filters bring in false positives, and it is not clear how this would impact the quality of assembly. Xander does not keep track of the multiplicity of k-mers, which would have been an effective way to differentiate between erroneous k-mers and correct k-mers. RESULTS: In this paper, we present a new gene-targeted assembler MegaGTA, which attempts to improve Xander in different aspects. Quality-wise, it utilizes iterative de Bruijn graphs to take full advantage of multiple k-mer sizes to make the best of both sensitivity and accuracy. Computation-wise, it employs succinct de Bruijn graphs (SdBG) to achieve low memory footprint and high speed (the latter is benefited from a highly efficient parallel algorithm for constructing SdBG). Unlike Bloom filters, an SdBG is an exact representation of a de Bruijn graph. It enables MegaGTA to avoid false-positive contigs and to easily incorporate the multiplicity of k-mers for building better HMM model. We have compared MegaGTA and Xander on an HMP-defined mock metagenomic dataset, and showed that MegaGTA excelled in both sensitivity and accuracy. On a large rhizosphere soil metagenomic sample (327Gbp), MegaGTA produced 9.7-19.3% more contigs than Xander, and these contigs were assigned to 10-25% more gene references. In our experiments, MegaGTA, depending on the number of k-mers used, is two to ten times faster than Xander. CONCLUSION: MegaGTA improves on the algorithm of Xander and achieves higher sensitivity, accuracy and speed. Moreover, it is capable of assembling gene sequences from ultra-large metagenomic datasets. Its source code is freely available at https://github.com/HKU-BAL/megagta .

A simple and economical method for improving whole genome alignment.

BACKGROUND: The recent advancement of whole genome alignment software has made it possible to align two genomes very efficiently and with only a small sacrifice in sensitivity. Yet it becomes very slow if the extra sensitivity is needed. This paper proposes a simple but effective method to improve the sensitivity of existing whole-genome alignment software without paying much extra running time. RESULTS AND CONCLUSIONS: We have applied our method to a popular whole genome alignment tool LAST, and we called the resulting tool LASTM. Experimental results showed that LASTM could find more high quality alignments with a little extra running time. For example, when comparing human and mouse genomes, to produce the similar number of alignments with similar average length and similarity, LASTM was about three times faster than LAST. We conclude that our method can be used to improve the sensitivity, and the extra time it takes is small, and thus it is worthwhile to be implemented in existing tools.

MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices.

The study of metagenomics has been much benefited from low-cost and high-throughput sequencing technologies, yet the tremendous amount of data generated make analysis like de novo assembly to consume too much computational resources. In late 2014 we released MEGAHIT v0.1 (together with a brief note of Li et al. (2015) [1]), which is the first NGS metagenome assembler that can assemble genome sequences from metagenomic datasets of hundreds of Giga base-pairs (bp) in a time- and memory-efficient manner on a single server. The core of MEGAHIT is an efficient parallel algorithm for constructing succinct de Bruijn Graphs (SdBG), implemented on a graphical processing unit (GPU). The software has been well received by the assembly community, and there is interest in how to adapt the algorithms to integrate popular assembly practices so as to improve the assembly quality, as well as how to speed up the software using better CPU-based algorithms (instead of GPU). In this paper we first describe the details of the core algorithms in MEGAHIT v0.1, and then we show the new modules to upgrade MEGAHIT to version v1.0, which gives better assembly quality, runs faster and uses less memory. For the Iowa Prairie Soil dataset (252Gbp after quality trimming), the assembly quality of MEGAHIT v1.0, when compared with v0.1, has a significant improvement, namely, 36% increase in assembly size and 23% in N50. More interestingly, MEGAHIT v1.0 is no slower than before (even running with the extra modules). This is primarily due to a new CPU-based algorithm for SdBG construction that is faster and requires less memory. Using CPU only, MEGAHIT v1.0 can assemble the Iowa Prairie Soil sample in about 43h, reducing the running time of v0.1 by at least 25% and memory usage by up to 50%. MEGAHIT v1.0, exhibiting a smaller memory footprint, can process even larger datasets. The Kansas Prairie Soil sample (484Gbp), the largest publicly available dataset, can now be assembled using no more than 500GB of memory in 7.5days. The assemblies of these datasets (and other large metgenomic datasets), as well as the software, are available at the website https://hku-bal.github.io/megabox.

PnpProbs: a better multiple sequence alignment tool by better handling of guide trees.

BACKGROUND: This paper describes a new MSA tool called PnpProbs, which constructs better multiple sequence alignments by better handling of guide trees. It classifies sequences into two types: normally related and distantly related. For normally related sequences, it uses an adaptive approach to construct the guide tree needed for progressive alignment; it first estimates the input's discrepancy by computing the standard deviation of their percent identities, and based on this estimate, it chooses the better method to construct the guide tree. For distantly related sequences, PnpProbs abandons the guide tree and uses instead some non-progressive alignment method to generate the alignment. RESULTS: To evaluate PnpProbs, we have compared it with thirteen other popular MSA tools, and PnpProbs has the best alignment scores in all but one test. We have also used it for phylogenetic analysis, and found that the phylogenetic trees constructed from PnpProbs' alignments are closest to the model trees. CONCLUSIONS: By combining the strength of the progressive and non-progressive alignment methods, we have developed an MSA tool called PnpProbs. We have compared PnpProbs with thirteen other popular MSA tools and our results showed that our tool usually constructed the best alignments.

BASE: a practical de novo assembler for large genomes using long NGS reads.

BACKGROUND: De novo genome assembly using NGS data remains a computation-intensive task especially for large genomes. In practice, efficiency is often a primary concern and favors using a more efficient assembler like SOAPdenovo2. Yet SOAPdenovo2, based on de Bruijn graph, fails to take full advantage of longer NGS reads (say, 150 bp to 250 bp from Illumina HiSeq and MiSeq). Assemblers that are based on string graphs (e.g., SGA), though less popular and also very slow, are more favorable for longer reads. METHODS: This paper shows a new de novo assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs. RESULTS: Experiments on two bacteria and four human datasets shows the advantage of BASE in both contig quality and speed in dealing with longer reads. In the experiment on bacteria, two datasets with read length of 100 bp and 250 bp were used.. Especially for the 250 bp dataset, BASE gives much better quality than SOAPdenovo2 and SGA and is simlilar to SPAdes. Regarding speed, BASE is consistently a few times faster than SPAdes and SGA, but still slower than SOAPdenovo2. BASE and Soapdenov2 are further compared using human datasets with read length 100 bp, 150 bp and 250 bp. BASE shows a higher N50 for all datasets, while the improvement becomes more significant when read length reaches 250 bp. Besides, BASE is more-meory efficent than SOAPdenovo2 when sequencing data with error rate. CONCLUSIONS: BASE is a practically efficient tool for constructing contig, with significant improvement in quality for long NGS reads. It is relatively easy to extend BASE to include scaffolding.

MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph.

MEGAHIT is a NGS de novo assembler for assembling large and complex metagenomics data in a time- and cost-efficient manner. It finished assembling a soil metagenomics dataset with 252 Gbps in 44.1 and 99.6 h on a single computing node with and without a graphics processing unit, respectively. MEGAHIT assembles the data as a whole, i.e. no pre-processing like partitioning and normalization was needed. When compared with previous methods on assembling the soil data, MEGAHIT generated a three-time larger assembly, with longer contig N50 and average contig length; furthermore, 55.8% of the reads were aligned to the assembly, giving a fourfold improvement.

database.bio: a web application for interpreting human variations.

UNLABELLED: Rapid advances of next-generation sequencing technology have led to the integration of genetic information with clinical care. Genetic basis of diseases and response to drugs provide new ways of disease diagnosis and safer drug usage. This integration reveals the urgent need for effective and accurate tools to analyze genetic variants. Due to the number and diversity of sources for annotation, automating variant analysis is a challenging task. Here, we present database.bio, a web application that combines variant annotation, prioritization and visualization so as to support insight into the individual genetic characteristics. It enhances annotation speed by preprocessing data on a supercomputer, and reduces database space via a unified database representation with compressed fields. AVAILABILITY AND IMPLEMENTATION: Freely available at https://database.bio.

Improving multiple sequence alignment by using better guide trees.

Progressive sequence alignment is one of the most commonly used method for multiple sequence alignment. Roughly speaking, the method first builds a guide tree, and then aligns the sequences progressively according to the topology of the tree. It is believed that guide trees are very important to progressive alignment; a better guide tree will give an alignment with higher accuracy. Recently, we have proposed an adaptive method for constructing guide trees. This paper studies the quality of the guide trees constructed by such method. Our study showed that our adaptive method can be used to improve the accuracy of many different progressive MSA tools. In fact, we give evidences showing that the guide trees constructed by the adaptive method are among the best.

MICA: A fast short-read aligner that takes full advantage of Many Integrated Core Architecture (MIC).

BACKGROUND: Short-read aligners have recently gained a lot of speed by exploiting the massive parallelism of GPU. An uprising alterative to GPU is Intel MIC; supercomputers like Tianhe-2, currently top of TOP500, is built with 48,000 MIC boards to offer ~55 PFLOPS. The CPU-like architecture of MIC allows CPU-based software to be parallelized easily; however, the performance is often inferior to GPU counterparts as an MIC card contains only ~60 cores (while a GPU card typically has over a thousand cores). RESULTS: To better utilize MIC-enabled computers for NGS data analysis, we developed a new short-read aligner MICA that is optimized in view of MIC's limitation and the extra parallelism inside each MIC core. By utilizing the 512-bit vector units in the MIC and implementing a new seeding strategy, experiments on aligning 150 bp paired-end reads show that MICA using one MIC card is 4.9 times faster than BWA-MEM (using 6 cores of a top-end CPU), and slightly faster than SOAP3-dp (using a GPU). Furthermore, MICA's simplicity allows very efficient scale-up when multiple MIC cards are used in a node (3 cards give a 14.1-fold speedup over BWA-MEM). SUMMARY: MICA can be readily used by MIC-enabled supercomputers for production purpose. We have tested MICA on Tianhe-2 with 90 WGS samples (17.47 Tera-bases), which can be aligned in an hour using 400 nodes. MICA has impressive performance even though MIC is only in its initial stage of development. AVAILABILITY AND IMPLEMENTATION: MICA's source code is freely available at http://sourceforge.net/projects/mica-aligner under GPL v3. SUPPLEMENTARY INFORMATION: Supplementary information is available as "Additional File 1". Datasets are available at www.bio8.cs.hku.hk/dataset/mica.

FaSD-somatic: a fast and accurate somatic SNV detection algorithm for cancer genome sequencing data.

UNLABELLED: Recent advances in high-throughput sequencing technologies have enabled us to sequence large number of cancer samples to reveal novel insights into oncogenetic mechanisms. However, the presence of intratumoral heterogeneity, normal cell contamination and insufficient sequencing depth, together pose a challenge for detecting somatic mutations. Here we propose a fast and an accurate somatic single-nucleotide variations (SNVs) detection program, FaSD-somatic. The performance of FaSD-somatic is extensively assessed on various types of cancer against several state-of-the-art somatic SNV detection programs. Benchmarked by somatic SNVs from either existing databases or de novo higher-depth sequencing data, FaSD-somatic has the best overall performance. Furthermore, FaSD-somatic is efficient, it finishes somatic SNV calling within 14 h on 50X whole genome sequencing data in paired samples. AVAILABILITY AND IMPLEMENTATION: The program, datasets and supplementary files are available at http://jjwanglab.org/FaSD-somatic/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads.

MOTIVATION: Transcriptome sequencing has long been the favored method for quickly and inexpensively obtaining a large number of gene sequences from an organism with no reference genome. Owing to the rapid increase in throughputs and decrease in costs of next- generation sequencing, RNA-Seq in particular has become the method of choice. However, the very short reads (e.g. 2 × 90 bp paired ends) from next generation sequencing makes de novo assembly to recover complete or full-length transcript sequences an algorithmic challenge. RESULTS: Here, we present SOAPdenovo-Trans, a de novo transcriptome assembler designed specifically for RNA-Seq. We evaluated its performance on transcriptome datasets from rice and mouse. Using as our benchmarks the known transcripts from these well-annotated genomes (sequenced a decade ago), we assessed how SOAPdenovo-Trans and two other popular transcriptome assemblers handled such practical issues as alternative splicing and variable expression levels. Our conclusion is that SOAPdenovo-Trans provides higher contiguity, lower redundancy and faster execution. AVAILABILITY AND IMPLEMENTATION: Source code and user manual are available at http://sourceforge.net/projects/soapdenovotrans/.

SOAPfusion: a robust and effective computational fusion discovery tool for RNA-seq reads.

MOTIVATION: RNA-Seq provides a powerful approach to carry out ab initio investigation of fusion transcripts representing critical translocation and post-transcriptional events that recode hereditary information. Most of the existing computational fusion detection tools are challenged by the issues of accuracy and how to handle multiple mappings. RESULTS: We present a novel tool SOAPfusion for fusion discovery with paired-end RNA-Seq reads. SOAPfusion is accurate and efficient for fusion discovery with high sensitivity (≥93%), low false-positive rate (≤1.36%), even the coverage is as low as 10×, highlighting its ability to detect fusions efficiently at low sequencing cost. From real data of Universal Human Reference RNA (UHRR) samples, SOAPfusion detected 7 novel fusion genes, more than other existing tools and all genes have been validated through reverse transcription-polymerase chain reaction followed by Sanger sequencing. SOAPfusion thus proves to be an effective method with precise applicability in search of fusion transcripts, which is advantageous to accelerate pathological and therapeutic cancer studies.

ClusterV-Web: a user-friendly tool for profiling HIV quasispecies and generating drug resistance reports from nanopore long-read data.

Summary: Third-generation long-read sequencing is an increasingly utilized technique for profiling human immunodeficiency virus (HIV) quasispecies and detecting drug resistance mutations due to its ability to cover the entire viral genome in individual reads. Recently, the ClusterV tool has demonstrated accurate detection of HIV quasispecies from Nanopore long-read sequencing data. However, the need for scripting skills and a computational environment may act as a barrier for many potential users. To address this issue, we have introduced ClusterV-Web, a user-friendly web-based application that enables easy configuration and execution of ClusterV, both remotely and locally. Our tool provides interactive tables and data visualizations to aid in the interpretation of results. This development is expected to democratize access to long-read sequencing data analysis, enabling a wider range of researchers and clinicians to efficiently profile HIV quasispecies and detect drug resistance mutations. Availability and implementation: ClusterV-Web is freely available and open source, with detailed documentation accessible at http://www.bio8.cs.hku.hk/ClusterVW/. The standalone Docker image and source code are also available at https://github.com/HKU-BAL/ClusterV-Web.

Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model.

Aims: Cardiovascular disease (CVD) is a leading cause of mortality, especially in developing countries. This study aimed to develop and validate a CVD risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using machine learning technique. Methods and results: Three cohorts of Chinese patients with established CVD were included if they had used any of the public healthcare services provided by the Hong Kong Hospital Authority (HA) since 2004 and categorized by their geographical locations. The 10-year CVD outcome was a composite of diagnostic or procedure codes with specific International Classification of Diseases, Ninth Revision, Clinical Modification. Multivariate imputation with chained equations and XGBoost were applied for the model development. The comparison with Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2°P) and Secondary Manifestations of ARTerial disease (SMART2) used the validation cohorts with 1000 bootstrap replicates. A total of 48 799, 119 672 and 140 533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which 8 classes of CVD-related drugs were considered interactive covariates. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C statistic of 0.69. Internal validation showed good discrimination and calibration performance with C statistic over 0.6. The P-CARDIAC also showed better performance than TRS-2°P and SMART2. Conclusion: Compared with other risk scores, the P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that the P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden.

COPE: an accurate k-mer-based pair-end reads connection tool to facilitate genome assembly.

MOTIVATION: The boost of next-generation sequencing technologies provides us with an unprecedented opportunity for elucidating genetic mysteries, yet the short-read length hinders us from better assembling the genome from scratch. New protocols now exist that can generate overlapping pair-end reads. By joining the 3' ends of each read pair, one is able to construct longer reads for assembling. However, effectively joining two overlapped pair-end reads remains a challenging task. RESULT: In this article, we present an efficient tool called Connecting Overlapped Pair-End (COPE) reads, to connect overlapping pair-end reads using k-mer frequencies. We evaluated our tool on 30× simulated pair-end reads from Arabidopsis thaliana with 1% base error. COPE connected over 99% of reads with 98.8% accuracy, which is, respectively, 10 and 2% higher than the recently published tool FLASH. When COPE is applied to real reads for genome assembly, the resulting contigs are found to have fewer errors and give a 14-fold improvement in the N50 measurement when compared with the contigs produced using unconnected reads. AVAILABILITY AND IMPLEMENTATION: COPE is implemented in C++ and is freely available as open-source code at ftp://ftp.genomics.org.cn/pub/cope. CONTACT: twlam@cs.hku.hk or luoruibang@genomics.org.cn