De novo diploid genome assembly using long noisy reads.

The high sequencing error rate has impeded the application of long noisy reads for diploid genome assembly. Most existing assemblers failed to generate high-quality phased assemblies using long noisy reads. Here, we present PECAT, a Phased Error Correction and Assembly Tool, for reconstructing diploid genomes from long noisy reads. We design a haplotype-aware error correction method that can retain heterozygote alleles while correcting sequencing errors. We combine a corrected read SNP caller and a raw read SNP caller to further improve the identification of inconsistent overlaps in the string graph. We use a grouping method to assign reads to different haplotype groups. PECAT efficiently assembles diploid genomes using Nanopore R9, PacBio CLR or Nanopore R10 reads only. PECAT generates more contiguous haplotype-specific contigs compared to other assemblers. Especially, PECAT achieves nearly haplotype-resolved assembly on B. taurus (Bison×Simmental) using Nanopore R9 reads and phase block NG50 with 59.4/58.0 Mb for HG002 using Nanopore R10 reads.

Quantitative structure-activity relationship model development for estimating the predicted No-effect concentration of petroleum hydrocarbon and derivatives in the ecological risk assessment.

Quantitative structure-activity relationship (QSAR) is a cost-effective solution to directly and accurately estimating the environmental safety thresholds (ESTs) of pollutants in the ecological risk assessment due to the lack of toxicity data. In this study, QSAR models were developed for estimating the Predicted No-Effect Concentrations (PNECs) of petroleum hydrocarbons and their derivatives (PHDs) under dietary exposure, based on the quantified molecular descriptors and the obtained PNECs of 51 PHDs with given acute or chronic toxicity concentrations. Three high-reliable QSAR models were respectively developed for PHDs, aromatic hydrocarbons and their derivatives (AHDs), and alkanes, alkenes and their derivatives (ALKDs), with excellent fitting performance evidenced by high correlation coefficient (0.89-0.95) and low root mean square error (0.13-0.2 mg/kg), and high stability and predictive performance reflected by high internal and external verification coefficient (Q2LOO, 0.66-0.89; Q2F1, 0.62-0.78; Q2F2, 0.60-0.73). The investigated quantitative relationships between molecular structure and PNECs indicated that 18 autocorrelation descriptors, 3 information index descriptors, 4 barysz matrix descriptors, 6 burden modified eigenvalues descriptors, and 1 BCUT descriptor were important molecular descriptors affecting the PNECs of PHDs. The obtained results supported that PNECs of PHDs can be accurately estimated by the influencing molecular descriptors and the quantitative relationship from the developed QSAR models, that provided a new feasible solution for ESTs derivation in the ecological risk assessment.

Construction of quercetin-fucoidan nanoparticles and their application in cancer chemo-immunotherapy treatment.

Fucoidan (FU), a natural marine polysaccharide, is an immunomodulator with great potential in tumor immunotherapy. In this work, a FU encapsulated nanoparticle named QU@FU-TS was developed, which contained the anticancer phytochemical quercetin (QU) and had the potential for cancer chemo-immunotherapy. QU@FU-TS were constructed through molecular self-assembly using green material tea saponin (TS) as the linking molecule. The molecular dynamics (MD) simulation showed that QU was bound to the hydrophobic tail of TS. At the same time, FU spontaneously assembled with the hydrophilic head of TS to form the outer layer of the QU@FU-TS. The molecular interactions between QU and TS were mainly π-stacking and hydrogen bonds. The bonding of FU and TS was maintained through the formation of multiple hydrogen bonds between the sulfate ester group and the hydroxy group. The inhibitory effects of QU@FU-TS on A549 cell proliferation were more potent than that by free QU. The antitumor activity of QU@FU-TS was mediated through various mechanisms, including the induction of oxidative stress, blocking cell cycle progression, and promoting cell apoptosis. Moreover, QU@FU-TS has been demonstrated to impede the proliferation and migration of cancer cells in vivo. The expression levels of macrophage surface markers increased under the treatment of QU@FU-TS, suggesting the potential of QU@FU-TS to serve as an immunotherapeutic agent by promoting macrophage activation.

DNA 5-methylcytosine detection and methylation phasing using PacBio circular consensus sequencing.

Long single-molecular sequencing technologies, such as PacBio circular consensus sequencing (CCS) and nanopore sequencing, are advantageous in detecting DNA 5-methylcytosine in CpGs (5mCpGs), especially in repetitive genomic regions. However, existing methods for detecting 5mCpGs using PacBio CCS are less accurate and robust. Here, we present ccsmeth, a deep-learning method to detect DNA 5mCpGs using CCS reads. We sequence polymerase-chain-reaction treated and M.SssI-methyltransferase treated DNA of one human sample using PacBio CCS for training ccsmeth. Using long (≥10 Kb) CCS reads, ccsmeth achieves 0.90 accuracy and 0.97 Area Under the Curve on 5mCpG detection at single-molecule resolution. At the genome-wide site level, ccsmeth achieves >0.90 correlations with bisulfite sequencing and nanopore sequencing using only 10× reads. Furthermore, we develop a Nextflow pipeline, ccsmethphase, to detect haplotype-aware methylation using CCS reads, and then sequence a Chinese family trio to validate it. ccsmeth and ccsmethphase can be robust and accurate tools for detecting DNA 5-methylcytosines.

Iridoids from Patrinia heterophylla and their anti-inflammatory activity.

A phytochemical investigation led to the isolation of five undescribed compounds (1-5) from the methanol extract of the rhizomes and roots of Patrinia heterophylla. The structures and configurations of these compounds were characterized by HRESIMS, ECD, and NMR data analyses. These compounds were assayed for their anti-inflammatory potential using LPS-stimulated BV-2 cells, of which compound 4 showed strong nitric oxide (NO) inhibitory effects with an IC50 of 6.48 µM. The potential anti-inflammatory mechanism was examined utilizing Western blotting and molecular docking. Further in vivo anti-inflammatory experiments revealed that compound 4 inhibited the NO production and reactive oxygen species in the zebrafish model.

A Quantitative Structure-Activity Relationship Approach to Determine Biotoxicity of Amide Herbicides for Ecotoxicological Risk Assessment.

Amide herbicides have been widely applied in agriculture and found to be widespread and affect nontarget organisms in the environment. To better understand the biotoxicity mechanisms and determine the toxicity to the nontarget organisms for the hazard and risk assessment, five QSAR models were developed for the biotoxicity prediction of amide herbicides toward five aquatic and terrestrial organisms (including algae, daphnia, fish, earthworm and avian species), based on toxicity concentration and quantitative molecular descriptors. The results showed that the developed models complied with OECD principles for QSAR validation and presented excellent performances in predictive ability. In combination, the investigated QSAR relationship led to the toxicity mechanisms that eleven electrical descriptors (EHOMO, ELUMO, αxx, αyy, αzz, µ, qN-, Qxx, Qyy, qH+, and q-), four thermodynamic descriptors (Cv, Sθ, Hθ, and ZPVE), and one steric descriptor (Vm) were strongly associated with the biotoxicity of amide herbicides. Electrical descriptors showed the greatest impacts on the toxicity of amide herbicides, followed by thermodynamic and steric descriptors.

The role of MnO2 crystal morphological scale and crystal structure in selective catalytic degradation of azo dye.

MnO2, as a representative manganese-based catalyst with many kinds of crystal forms, has been widely used to activate PMS. However, the role of morphological scale and crystal structures on the catalytic capability of MnO2 still lacks further study. In this study, four different crystal forms of MnO2 (α-MnO2, ß-MnO2, γ-MnO2, and δ-MnO2) are succeeded in being fabricated via hydrothermal processes and evaluated by activating PMS for the removal of Reactive Yellow X-RG, typical azo dye. Experiment results indicate that α-MnO2 with a one-dimensional structure exhibits the best catalytic performance among the four as-prepared MnO2, which can be attributed to its broadest crystal interplanar distance (0.692), the highest portion of Mn (III)/Mn (IV) (4.194), and lowest value of average oxidation state AOS (2.696). Correlation analysis confirms that interplanar distance is the most relative factor with the catalytic activity of MnO2 among the three studied factors (R2 = 0.99715). Meanwhile, the morphological scale structure of α-MnO2 can also account for its highest catalytic ability among the four as-prepared MnO2, including its large specific area and advantageous one-dimensional nanostructure. Furthermore, according to the response surface methodology, when the dosage of PMS is 2.369 g/L, the dosage of α-MnO2 is 0.991 g/L, and the initial dye concentration is 1025 mg/L, the maximum removal rate of Reactive Yellow X-RG is up to 97.38%.

NanoSNP: a progressive and haplotype-aware SNP caller on low-coverage nanopore sequencing data.

MOTIVATION: Oxford Nanopore sequencing has great potential and advantages in population-scale studies. Due to the cost of sequencing, the depth of whole-genome sequencing for per individual sample must be small. However, the existing single nucleotide polymorphism (SNP) callers are aimed at high-coverage Nanopore sequencing reads. Detecting the SNP variants on low-coverage Nanopore sequencing data is still a challenging problem. RESULTS: We developed a novel deep learning-based SNP calling method, NanoSNP, to identify the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. In this method, we design a multi-step, multi-scale and haplotype-aware SNP detection pipeline. First, the pileup model in NanoSNP utilizes the naive pileup feature to predict a subset of SNP sites with a Bi-long short-term memory (LSTM) network. These SNP sites are phased and used to divide the low-coverage Nanopore reads into different haplotypes. Finally, the long-range haplotype feature and short-range pileup feature are extracted from each haplotype. The haplotype model combines two features and predicts the genotype for the candidate site using a Bi-LSTM network. To evaluate the performance of NanoSNP, we compared NanoSNP with Clair, Clair3, Pepper-DeepVariant and NanoCaller on the low-coverage (∼16×) Nanopore sequencing reads. We also performed cross-genome testing on six human genomes HG002-HG007, respectively. Comprehensive experiments demonstrate that NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller in identifying SNPs on low-coverage Nanopore sequencing data, including the difficult-to-map regions and major histocompatibility complex regions in the human genome. NanoSNP is comparable to Clair3 when the coverage exceeds 16×. AVAILABILITY AND IMPLEMENTATION: https://github.com/huangnengCSU/NanoSNP.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Semi-automated assembly of high-quality diploid human reference genomes.

The current human reference genome, GRCh38, represents over 20 years of effort to generate a high-quality assembly, which has benefitted society1,2. However, it still has many gaps and errors, and does not represent a biological genome as it is a blend of multiple individuals3,4. Recently, a high-quality telomere-to-telomere reference, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a nearly homozygous genome5. To address these limitations, the Human Pangenome Reference Consortium formed with the goal of creating high-quality, cost-effective, diploid genome assemblies for a pangenome reference that represents human genetic diversity6. Here, in our first scientific report, we determined which combination of current genome sequencing and assembly approaches yield the most complete and accurate diploid genome assembly with minimal manual curation. Approaches that used highly accurate long reads and parent-child data with graph-based haplotype phasing during assembly outperformed those that did not. Developing a combination of the top-performing methods, we generated our first high-quality diploid reference assembly, containing only approximately four gaps per chromosome on average, with most chromosomes within ±1% of the length of CHM13. Nearly 48% of protein-coding genes have non-synonymous amino acid changes between haplotypes, and centromeric regions showed the highest diversity. Our findings serve as a foundation for assembling near-complete diploid human genomes at scale for a pangenome reference to capture global genetic variation from single nucleotides to structural rearrangements.

Fec: a fast error correction method based on two-rounds overlapping and caching.

The third-generation sequencing technology has advanced genome analysis with long-read length, but the reads need error correction due to the high error rate. Error correction is a time-consuming process especially when the sequencing coverage is high. Generally, for a pair of overlapping reads A and B, the existing error correction methods perform a base-level alignment from B to A when correcting the read A. And another base-level alignment from A to B is performed when correcting the read B. However, based on our observation, the base-level alignment information can be reused. In this article, we present a fast error correction tool Fec, using two-rounds overlapping and caching. Fec can be used independently or as an error correction step in an assembly pipeline. In the first round, Fec uses a large window size (20) to quickly find enough overlaps to correct most of the reads. In the second round, a small window size (5) is used to find more overlaps for the reads with insufficient overlaps in the first round. When performing base-level alignment, Fec searches the cache first. If the alignment exists in the cache, Fec takes this alignment out and deduces the second alignment from it. Otherwise, Fec performs base-level alignment and stores the alignment in the cache. We test Fec on nine datasets, and the results show that Fec has 1.24-38.56 times speed-up compared to MECAT, CANU and MINICNS on five PacBio datasets and 1.16-27.8 times speed-up compared to NECAT and CANU on four nanopore datasets. AVAILABILITY AND IMPLEMENTATION: Fec is available at https://github.com/zhangjuncsu/Fec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide identification and expression analyses of C2H2 zinc finger transcription factors in Pleurotus ostreatus.

The C2H2-type zinc finger proteins (C2H2-ZFPs) regulate various developmental processes and abiotic stress responses in eukaryotes. Yet, a comprehensive analysis of these transcription factors which could be used to find candidate genes related to the control the development and abiotic stress tolerance has not been performed in Pleurotus ostreatus. To fill this knowledge gap, 18 C2H2-ZFs were identified in the P. ostreatus genome. Phylogenetic analysis indicated that these proteins have dissimilar amino acid sequences. In addition, these proteins had variable protein characteristics, gene intron-exon structures, and motif compositions. The expression patterns of PoC2H2-ZFs in mycelia, primordia, and young and mature fruiting bodies were investigated using qRT-PCR. The expression of some PoC2H2-ZFs is regulated by auxin and cytokinin. Moreover, members of PoC2H2-ZFs expression levels are changed dramatically under heat and cold stress, suggesting that these genes may participate in abiotic stress responses. These findings could be used to study the role of P. ostreatus-derived C2H2-ZFs in development and stress tolerance.

SACall: A Neural Network Basecaller for Oxford Nanopore Sequencing Data Based on Self-Attention Mechanism.

Highly portable Oxford Nanopore sequencer producing long reads in real-time at low cost has made many breakthroughs in genomics studies. However, a major limitation of nanopore sequencing is its high errors when deciphering DNA sequences from noisy and complex raw data. In this paper, we developed an end-to-end basecaller, SACall, based on convolution layers, transformer self-attention layers and a CTC decoder. In SACall, the convolution layers are used to downsample the signals and capture the local patterns. To achieve the contextual relevance of signals, self-attention layers are adopted to calculate the similarity of the signals at any two positions in the raw signal sequence. Finally, the CTC decoder generates the DNA sequence by a beam search algorithm. We use a benchmark consisting of nine isolated genomes to test the quality of different basecallers including SACall, Albacore, and Guppy. The performances of basecallers are evaluated from the perspective of read accuracy, assembly quality, and consensus accuracy. Among most of the genomes in the test benchmark, the reads basecalled by SACall have fewer errors than the reads basecalled by other basecallers. When assembling the basecalled reads of each genome, the assembly from SACall basecalled reads achieves a higher assembly identity. In addition, there are fewer errors in the polished assembly from reads basecalled by SACall compared to those basecalled by Albacore and Guppy. In general, SACall outperforms the Nanopore official basecallers Albacore and Guppy in the benchmark. Moreover, SACall is an open-source and freely available basecaller, which gives a chance for researchers to train their own basecalling models on specific data and basecall Nanopore reads.

Comprehensive chemical characterization of dissolved organic matter in typical point-source refinery wastewaters.

In petroleum refineries, the electric desalting, distillation, and stripping processes could generate large amounts of wastewaters that contain toxic substances. In this study, eight wastewater samples were collected from the three typical refining processes for comprehensive chemical characterization of the dissolved organic matter (DOM) using excitation emission matrix fluorescence spectroscopy, gas chromatography-mass spectrometry, and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Results showed that protein-like components and benzene were ubiquitous in all these wastewaters. Oxygen-containing volatile organic compounds had higher contents in crude distillation and stripping wastewater than those in electric desalting wastewater. Among the three refinery processes, molecular composition of DOM in the stripping wastewater had the highest complexity. The Ox and OxSy class species assigned from the negative-ion electrospray ionization FT-ICR MS were dominant in all wastewaters. The OxS2 class species which were effectively removed during stripping treatment had highest relative abundance in stripping influent. These results are instructive to guide the development of "divide and conquer" and would improve the treatment and management of refinery wastewater streams.

BlockPolish: accurate polishing of long-read assembly via block divide-and-conquer.

Long-read sequencing technology enables significant progress in de novo genome assembly. However, the high error rate and the wide error distribution of raw reads result in a large number of errors in the assembly. Polishing is a procedure to fix errors in the draft assembly and improve the reliability of genomic analysis. However, existing methods treat all the regions of the assembly equally while there are fundamental differences between the error distributions of these regions. How to achieve very high accuracy in genome assembly is still a challenging problem. Motivated by the uneven errors in different regions of the assembly, we propose a novel polishing workflow named BlockPolish. In this method, we divide contigs into blocks with low complexity and high complexity according to statistics of aligned nucleotide bases. Multiple sequence alignment is applied to realign raw reads in complex blocks and optimize the alignment result. Due to the different distributions of error rates in trivial and complex blocks, two multitask bidirectional Long short-term memory (LSTM) networks are proposed to predict the consensus sequences. In the whole-genome assemblies of NA12878 assembled by Wtdbg2 and Flye using Nanopore data, BlockPolish has a higher polishing accuracy than other state-of-the-arts including Racon, Medaka and MarginPolish & HELEN. In all assemblies, errors are predominantly indels and BlockPolish has a good performance in correcting them. In addition to the Nanopore assemblies, we further demonstrate that BlockPolish can also reduce the errors in the PacBio assemblies. The source code of BlockPolish is freely available on Github (https://github.com/huangnengCSU/BlockPolish).

Genome-wide detection of cytosine methylations in plant from Nanopore data using deep learning.

In plants, cytosine DNA methylations (5mCs) can happen in three sequence contexts as CpG, CHG, and CHH (where H = A, C, or T), which play different roles in the regulation of biological processes. Although long Nanopore reads are advantageous in the detection of 5mCs comparing to short-read bisulfite sequencing, existing methods can only detect 5mCs in the CpG context, which limits their application in plants. Here, we develop DeepSignal-plant, a deep learning tool to detect genome-wide 5mCs of all three contexts in plants from Nanopore reads. We sequence Arabidopsis thaliana and Oryza sativa using both Nanopore and bisulfite sequencing. We develop a denoising process for training models, which enables DeepSignal-plant to achieve high correlations with bisulfite sequencing for 5mC detection in all three contexts. Furthermore, DeepSignal-plant can profile more 5mC sites, which will help to provide a more complete understanding of epigenetic mechanisms of different biological processes.

NeuralPolish: a novel Nanopore polishing method based on alignment matrix construction and orthogonal Bi-GRU Networks.

MOTIVATION: Oxford Nanopore sequencing producing long reads at low cost has made many breakthroughs in genomics studies. However, the large number of errors in Nanopore genome assembly affect the accuracy of genome analysis. Polishing is a procedure to correct the errors in genome assembly and can improve the reliability of the downstream analysis. However, the performances of the existing polishing methods are still not satisfactory. RESULTS: We developed a novel polishing method, NeuralPolish, to correct the errors in assemblies based on alignment matrix construction and orthogonal Bi-GRU networks. In this method, we designed an alignment feature matrix for representing read-to-assembly alignment. Each row of the matrix represents a read, and each column represents the aligned bases at each position of the contig. In the network architecture, a bi-directional GRU network is used to extract the sequence information inside each read by processing the alignment matrix row by row. After that, the feature matrix is processed by another bi-directional GRU network column by column to calculate the probability distribution. Finally, a CTC decoder generates a polished sequence with a greedy algorithm. We used five real datasets and three assembly tools including Wtdbg2, Flye and Canu for testing, and compared the results of different polishing methods including NeuralPolish, Racon, MarginPolish, HELEN and Medaka. Comprehensive experiments demonstrate that NeuralPolish achieves more accurate assembly with fewer errors than other polishing methods and can improve the accuracy of assembly obtained by different assemblers. AVAILABILITY AND IMPLEMENTATION: https://github.com/huangnengCSU/NeuralPolish.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Endogenous asymmetric dimethylarginine accumulation precipitates the cardiac and mitochondrial dysfunctions in type 1 diabetic rats.

Myocardial mitochondrial function and biogenesis are suppressed in diabetes, but the mechanisms are unclear. Increasing evidence suggests that asymmetric dimethylarginine (ADMA) is associated with diabetic cardiovascular complications. This study was to determine whether endogenous ADMA accumulation contributes to cardiac and mitochondrial dysfunctions of diabetic rats and elucidate the potential mechanisms. Diabetic rat was induced by single intraperitoneal injection of streptozotocin (50 mg/kg). N-acetylcysteine was given (250 mg/kg/d) by gavage for 12w. Cardiac function was detected by echocardiography. Left ventricle papillary muscles were isolated to examine myocardial contractility. Myocardial ATP and mitochondrial DNA contents were measured to evaluate mitochondrial function and biogenesis. Endogenous ADMA accumulation was augmented resulting in decreased nitric oxide (NO) production and increased oxidative stress, suggesting NO synthase (NOS) uncoupling in the myocardium of T1DM rats compared with control rats. ADMA augmentation was associated with cardiac and mitochondrial dysfunctions along with myocardial uncoupling protein-2 (UCP2) upregulation and peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) downregulation in T1DM rats. Exogenous ADMA could directly inhibit myocardial contractility, mitochondrial function and biogenesis in parallel with decreasing NO content and PGC-1α expression while increasing oxidative stress and UCP2 expression in papillary muscles and cardiomyocytes. Treatment with antioxidant N-acetylcysteine, also an inhibitor of NOS uncoupling, either ameliorated ADMA-associated cardiac and mitochondrial dysfunctions or reversed ADMA-induced NO reduction and oxidative stress enhance in vivo and in vitro. These results indicate that myocardial ADMA accumulation precipitates cardiac and mitochondrial dysfunctions in T1DM rats. The underlying mechanism may be related to NOS uncoupling, resulting in NO reduction and oxidative stress increment, ultimate PGC-1α down-regulation and UCP2 up-regulation.

Efficient assembly of nanopore reads via highly accurate and intact error correction.

Long nanopore reads are advantageous in de novo genome assembly. However, nanopore reads usually have broad error distribution and high-error-rate subsequences. Existing error correction tools cannot correct nanopore reads efficiently and effectively. Most methods trim high-error-rate subsequences during error correction, which reduces both the length of the reads and contiguity of the final assembly. Here, we develop an error correction, and de novo assembly tool designed to overcome complex errors in nanopore reads. We propose an adaptive read selection and two-step progressive method to quickly correct nanopore reads to high accuracy. We introduce a two-stage assembler to utilize the full length of nanopore reads. Our tool achieves superior performance in both error correction and de novo assembling nanopore reads. It requires only 8122 hours to assemble a 35X coverage human genome and achieves a 2.47-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line shows an NG50 of 22 Mbp. The high-quality assembly of nanopore reads can significantly reduce false positives in structure variation detection.

Molecular characterization and overexpression of mnp6 and vp3 from Pleurotus ostreatus revealed their involvement in biodegradation of cotton stalk lignin.

Fungal secretory heme peroxidase (Class II POD) plays a significant role in biomass conversion due to its lignin-degrading activity. In this study, genome-wide identification and bioinformatics were performed to analyze P leurotus ostreatus peroxidases (PoPODs). A total of six manganese peroxidases (MnPs) and three versatile peroxidases (VPs) were obtained. Bioinformatics analysis and qRT-PCR showed that P. ostreatus mnp6 (Pomnp6) and P. ostreatus vp3 (Povp3) could be involved in lignin degradation. Both Pomnp6 and Povp3 transgenetic fungi showed significantly increased lignin degradation of cotton stalks. 1H-NMR revealed that Pomnp6 and Povp3 may preferentially degrade S-lignin in cotton stalks and mainly break ß-O-4' bond linkages and hydroxyl. These results support the possible utility of Pomnp6 and Povp3 in natural straw resources and development of sustainable energy.