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With the past decades of diamond inclusion research, it is now well established that the mantle-derived diamonds are originated either from the lithospheric mantle or sublithospheric mantle. The lithospheric diamonds can be further divided into mainly the peridotitic and eclogitic suites, which can be distinguished based on their inclusion chemistry, carbon, and nitrogen isotopic compositions (1, 2). The parental lithology of sublithospheric diamonds is less well established, partly due to their much lower occurrence relative to the lithospheric diamonds. But there has been growing isotopic evidence for the involvement of subducted materials in the source region of sublithospheric diamonds, such as carbon, boron, oxygen, and iron (3–6). Precipitation of diamonds in the Earth’s mantle has been thought to require the presence of a fluid phase. Being C-O-H, saline, carbonatitic, silicic, or metallic in composition, these fluids were released upon dehydration or partial melting of the parental lithology and migrate through the mantle until they reach diamond saturation point due to either the change in pressure-temperature, or redox conditions. Understanding the parental lithology and fluid composition of different diamonds has primarily relied on their carbon and nitrogen isotope compositions and major/trace element compositions of mineral/fluid inclusions. These tools have been shown to be powerful in many cases but each could have their own disadvantages. Nitrogen isotopes, for example, are less applicable to sublithospheric diamonds due to their low N concentration. Trace element compositions, on the other hand, can be easily manipulated by small mass fractions of low degree-melt that are enriched in incompatible elements. Understanding the diamond-forming fluids and their parental lithology require new tools that can provide a different perspective than the ones discussed above. In this presentation, we show recent developments in adapting Fe, Mg, and K isotope systems to diamond inclusion studies for a better understanding of their formation. These so-called “non-traditional” stable isotope systems were typically developed for large rocks that are not limited by sample amount. In order to adapt them to mineral inclusions tens to hundreds of micrometers in size, we’ve developed dedicated procedures to: 1) clean the diamond surface to remove contamination before extracting individual inclusions; 2) scale down the columns used for chemical purification to minimize blanks; and 3) improving sensitivity on the mass spectrometer to analyze small samples. With a Nu Plasma II at the Carnegie Institution for Science, we have shown to be able to analyze inclusion samples containing as little as 200 ng of Fe (6). With an upgraded Nu Plasma Sapphire at UCLA that is equipped with a collision cell, we are now able to analyze samples with >25 ng Fe. The same strategy has now been expanded to Mg and K isotope systems, for which a low sample limit of 25 ng and 300 ng has been achieved. With examples of Fe and Mg isotopic compositions of ferropericlase in sublithospheric diamond and K isotopic composition of fluid inclusions in fibrous diamonds, we show how isotopic compositions of major elements of mineral/fluid inclusions in diamond bring us new perspectives on their origin. Our tests show promising results to extend existing Mg and Fe protocols to silicate minerals and potentially applying similar strategies to silicon, calcium, and barium isotopes in the future. 

Copper shows limited isotopic variation in equilibrated mantle-derived silicate rocks, but large isotopic fractionation during kinetic processes. For example, lunar and terrestrial samples that have experienced evaporation were found to have an isotopic fractionation of up to 12.5‰ in their 65Cu/63Cu ratios, while komatiites, lherzolites, mid-ocean ridge and ocean island basalts show negligible Cu isotope fractionation as a result of equilibrium partial melting and crystal fractionation. The contrast between the observed magnitudes of equilibrium and kinetic isotope fractionation for Cu calls for a better understanding of kinetic Cu isotope fractionation. One of the mechanisms for creating large kinetic isotopic fractionation even at magmatic temperatures is diffusion. In this study, we performed Cu isotopic measurements on Cu diffusion couple experiments to constrain the beta factor for Cu isotopic fractionation by diffusion. We demonstrate a Monte Carlo approach for the regression and error estimation of the measured isotope profiles, which yielded beta values of 0.16 ± 0.03 and 0.18 ± 0.03 for the two experimental charges measured. Our results are subsequently applied to a quantitative model for the evaporation of a molten sphere to discuss the role of diffusion in affecting the bulk Cu isotopic fractionation between liquid and vapor during evaporation. We apply the model to Cu evaporation experiments and tektite data to show that convection primarily governs mass transport for evaporation during tektite formation. In addition, we show that Cu isotopes can be used as a tool to test the role of kinetics during various magmatic processes such as magmatic sulfide ore deposit formation, porphyry-type ore deposit formation, and fluid-rock interactions. 

Abstract 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, aPhasedErrorCorrection andAssemblyTool, 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 onB. taurus(Bison×Simmental) using Nanopore R9 reads and phase block NG50 with 59.4/58.0 Mb for HG002 using Nanopore R10 reads. 

Abstract 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. 

Abstract Mineral/melt partition coefficients have been widely used to provide insights into magmatic processes. Olivine is one of the most abundant and important minerals in the lunar mantle and mare basalts. Yet, no systematic olivine/melt partitioning data are available for lunar conditions. We report trace element partition data between host mineral olivine and its melt inclusions in lunar basalts. Equilibrium is evaluated using the Fe-Mg exchange coefficient, leading to the choice of melt inclusion-host olivine pairs in lunar basalts 12040, 12009, 15016, 15647, and 74235. Partition coefficients of 21 elements (Li, Mg, Al, Ca, Ti, V, Cr, Mn, Fe, Co, Y, Zr, Nb, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu) were measured. Except for Li, V, and Cr, these elements show no significant difference in olivine-melt partitioning compared to the data for terrestrial samples. The partition coefficient of Li between olivine and melt in some lunar basalts with low Mg# (Mg# < 0.75 in olivine, or < ~0.5 in melt) is higher than published data for terrestrial samples, which is attributed to the dependence of DLi on Mg# and the lack of literature DLi data with low Mg#. The partition coefficient of V in lunar basalts is measured to be 0.17 to 0.74, significantly higher than that in terrestrial basalts (0.003 to 0.21), which can be explained by the lower oxygen fugacity in lunar basalts. The significantly higher DV can explain why V is less enriched in evolved lunar basalts than terrestrial basalts. The partition coefficient of Cr between olivine and basalt melt in the Moon is 0.11 to 0.62, which is lower than those in terrestrial settings by a factor of ~2. This is surprising because previous authors showed that Cr partition coefficient is independent of fO2. A quasi-thermodynamically based model is developed to correlate Cr partition coefficient to olivine and melt composition and fO2. The lower Cr partition coefficient between olivine and basalt in the Moon can lead to more Cr enrichment in the lunar magma ocean, as well as more Cr enrichment in mantle-derived basalts in the Moon. Hence, even though Cr is typically a compatible element in terrestrial basalts, it is moderately incompatible in primitive lunar basalts, with a similar degree of incompatibility as V based on partition coefficients in this work, as also evidenced by the relatively constant V/Cr ratio of 0.039 ± 0.011 in lunar basalts. The confirmation of constant V/Cr ratio is important for constraining concentrations of Cr (slightly volatile and siderophile) and V (slightly siderophile) in the bulk silicate Moon. 

Abstract 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. 

Abstract 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). 

Abstract 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. 

Subducting tectonic plates carry water and other surficial components into Earth’s interior. Previous studies suggest that serpentinized peridotite is a key part of deep recycling, but this geochemical pathway has not been directly traced. Here, we report Fe-Ni–rich metallic inclusions in sublithospheric diamonds from a depth of 360 to 750 km with isotopically heavy iron (δ 56 Fe = 0.79 to 0.90‰) and unradiogenic osmium ( 187 Os/ 188 Os = 0.111). These iron values lie outside the range of known mantle compositions or expected reaction products at depth. This signature represents subducted iron from magnetite and/or Fe-Ni alloys precipitated during serpentinization of oceanic peridotite, a lithology known to carry unradiogenic osmium inherited from prior convection and melt depletion. These diamond-hosted inclusions trace serpentinite subduction into the mantle transition zone. We propose that iron-rich phases from serpentinite contribute a labile heavy iron component to the heterogeneous convecting mantle eventually sampled by oceanic basalts.