Enhancing discoveries of molecular QTL studies with small sample size using summary statistic imputation.

Quantitative trait locus (QTL) analyses of multiomic molecular traits, such as gene transcription (eQTL), DNA methylation (mQTL) and histone modification (haQTL), have been widely used to infer the functional effects of genome variants. However, the QTL discovery is largely restricted by the limited study sample size, which demands higher threshold of minor allele frequency and then causes heavy missing molecular trait-variant associations. This happens prominently in single-cell level molecular QTL studies because of sample availability and cost. It is urgent to propose a method to solve this problem in order to enhance discoveries of current molecular QTL studies with small sample size. In this study, we presented an efficient computational framework called xQTLImp to impute missing molecular QTL associations. In the local-region imputation, xQTLImp uses multivariate Gaussian model to impute the missing associations by leveraging known association statistics of variants and the linkage disequilibrium (LD) around. In the genome-wide imputation, novel procedures are implemented to improve efficiency, including dynamically constructing a reused LD buffer, adopting multiple heuristic strategies and parallel computing. Experiments on various multiomic bulk and single-cell sequencing-based QTL datasets have demonstrated high imputation accuracy and novel QTL discovery ability of xQTLImp. Finally, a C++ software package is freely available at https://github.com/stormlovetao/QTLIMP.

An integrated approach for copy number variation discovery in parent-offspring trios.

Whole-genome sequencing (WGS) of parent-offspring trios has become widely used to identify causal copy number variations (CNVs) in rare and complex diseases. Existing CNV detection approaches usually do not make effective use of Mendelian inheritance in parent-offspring trios and yield low accuracy. In this study, we propose a novel integrated approach, TrioCNV2, for jointly detecting CNVs from WGS data of the parent-offspring trio. TrioCNV2 first makes use of the read depth and discordant read pairs to infer approximate locations of CNVs and then employs the split read and local de novo assembly approaches to refine the breakpoints. We use the real WGS data of two parent-offspring trios to demonstrate TrioCNV2's performance and compare it with other CNV detection approaches. The software TrioCNV2 is implemented using a combination of Java and R and is freely available from the website at https://github.com/yongzhuang/TrioCNV2.

A deep learning approach for filtering structural variants in short read sequencing data.

Short read whole genome sequencing has become widely used to detect structural variants in human genetic studies and clinical practices. However, accurate detection of structural variants is a challenging task. Especially existing structural variant detection approaches produce a large proportion of incorrect calls, so effective structural variant filtering approaches are urgently needed. In this study, we propose a novel deep learning-based approach, DeepSVFilter, for filtering structural variants in short read whole genome sequencing data. DeepSVFilter encodes structural variant signals in the read alignments as images and adopts the transfer learning with pre-trained convolutional neural networks as the classification models, which are trained on the well-characterized samples with known high confidence structural variants. We use two well-characterized samples to demonstrate DeepSVFilter's performance and its filtering effect coupled with commonly used structural variant detection approaches. The software DeepSVFilter is implemented using Python and freely available from the website at https://github.com/yongzhuang/DeepSVFilter.

Hydrogen-Transfer Reductive Catalytic Fractionation of Lignocellulose: High Monomeric Yield with Switchable Selectivity.

Lignin solubilization and in situ hydrogenolysis are crucial for reductive catalytic fractionation (RCF) of lignocellulose to aromatic monomers. In this study, we reported a typical hydrogen bond acceptor of choline chloride (ChCl) to tailor the hydrogen-donating environment of the Ru/C-catalyzed hydrogen-transfer RCF of lignocellulose. The ChCl-tailored hydrogen-transfer RCF of lignocellulose was conducted under mild temperature and low-pressure (<1 bar) conditions, which was applicable to other lignocellulosic biomass sources. We obtained an approximate theoretical yield of propylphenol monomer of 59.2 wt % and selectivity of 97.3 % using an optimal content of ChCl (10 wt %) in ethylene glycol at 190 °C for 8 h. When the content of ChCl in ethylene glycol was increased to 110 wt %, the selectivity of propylphenol switched toward propylenephenol (yield of 36.2 wt % and selectivity of 87.6 %). The findings in this work provide valuable information for transforming lignin from lignocellulose into value-added products.

abPOA: an SIMD-based C library for fast partial order alignment using adaptive band.

SUMMARY: Partial order alignment, which aligns a sequence to a directed acyclic graph, is now frequently used as a key component in long-read error correction and assembly. We present abPOA (adaptive banded Partial Order Alignment), a Single Instruction Multiple Data (SIMD)-based C library for fast partial order alignment using adaptive banded dynamic programming. It can work as a stand-alone multiple sequence alignment and consensus calling tool or be easily integrated into any long-read error correction and assembly workflow. Compared to a state-of-the-art tool (SPOA), abPOA is up to 10 times faster with a comparable alignment accuracy. AVAILABILITY AND IMPLEMENTATION: abPOA is implemented in C. A stand-alone tool and a C/Python software interface are freely available at https://github.com/yangao07/abPOA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A pipeline for RNA-seq based eQTL analysis with automated quality control procedures.

BACKGROUND: Advances in the expression quantitative trait loci (eQTL) studies have provided valuable insights into the mechanism of diseases and traits-associated genetic variants. However, it remains challenging to evaluate and control the quality of multi-source heterogeneous eQTL raw data for researchers with limited computational background. There is an urgent need to develop a powerful and user-friendly tool to automatically process the raw datasets in various formats and perform the eQTL mapping afterward. RESULTS: In this work, we present a pipeline for eQTL analysis, termed eQTLQC, featured with automated data preprocessing for both genotype data and gene expression data. Our pipeline provides a set of quality control and normalization approaches, and utilizes automated techniques to reduce manual intervention. We demonstrate the utility and robustness of this pipeline by performing eQTL case studies using multiple independent real-world datasets with RNA-seq data and whole genome sequencing (WGS) based genotype data. CONCLUSIONS: eQTLQC provides a reliable computational workflow for eQTL analysis. It provides standard quality control and normalization as well as eQTL mapping procedures for eQTL raw data in multiple formats. The source code, demo data, and instructions are freely available at https://github.com/stormlovetao/eQTLQC .

Filtering de novo indels in parent-offspring trios.

BACKGROUND: Identification of de novo indels from whole genome or exome sequencing data of parent-offspring trios is a challenging task in human disease studies and clinical practices. Existing computational approaches usually yield high false positive rate. RESULTS: In this study, we developed a gradient boosting approach for filtering de novo indels obtained by any computational approaches. Through application on the real genome sequencing data, our approach showed it could significantly reduce the false positive rate of de novo indels without a significant compromise on sensitivity. CONCLUSIONS: The software DNMFilter_Indel was written in a combination of Java and R and freely available from the website at https://github.com/yongzhuang/DNMFilter_Indel .

Joint detection of germline and somatic copy number events in matched tumor-normal sample pairs.

MOTIVATION: Whole-genome sequencing (WGS) of tumor-normal sample pairs is a powerful approach for comprehensively characterizing germline copy number variations (CNVs) and somatic copy number alterations (SCNAs) in cancer research and clinical practice. Existing computational approaches for detecting copy number events cannot detect germline CNVs and SCNAs simultaneously, and yield low accuracy for SCNAs. RESULTS: In this study, we developed TumorCNV, a novel approach for jointly detecting germline CNVs and SCNAs from WGS data of the matched tumor-normal sample pair. We compared TumorCNV with existing copy number event detection approaches using the simulated data and real data for the COLO-829 melanoma cell line. The experimental results showed that TumorCNV achieved superior performance than existing approaches. AVAILABILITY AND IMPLEMENTATION: The software TumorCNV is implemented using a combination of Java and R, and it is freely available from the website at https://github.com/yongzhuang/TumorCNV. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Joint detection of copy number variations in parent-offspring trios.

MOTIVATION: Whole genome sequencing (WGS) of parent-offspring trios is a powerful approach for identifying disease-associated genes via detecting copy number variations (CNVs). Existing approaches, which detect CNVs for each individual in a trio independently, usually yield low-detection accuracy. Joint modeling approaches leveraging Mendelian transmission within the parent-offspring trio can be an efficient strategy to improve CNV detection accuracy. RESULTS: In this study, we developed TrioCNV, a novel approach for jointly detecting CNVs in parent-offspring trios from WGS data. Using negative binomial regression, we modeled the read depth signal while considering both GC content bias and mappability bias. Moreover, we incorporated the family relationship and used a hidden Markov model to jointly infer CNVs for three samples of a parent-offspring trio. Through application to both simulated data and a trio from 1000 Genomes Project, we showed that TrioCNV achieved superior performance than existing approaches. AVAILABILITY AND IMPLEMENTATION: The software TrioCNV implemented using a combination of Java and R is freely available from the website at https://github.com/yongzhuang/TrioCNV CONTACT: ydwang@hit.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

InteGO2: a web tool for measuring and visualizing gene semantic similarities using Gene Ontology.

BACKGROUND: The Gene Ontology (GO) has been used in high-throughput omics research as a major bioinformatics resource. The hierarchical structure of GO provides users a convenient platform for biological information abstraction and hypothesis testing. Computational methods have been developed to identify functionally similar genes. However, none of the existing measurements take into account all the rich information in GO. Similarly, using these existing methods, web-based applications have been constructed to compute gene functional similarities, and to provide pure text-based outputs. Without a graphical visualization interface, it is difficult for result interpretation. RESULTS: We present InteGO2, a web tool that allows researchers to calculate the GO-based gene semantic similarities using seven widely used GO-based similarity measurements. Also, we provide an integrative measurement that synergistically integrates all the individual measurements to improve the overall performance. Using HTML5 and cytoscape.js, we provide a graphical interface in InteGO2 to visualize the resulting gene functional association networks. CONCLUSIONS: InteGO2 is an easy-to-use HTML5 based web tool. With it, researchers can measure gene or gene product functional similarity conveniently, and visualize the network of functional interactions in a graphical interface. InteGO2 can be accessed via http://mlg.hit.edu.cn:8089/ .

Family genome browser: visualizing genomes with pedigree information.

MOTIVATION: Families with inherited diseases are widely used in Mendelian/complex disease studies. Owing to the advances in high-throughput sequencing technologies, family genome sequencing becomes more and more prevalent. Visualizing family genomes can greatly facilitate human genetics studies and personalized medicine. However, due to the complex genetic relationships and high similarities among genomes of consanguineous family members, family genomes are difficult to be visualized in traditional genome visualization framework. How to visualize the family genome variants and their functions with integrated pedigree information remains a critical challenge. RESULTS: We developed the Family Genome Browser (FGB) to provide comprehensive analysis and visualization for family genomes. The FGB can visualize family genomes in both individual level and variant level effectively, through integrating genome data with pedigree information. Family genome analysis, including determination of parental origin of the variants, detection of de novo mutations, identification of potential recombination events and identical-by-decent segments, etc., can be performed flexibly. Diverse annotations for the family genome variants, such as dbSNP memberships, linkage disequilibriums, genes, variant effects, potential phenotypes, etc., are illustrated as well. Moreover, the FGB can automatically search de novo mutations and compound heterozygous variants for a selected individual, and guide investigators to find high-risk genes with flexible navigation options. These features enable users to investigate and understand family genomes intuitively and systematically. AVAILABILITY AND IMPLEMENTATION: The FGB is available at http://mlg.hit.edu.cn/FGB/.

A Bayesian framework for de novo mutation calling in parents-offspring trios.

MOTIVATION: Spontaneous (de novo) mutations play an important role in the disease etiology of a range of complex diseases. Identifying de novo mutations (DNMs) in sporadic cases provides an effective strategy to find genes or genomic regions implicated in the genetics of disease. High-throughput next-generation sequencing enables genome- or exome-wide detection of DNMs by sequencing parents-proband trios. It is challenging to sift true mutations through massive amount of noise due to sequencing error and alignment artifacts. One of the critical limitations of existing methods is that for all genomic regions the same pre-specified mutation rate is assumed, which has a significant impact on the DNM calling accuracy. RESULTS: In this study, we developed and implemented a novel Bayesian framework for DNM calling in trios (TrioDeNovo), which overcomes these limitations by disentangling prior mutation rates from evaluation of the likelihood of the data so that flexible priors can be adjusted post-hoc at different genomic sites. Through extensively simulations and application to real data we showed that this new method has improved sensitivity and specificity over existing methods, and provides a flexible framework to further improve the efficiency by incorporating proper priors. The accuracy is further improved using effective filtering based on sequence alignment characteristics. AVAILABILITY AND IMPLEMENTATION: The C++ source code implementing TrioDeNovo is freely available at https://medschool.vanderbilt.edu/cgg. CONTACT: bingshan.li@vanderbilt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The personal genome browser: visualizing functions of genetic variants.

Advances in high-throughput sequencing technologies have brought us into the individual genome era. Projects such as the 1000 Genomes Project have led the individual genome sequencing to become more and more popular. How to visualize, analyse and annotate individual genomes with knowledge bases to support genome studies and personalized healthcare is still a big challenge. The Personal Genome Browser (PGB) is developed to provide comprehensive functional annotation and visualization for individual genomes based on the genetic-molecular-phenotypic model. Investigators can easily view individual genetic variants, such as single nucleotide variants (SNVs), INDELs and structural variations (SVs), as well as genomic features and phenotypes associated to the individual genetic variants. The PGB especially highlights potential functional variants using the PGB built-in method or SIFT/PolyPhen2 scores. Moreover, the functional risks of genes could be evaluated by scanning individual genetic variants on the whole genome, a chromosome, or a cytoband based on functional implications of the variants. Investigators can then navigate to high risk genes on the scanned individual genome. The PGB accepts Variant Call Format (VCF) and Genetic Variation Format (GVF) files as the input. The functional annotation of input individual genome variants can be visualized in real time by well-defined symbols and shapes. The PGB is available at http://www.pgbrowser.org/.

Sexually Dimorphic Gene Expression Associated with Growth and Reproduction of Tongue Sole (Cynoglossus semilaevis) Revealed by Brain Transcriptome Analysis.

In this study, we performed a comprehensive analysis of the transcriptome of one- and two-year-old male and female brains of Cynoglossus semilaevis by high-throughput Illumina sequencing. A total of 77,066 transcripts, corresponding to 21,475 unigenes, were obtained with a N50 value of 4349 bp. Of these unigenes, 33 genes were found to have significant differential expression and potentially associated with growth, from which 18 genes were down-regulated and 12 genes were up-regulated in two-year-old males, most of these genes had no significant differences in expression among one-year-old males and females and two-year-old females. A similar analysis was conducted to look for genes associated with reproduction; 25 genes were identified, among them, five genes were found to be down regulated and 20 genes up regulated in two-year-old males, again, most of the genes had no significant expression differences among the other three. The performance of up regulated genes in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was significantly different between two-year-old males and females. Males had a high gene expression in genetic information processing, while female's highly expressed genes were mainly enriched on organismal systems. Our work identified a set of sex-biased genes potentially associated with growth and reproduction that might be the candidate factors affecting sexual dimorphism of tongue sole, laying the foundation to understand the complex process of sex determination of this economic valuable species.

A gradient-boosting approach for filtering de novo mutations in parent-offspring trios.

MOTIVATION: Whole-genome and -exome sequencing on parent-offspring trios is a powerful approach to identifying disease-associated genes by detecting de novo mutations in patients. Accurate detection of de novo mutations from sequencing data is a critical step in trio-based genetic studies. Existing bioinformatic approaches usually yield high error rates due to sequencing artifacts and alignment issues, which may either miss true de novo mutations or call too many false ones, making downstream validation and analysis difficult. In particular, current approaches have much worse specificity than sensitivity, and developing effective filters to discriminate genuine from spurious de novo mutations remains an unsolved challenge. RESULTS: In this article, we curated 59 sequence features in whole genome and exome alignment context which are considered to be relevant to discriminating true de novo mutations from artifacts, and then employed a machine-learning approach to classify candidates as true or false de novo mutations. Specifically, we built a classifier, named De Novo Mutation Filter (DNMFilter), using gradient boosting as the classification algorithm. We built the training set using experimentally validated true and false de novo mutations as well as collected false de novo mutations from an in-house large-scale exome-sequencing project. We evaluated DNMFilter's theoretical performance and investigated relative importance of different sequence features on the classification accuracy. Finally, we applied DNMFilter on our in-house whole exome trios and one CEU trio from the 1000 Genomes Project and found that DNMFilter could be coupled with commonly used de novo mutation detection approaches as an effective filtering approach to significantly reduce false discovery rate without sacrificing sensitivity. AVAILABILITY: The software DNMFilter implemented using a combination of Java and R is freely available from the website at http://humangenome.duke.edu/software.

An evaluation of copy number variation detection tools from whole-exome sequencing data.

Copy number variation (CNV) has been found to play an important role in human disease. Next-generation sequencing technology, including whole-genome sequencing (WGS) and whole-exome sequencing (WES), has become a primary strategy for studying the genetic basis of human disease. Several CNV calling tools have recently been developed on the basis of WES data. However, the comparative performance of these tools using real data remains unclear. An objective evaluation study of these tools in practical research situations would be beneficial. Here, we evaluated four well-known WES-based CNV detection tools (XHMM, CoNIFER, ExomeDepth, and CONTRA) using real data generated in house. After evaluation using six metrics, we found that the sensitive and accurate detection of CNVs in WES data remains challenging despite the many algorithms available. Each algorithm has its own strengths and weaknesses. None of the exome-based CNV calling methods performed well in all situations; in particular, compared with CNVs identified from high coverage WGS data from the same samples, all tools suffered from limited power. Our evaluation provides a comprehensive and objective comparison of several well-known detection tools designed for WES data, which will assist researchers in choosing the most suitable tools for their research needs.

Hydrogen-transfer strategy in lignin refinery: Towards sustainable and versatile value-added biochemicals.

Lignin, the most prevalent natural source of polyphenols on Earth, offers substantial possibilities for the conversion into aromatic compounds, which is critical for attaining sustainability and carbon neutrality. The hydrogen-transfer method has garnered significant interest owing to its environmental compatibility and economic viability. The efficacy of this approach is contingent upon the careful selection of catalytic and hydrogen-donating systems that decisively affect the yield and selectivity of the monomeric products resulting from lignin degradation. This paper highlights the hydrogen-transfer technique in lignin refinery, with a specific focus on the influence of hydrogen donors on the depolymerization pathways of lignin. It delineates the correlation between the structure and activity of catalytic hydrogen-transfer arrangements and the gamut of lignin-derived biochemicals, utilizing data from lignin model compounds, separated lignin, and lignocellulosic biomass. Additionally, the paper delves into the advantages and future directions of employing the hydrogen-transfer approach for lignin conversion. In essence, this concept investigation illuminates the efficacy of the hydrogen-transfer paradigm in lignin valorization, offering key insights and strategic directives to maximize lignin's value sustainably.

Lattice Strain Engineering on Metal-Organic Frameworks by Ligand Doping to Boost the Electrocatalytic Biomass Valorization.

As an efficient and environmental-friendly strategy, electrocatalytic oxidation can realize biomass lignin valorization by cleaving its aryl ether bonds to produce value-added chemicals. However, the complex and polymerized structure of lignin presents challenges in terms of reactant adsorption on the catalyst surface, which hinders further refinement. Herein, NiCo-based metal-organic frameworks (MOFs) are employed as the electrocatalyst to enhance the adsorption of reactant molecules through π-π interaction. More importantly, lattice strain is introduced into the MOFs via curved ligand doping, which enables tuning of the d-band center of metal active sites to align with the reaction intermediates, leading to stronger adsorption and higher electrocatalytic activity toward bond cleavage within lignin model compounds and native lignin. When 2'-phenoxyacetophenone is utilized as the model compound, high yields of phenol (76.3%) and acetophenone (21.7%) are achieved, and the conversion rate of the reactants reaches 97%. Following pre-oxidation of extracted poplar lignin, >10 kinds of phenolic compounds are received using the as-designed MOFs electrocatalyst, providing ≈12.48% of the monomer, including guaiacol, vanillin, eugenol, etc., and p-hydroxybenzoic acid dominates all the products. This work presents a promising and deliberately designed electrocatalyst for realizing lignin valorization, making significant strides for the sustainability of this biomass resource.