ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data

Identification of rare-variant effect in complex human traits using whole-genome and whole- exome sequencing data

For recent advancements in sequencing technologies, genetic information can be obtained from a large population at a relatively low cost. This provides an unprecedented opportunity to understand the role of genetic variability in association with complex human traits. One common strategy is to conduct genome-wide association studies to identify loci significantly associated with phenotypes of interest. However, the findings are usually limited to common variants with small effect sizes. Collectively, these identified loci can not fully explain the observed heritability, which is a problem commonly referred to as “the missing heritability.” To uncover this problem, human genetic research has shifted more focus to other types of genetic variations, including rare variants, which is further capacitated and facilitated by the next-generation sequencing technique. These rare mutations are believed to harbor large effect sizes and, therefore to be one of the major contributors to complex traits.Here, we describe our effort in analyzing the effect of rare variants in two complex human traits, Alzheimer’s Disease and Tourette Syndrome, followed by conducting a genome-wide association study on human blood lipids. Exploring large whole-genome sequencing datasets, we have first demonstrated that rare variants were strongly associated with Alzheimer’s Disease, neurofibrillary tangles, and age-related phenotypes within the endocytic pathway using a gene-set burden analysis framework. Subsequent gene-based analyses identified one AD-associated gene, ANKRD13D, and two e-Genes, HLA-A and SLC26A7. Leveraging bulk and scRNA-Seq data, we observed significant differential expression patterns in all three implicated genes. Secondly, we have explored a specific type of rare variants, de novo mutations, within Tourette Syndrome patients using a whole-exome sequencing trio dataset and identified a recurrent mutation in one gene, FBN2, previously implicated in TS. Comparing to the expected mutation rate, we demonstrated that the protein-truncating variants were enriched in probands. In addition, gene-set analysis displayed differential expression patterns across different tissue types and brain developmental stages. Lastly, we have performed a multi-population meta-analysis on blood lipid levels using electronic health records and genotyping information from the UCLA ATLAS database. We have observed genetic effects both specific to and shared across five different populations. Compared to previous large-scale GWASes, our results demonstrated consistent effect estimates while identifying one novel locus, rs72552763

Mechanical condition diagnosis method of high‐voltage reactor using its oil tank operation deflection shape

Abstract After the long‐term operation, multiple mechanical defects (i.e. loosening of winding and core) of the high‐voltage reactor will emerge. Mechanical conditions of these critical components inside the high‐voltage reactors differ vibration of the oil tank. Thus, in this paper, array vibration sensors attached to the external surface of the tank are utilized to derive the operation deflection shape, which provides effective‐rich information for defects diagnosis. The influence of the applied voltage and mechanical conditions are deeply investigated, and the results indicate that the vibration of the frequency components at 100 Hz is only related to mechanical conditions but not sensitive to the applied voltage. Thereby, nodal lines are extracted from operation deflection shape at the same frequency (100 Hz) using edge extraction, which contains less interference information compared to the traditional greyscale image. The distance between nodal line features, estimated by the Siamese network, is adopted as an effective indicator to classify various defect types and levels of multiple mechanical conditions. Experimental results confirm the accuracy and feasibility of this proposed method in identifying mechanical conditions, and the respective identification accuracy for defects of winding and iron core reaches 100% and 92.9%, which is better than that from greyscale samples

ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data

ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data

Bacterial Metabolite Reuterin Attenuated LPS-Induced Oxidative Stress and Inflammation Response in HD11 Macrophages

Reuterin is well-known for its broad-spectrum antimicrobial ability, while the other potential bioactivity is not yet clear. The present study aims to investigate the immunomodulatory activity of reuterin on chicken macrophage HD11 cells for the first time and evaluate whether reuterin is able to regulate the lipopolysaccharide-stimulated inflammatory response. The results showed that the safe medication range of reuterin was less than 250 μM. Reuterin treatment for 6 h decreased the transcriptional of CD86, IL-1β and iNOS and increased the expression of CD206 in a dose-dependent way, but reuterin treatment for 12 h contrary increased the expression of IL-1β, IL-6 and IL-10. However, it was noticed that reuterin treatment for 12 h significantly decreased the production of reactive oxygen species (ROS) and suppressed the phagocytosis activity of HD11 macrophages against bacteria. Further, the results showed that preincubation or coincubation with reuterin significantly attenuated the promotive effects of lipopolysaccharide (LPS) on transcription of proinflammatory cytokines (including IL-1β, IL-6 and TNF-α) and obviously inhibited nitric oxide (NO) production as well as the protein expression of inducible nitric oxide synthase (iNOS). Meanwhile, Mechanism studies implied that reuterin might exert an anti-inflammatory effect on LPS-stimulated cells by downregulating the expression of TLR4/MyD88/TRAF6 and blocking the activation of NF-κB as well as MAPKs signaling pathways. Additionally, it was found that both pretreatment and cotreatment with reuterin remarkably inhibited the oxidative stress induced by LPS stimulation by activating the Nrf2/HO-1 signaling pathway and enhancing the activities of antioxidative enzymes. These findings suggested the immunoregulatory function of reuterin and indicated this bacterial metabolite was able to inhibit the inflammation and oxidative stress of HD11 macrophages once exposed to LPS stimulation

Selenomethionine Attenuated H₂O₂-Induced Oxidative Stress and Apoptosis by Nrf2 in Chicken Liver Cells

Earlier studies have shown that selenomethionine (SM) supplements in broiler breeders had higher deposition in eggs, further reduced the mortality of chicken embryos, and exerted a stronger antioxidant ability in offspring than sodium selenite (SS). Since previous studies also confirmed that Se deposition in eggs was positively correlated with maternal supplementation, this study aimed to directly investigate the antioxidant activities and underlying mechanisms of SS and SM on the chicken hepatocellular carcinoma cell line (LMH). The cytotoxicity results showed that the safe concentration of SM was up to 1000 ng/mL, while SS was 100 ng/mL. In Se treatments, both SS and SM significantly elevated mRNA stability and the protein synthesis rate of glutathione peroxidase (GPx) and thioredoxin reductase (TrxR), two Se-containing antioxidant enzymes. Furthermore, SM exerted protective effects in the H2O2-induced oxidant stress model by reducing free radicals (including ROS, MDA, and NO) and elevating the activities of antioxidative enzymes, which performed better than SS. Furthermore, the results showed that cotreatment with SM significantly induced apoptosis induced by H2O2 on elevating the content of Bcl-2 and decreasing caspase-3. Moreover, investigations of the mechanism revealed that SM might exert antioxidant effects on H2O2-induced LMHs by activating the Nrf2 pathway and enhancing the activities of major antioxidant selenoenzymes downstream. These findings provide evidence for the effectiveness of SM on ameliorating H2O2-induced oxidative impairment and suggest SM has the potential to be used in the prevention or adjuvant treatment of oxidative-related impairment in poultry feeds

Mechanisms Underlying the Protective Effect of Maternal Zinc (ZnSO4 or Zn-Gly) against Heat Stress-Induced Oxidative Stress in Chicken Embryo

Environmental factors such as high temperature can cause oxidative stress and negatively affect the physiological status and meat quality of broiler chickens. The study was conducted to evaluate the effects of dietary maternal Zn-Gly or ZnSO4 supplementation on embryo mortality, hepatocellular mitochondrial morphology, liver antioxidant capacity and the expression of related genes involved in liver oxidative mechanisms in heat-stressed broilers. A total of 300 36-week-old Lingnan Yellow broiler breeders were randomly divided into three treatments: (1) control (basal diet, 24 mg zinc/kg); (2) inorganic ZnSO4 group (basal diet +80 mg ZnSO4/kg); (3) organic Zn-Gly group (basal diet +80 mg Zn-Gly/kg). The results show that maternal zinc alleviated heat stress-induced chicken embryo hepatocytes’ oxidative stress by decreasing the content of ROS, MDA, PC, 8-OHdG, and levels of HSP70, while enhancing T-SOD, T-AOC, CuZn-SOD, GSH-Px, CTA activities and the content of MT. Maternal zinc alleviated oxidative stress-induced mitochondrial damage in chick embryo hepatocytes by increasing mitochondrial membrane potential and UCP gene expression; and Caspase-3-mediated apoptosis was alleviated by increasing CuZn-SOD and MT gene expression and decreasing Bax gene expression and reducing the activity of caspase 3. Furthermore, maternal zinc treatment significantly increased Nrf2 gene expression. The results above suggest that maternal zinc can activate the Nrf2 signaling pathway in developing chick embryos, enhance its antioxidant function and reduce the apoptosis-effecting enzyme caspase-3 activities, thereby slowing oxidative stress injury and tissue cell apoptosis