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Deletions and tandem duplications (commonly called CNVs) represent the majority of structural variations in a human genome. They can be identified using short reads, but because they frequently occur in repetitive regions, existing methods fail to detect most of them. This is because CNVs in repetitive regions often do not produce the evidence needed by existing short reads-based callers (split reads, discordant pairs or read depth change). Here, we introduce a new CNV short reads-based caller named SurVIndel2. SurVindel2 builds on statistical techniques we previously developed, but also employs a novel type of evidence, hidden split reads, that can uncover many CNVs missed by existing algorithms. We use public benchmarks to show that SurVIndel2 outperforms other popular callers, both on human and non-human datasets. Then, we demonstrate the practical utility of the method by generating a catalogue of CNVs for the 1000 Genomes Project that contains hundreds of thousands of CNVs missing from the most recent public catalogue. We also show that SurVIndel2 is able to complement small indels predicted by Google DeepVariant, and the two software used in tandem produce a remarkably complete catalogue of variants in an individual. Finally, we characterise how the limitations of current sequencing technologies contribute significantly to the missing CNVs., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Structural variants (SVs) are significant contributors to inter-individual genetic variation associated with traits and diseases. Current SV studies using whole-genome sequencing (WGS) have a largely Eurocentric composition, with little known about SV diversity in other ancestries, particularly from Asia. Here, we present a WGS catalogue of 73,035 SVs from 8392 Singaporeans of East Asian, Southeast Asian and South Asian ancestries, of which ~65% (47,770 SVs) are novel. We show that Asian populations can be stratified by their global SV patterns and identified 42,239 novel SVs that are specific to Asian populations. 52% of these novel SVs are restricted to one of the three major ancestry groups studied (Indian, Chinese or Malay). We uncovered SVs affecting major clinically actionable loci. Lastly, by identifying SVs in linkage disequilibrium with single-nucleotide variants, we demonstrate the utility of our SV catalogue in the fine-mapping of Asian GWAS variants and identification of potential causative variants. These results augment our knowledge of structural variation across human populations, thereby reducing current ancestry biases in global references of genetic variation afflicting equity, diversity and inclusion in genetic research., (© 2024. The Author(s).)

Draft genomes generated from Oxford Nanopore Technologies (ONT) long reads are known to have a higher error rate. Although existing genome polishers can enhance their quality, the error rate (including mismatches, indels and switching errors between paternal and maternal haplotypes) can be significant. Here, we develop two polishers, hypo-short and hypo-hybrid to address this issue. Hypo-short utilizes Illumina short reads to polish an ONT-based draft assembly, resulting in a high-quality assembly with low error rates and switching errors. Expanding on this, hypo-hybrid incorporates ONT long reads to further refine the assembly into a diploid representation. Leveraging on hypo-hybrid, we have created a diploid genome assembly pipeline called hypo-assembler. Hypo-assembler automates the generation of highly accurate, contiguous and nearly complete diploid assemblies using ONT long reads, Illumina short reads and optionally Hi-C reads. Notably, our solution even allows for the production of telomere-to-telomere diploid genomes with additional manual steps. As a proof of concept, we successfully assembled a fully phased telomere-to-telomere diploid genome of HG00733, achieving a quality value exceeding 50., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Photoperiods integrate with the circadian clock to coordinate gene expression rhythms and thus ensure plant fitness to the environment. Genome-wide characterization and comparison of rhythmic genes under different light conditions revealed delayed phase under constant darkness (DD) and reduced amplitude under constant light (LL) in rice. Interestingly, ChIP-seq and RNA-seq profiling of rhythmic genes exhibit synchronous circadian oscillation in H3K9ac modifications at their loci and long non-coding RNAs (lncRNAs) expression at proximal loci. To investigate how gene expression rhythm is regulated in rice, we profiled the open chromatin regions and transcription factor (TF) footprints by time-series ATAC-seq. Although open chromatin regions did not show circadian change, a significant number of TFs were identified to rhythmically associate with chromatin and drive gene expression in a time-dependent manner. Further transcriptional regulatory networks mapping uncovered significant correlation between core clock genes and transcription factors involved in light/temperature signaling. In situ Hi-C of ZT8-specific expressed genes displayed highly connected chromatin association at the same time, whereas this ZT8 chromatin connection network dissociates at ZT20, suggesting the circadian control of gene expression by dynamic spatial chromatin conformation. These findings together implicate the existence of a synchronization mechanism between circadian H3K9ac modifications, chromatin association of TF and gene expression, and provides insights into circadian dynamics of spatial chromatin conformation that associate with gene expression rhythms., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Histopathology is a crucial diagnostic tool in cancer and involves the analysis of gigapixel slides. Multiple instance learning (MIL) promises success in digital histopathology thanks to its ability to handle gigapixel slides and work with weak labels. MIL is a machine learning paradigm that learns the mapping between bags of instances and bag labels. It represents a slide as a bag of patches and uses the slide's weak label as the bag's label. This paper introduces distribution-based pooling filters that obtain a bag-level representation by estimating marginal distributions of instance features. We formally prove that the distribution-based pooling filters are more expressive than the classical point estimate-based counterparts, like 'max' and 'mean' pooling, in terms of the amount of information captured while obtaining bag-level representations. Moreover, we empirically show that models with distribution-based pooling filters perform equal to or better than those with point estimate-based pooling filters on distinct real-world MIL tasks defined on the CAMELYON16 lymph node metastases dataset. Our model with a distribution pooling filter achieves an area under the receiver operating characteristics curve value of 0.9325 (95% confidence interval: 0.8798 - 0.9743) in the tumor vs. normal slide classification task., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Insertions are one of the major types of structural variations and are defined as the addition of 50 nucleotides or more into a DNA sequence. Several methods exist to detect insertions from next-generation sequencing short read data, but they generally have low sensitivity. Our contribution is two-fold. First, we introduce INSurVeyor, a fast, sensitive and precise method that detects insertions from next-generation sequencing paired-end data. Using publicly available benchmark datasets (both human and non-human), we show that INSurVeyor is not only more sensitive than any individual caller we tested, but also more sensitive than all of them combined. Furthermore, for most types of insertions, INSurVeyor is almost as sensitive as long reads callers. Second, we provide state-of-the-art catalogues of insertions for 1047 Arabidopsis Thaliana genomes from the 1001 Genomes Project and 3202 human genomes from the 1000 Genomes Project, both generated with INSurVeyor. We show that they are more complete and precise than existing resources, and important insertions are missed by existing methods., (© 2023. The Author(s).)

Structural variations (SVs) represent genomic rearrangements (such as deletions, insertions, and inversions) whose sizes are larger than 50bp. They play important roles in genetic diseases and evolution mechanism. Due to the advance of long-read sequencing (i.e. PacBio long-read sequencing and Oxford Nanopore (ONT) long-read sequencing), we can call SVs accurately. However, for ONT long reads, we observe that existing long read SV callers miss a lot of true SVs and call a lot of false SVs in repetitive regions and in regions with multi-allelic SVs. Those errors are caused by messy alignments of ONT reads due to their high error rate. Hence, we propose a novel method, SVsearcher, to solve these issues. We run SVsearcher and other callers in three real datasets and find that SVsearcher improves the F1 score by approximately 10% for high coverage (50×) datasets and more than 25% for low coverage (10×) datasets. More importantly, SVsearcher can identify 81.7%-91.8% multi-allelic SVs while existing methods only identify 13.2% (Sniffles)-54.0% (nanoSV) of them. SVsearcher is available at https://github.com/kensung-lab/SVsearcher., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Pathologists diagnose prostate cancer by core needle biopsy. In low-grade and low-volume cases, they look for a few malignant glands out of hundreds within a core. They may miss a few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep-learning pipeline to assist pathologists in detecting malignant glands in core needle biopsies of low-grade and low-volume cases. Analyzing a gland at multiple resolutions, our model exploited morphology and neighborhood information, which were crucial in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. Besides, we made the images publicly available, becoming the first digital histopathology dataset of patients of Asian ancestry with prostatic carcinoma. Our multi-resolution classification model achieved an area under the receiver operating characteristic curve (AUROC) value of 0.992 (95% confidence interval [CI]: 0.985-0.997) in the external validation study, showing the generalizability of our multi-resolution approach., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

Neural stem cells (NSCs) divide asymmetrically to balance their self-renewal and differentiation, an imbalance in which can lead to NSC overgrowth and tumor formation. The functions of Parafibromin, a conserved tumor suppressor, in the nervous system are not established. Here, we demonstrate that Drosophila Parafibromin/Hyrax (Hyx) inhibits ectopic NSC formation by governing cell polarity. Hyx is essential for the asymmetric distribution and/or maintenance of polarity proteins. hyx depletion results in the symmetric division of NSCs, leading to the formation of supernumerary NSCs in the larval brain. Importantly, we show that human Parafibromin rescues the ectopic NSC phenotype in Drosophila hyx mutant brains. We have also discovered that Hyx is required for the proper formation of interphase microtubule-organizing center and mitotic spindles in NSCs. Moreover, Hyx is required for the proper localization of 2 key centrosomal proteins, Polo and AurA, and the microtubule-binding proteins Msps and D-TACC in dividing NSCs. Furthermore, Hyx directly regulates the polo and aurA expression in vitro. Finally, overexpression of polo and aurA could significantly suppress ectopic NSC formation and NSC polarity defects caused by hyx depletion. Our data support a model in which Hyx promotes the expression of polo and aurA in NSCs and, in turn, regulates cell polarity and centrosome/microtubule assembly. This new paradigm may be relevant to future studies on Parafibromin/HRPT2-associated cancers., Competing Interests: The authors have declared that no competing interests exist.

Esophageal cancer (EC) remains a significant challenge globally, having the 8th highest incidence and 6th highest mortality worldwide. Esophageal squamous cell carcinoma (ESCC) is the most common form of EC in Asia. Crucially, more than 90% of EC cases in China are ESCC. The high mortality rate of EC is likely due to the limited number of effective therapeutic options. To increase patient survival, novel therapeutic strategies for EC patients must be devised. Unfortunately, the development of novel drugs also presents its own significant challenges as most novel drugs do not make it to market due to lack of efficacy or safety concerns. A more time and cost-effective strategy is to identify existing drugs, that have already been approved for treatment of other diseases, which can be repurposed to treat EC patients, with drug repositioning. This can be achieved by comparing the gene expression profiles of disease-states with the effect on gene-expression by a given drug. In our analysis, we used previously published microarray data and identified 167 differentially expressed genes (DEGs). Using weighted key driver analysis, 39 key driver genes were then identified. These driver genes were then used in Overlap Analysis and Network Analysis in Pharmomics. By extracting drugs common to both analyses, 24 drugs are predicted to demonstrate therapeutic effect in EC patients. Several of which have already been shown to demonstrate a therapeutic effect in EC, most notably Doxorubicin, which is commonly used to treat EC patients, and Ixazomib, which was recently shown to induce apoptosis and supress growth of EC cell lines. Additionally, our analysis predicts multiple psychiatric drugs, including Venlafaxine, as repositioned drugs. This is in line with recent research which suggests that psychiatric drugs should be investigated for use in gastrointestinal cancers such as EC. Our study shows that a drug repositioning approach is a feasible strategy for identifying novel ESCC therapies and can also improve the understanding of the mechanisms underlying the drug targets., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Bennett, Huang, He, Lee, Sung and Chan.)

Singapore's National Flower, Papilionanthe (Ple.) Miss Joaquim 'Agnes' (PMJ) is highly prized as a horticultural flower from the Orchidaceae family. A combination of short-read sequencing, single-molecule long-read sequencing and chromatin contact mapping was used to assemble the PMJ genome, spanning 2.5 Gb and 19 pseudo-chromosomal scaffolds. Genomic resources and chemical profiling provided insights towards identifying, understanding and elucidating various classes of secondary metabolite compounds synthesized by the flower. For example, presence of the anthocyanin pigments detected by chemical profiling coincides with the expression of ANTHOCYANIN SYNTHASE (ANS), an enzyme responsible for the synthesis of the former. Similarly, the presence of vandaterosides (a unique class of glycosylated organic acids with the potential to slow skin aging) discovered using chemical profiling revealed the involvement of glycosyltransferase family enzymes candidates in vandateroside biosynthesis. Interestingly, despite the unnoticeable scent of the flower, genes involved in the biosynthesis of volatile compounds and chemical profiling revealed the combination of oxygenated hydrocarbons, including traces of linalool, beta-ionone and vanillin, forming the scent profile of PMJ. In summary, by combining genomics and biochemistry, the findings expands the known biodiversity repertoire of the Orchidaceae family and insights into the genome and secondary metabolite processes of PMJ., (© 2022. The Author(s).)

HFE (Hemochromatosis) is a conventional iron level regulator and its loss of function due to gene mutations increases the risk of cancers including hepatocellular carcinoma (HCC). Likewise, studies focusing on HFE overexpression in cancers are all limited to linking up these events as a consequence of iron level deregulation. No study has explored any iron unrelated role of HFE in cancers. Here, we first reported HFE as an oncogene in HCC and its undescribed function on promoting abscission in cytokinesis during mitotic cell division, independent of its iron-regulating ability. Clinical analyses revealed HFE upregulation in tumors linking to large tumor size and poor prognosis. Functionally and mechanistically, HFE promoted cytokinetic abscission via facilitating ESCRT abscission machinery recruitment to the abscission site through signaling a novel HFE/ALK3/Smads/LIF/Hippo/YAP/YY1/KIF13A axis. Pharmacological blockage of HFE signaling axis impeded tumor phenotypes in vitro and in vivo. Our data on HFE-driven HCC unveiled a new mechanism utilized by cancer cells to propel rapid cell division. This study also laid the groundwork for tumor intolerable therapeutics development given the high cytokinetic dependency of cancer cells and their vulnerability to cytokinetic blockage., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Peach diseases seriously affect peach yield and people's health. The precise identification of peach diseases and the segmentation of the diseased areas can provide the basis for disease control and treatment. However, the complex background and imbalanced samples bring certain challenges to the segmentation and recognition of lesion area, and the hard samples and imbalance samples can lead to a decline in classification of foreground class and background class. In this paper we applied deep network models (Mask R-CNN and Mask Scoring R-CNN) for segmentation and recognition of peach diseases. Mask R-CNN and Mask Scoring R-CNN are classic instance segmentation models. Using instance segmentation model can obtain the disease names, disease location and disease segmentation, and the foreground area is the basic feature for next segmentation. Focal Loss can solve the problems caused by difficult samples and imbalance samples, and was used for this dataset to improve segmentation accuracy. Experimental results show that Mask Scoring R-CNN with Focal Loss function can improve recognition rate and segmentation accuracy comparing to Mask Scoring R-CNN with CE loss or comparing to Mask R-CNN. When ResNet50 is used as the backbone network based on Mask R-CNN, the segmentation accuracy of segm_mAP_50 increased from 0.236 to 0.254. When ResNetx101 is used as the backbone network, the segmentation accuracy of segm_mAP_50 increased from 0.452 to 0.463. In summary, this paper used Focal Loss on Mask R-CNN and Mask Scoring R-CNN to generate better mAP of segmentation and output more detailed information about peach diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yao, Ni, Wu, Wang, Li and Sung.)

DNA methylation is known to be the most stable epigenetic modification and has been extensively studied in relation to cell differentiation, development, X chromosome inactivation and disease. Allele-specific DNA methylation (ASM) is a well-established mechanism for genomic imprinting and regulates imprinted gene expression. Previous studies have confirmed that certain special regions with ASM are susceptible and closely related to human carcinogenesis and plant development. In addition, recent studies have proven ASM to be an effective tumour marker. However, research on the functions of ASM in diseases and development is still extremely scarce. Here, we collected 4400 BS-Seq datasets and 1598 corresponding RNA-Seq datasets from 47 species, including human and mouse, to establish a comprehensive ASM database. We obtained the data on DNA methylation level, ASM and allele-specific expressed genes (ASEGs) and further analysed the ASM/ASEG distribution patterns of these species. In-depth ASM distribution analysis and differential methylation analysis conducted in nine cancer types showed results consistent with the reported changes in ASM in key tumour genes and revealed several potential ASM tumour-related genes. Finally, integrating these results, we constructed the first well-resourced and comprehensive ASM database for 47 species (ASMdb, www.dna-asmdb.com)., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Tumor purity is the percentage of cancer cells within a tissue section. Pathologists estimate tumor purity to select samples for genomic analysis by manually reading hematoxylin-eosin (H&E)-stained slides, which is tedious, time consuming, and prone to inter-observer variability. Besides, pathologists' estimates do not correlate well with genomic tumor purity values, which are inferred from genomic data and accepted as accurate for downstream analysis. We developed a deep multiple instance learning model predicting tumor purity from H&E-stained digital histopathology slides. Our model successfully predicted tumor purity in eight The Cancer Genome Atlas (TCGA) cohorts and a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values. Thus, our model can be utilized to select samples for genomic analysis, which will help reduce pathologists' workload and decrease inter-observer variability. Furthermore, our model provided tumor purity maps showing the spatial variation within sections. They can help better understand the tumor microenvironment., Competing Interests: The authors declare no competing interests., (© 2021 The Authors.)

Large indels greatly impact the observable phenotypes in different organisms including plants and human. Hence, extracting large indels with high precision and sensitivity is important. Here, we developed IndelEnsembler to detect large indels in 1047 Arabidopsis whole-genome sequencing data. IndelEnsembler identified 34 093 deletions, 12 913 tandem duplications and 9773 insertions. Our large indel dataset was more comprehensive and accurate compared with the previous dataset of AthCNV (1). We captured nearly twice of the ground truth deletions and on average 27% more ground truth duplications compared with AthCNV, though our dataset has less number of large indels compared with AthCNV. Our large indels were positively correlated with transposon elements across the Arabidopsis genome. The non-homologous recombination events were the major formation mechanism of deletions in Arabidopsis genome. The Neighbor joining (NJ) tree constructed based on IndelEnsembler's deletions clearly divided the geographic subgroups of 1047 Arabidopsis. More importantly, our large indels represent a previously unassessed source of genetic variation. Approximately 49% of the deletions have low linkage disequilibrium (LD) with surrounding single nucleotide polymorphisms. Some of them could affect trait performance. For instance, using deletion-based genome-wide association study (DEL-GWAS), the accessions containing a 182-bp deletion in AT1G11520 had delayed flowering time and all accessions in north Sweden had the 182-bp deletion. We also found the accessions with 65-bp deletion in the first exon of AT4G00650 (FRI) flowered earlier than those without it. These two deletions cannot be detected in AthCNV and, interestingly, they do not co-occur in any Arabidopsis thaliana accession. By SNP-GWAS, surrounding SNPs of these two deletions do not correlate with flowering time. This example demonstrated that existing large indel datasets miss phenotypic variations and our large indel dataset filled in the gap., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Androgen receptor (AR) plays a central role in driving prostate cancer (PCa) progression. How AR promotes this process is still not completely clear. Herein, we used single-cell transcriptome analysis to reconstruct the transcriptional network of AR in PCa. Our work shows AR directly regulates a set of signature genes in the ER-to-Golgi protein vesicle-mediated transport pathway. The expression of these genes is required for maximum androgen-dependent ER-to-Golgi trafficking, cell growth, and survival. Our analyses also reveal the signature genes are associated with PCa progression and prognosis. Moreover, we find inhibition of the ER-to-Golgi transport process with a small molecule enhanced antiandrogen-mediated tumor suppression of hormone-sensitive and insensitive PCa. Finally, we demonstrate AR collaborates with CREB3L2 in mediating ER-to-Golgi trafficking in PCa. In summary, our findings uncover a critical role for dysregulation of ER-to-Golgi trafficking expression and function in PCa progression, provide detailed mechanistic insights for how AR tightly controls this process, and highlight the prospect of targeting the ER-to-Golgi pathway as a therapeutic strategy for advanced PCa., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

The ubiquity of vibration sensors and accelerometers, as well as advances in microfabrication technologies, have led to the development of implantable devices for biomedical applications. This work describes a piezoelectric microelectromechanical systems accelerometer designed for potential use in auditory prostheses. The design includes an aluminum nitride bimorph beam with a silicon proof mass. Analytic models of the device sensitivity and noise are presented. These lead to a minimum detectable acceleration cost function for the sensor that can be used to optimize sensor designs more effectively than typical sensitivity maximizing or electrical noise minimizing approaches. A fabricated device with a 1 μm thick, 100 μm long, and 700 μm wide beam and a 400 μm thick, 63 μm long, and 740 μm wide proof mass is tested experimentally. Results indicate accurate modeling of the system sensitivity up to the first resonant frequency (1420 Hz). The low-frequency sensitivity of the device is 1.3 mV/g, and the input referred noise is 36.3 nV / Hz at 100 Hz and 11.8 nV / Hz at 1 kHz. The resulting minimum detectable acceleration at 100 Hz and 1 kHz is 28 μg / Hz and 9.1 μg / Hz , respectively. A brief explanation of the use of the validated cost function for sensor design is provided, as well as an example comparing the piezoelectric sensor design to another from the literature. It is concluded that a traditional single-resonance design cannot compete with the performance of acoustic sensors; therefore, novel device designs must be considered for implantable auditory prosthesis applications.

Background: The Muscovy duck (Cairina moschata) is an economically important duck species, with favourable growth and carcass composition parameters in comparison to other ducks. However, limited genomic resources for Muscovy duck hinder our understanding of its evolution and genetic diversity., Results: We combined linked-reads sequencing technology and reference-guided methods for de novo genome assembly. The final draft assembly was 1.12 Gbp with 29 autosomes, one sex chromosome and 4,583 unlocalized scaffolds with an N50 size of 77.35 Mb. Based on universal single-copy orthologues (BUSCO), the draft genome assembly completeness was estimated to be 93.30 %. Genome annotation identified 15,580 genes, with 15,537 (99.72 %) genes annotated in public databases. We conducted comparative genomic analyses and found that species-specific and rapidly expanding gene families (compared to other birds) in Muscovy duck are mainly involved in Calcium signaling, Adrenergic signaling in cardiomyocytes, and GnRH signaling pathways. In comparison to the common domestic duck (Anas platyrhynchos), we identified 104 genes exhibiting strong signals of adaptive evolution (Ka/Ks > 1). Most of these genes were associated with immune defence pathways (e.g. IFNAR1 and TLR5). This is indicative of the existence of differences in the immune responses between the two species. Additionally, we combined divergence and polymorphism data to demonstrate the "faster-Z effect" of chromosome evolution., Conclusions: The chromosome-level genome assembly of Muscovy duck and comparative genomic analyses provide valuable resources for future molecular ecology studies, as well as the evolutionary arms race between the host and influenza viruses., (© 2021. The Author(s).)

Motivation: Virus integration in the host genome is frequently reported to be closely associated with many human diseases, and the detection of virus integration is a critically challenging task. However, most existing tools show limited specificity and sensitivity. Therefore, the objective of this study is to develop a method for accurate detection of virus integration into host genomes., Results: Herein, we report a novel method termed HIVID2 that is a significant upgrade of HIVID. HIVID2 performs a paired-end combination (PE-combination) for potentially integrated reads. The resulting sequences are then remapped onto the reference genomes, and both split and discordant chimeric reads are used to identify accurate integration breakpoints with high confidence. HIVID2 represents a great improvement in specificity and sensitivity, and predicts breakpoints closer to the real integrations, compared with existing methods. The advantage of our method was demonstrated using both simulated and real datasets. HIVID2 uncovered novel integration breakpoints in well-known cervical cancer-related genes, including FHIT and LRP1B, which was verified using protein expression data. In addition, HIVID2 allows the user to decide whether to automatically perform advanced analysis using the identified virus integrations. By analyzing the simulated data and real data tests, we demonstrated that HIVID2 is not only more accurate than HIVID but also better than other existing programs with respect to both sensitivity and specificity. We believe that HIVID2 will help in enhancing future research associated with virus integration., Availabilityand Implementation: HIVID2 can be accessed at https://github.com/zengxi-hada/HIVID2/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Motivation: Structural variations (SVs) are large scale mutations in a genome; although less frequent than point mutations, due to their large size they are responsible for more heritable differences between individuals. Two prominent classes of SVs are deletions and tandem duplications. They play important roles in many devastating genetic diseases, such as Smith-Magenis syndrome, Potocki-Lupski syndrome and Williams-Beuren syndrome. Since paired-end whole genome sequencing data have become widespread and affordable, reliably calling deletions and tandem duplications has been a major target in bioinformatics; unfortunately, the problem is far from being solved, since existing solutions often offer poor results when applied to real data., Results: We developed a novel caller, SurVIndel, which focuses on detecting deletions and tandem duplications from paired next-generation sequencing data. SurVIndel uses discordant paired reads, clipped reads as well as statistical methods. We show that SurVIndel outperforms existing methods on both simulated and real biological datasets., Availability and Implementation: SurVIndel is available at https://github.com/Mesh89/SurVIndel., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: The most prolific duck genetic resource in the world is located in Southeast/South Asia but little is known about the domestication and complex histories of these duck populations., Results: Based on whole-genome resequencing data of 78 ducks (Anas platyrhynchos) and 31 published whole-genome duck sequences, we detected three geographic distinct genetic groups, including local Chinese, wild, and local Southeast/South Asian populations. We inferred the demographic history of these duck populations with different geographical distributions and found that the Chinese and Southeast/South Asian ducks shared similar demographic features. The Chinese domestic ducks experienced the strongest population bottleneck caused by domestication and the last glacial maximum (LGM) period, whereas the Chinese wild ducks experienced a relatively weak bottleneck caused by domestication only. Furthermore, the bottleneck was more severe in the local Southeast/South Asian populations than in the local Chinese populations, which resulted in a smaller effective population size for the former (7100-11,900). We show that extensive gene flow has occurred between the Southeast/South Asian and Chinese populations, and between the Southeast Asian and South Asian populations. Prolonged gene flow was detected between the Guangxi population from China and its neighboring Southeast/South Asian populations. In addition, based on multiple statistical approaches, we identified a genomic region that included three genes (PNPLA8, THAP5, and DNAJB9) on duck chromosome 1 with a high probability of gene flow between the Guangxi and Southeast/South Asian populations. Finally, we detected strong signatures of selection in genes that are involved in signaling pathways of the nervous system development (e.g., ADCYAP1R1 and PDC) and in genes that are associated with morphological traits such as cell growth (e.g., IGF1R)., Conclusions: Our findings provide valuable information for a better understanding of the domestication and demographic history of the duck, and of the gene flow between local duck populations from Southeast/South Asia and China.

The ability of neural stem cells (NSCs) to switch between quiescence and proliferation is crucial for brain development and homeostasis. Increasing evidence suggests that variants of histone lysine methyltransferases including KMT5A are associated with neurodevelopmental disorders. However, the function of KMT5A/Pr-set7/SETD8 in the central nervous system is not well established. Here, we show that Drosophila Pr-Set7 is a novel regulator of NSC reactivation. Loss of function of pr-set7 causes a delay in NSC reactivation and loss of H4K20 monomethylation in the brain. Through NSC-specific in vivo profiling, we demonstrate that Pr-set7 binds to the promoter region of cyclin-dependent kinase 1 (cdk1) and Wnt pathway transcriptional co-activator earthbound1/jerky (ebd1). Further validation indicates that Pr-set7 is required for the expression of cdk1 and ebd1 in the brain. Similar to Pr-set7, Cdk1 and Ebd1 promote NSC reactivation. Finally, overexpression of Cdk1 and Ebd1 significantly suppressed NSC reactivation defects observed in pr-set7-depleted brains. Therefore, Pr-set7 promotes NSC reactivation by regulating Wnt signaling and cell cycle progression. Our findings may contribute to the understanding of mammalian KMT5A/PR-SET7/SETD8 during brain development., (© 2021 The Authors. Published under the terms of the CC BY NC ND 4.0 license.)

A significant portion of human cancers are due to viruses integrating into human genomes. Therefore, accurately predicting virus integrations can help uncover the mechanisms that lead to many devastating diseases. Virus integrations can be called by analysing second generation high-throughput sequencing datasets. Unfortunately, existing methods fail to report a significant portion of integrations, while predicting a large number of false positives. We observe that the inaccuracy is caused by incorrect alignment of reads in repetitive regions. False alignments create false positives, while missing alignments create false negatives. This paper proposes SurVirus, an improved virus integration caller that corrects the alignment of reads which are crucial for the discovery of integrations. We use publicly available datasets to show that existing methods predict hundreds of thousands of false positives; SurVirus, on the other hand, is significantly more precise while it also detects many novel integrations previously missed by other tools, most of which are in repetitive regions. We validate a subset of these novel integrations, and find that the majority are correct. Using SurVirus, we find that HPV and HBV integrations are enriched in LINE and Satellite regions which had been overlooked, as well as discover recurrent HBV and HPV breakpoints in human genome-virus fusion transcripts., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue., Results: This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%., Conclusions: The proposed L2MXception network may have great potential in early identification of peach diseases.

This study suggests a method to select core data that will be helpful for machine learning. Specifically, we form a two-dimensional distribution based on the similarity of the training data and compose grids with fixed ratios on the distribution. In each grid, we select data based on the distribution consistency (DC) of the target class data and examine how it affects the classifier. We use CIFAR-10 for the experiment and set various grid ratios from 0.5 to 0.005. The influences of these variables were analyzed with the use of different training data sizes selected based on high-DC, low-DC (inverse of high DC), and random (no criteria) selections. As a result, the average point accuracy at 0.95% (±0.65) and the point accuracy at 1.54% (±0.59) improved for the grid configurations of 0.008 and 0.005, respectively. These outcomes justify an improved performance compared with that of the existing approach (data distribution search). In this study, we confirmed that the learning performance improved when the training data were selected for very small grid and high-DC settings.

Background: DNA methylation is an important epigenetic modification that plays a critical role in most eukaryotic organisms. Parental alleles in haploid genomes may exhibit different methylation patterns, which can lead to different phenotypes and even different therapeutic and drug responses to diseases. However, to our knowledge, no software is available for the identification of DNA methylation haplotype regions with combined allele-specific DNA methylation, single nucleotide polymorphisms (SNPs) and high-throughput chromosome conformation capture (Hi-C) data., Results: In this paper, we developed a new method, MethHaplo, that identify DNA methylation haplotype regions with allele-specific DNA methylation and SNPs from whole-genome bisulfite sequencing (WGBS) data. Our results showed that methylation haplotype regions were ten times longer than haplotypes with SNPs only. When we integrate WGBS and Hi-C data, MethHaplo could call even longer haplotypes., Conclusions: This study illustrates the usefulness of methylation haplotypes. By constructing methylation haplotypes for various cell lines, we provide a clearer picture of the effect of DNA methylation on gene expression, histone modification and three-dimensional chromosome structure at the haplotype level. Our method could benefit the study of parental inheritance-related disease and hybrid vigor in agriculture.

Integration of human papillomavirus (HPV) DNA into the human genome is a reputed key driver of cervical cancer. However, the effects of HPV integration on chromatin structural organization and gene expression are largely unknown. We studied a cohort of 61 samples and identified an integration hot spot in the CCDC106 gene on chromosome 19. We then selected fresh cancer tissue that contained the unique integration loci at CCDC106 with no HPV episomal DNA and performed whole-genome, RNA, chromatin immunoprecipitation and high-throughput chromosome conformation capture (Hi-C) sequencing to identify the mechanisms of HPV integration in cervical carcinogenesis. Molecular analyses indicated that chromosome 19 exhibited significant genomic variation and differential expression densities, with correlation found between three-dimensional (3D) structural change and gene expression. Importantly, HPV integration divided one topologically associated domain (TAD) into two smaller TADs and hijacked an enhancer from PEG3 to CCDC106, with a decrease in PEG3 expression and an increase in CCDC106 expression. This expression dysregulation was further confirmed using 10 samples from our cohort, which exhibited the same HPV-CCDC106 integration. In summary, we found that HPV-CCDC106 integration altered local chromosome architecture and hijacked an enhancer via 3D genome structure remodeling. Thus, this study provides insight into the 3D structural mechanism underlying HPV integration in cervical carcinogenesis., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The rice stem borer, Chilo suppressalis, is one of the most damaging insect pests to rice production worldwide. Although C. suppressalis has been the focus of numerous studies examining cold tolerance and diapause, plant-insect interactions, pesticide targets and resistance, and the development of RNAi-mediated pest management, the absence of a high-quality genome has limited deeper insights. To address this limitation, we generated a draft C. suppressalis genome constructed from both Illumina and PacBio sequences. The assembled genome size was 824.35 Mb with a contig N 50 of 307 kb and a scaffold N 50 of 1.75 Mb. Hi-C scaffolding assigned 99.2% of the bases to one of 29 chromosomes. Based on universal single-copy orthologues (BUSCO), the draft genome assembly was estimated to be 97% complete and is predicted to encompass 15,653 protein-coding genes. Cold tolerance is an extreme survival strategy found in animals. However, little is known regarding the genetic basis of the winter ecology of C. suppressalis. Here, we focused our orthologous analysis on those gene families associated with animal cold tolerance. Our finding provided the first genomic evidence revealing specific cold-tolerant strategies in C. suppressalis, including those involved in glucose-originated glycerol biosynthesis, triacylglycerol-originated glycerol biosynthesis, fatty acid synthesis and trehalose transport-intermediate cold tolerance. The high-quality C. suppressalis genome provides a valuable resource for research into a broad range of areas in molecular ecology, and subsequently benefits developing modern pest control strategies., (© 2019 John Wiley & Sons Ltd.)

The previous fastest algorithm for computing the rooted triplet distance between two input galled trees (i.e., phylogenetic networks whose cycles are vertex-disjoint) runs in [Formula: see text] time, where n is the cardinality of the leaf label set. In this article, we present an [Formula: see text]-time solution. Our strategy is to transform the input so that the answer can be obtained by applying an existing [Formula: see text]-time algorithm for the simpler case of two phylogenetic trees a constant number of times. The new algorithm has been implemented, and applying it to pairs of randomly generated galled trees with up to [Formula: see text] leaves confirms that it is fast in practice.

Background: Hepatocellular carcinoma is the second most deadly cancer with late presentation and limited treatment options, highlighting an urgent need to better understand HCC to facilitate the identification of early-stage biomarkers and uncover therapeutic targets for the development of novel therapies for HCC., Methods: Deep transcriptome sequencing of tumor and paired non-tumor liver tissues was performed to comprehensively evaluate the profiles of both the host and HBV transcripts in HCC patients. Differential gene expression patterns and the dys-regulated genes associated with clinical outcomes were analyzed. Somatic mutations were identified from the sequencing data and the deleterious mutations were predicted. Lastly, human-HBV chimeric transcripts were identified, and their distribution, potential function and expression association were analyzed., Results: Expression profiling identified the significantly upregulated TP73 as a nodal molecule modulating expression of apoptotic genes. Approximately 2.5% of dysregulated genes significantly correlated with HCC clinical characteristics. Of the 110 identified genes, those involved in post-translational modification, cell division and/or transcriptional regulation were upregulated, while those involved in redox reactions were downregulated in tumors of patients with poor prognosis. Mutation signature analysis identified that somatic mutations in HCC tumors were mainly non-synonymous, frequently affecting genes in the micro-environment and cancer pathways. Recurrent mutations occur mainly in ribosomal genes. The most frequently mutated genes were generally associated with a poorer clinical prognosis. Lastly, transcriptome sequencing suggest that HBV replication in the tumors of HCC patients is rare. HBV-human fusion transcripts are a common observation, with favored HBV and host insertion sites being the HBx C-terminus and gene introns (in tumors) and introns/intergenic-regions (in non-tumors), respectively. HBV-fused genes in tumors were mainly involved in RNA binding while those in non-tumors tissues varied widely. These observations suggest that while HBV may integrate randomly during chronic infection, selective expression of functional chimeric transcripts may occur during tumorigenesis., Conclusions: Transcriptome sequencing of HCC patients reveals key cancer molecules and clinically relevant pathways deregulated/mutated in HCC patients and suggests that while HBV may integrate randomly during chronic infection, selective expression of functional chimeric transcripts likely occur during the process of tumorigenesis.

Insight into high-resolution three-dimensional genome organization and its effect on transcription remains largely elusive in plants. Here, using a long-read ChIA-PET approach, we map H3K4me3- and RNA polymerase II (RNAPII)-associated promoter-promoter interactions and H3K9me2-marked heterochromatin interactions at nucleotide/gene resolution in rice. The chromatin architecture is separated into different independent spatial interacting modules with distinct transcriptional potential and covers approximately 82% of the genome. Compared to inactive modules, active modules possess the majority of active loop genes with higher density and contribute to most of the transcriptional activity in rice. In addition, promoter-promoter interacting genes tend to be transcribed cooperatively. In contrast, the heterochromatin-mediated loops form relative stable structure domains in chromatin configuration. Furthermore, we examine the impact of genetic variation on chromatin interactions and transcription and identify a spatial correlation between the genetic regulation of eQTLs and e-traits. Thus, our results reveal hierarchical and modular 3D genome architecture for transcriptional regulation in rice.

Mutations of the Integrator subunits are associated with neurodevelopmental disorders and cancers. However, their role during neural development is poorly understood. Here, we demonstrate that the Drosophila Integrator complex prevents dedifferentiation of intermediate neural progenitors (INPs) during neural stem cell (neuroblast) lineage development. Loss of intS5, intS8, and intS1 generated ectopic type II neuroblasts. INP-specific knockdown of intS8, intS1, and intS2 resulted in the formation of excess type II neuroblasts, indicating that Integrator prevents INP dedifferentiation. Cell-type-specific DamID analysis identified 1413 IntS5-binding sites in INPs, including zinc-finger transcription factor earmuff (erm). Furthermore, erm expression is lost in intS5 and intS8 mutant neuroblast lineages, and intS8 genetically interacts with erm to suppress the formation of ectopic neuroblasts. Taken together, our data demonstrate that the Drosophila Integrator complex plays a critical role in preventing INP dedifferentiation primarily by regulating a key transcription factor Erm that also suppresses INP dedifferentiation., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

In the version of this article initially published, '+' and '-' labels were missing from the graph keys at the bottom of Fig. 8d. The error has been corrected in the HTML and PDF versions of the article.

The aberrant activities of transcription factors such as the androgen receptor (AR) underpin prostate cancer development. While the AR cis -regulation has been extensively studied in prostate cancer, information pertaining to the spatial architecture of the AR transcriptional circuitry remains limited. In this paper, we propose a novel framework to profile long-range chromatin interactions associated with AR and its collaborative transcription factor, erythroblast transformation-specific related gene (ERG), using chromatin interaction analysis by paired-end tag (ChIA-PET). We identified ERG-associated long-range chromatin interactions as a cooperative component in the AR-associated chromatin interactome, acting in concert to achieve coordinated regulation of a subset of AR target genes. Through multifaceted functional data analysis, we found that AR-ERG interaction hub regions are characterized by distinct functional signatures, including bidirectional transcription and cotranscription factor binding. In addition, cancer-associated long noncoding RNAs were found to be connected near protein-coding genes through AR-ERG looping. Finally, we found strong enrichment of prostate cancer genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) at AR-ERG co-binding sites participating in chromatin interactions and gene regulation, suggesting GWAS target genes identified from chromatin looping data provide more biologically relevant findings than using the nearest gene approach. Taken together, our results revealed an AR-ERG-centric higher-order chromatin structure that drives coordinated gene expression in prostate cancer progression and the identification of potential target genes for therapeutic intervention., (© 2019 Zhang et al.; Published by Cold Spring Harbor Laboratory Press.)

Background: DNA methylation plays crucial roles in most eukaryotic organisms. Bisulfite sequencing (BS-Seq) is a sequencing approach that provides quantitative cytosine methylation levels in genome-wide scope and single-base resolution. However, genomic variations such as insertions and deletions (indels) affect methylation calling, and the alignment of reads near/across indels becomes inaccurate in the presence of polymorphisms. Hence, the simultaneous detection of DNA methylation and indels is important for exploring the mechanisms of functional regulation in organisms., Results: These problems motivated us to develop the algorithm BatMeth2, which can align BS reads with high accuracy while allowing for variable-length indels with respect to the reference genome. The results from simulated and real bisulfite DNA methylation data demonstrated that our proposed method increases alignment accuracy. Additionally, BatMeth2 can calculate the methylation levels of individual loci, genomic regions or functional regions such as genes/transposable elements. Additional programs were also developed to provide methylation data annotation, visualization, and differentially methylated cytosine/region (DMC/DMR) detection. The whole package provides new tools and will benefit bisulfite data analysis., Conclusion: BatMeth2 improves DNA methylation calling, particularly for regions close to indels. It is an autorun package and easy to use. In addition, a DNA methylation visualization program and a differential analysis program are provided in BatMeth2. We believe that BatMeth2 will facilitate the study of the mechanisms of DNA methylation in development and disease. BatMeth2 is an open source software program and is available on GitHub ( https://github.com/GuoliangLi-HZAU/BatMeth2 /).

Accumulation of trimethylation of histone H3 at lysine 4 (H3K4me3) on immune-related gene promoters underlies robust transcription during trained immunity. However, the molecular basis for this remains unknown. Here we show three-dimensional chromatin topology enables immune genes to engage in chromosomal contacts with a subset of long noncoding RNAs (lncRNAs) we have defined as immune gene-priming lncRNAs (IPLs). We show that the prototypical IPL, UMLILO, acts in cis to direct the WD repeat-containing protein 5 (WDR5)-mixed lineage leukemia protein 1 (MLL1) complex across the chemokine promoters, facilitating their H3K4me3 epigenetic priming. This mechanism is shared amongst several trained immune genes. Training mediated by β-glucan epigenetically reprograms immune genes by upregulating IPLs in manner dependent on nuclear factor of activated T cells. The murine chemokine topologically associating domain lacks an IPL, and the Cxcl genes are not trained. Strikingly, the insertion of UMLILO into the chemokine topologically associating domain in mouse macrophages resulted in training of Cxcl genes. This provides strong evidence that lncRNA-mediated regulation is central to the establishment of trained immunity.

Transpositions transfer DNA segments between different loci within a genome; in particular, when a transposition is found in a sample but not in a reference genome, it is called a non-reference transposition. They are important structural variations that have clinical impact. Transpositions can be called by analyzing second generation high-throughput sequencing datasets. Current methods follow either a database-based or a database-free approach. Database-based methods require a database of transposable elements. Some of them have good specificity; however this approach cannot detect novel transpositions, and it requires a good database of transposable elements, which is not yet available for many species. Database-free methods perform de novo calling of transpositions, but their accuracy is low. We observe that this is due to the misalignment of the reads; since reads are short and the human genome has many repeats, false alignments create false positive predictions while missing alignments reduce the true positive rate. This paper proposes new techniques to improve database-free non-reference transposition calling: first, we propose a realignment strategy called one-end remapping that corrects the alignments of reads in interspersed repeats; second, we propose a SNV-aware filter that removes some incorrectly aligned reads. By combining these two techniques and other techniques like clustering and positive-to-negative ratio filter, our proposed transposition caller TranSurVeyor shows at least 3.1-fold improvement in terms of F1-score over existing database-free methods. More importantly, even though TranSurVeyor does not use databases of prior information, its performance is at least as good as existing database-based methods such as MELT, Mobster and Retroseq. We also illustrate that TranSurVeyor can discover transpositions that are not known in the current database.

The [Formula: see text] Consistency problem takes as input two sets [Formula: see text] and [Formula: see text] of resolved triplets and two sets [Formula: see text] and [Formula: see text] of fan triplets, and asks for a distinctly leaf-labeled tree that contains all elements in [Formula: see text] and no elements in [Formula: see text] as embedded subtrees, if such a tree exists. This article presents a detailed characterization of how the computational complexity of the problem changes under various restrictions. Our main result is an efficient algorithm for dense inputs satisfying [Formula: see text] whose running time is linear in the size of the input and therefore optimal.

This article presents two new deterministic algorithms for constructing consensus trees. Given an input of  phylogenetic trees with identical leaf label sets and  leaves each, the first algorithm constructs the majority rule (+) consensus tree in time, which is optimal since the input size is , and the second one constructs the frequency difference consensus tree in time.

A correction to this article has been published and is linked from the HTML version of this article.

Background: Hepatocellular carcinoma (HCC) is the most common type of liver cancer worldwide. Previously, we reported that cadherin-17 (CDH17) and its related CDH17/β-catenin axis may be responsible for inducing HCC in a subset of patients exhibiting CDH17 over-expression. Here we aimed at obtaining a better understanding of the CDH17-related HCC biology and to obtain further indications for the design of targeted therapies in CDH17 over-expressing HCC patients., Results: We found that SPINK1 acts as a downstream effector of the CDH17/β-catenin axis in HCC. In addition, we found that SPINK1 expression exhibited a positive correlation with CDH17 expression in human HCCs and was over-expressed in up to 70% of the tumors. We identified SPINK1 as a downstream effector of the CDH17/β-catenin axis using a spectrum of in vitro assays, including gene expression modulation and inhibitor assays, bioinformatics analyses and luciferase reporter assays. These in vitro results were validated in primary human HCCs, including the observation that alteration in β-catenin expression (a core component of the CDH17/β-catenin axis) in tumors affects SPINK1 serum levels in HCC patients. Similar to CDH17, SPINK1 expression in HCC cells was found to be associated with specific tumor-related properties via activating the c-Raf/MEK/ERK pathway., Conclusions: Our current data substantiate our knowledge on the role of CDH17 in the biology of HCC and suggest that components of the CDH17/β-catenin axis may serve as therapeutic targets in CDH17 over-expressing HCC patients.

The switch between quiescence and proliferation is central for neurogenesis and its alteration is linked to neurodevelopmental disorders such as microcephaly. However, intrinsic mechanisms that reactivate Drosophila larval neural stem cells (NSCs) to exit from quiescence are not well established. Here we show that the spindle matrix complex containing Chromator (Chro) functions as a key intrinsic regulator of NSC reactivation downstream of extrinsic insulin/insulin-like growth factor signalling. Chro also prevents NSCs from re-entering quiescence at later stages. NSC-specific in vivo profiling has identified many downstream targets of Chro, including a temporal transcription factor Grainy head (Grh) and a neural stem cell quiescence-inducing factor Prospero (Pros). We show that spindle matrix proteins promote the expression of Grh and repress that of Pros in NSCs to govern their reactivation. Our data demonstrate that nuclear Chro critically regulates gene expression in NSCs at the transition from quiescence to proliferation.The spindle matrix proteins, including Chro, are known to regulate mitotic spindle assembly in the cytoplasm. Here the authors show that in Drosophila larval brain, Chro promotes neural stem cell (NSC) reactivation and prevents activated NSCs from entering quiescence, and that Chro carries out such a role by regulating the expression of key transcription factors in the nucleus.

A rise in the occurrence of allergic diseases is attributed to the dysregulated balance of type 1/type 2 immunity, where type 2 T-helper (Th2) cells predominate over type 1 T-helper (Th1) cells, leading to an abnormally increased production of IgE in response to unharmful antigens. Kimchi, a traditional Korean fermented food, is a rich source of beneficial lactic acid bacteria. In this study, we investigated the ability of Enterococcus faecium FC-K derived from kimchi to induce type I immunity in the presence of Th2 polarizing conditions in vitro and in vivo. Stimulation of mouse peritoneal macrophages with E. faecium FC-K induced the production of tumor necrosis factor alpha, interleukin (IL)-6, and IL-12. Under the in vitro Th2 conditions in which splenic T cells were activated in the presence of IL-4, E. faecium FC-K enhanced the ability of T cells to produce interferon (IFN)-γ. Using the ovalbumin (OVA)-induced allergy model, male BALB/c mice receiving E. faecium FC-K reduced the serum level of total IgE, but not that of OVA-specific IgE. Furthermore, the population of activated splenic B cells during OVA immunization was decreased in E. faecium FC-K-treated mice, accounting for a reduction of total IgE in the serum. Restimulating splenocytes from OVA-immunized mice with OVA ex vivo resulted in an increased production of IFN-γ, with no effect on IL-4, in E. faecium FC-Ktreated mice. These observations provide the evidence that E. faecium FC-K can be a beneficial probiotic strain that can modulate the Th2-mediated pathologic response.

Background: The study of virus integrations in human genome is important since virus integrations were shown to be associated with diseases. In the literature, few methods have been proposed that predict virus integrations using next generation sequencing datasets. Although they work, they are slow and are not very sensitive., Results and Discussion: This paper introduces a new method BatVI to predict viral integrations. Our method uses a fast screening method to filter out chimeric reads containing possible viral integrations. Next, sensitive alignments of these candidate chimeric reads are called by BLAST. Chimeric reads that are co-localized in the human genome are clustered. Finally, by assembling the chimeric reads in each cluster, high confident virus integration sites are extracted., Conclusion: We compared the performance of BatVI with existing methods VirusFinder and VirusSeq using both simulated and real-life datasets of liver cancer patients. BatVI ran an order of magnitude faster and was able to predict almost twice the number of true positives compared to other methods while maintaining a false positive rate less than 1%. For the liver cancer datasets, BatVI uncovered novel integrations to two important genes TERT and MLL4, which were missed by previous studies. Through gene expression data, we verified the correctness of these additional integrations. BatVI can be downloaded from http://biogpu.ddns.comp.nus.edu.sg/~ksung/batvi/index.html .

Structural variations (SVs) are mutations in the genome of size at least fifty nucleotides. They contribute to the phenotypic differences among healthy individuals, cause severe diseases and even cancers by breaking or linking genes. Thus, it is crucial to systematically profile SVs in the genome. In the past decade, many next-generation sequencing (NGS)-based SV detection methods have been proposed due to the significant cost reduction of NGS experiments and their ability to unbiasedly detect SVs to the base-pair resolution. These SV detection methods vary in both sensitivity and specificity, since they use different SV-property-dependent and library-property-dependent features. As a result, predictions from different SV callers are often inconsistent. Besides, the noises in the data (both platform-specific sequencing error and artificial chimeric reads) impede the specificity of SV detection. Poorly characterized regions in the human genome (e.g., repeat regions) greatly impact the reads mapping and in turn affect the SV calling accuracy. Calling of complex SVs requires specialized SV callers. Apart from accuracy, processing speed of SV caller is another factor deciding its usability. Knowing the pros and cons of different SV calling techniques and the objectives of the biological study are essential for biologists and bioinformaticians to make informed decisions. This paper describes different components in the SV calling pipeline and reviews the techniques used by existing SV callers. Through simulation study, we also demonstrate that library properties, especially insert size, greatly impact the sensitivity of different SV callers. We hope the community can benefit from this work both in designing new SV calling methods and in selecting the appropriate SV caller for specific biological studies., (Copyright © 2016 Elsevier Inc. All rights reserved.)