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Meng, J.

Publications and source records attributed to Meng, J..

At least 73 records · Page 4Linked to original sources

Global Profiling of the Lysine Crotonylome in Different Pluripotent States

Pluripotent stem cells (PSCs) can be expanded in vitro in different culture conditions, resulting in a spectrum of cell states with distinct properties. Understanding how PSCs transition from one state to another, ultimately leading to lineage-specific differentiation, is important for developmental biology and regenerative medicine. Although there is significant information regarding gene expression changes controlling these transitions, less is known about post-translational modifications of proteins. Protein crotonylation is a newly discovered post-translational modification where lysine residues are modified with a crotonyl group. Here, we employed affinity purification of crotonylated peptides and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to systematically profile protein crotonylation in mouse PSCs in different states including ground, metastable and primed state, as well as metastable PSCs undergoing early pluripotency exit. We successfully identified 3628 high-confidence sites in 1426 proteins. These crotonylated proteins are enriched for factors involved in functions related to pluripotency such as RNA biogenesis, central carbon metabolism and proteasome function. Moreover, we found that increasing the cellular levels of crotonyl-CoA through crotonic acid treatment promotes proteasome activity in metastable PSCs and delays their differentiation, consistent with previous observations showing that enhanced proteasomal activity helps to sustain pluripotency. Our atlas of protein crotonylation will be valuable for further studies of pluripotency regulation and may also provide insights into the role of metabolism in other cell fate transitions.

cell biology↗

MOVICS: an R package for multi-omics integration and visualization in cancer subtyping

SummaryStratification of cancer patients into distinct molecular subgroups based on multi-omics data is an important issue in the context of precision medicine. Here we present MOVICS, an R package for multi-omics integration and visualization in cancer subtyping. MOVICS provides a unified interface for 10 state-of-the-art multi-omics integrative clustering algorithms, and incorporates the most commonly used downstream analyses in cancer subtyping researches, including characterization and comparison of identified subtypes from multiple perspectives, and verification of subtypes in external cohort using a model-free approach for multiclass prediction. MOVICS also creates feature rich customizable visualizations with minimal effort. Availability and implementationMOVICS package and online tutorial are freely available at https://github.com/xlucpu/MOVICS.

bioinformatics↗

Tumor Immunity Microenvironment-based classifications of bladder cancer for enhancing cancer immunotherapy

BackgroundBladder cancer is composed by a mass of heterogenetic characteristics, immunotherapy is a potential way to save the life of bladder cancer patients, but only benefit to about 20% patients. Methods and materialsA total of 4003 bladder cancer patients from 19 cohorts was enrolled in this study, collecting the clinical information and mRNA expression profile. The unsupervised non-negative matrix factorization (NMF) and nearest template prediction (NTP) algorithm was used to divide the patients to immune activated, immune exhausted and non-immune class. Verified gene sets of signatures were used to illustrate the characteristic of immunophenotypes. Clinical and genetic features were compared in different immunophenotypes. ResultsWe identified the immune class and non-immune classes in from TCGA-BLCA cohort. The 150 top different expression genes between these two classes was extracted as the input profile for the reappearing of the classification in the other 19 cohorts. As to the activated and exhausted subgroups, a stromal activation signature was conducted by NTP algorithm. Patients in the immune classes shown the highly enriched signatures of immunocytes, while the exhausted subgroup also shown an increased signature of TITR, WNT/TGF-{beta}, TGF-{beta}1 activated, and C-ECM signatures. Patients in the immune activated shown a lower CNA burden, better overall survival, and favorable response to anti-PD-1 therapy. ConclusionWe defined and validated a novel classifier among the 4003 bladder cancer patients. Anti-PD-1 immunotherapy could benefit more for the patients belong to immune activated subgroup, while ICB therapy plus TGF-{beta} inhibitor or EP300 inhibitor might be more effectiveness for patients in immune exhausted subgroup.

immunology↗

VIP-HL: Semi-automated ACMG/AMP variant interpretation platform for genetic hearing loss

PurposeThe American College of Medical Genetics and Genomics, and the Association for Molecular Pathology (ACMG/AMP) have proposed a set of evidence-based guidelines to support sequence variant interpretation. The ClinGen hearing loss expert panel (HL-EP) introduced further specifications into the ACMG/AMP framework for genetic hearing loss. This study aimed to semi-automate the HL ACMG/AMP rules. MethodsVIP-HL aggregates information from external databases to automate 13 out of 24 ACMG/AMP rules specified by HL-EP, namely PVS1, PS1, PM1, PM2, PM4, PM5, PP3, BA1, BS1, BS2, BP3, BP4, and BP7. ResultsWe benchmarked VIP-HL using 50 variants where 83 rules were activated by the HL expert panel. VIP-HL concordantly activated 96% (80/83) rules, significantly higher than that of by InterVar (47%; 39/83). Of 4948 ClinVar star 2+ variants from 142 deafness-related genes, VIP-HL achieved an overall variant interpretation concordance in 88.0% (4353/4948). VIP-HL is available with a user-friendly web interface at http://hearing.genetics.bgi.com/. ConclusionVIP-HL is an integrated online tool for reliable automated variant classification in hearing loss genes. It assists curators in variant interpretation and provides a platform for users to share classifications with each other.

bioinformatics↗

Multi-Task Learning based 3-Dimensional Striatal Segmentation of MRI - a Multi-modal Objective Assessment

The human striatum is a collection of subcortical nuclei that serves as a critical node in an information processing network. Recent studies have established a clear topographical and functional organization of projections to and from striatal regions that involves detailed and complex subdivisions of the striatum. Manual segmentation of these functional subdivisions is labor-intensive and time-consuming, yet automated methods have also been a challenging computational problem. Recently, deep learning has emerged as a powerful tool for various tasks, including semantic segmentation. Multi-Task Learning (MTL) is a machine learning technique that allows latent representations between related tasks to be shared during model prediction of multiple tasks with better accuracy. We utilized MTL to segment subregions of the striatum consisting of pre-commissural putamen (prePU), pre-commissural caudate (preCA), post-commissural putamen (postPU), post-commissural caudate (postCA), and ventral striatum (VST). Dice similarity coefficients (DSC) demonstrate excellent spatial agreement between manual and MTL-generated segmentations ([≥] 0.72 across all striatal subregions). Further quantitative task-based analysis was also conducted. Binding potential values, BPND, of [11C]raclopride PET, and ROI time-series and whole-brain connectivity using fMRI images were compared between results generated from manual segmentations and MTL-generated segmentations. BPND values from MTL-generated segmentations were shown to correlate well with manual segmentations with R2 [≥] 0.91 in all caudate and putamen subregions, and R2=0.69 in VST. Mean Pearson correlation coefficients of the fMRI data between MTL-generated and manual segmentations were also high in time-series ([≥]0.86) and whole-brain connectivity ([≥]0.89) across all subregions. We conclude that the MTL approach is a fast, robust and reliable method for 3D striatal subregion segmentation with results comparable to manually segmented ROIs.

bioengineering↗

Androgen Receptor promotes renal cell carcinoma (RCC) vasculogenic mimicry (VM) via altering TWIST1 nonsense-mediated decay through lncRNA-TANAR

While the androgen receptor (AR) may influence the progression of clear cell renal cell carcinoma (ccRCC), its role to impact vasculogenic mimicry (VM) to alter the ccRCC progression and metastasis remains obscure. Here we demonstrated that elevated AR expression was positively correlated with tumor-originated vasculogenesis in ccRCC patients. Consistently, in vitro research revealed AR promoted VM formation in ccRCC cell lines via modulating lncRNA-TANAR/TWIST1 signals. Mechanism dissection showed that AR could increase lncRNA-TANAR (TANAR) expression through binding to the androgen response elements (AREs) located on its promoter region. Moreover, we found that TANAR could impede nonsense-mediated mRNA decay (NMD) of TWIST1 mRNA by direct interaction with TWIST1 5’UTR. A preclinical study using in vivo mouse model with orthotopic xenografts of ccRCC cells further confirmed the in vitro data. Together, these results illustrated that AR-mediated lnc-TANAR signals might play a crucial role in ccRCC VM formation and metastasis, and targeting this newly identified AR/lncRNA-TANAR/TWIST1 signaling may help in the development of a novel anti-angiogenesis therapy to better suppress the ccRCC progression.View Full Text

cancer biology↗

Development and validation of a six-RNA binding proteins prognostic signature and candidate drugs for prostate cancer

The dysregulation of RNA binding proteins (RBPs) play critical roles in the progression of several cancers. However, the overall functions of RBPs in prostate cancer (PCa) remain poorly understood. Therefore, we first identified 144 differentially expressed RBPs in tumors compared to normal tissues based on the TCGA dataset. Next, six RBP genes (MSI1, MBNL2, LENG9, REXO2, RNASE1, PABPC1L) were screened out as prognosis hub genes by univariate, LASSO and multivariate Cox regression and used to establish the prognostic signature. Further analysis indicated that high risk group was significantly associated with poor RFS, which was validated in the MSKCC cohort. Besides, patients in high risk group was closely associated with dysregulation of DNA damage repair pathway, copy number alteration, tumor burden mutation and low-respond to cisplatin (P < 0.001), bicalutamide (P < 0.001). Finally, three drugs (ribavirin, carmustine, carbenoxolone) were predicted using Connectivity Map. In summary, we identified a six-RBP gene signature and three candidate drugs against PCa, which may promote the individualized treatment and further improve the life quality of PCa patients.

genomics↗

Neuroinflammation in the normal appearing white matter of multiple sclerosis brain causes abnormalities at the node of Ranvier

Changes to the structure of nodes of Ranvier in the normal-appearing white matter (NAWM) of MS brains are associated with chronic inflammation. We show that the paranodal domains in MS NAWM are longer on average than control, with Kv1.2 channels dislocated into the paranode. These pathological features are reproduced in a model of chronic meningeal inflammation generated by the injection of lentiviral vectors for the lymphotoxin- (LT) and interferon-{gamma} (IFN{gamma}) genes. We show that tumour necrosis factor (TNF), IFN{gamma} and glutamate can provoke paranodal elongation in cerebellar slice cultures, which could be reversed by an NMDA blocker. When these changes were inserted into a computational model to simulate axonal conduction, a rapid decrease in velocity was observed, reaching conduction failure in small diameter axons. We suggest that glial cells activated by proinflammatory cytokines can produce high levels of glutamate, which triggers paranodal pathology, contributing to axonal damage and conduction deficits.

neuroscience↗

Immune Response Drives Outcomes in Prostate Cancer: Implications for Immunotherapy

BackgroundThe heterogeneity of the immune microenvironment leads to the different response results of immune checkpoint blockade therapy. We aimed to propose a robust molecular classification of prostate cancer microenvironment to identify ideal patients for delivering effective immunotherapy. MethodsA total of 1,557 prostate cancer patients were enrolled in the current study, including 69 real-world samples from the AHMU-PC cohort. Non-negative matrix factorization algorithm was employed to virtually microdissect the patients to immune and non-immune subclasses. The patients in the immune class were dichotomized to immune activated and suppressed subtypes by the nearest template prediction of activated stroma signature. The curative effects of different immune subclasses in response to immunotherapy were also predicted. ResultsWe termed the newly identified molecular class of tumors as "immune class", which was characterized by a high enrichment of T cell, B cell, NK cell, macrophage associated signatures, etc., compared with the non-immune class (all, P < 0.05). Subsequently, the immune class was subclassified into immune activated and suppressed subtypes determined by the activation status of WNT/TGF-{beta}, TGF-{beta}1, and C-ECM signatures. As a result, around 14.9% to 24.3% of overall patients belonged to the immune activated subgroup, and they were proved with the favorable recurrence-free survival outcomes than others and showed potential curative effects in response to the anti-PD-1/PD-L1 immunotherapy. ConclusionOur study identifies a novel immune class in prostate cancer, which is further described by active or exhausted immune responses. These findings provide novel insights into tailoring immunotherapeutic strategies for different prostate cancer subgroups. Highlights{blacksquare} Immunotherapy could trigger a durable response in advanced prostate cancer, but it only benefits a minority of patients; {blacksquare}Immune response drives recurrence-free survival outcomes in prostate cancer; {blacksquare}The robust molecular classification system helps identify more ideal patients for delivering anti-PD-1/PD-L1 immunotherapy.

immunology↗

Effects of rhein on bile acid homeostasis in rats

Rhein, the active ingredient of rhubarb, a medicinal and edible plant, is widely used in clinical practice. In this work, we investigated the alterations of 14 bile acids and hepatic transporters after rats were administered rhein for 5 consecutive weeks. There was no obvious injury to the liver and kidney, and there were no significant changes in biochemical indicators. However, 1,000 mg/kg rhein increased the liver total bile acid (TBA) levels, especially taurine-conjugated bile acids (t-CBAs), inhibited the expression of Farnesoid X receptor (FXR) and (bile salt export pump) BSEP mRNA, and upregulated the expression of (cholesterol 7-hydroxylase) CYP7A1 mRNA. Rhein close to the clinical dose reduced the amounts of TBAs, especially unconjugated bile acids (UCBAs), and elevated the expression of FXR and multidrug resistance-associated protein 3 (Mrp3) mRNA. These results denote that rhein is not toxic and is safe to use at a reasonable dose and timing.

pharmacology and toxicology↗

Regulation of olfactory-based sex behaviors in the silkworm by genes in the sex-determination cascade

Insect courtship and mating depend on integration of olfactory, visual, and tactile cues. Compared to other insects, Bombyx mori, the domesticated silkworm, has relatively simple sexual behaviors as it cannot fly. Here by using CRISPR/Cas and electrophysiological techniques we found that courtship and mating behaviors are regulated in male silk moths by mutating genes in the sex determination cascade belonging to two conserved pathways. Loss of Bmdsx gene expression significantly reduced the peripheral perception of the major pheromone component bombykol by reducing expression of the product of the BmOR1 gene which completely blocked courtship in adult males. Interestingly, we found that mating behavior was regulated independently by another sexual differentiation gene, Bmfru. Loss of Bmfru completely blocked mating, but males displayed normal courtship behavior. Lack of Bmfru expression significantly reduced the perception of the minor pheromone component bombykal due to the down regulation of BmOR3 expression; further, functional analysis revealed that loss of the product of BmOR3 played a key role in terminating male mating behavior. Our results suggest that Bmdsx and Bmfru are at the base of the two primary pathways that regulate olfactory-based sexual behavior. Author SummaryThe fundamental insect sexual behaviors, courtship and mating, result from successful integration of olfactory, vision, tactile and other complex innate behaviors. In the widely used insect model, Drosophila melanogaster, the sex determination cascade genes fruitless and doublesex are involved in the regulation of courtship and mating behaviors; however, little is known about the function of these sexual differentiation genes in regulating sex behaviors of Lepidoptera. Here we combine genetics and electrophysiology to investigate regulation pathway of sexual behaviors in the model lepidopteran insect, the domesticated silk moth, Bombyx mori. Our results support the presence of two genetic pathways in B. mori, named Bmdsx-BmOR1-bombykol and Bmfru-BmOR3-bombykal, which control distinct aspects of male sexual behavior that are modulated by olfaction. This is the first comprehensive report about the role of sex differentiation genes in the male sexual behavior in the silk moth.

genetics↗

DeepSSV: detecting somatic small variants in paired tumor and normal sequencing data with convolutional neural network

MotivationIt is of considerable interest to detect somatic mutations in paired tumor and normal sequencing data. A number of callers that are based on statistical or machine learning approaches have been developed to detect somatic small variants. However, they take into consideration only limited information about the reference and potential variant allele in both samples at a candidate somatic site. Also, they differ in how biological and technological noises are addressed. Hence, they are expected to produce divergent outputs.\n\nResultsTo overcome the drawbacks of existing somatic callers, we develop a deep learning-based tool called DeepSSV, which employs a convolutional neural network (CNN) model to learn increasingly abstract feature representations from the raw data in higher feature layers. DeepSSV creates a spatially-oriented representation of read alignments around the candidate somatic sites adapted for the convolutional architecture, which enables it to expand to effectively gather scattered evidences. Moreover, DeepSSV incorporates the mapping information of both reference-allele-supporting and variant-allele-supporting reads in the tumor and normal samples at a genomic site that are readily available in the pileup format file. Together, the CNN model can process the whole alignment information. Such representational richness allows the model to capture the dependencies in the sequence and identify context-based sequencing artifacts, and alleviates the need of post-call filters that heavily depend on prior knowledge. We fitted the model on ground truth somatic mutations, and did benchmarking experiments on simulated and real tumors. The benchmarking results demonstrate that DeepSSV outperforms its state-of-the-art competitors in overall F1 score.\n\nAvailability and Implementationhttps://github.com/jingmeng-bioinformatics/DeepSSV\n\nContacta.salim@latrobe.edu.au\n\nSupplementary informationSupplementary data are available at online.

bioinformatics↗

Prevention of enteric bacterial infections and modulation of gut microbiota composition with conjugated linoleic acids producing Lactobacillus in mice

Probiotics are recognized to outcompete pathogenic bacteria by receptor-mediated colonizing and secreting functional metabolites which have direct antimicrobial activities towards pathogens and/or improving hosts gut health and immunity. We have constructed a Lactobacillus casei (LC) probiotic strain, LC+mcra, by inserting mcra (myosin cross-reactive antigen) gene, which stimulates the conversion of conjugated linoleic acids. In this study, we evaluated the protective roles of LC+mcra against pathogenic Salmonella enterica serovar Typhimurium (ST) and enterohaemorrhagic E. coli (EHEC) infection in BALB/cJ mice. Through a series of in vivo investigation, we observed that LC+mcra colonized efficiently in mice gut and competitively reduced the infection with ST and EHEC in various locations of small and large intestine, specifically cecum, jejunum, and ileum (p<0.05). The cecal microbiota in ST-challenged mice with LC+mcra protection were positively modulated with higher relative abundances Firmicutes but lower Proteobacteria plus increased bacterial species diversity/richness based on 16S metagenomic sequencing. Based on cytokine gene expression analysis by qRT-PCR, mice pretreated with LC+mcra were found with attenuated bacterial pathogen-induced gut inflammation. Furthermore, mice fed LC+mcra daily for one week could protect themselves from the impairments caused by enteric infections with ST or EHEC. These impairments include weight loss, negative hematological changes, intestinal histological alterations, and potential death. This in vivo study suggests that daily consumption of novel conjugated linoleic acids over-producing probiotic might be efficient in improving gut intestinal microbiome composition and preventing/combating foodborne enteric bacterial infections with pathogenic Salmonella and diarrheagenic E. coli.\n\nAuthor summaryNumerous bacteria colonize throughout the gastrointestinal tract and form a complex microbial ecosystem known as gut microbiota. A balanced microbial composition is crucial for maintaining proper gut health and host defense against pathogenic microbes. However, enteric bacterial infections could cause illness and even lead to death of host when foodborne pathogens like Salmonella and enterohaemorrhagic E. coli (EHEC) invade gut intestine and cause imbalance of gut microbiota. Beneficial microbes in gastrointestinal tract such as Lactobacillus and their secreted bio-active metabolites, are potential bio-agents to improve gut immunity and outcompete bacterial pathogens. In this study, to evaluate roles of novel Lactobacillus strain LC+mcra which produce higher amount of a group of beneficial secondary metabolites called conjugated linoleic acids, we have shown that daily oral administration of this LC+mcra for one-week in mice lead to higher proportion of beneficial bacterial colonization in different locations of intestine and a significant reduction of pathogenic Salmonella and EHEC colonization. Furthermore, mice fed with LC+mcra restore and modulate Salmonella infection-induced negative impact on gut microbiota composition and protect themselves from various levels of physiological damage.

microbiology↗

Reproducibility of functional brain alterations in major depressive disorder: evidence from a multisite resting-state functional MRI study with 1,434 individuals

Resting-state functional MRI (R-fMRI) studies have demonstrated widespread alterations in brain function in patients with major depressive disorder (MDD). However, a clear and consistent conclusion regarding a repeatable pattern of MDD-relevant alterations is still limited due to the scarcity of large-sample, multisite datasets. Here, we address this issue by including a large R-fMRI dataset with 1,434 participants (709 patients with MDD and 725 healthy controls) from five centers in China. Individual functional activity maps that represent very local to long-range connections are computed using the amplitude of low-frequency fluctuations, regional homogeneity and distance-related functional connectivity strength. The reproducibility analyses involve different statistical strategies, global signal regression, across-center consistency, clinical variables, and sample size. We observed significant hypoactivity in the orbitofrontal, sensorimotor, and visual cortices and hyperactivity in the frontoparietal cortices in MDD patients compared to the controls. These alterations are not affected by different statistical analysis strategies, global signal regression and medication status and are generally reproducible across centers. However, these between-group differences are partially influenced by the episode status and the age of disease onset in patients, and the brain-clinical variable relationship exhibits poor cross-center reproducibility. Bootstrap analyses reveal that at least 400 subjects in each group are required to replicate significant alterations (an extent threshold of P<.05 and a height threshold of P<.001) at 50% reproducibility. Together, these results highlight reproducible patterns of functional alterations in MDD and relevant influencing factors, which provides crucial guidance for future neuroimaging studies of this disorder.

neuroscience↗

Genome-wide analysis reveals signatures of selection for gait traits in Yili horse

High quality gaits play an important role in many breeding programs, but the genes of gait were limited at the moment. Here, we present an analysis of genomic selection signatures in 53 individuals from two breeds, using genotype data from the Affymetrix Equine 670K SNP genotyping array. The 11 selection regions of Yili horse were identified using an FST statistic and XP-EHH calculated in 200-kb windows across the genome. In total, 50 genes could be found in the 11 regions, and two candidate genes related to locomotory behavior (CLN6, FZD4). The genome of Yili horse and Russian horse were shaped by natural and artificial selection. Our results suggest that gait trait of Yili horse may related to two genes. This is the first time when whole genome array data is utilized to study genomic regions affecting gait in Yili horse breed.

genetics↗

Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods

N6-methyladenosine (m6A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m6A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m6A levels are controlled and whether and how regulation of m6A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m6A-regulated genes and m6A-associated disease, which includes Deep-m6A, the first model for detecting condition-specific m6A sites from MeRIP-Seq data with a single base resolution using deep learning and a new network-based pipeline that prioritizes functional significant m6A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m6A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m6A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m6A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway. The m6A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m6A regulatory functions and its roles in diseases.\n\nAuthor summaryThe goal of this work is to identify functional significant m6A-regulated genes and m6A-associated diseases from analyzing an extensive collection of MeRIP-seq data. To achieve this, we first developed Deep-m6A, a CNN model for single-base m6A prediction. To our knowledge, this is the first condition-specific single-base m6A site prediction model that combines mRNA sequence feature and MeRIP-Seq data. The 10-fold cross-validation and test on an independent dataset showthat Deep-m6A outperformed two sequence-based models. We applied Deep-m6A followed by network-based analysis using HotNet2 and RWRH to 75 human MeRIP-Seq samples from various cells and tissue under different conditions to globally detect m6A-regulated genes and further predict m6A mediated functions and associated diseases. This is also to our knowledge the first attempt to predict m6A functions and associated diseases using only computational methods in a global manner on a large number of human MeRIP-Seq samples. The predicted functions and diseases show considerable consistent with those reported in the literature, which demonstrated the power of our proposed pipeline to predict potential m6A mediated functions and associated diseases.

bioinformatics↗

Genome-wide selection footprints and deleterious variations in young Asian allotetraploid rapeseed

Brassica napus (AACC, 2n=38), is an important oilseed crop grown worldwide. However, little is known about the population evolution of this species, the genomic difference between its major genetic clusters, such as European and Asian rapeseed, and impacts of historical large-sale introgression events in this young tetraploid. In this study, we reported the de novo assembly of the genome sequences of an Asian rapeseed (B. napus), Ningyou 7 and its four progenitors and carried out de novo assembly-based comparison, pedigree and population analysis with other available genomic data from diverse European and Asian cultivars. Our results showed that Asian rapeseed originally derived from European rapeseed, but it had subsequently significantly diverged, with rapid genome differentiation after intensive local breeding selection. The first historical introgression of B. rapa dramatically broadened the allelic pool of Asian B. napus, but decreased their deleterious variations. The secondary historical introgression of European rapeseed (canola-quality) has reshaped Asian rapeseed into two groups, accompanied by an increase in genetic load. This study demonstrates distinctive genomic footprints by recent intra- and inter-species introgression events for local adaptation, and provide novel insights for understanding the rapid genome evolution of a young allopolyploid crop.

genomics↗

A database of simulated tumor genomes towards accurate detection of somatic small variants in cancer

BackgroundSomatic mutations promote the transformation of normal cells to cancer. Accurate identification of such mutations facilitates cancer diagnosis and treatment. A number of callers have been developed to predict them from paired tumor/normal or unpaired tumor sequencing data. However, the small size of currently available experimentally validated somatic sites limits evaluation and then improvement of callers. Fortunately, NIST reference material NA12878 genome has been well-characterized with publicly available high-confidence genotype calls.\n\nResultsWe used BAMSurgeon to create simulated tumors by introducing somatic small variants (SNVs and small indels) into homozygous reference or wildtype sites of NA12878. We generated 135 simulated tumors from 5 pre-tumors/normals. These simulated tumors vary in sequencing and subsequent mapping error profiles, read length, the number of sub-clones, the VAF, the mutation frequency across the genome and the genomic context. Furthermore, these pure tumor/normal pairs can be mixed at desired ratios within each pair to simulate sample contamination.\n\nConclusionsThis database (a total size of 15 terabytes) will be of great use to benchmark somatic small variant callers and guide their improvement.\n\nContact informationjing.mengrabbit@gmail.com

bioinformatics↗