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Oesophageal squamous cell carcinoma (ESCC) is one of the most lethal cancers, owing to a high frequency of metastasis. However, little is known about the genomic landscape of metastatic ESCC. To identify the genetic alterations that underlie ESCC metastasis, whole-exome sequencing was performed for 41 primary tumours and 15 lymph nodes (LNs) with metastatic ESCCs. Eleven cases included matched primary tumours, synchronous LN metastases, and non-neoplastic mucosa. Approximately 50-76% of the mutations identified in primary tumours appeared in the synchronous LN metastases. Metastatic ESCCs harbour frequent mutations of TP53, KMT2D, ZNF750, and IRF5. Importantly, ZNF750 was recurrently mutated in metastatic ESCC. Combined analysis from current and previous genomic ESCC studies indicated more frequent ZNF750 mutation in diagnosed cases with LN metastasis than in those without metastasis (14% versus 3.4%, n = 629, P = 1.78 × 10-5 ). The Cancer Genome Atlas data further showed that ZNF750 genetic alterations were associated with early disease relapse. Previous ESCC studies have demonstrated that ZNF750 knockdown strongly promotes proliferation, migration, and invasion. Collectively, these results suggest a role for ZNF750 as a metastasis suppressor. TP53 is highly mutated in ESCC, and missense mutations are associated with poor overall survival, independently of pathological stage, suggesting that these missense mutations have important functional impacts on tumour progression, and are thus likely to be gain-of-function (GOF) mutations. Additionally, mutations of epigenetic regulators, including KMT2D, TET2, and KAT2A, and chromosomal 6p22 and 11q23 deletions of histone variants, which are important for nucleosome assembly, were detected in 80% of LN metastases. Our study highlights the important role of critical genetic events including ZNF750 mutations, TP53 putative GOF mutations and nucleosome disorganization caused by genetic lesions seen with ESCC metastasis. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

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We present Virtual Pharmacist, a web-based platform that takes common types of high-throughput data, namely microarray SNP genotyping data, FASTQ and Variant Call Format (VCF) files as inputs, and reports potential drug responses in terms of efficacy, dosage and toxicity at one glance. Batch submission facilitates multivariate analysis or data mining of targeted groups. Individual analysis consists of a report that is readily comprehensible to patients and practioners who have basic knowledge in pharmacology, a table that summarizes variants and potential affected drug response according to the US Food and Drug Administration pharmacogenomic biomarker labeled drug list and PharmGKB, and visualization of a gene-drug-target network. Group analysis provides the distribution of the variants and potential affected drug response of a target group, a sample-gene variant count table, and a sample-drug count table. Our analysis of genomes from the 1000 Genome Project underlines the potentially differential drug responses among different human populations. Even within the same population, the findings from Watson's genome highlight the importance of personalized medicine. Virtual Pharmacist can be accessed freely at http://www.sustc-genome.org.cn/vp or installed as a local web server. The codes and documentation are available at the GitHub repository (https://github.com/VirtualPharmacist/vp). Administrators can download the source codes to customize access settings for further development.

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Single-cell genomic analysis has grown rapidly in recent years and finds widespread applications in various fields of biology, including cancer biology, development, immunology, pre-implantation genetic diagnosis, and neurobiology. To date, the amplification bias, amplification uniformity and reproducibility of the three major single cell whole genome amplification methods (GenomePlex WGA4, MDA and MALBAC) have not been systematically investigated using mammalian cells. In this study, we amplified genomic DNA from individual hippocampal neurons using three single-cell DNA amplification methods, and sequenced them at shallow depth. We then systematically evaluated the GC-bias, reproducibility, and copy number variations among individual neurons. Our results showed that single-cell genome sequencing results obtained from the MALBAC and WGA4 methods are highly reproducible and have a high success rate. The MALBAC displays significant biases towards high GC content. We then attempted to correct the GC bias issue by developing a bioinformatics pipeline, which allows us to call CNVs in single cell sequencing data, and chromosome level and sub-chromosomal level CNVs among individual neurons can be detected. We also proposed a metric to determine the CNV detection limits. Overall, MALBAC and WGA4 have better performance than MDA in detecting CNVs.

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Essential genes are those genes indispensable for the survival of any living cell. Bacterial essential genes constitute the cornerstones of synthetic biology and are often attractive targets in the development of antibiotics and vaccines. Because identification of essential genes with wet-lab ways often means expensive economic costs and tremendous labor, scientists changed to seek for alternative way of computational prediction. Aiming to help to solve this issue, our research group (CEFG: group of Computational, Comparative, Evolutionary and Functional Genomics, http://cefg.uestc.edu.cn) has constructed three online services to predict essential genes in bacterial genomes. These freely available tools are applicable for single gene sequences without annotated functions, single genes with definite names, and complete genomes of bacterial strains. To ensure reliable predictions, the investigated species should belong to the same family (for EGP) or phylum (for CEG_Match and Geptop) with one of the reference species, respectively. As the pilot software for the issue, predicting accuracies of them have been assessed and compared with existing algorithms, and note that all of other published algorithms have not any formed online services. We hope these services at CEFG will help scientists and researchers in the field of essential genes.

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The GC contents of 2670 prokaryotic genomes that belong to diverse phylogenetic lineages were analyzed in this paper. These genomes had GC contents that ranged from 13.5% to 74.9%. We analyzed the distance of base frequencies at the three codon positions, codon frequencies, and amino acid compositions across genomes with respect to the differences in the GC content of these prokaryotic species. We found that although the phylogenetic lineages were remote among some species, a similar genomic GC content forced them to adopt similar base usage patterns at the three codon positions, codon usage patterns, and amino acid usage patterns. Our work demonstrates that in prokaryotic genomes: a) base usage, codon usage, and amino acid usage change with GC content with a linear correlation; b) the distance of each usage has a linear correlation with the GC content difference; and c) GC content is more essential than phylogenetic lineage in determining base usage, codon usage, and amino acid usage. This work is exceptional in that we adopted intuitively graphic methods for all analyses, and we used these analyses to examine as many as 2670 prokaryotes. We hope that this work is helpful for understanding common features in the organization of microbial genomes.

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Mutation is the ultimate source of genetic variation and evolution. Mutation accumulation (MA) experiments are an alternative approach to study de novo mutation events directly. We have constructed a resource of Spontaneous Mutation Accumulation Lines (SMAL; http://cefg.uestc.edu.cn/smal), which contains all the current publicly available MA lines identified by high-throughput sequencing. We have relocated and mapped the mutations based on the most recent genome annotations. A total of 5,608 single base mutations and 540 other mutations were obtained and are recorded in the current version of the SMAL database. The integrated data in SMAL provide detailed information that can be used in new theoretical analyses. We believe that the SMAL resource will help researchers better understand the processes of genetic variation and the incidence of disease.

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Single cell genomics is a rapidly growing field with many new techniques emerging in the past few years. However, few bioinformatics tools specific for single cell genomics analysis are available. Single cell DNA/RNA sequencing data usually have low genome coverage and high amplification bias, which makes bioinformatics analysis challenging. Many current bioinformatics tools developed for bulk cell sequencing do not work well with single cell sequencing data. Here, we summarize current challenges in the bioinformatics analysis of single cell genomic DNA sequencing and single cell transcriptomes. These challenges include calling copy number variations, identifying mutated genes in tumor samples, reconstructing cell lineages, recovering low abundant transcripts, and improving the accuracy of quantitative analysis of transcripts. Development in single cell genomics bioinformatics analysis will promote the application of this technology to basic biology and medical research.

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Integrative genomics predictors, which score highly in predicting bacterial essential genes, would be unfeasible in most species because the data sources are limited. We developed a universal approach and tool designated Geptop, based on orthology and phylogeny, to offer gene essentiality annotations. In a series of tests, our Geptop method yielded higher area under curve (AUC) scores in the receiver operating curves than the integrative approaches. In the ten-fold cross-validations among randomly upset samples, Geptop yielded an AUC of 0.918, and in the cross-organism predictions for 19 organisms Geptop yielded AUC scores between 0.569 and 0.959. A test applied to the very recently determined essential gene dataset from the Porphyromonas gingivalis, which belongs to a phylum different with all of the above 19 bacterial genomes, gave an AUC of 0.77. Therefore, Geptop can be applied to any bacterial species whose genome has been sequenced. Compared with the essential genes uniquely identified by the lethal screening, the essential genes predicted only by Gepop are associated with more protein-protein interactions, especially in the three bacteria with lower AUC scores (<0.7). This may further illustrate the reliability and feasibility of our method in some sense. The web server and standalone version of Geptop are available at http://cefg.uestc.edu.cn/geptop/ free of charge. The tool has been run on 968 bacterial genomes and the results are accessible at the website.

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Most bacterial genomes have one single chromosome. The species Burkholderia cenocepacia, a Gram-negative ß-proteobacterium, is one of the exceptions. Genomes of four strains of the species have been sequenced and each has three circular chromosomes. In the genus Burkholderia, there are another seven sequenced strains that have three chromosomes. In this paper, the numbers of essential genes and tRNA genes among the 11 strains of the genus Burkholderia are compared. Interestingly, it is found that the shortest chromosome of B. cenocepacia AU-1054 has much (over three times) more essential genes and tRNA genes than the corresponding chromosomes in the other 10 strains. However, no significant difference has been found on the two longer chromosomes among the 11 strains. Non-homologous chromosomal translocation between chromosomes I and III in the species B. cenocepacia is found to be responsible for the unusual distribution of essential genes. The present work may contribute to the understanding of how the secondary chromosomes of multipartite bacterial genomes originate and evolve. The computer program, DEG_match, for comparatively identifying essential genes in any annotated bacterial genomes is freely available at http://cobi.uestc.edu.cn/resource/AU1054/.

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