J Han / N Uberoi (@1.57) vs Y Zhang / Y Zhao C (@2.25)
10-09-2019

Our Prediction:

J Han / N Uberoi will win
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J Han / N Uberoi – Y Zhang / Y Zhao C Match Prediction | 10-09-2019 01:00

In addition, we used two independent cohorts to show the power of HUPAN for pan-genome analysis. This approach also could be extended to the study of other genomic variations, such as copy number variations and other structural variations. For example, the misassembled contigs could be further analyzed to call these large structural variations, which were less accessible by reference-based variation calling tools. In this manuscript, we considered the non-reference genes. All individuals were sampled from Han Chinese population, and this analysis could be extended to other populations to capture the global genetic variations and also various tumors to explore the dynamic variations of cancer genomes.

There were only 8.64Mb (28.11%) that could be aligned to the GM12878 [30], which was originated from European, indicating a significant portion of these 30.72Mb sequences may be Chinese-specific or East Asian-specific. These sequences were validated by the additional sequences of GRCh38 reference genome and previously published human genome assemblies [2, 26,27,28,29,30]. Most of the sequences (30.07Mb) could be fully or partially aligned to the above genomes at 90% identity (Fig. 3f and Table2). Overall, there were only 646.23Kb that could not be aligned to all the above genomes at all, and this indicates that the vast majority of the fully unaligned sequences were valid human DNA sequences. To our surprise, the percentage (48.0%) of sequences that could be aligned to HX1 genome [2], which is from a Chinese individual, is lower than that of the HuRef genome (55.3%) [28]. In particular, 24.10Mb (78.46%) could bealigned to the YH genome [26], which is the first assembled Asian individual, and 24.37Mb (79.35%) could bealigned to the KOREF genome [29], which is from a South Korean individual.

The sections were then incubated with anti-S100A14 antibody (1:200) overnight at 4C. Paraffin embedded specimens (each 4 m) were dewaxed, rehydrated in a series of ethanol solutions, and treated with an antigen retrieval solution with a microwave. Immunohistochemistry (IHC) was performed via the SP9001 Rabbit kit (Zhongshan Jinqiao Biotech Company, Beijing, China) according to manufacturers instructions. Endogenous peroxidase activity was blocked using 3% H2O2 for 10 min. Sections were counterstained with hematoxylin, dehydrated, and then mounted with coverslips. The positively stained area was evaluated as follows: 0, no staining; 1, 80% stained positive. All sections were examined microscopically and blindly evaluated by two independent pathologists according to a scoring method described previously by Zhang [23]. Unspecific staining was blocked for 15 min using normal goat serum. Each specimen was assessed with reference to staining intensity and positively stained area. The combined staining score (staining intensity times staining area) was then graded as 0, negative immunoreactivity; 1-4, low immunoreactivity; and >4, high immunoreactivity. Expression levels of S100A14 were determined using the standard SP immunohistochemical technique. Staining intensity was graded on the following scale: 0, no staining; 1, light yellow; 2, yellowish brown; 3, brown. The primary antibody was replaced with PBS as the negative control. The immunoreaction was visualized using 3, 3-diaminobenzidine (DAB) staining. At least 5 high-power fields were selected randomly, with >200 cells counted per field.

Abstract

Over the past decade, due to the rapid decrease of sequencing cost, pan-genome analysis has become popular in bacteria [10, 11] and plants [12,13,14,15,16]. [17] in Streptococcus agalactiae study and aimed to reveal gene or gene family presence-absence variation (PAVs) within a species or a population. [18]) containing genes in a subset of individuals of this species. The approach of pan-genome analysis was first introduced by Tettelin et al. The pan-genome is composed of a core genome containing genes present in all individual genomes and a distributed genome (or dispensable genome, which is somewhat misleading as discussed by Marroni et al.

We performed manual microdissection to collect cells of interest from lung adenocarcinoma and matched adjacent normal lung tissue, as previously described by Nowak [6] and Chen [20]. Under the guidance of an H&E slide, the adjacent unstained 10 to 14 m thick continuous frozen sections were dissected with a syringe needle and/or scalpel from the area identified by a pathologist, transferred to an Eppendorf tube, and stored at -80C until ready for use. Frozen sections (5 m each) from lung adenocarcinoma and matched normal lung tissues were cut in a Microm HM500 Cryostat at -25C and identified by routine H&E staining.

The procedure is also deficient in identifying low abundance proteins, proteins with extreme molecular weights and pI value, and hydrophobic proteins such as membrane proteins [28]. This technique yielded findings consistent with LCM data [6]. Although 2DGE remains an important tool in comparative proteomics, it is less than satisfactory when a large amount of sample handling is required. Most significantly, the enhanced technology increases the probability of detecting low-abundance proteins that may play a role in the development of cancer. To date, a number of studies have identified differential proteins in normal and cancer cells using traditional 2DGE, followed by mass spectrometry [25-27]. Subcellular proteomic analysis has many advantages over whole-cell proteome analysis. By delineating the differences in protein expression profiles between tumor and normal tissue, comparative quantitative proteomics offers a powerful method to investigate molecular mechanisms and identify potential tumor markers and therapeutic targets in cancer. iTRAQ technology can overcome these limitations and provide a better solution to identify cell membrane proteins. Manual microdissection from frozen sections provides a rapid and inexpensive means to substantially enrich tumor cells for downstream analysis.

In this study, we used iTRAQ labeling and 2D-LC-MS/MS to compare protein expression between pooled lung adenocarcinoma and matched normal lung tissue samples. As a result, S100A14 was significantly upregulated in lung adenocarcinoma (2.10-fold) compared with normal lung tissues. Of the differentially expressed proteins, 234 (41%) were identified by more than five unique peptides, 42 (7.4%) by four unique peptides, 48 (8.4%) by three unique peptides, 101 (14.7%) by two unique peptides, and 143 (25.2%) by one peptide. MS/MS spectra of the four peptides used for identification of S100A14 are shown in Figure 2. Among the 2486 proteins, 568 proteins were considered differentially expressed between lung adenocarcinoma and normal lung tissue according to ratios of fold-change (1.5 or 0.66). Two hundred fifty-seven proteins were upregulated and 311 were downregulated (Table 1, Supplementary Data). A total of 2486 proteins from both tumor and normal tissue were respectively identified using at least one peptide with 95% confidence.

Search results

NSCLC includes adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and other cell types. Lung cancer is divided into two classes: non-small cell lung cancer (NSCLC) and small cell lung cancer. Lung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer death worldwide [1]. Although many treatments are available, its prognosis is still poor. Smoking is the most common cause of lung cancer overall, but lung adenocarcinoma is the most frequently occurring cell type in nonsmokers, and its pathogenesis remains unclear. Lung adenocarcinoma is the most common type of lung cancer and has been increasing in recent years. The 5-year survival of all lung cancer patients is only approximately 16% [2].

We validated the novel predicted genes by two RNA-Seq data sets. In addition, about 30% (50/167) were expressed in at least one of the 1001 publicly available RNA sequencing datasets. The median length of novel predicted genes (614bp) was shorter than that of those genes located in the human reference genome (27.04 Kb). When the threshold of coverage decreased to 80%, 120 novel genes were validated (Additional file 1: Figure S9). At the threshold of 95% coverage, 46.71% of the full-length novel genes (78/167) were expressed in one or more of the 90 gastric tissues. In total, 167 full-length novel genes were predicted on non-redundant non-reference sequences from 185 deep sequencing individuals (Additional file 1: Figure S8).

The explosive growth of human whole-genome sequencing data brings significant challenges and tremendous opportunities to study the pan-genome of a specific population [21]. Instead of using all reads, only the unmapped reads were extracted to conduct de novo assembly [8, 20]. See more details in Additionalfile1: Supplementary methods). We compared the assembled results using all reads and unmapped reads with simulated sequencing data, and suggested that pseudo de novo assembly method may underestimate the size of non-reference sequences and produce more misassembled sequences at the meantime (Additional file 1: Table S1). Several previous studies reported non-reference genome sequences using the approach of pseudo de novo assembly [4, 6, 8, 20]. Nevertheless, due to the large size of the human genome, EUPAN cannot be applied for human pan-genome analysis because of the huge memory size requirement of the de novo assembly step (more than 500Gb memory is needed to assemble a human genome from a 30-fold sequencing data. Recently, we reported a tool EUPAN [22] based on a map-to-pan strategy and applied it to more than 3000 rice genomes [13]. However, constructing the pan-genome sequences from hundreds of individual genomes is a huge challenge. If all reads were used, aligning hundreds of assembled genomes to the human reference genome to extract the non-reference sequences and distinguishing the non-human sequences contaminated in sampling, sequencing, and other procedures are other challenges that need to be addressed.

A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries B. Li, J. Chen, N.K. Godil, T. Johan, R. Li, A. Aono, M. Liu, H. Wan, C. Zhang, and C. Fu, T. Furuya, H. Burtscher, Q. Li, Y. Chowdhury, B. Lu, C. Koyanagi, R. Schreck, M. Kosaka, H. Fang, H. Tatsuma, Y. Zou Computer Vision and Image Understanding (CVIU), 131:1-27, 2015. Ohbuchi, A.

Introduction

System diagram of pan-genome construction subsystem in HUPAN.