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
  • Home
  • Tennis
  • J Han / N Uberoi vs Y Zhang / Y Zhao C

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

In total, there are 19,817 protein-coding genes in the annotation database. The gene/transcript annotation of GRCh38 primary assembly sequences was based on GENCODE [37] (Release 26). If a gene has multiple transcripts, only the transcript with the longest open reading frame (ORF) was selected as a representative. The annotation of GRCh38 primary assembly sequences and non-reference sequences were independent. Since all genes located in chromosome Y were absent in all female individuals, we excluded 63 genes in chromosome Y.

Due to the large size of the human genome, this process using QUAST[35] directly is time-consuming and requires a huge amount of memory (Table 1). In order to obtain non-reference sequences from individual genomes, contigs unable to be aligned to the GRCh38 primary assembly sequence (with identity cutoff of 90%) were collected for each individual. We adopted a strategy based on a well-assembled and well-annotated reference genome. In HUPAN pipeline, we focused on two types of non-reference sequences: fully unaligned sequences and partially unaligned sequences. Fully unaligned sequences are defined as contigs with no alignment to the reference sequence while partially unaligned sequences are defined as contigs with at least one alignment and at least one unaligned fragment longer than a defined threshold (default, 500bp). We discarded those sequences whose best match were microorganisms including bacteria, fungi, archaea, and viruses and non-primate eukaryotes including all plants and non-primate animals, which could reflect possible contaminations (Additional file 1: Supplementary methods). In order to speed up this step, we developed a two-step strategy: discarding the contigs highly similar with the reference genome followed by extracting non-reference sequences (Additional file 1: Supplementary methods). After obtaining individual non-reference sequences, we merged them and removed redundant sequences by CDHIT[36] with the identity cutoff of 90%. Building pan-genome sequences from individual assemblies is another challenging task.

We used gene coverage and/or CDS coverage (covered bases in ORF / ORF length) to determine whether a gene was present in one individual. The subsets with different coverage were used to determine gene PAV analysis under different CDS coverages. To confirm that the sequencing depth of 30-fold was sufficient to analyze the gene PAV of one individual, we selected the individual GCH1N00001G and sampled the alignment result to form subsets of 3- to 27-folds with a step size of 3. The coding coverage and gene body coverage of each gene in each individual were calculated from the sorted .bam files. All reads of each individual were mapped to the pan-genome sequences using Bowtie2[39, 40] with default parameters. SAMTools[40] and Picard software (http://broadinstitute.github.io/picard/) were used to sort and index the alignment files.

We used the reads from one individual GCH1N00001G to explore the relationship of reads depth and the CDS coverage (the percentage of coding sequence (CDS) of a gene was covered by at least one mapped reads for each individual genome) on individual gene PAV. For example, ten genes showed common gene deletion polymorphisms with the coding exons missing; six of these genes (UGT2B17, UGT2B28, LCE3C, GSTM1, OR51A2, and AR4F5) were considered as distributed genes across 185 deep sequencing genomes. 4b), and the core genome included 19,315 (96.88%) genes (Fig. The percentage (69.46%) of distributed genes in 167 novel predicted genes were significantly higher than that of the reference genes (2.48%). 4c). We selected CDS coverage of 95% to determine the core genes (the genes present in all individuals) and distributed genes (the genes absent in at least one individual), since no big change had been observed when CDS coverage was decreased to lower than 95%. In total, there were 19,921 protein-coding genes, including 19,754 genes located on human reference genome and 167 novel predicted genes. In total, there were 606 distributed genes (Fig. Of the 490 distributed genes on the reference genome, several were known common gene deletion polymorphisms [31]. The number of genes present in the individual was increased as the sequencing depth was increased, and the gene number tended to be stable when the depth was larger than six (Fig.4a). On average, there were 19,817 (ranging from 19,763 to 19,851) genes in one individual genome (Fig. 4d), of which 490 (80.85%) were GRCh38 reference genes, and the rest 116 genes were the novel predicted genes. The gene number was decreased by increasing the threshold values of CDS coverage.

PubMed

The six individual assembled genomes were also downloaded from NCBI. The novel sequences of hs38d1 [9] were downloaded from NCBI with accession number GCA_000786075.2. The pan-genome of 910 Africans [20] were downloaded from NCBI under accession PDBU01000000. The six primate reference genomes were downloaded from NCBI with accession numbers GCA_000001515.5 (chimpanzee [45]), GCA_000151905.3 (gorilla [46]), GCF_000258655.2 (bonobo [47]), GCA_002880775.3 (orangutan [48]), GCA_000772875.3 (rhesus [49]), and GCF_000264685.3 (baboon [50]).

In another Chinese genome HX1, 12.8Mb sequences were detected not present in GRCh38 but 68% of these novel sequences could be found in Asian populations [2]. The first human pan-genome study was carried out in 2010, and only two representative genomes from Africa and Asia were analyzed [3]. In this study, about 5Mb novel sequences absent in the reference genome (hg19 assembly) were detected for each individual and the total sequences absent in the reference genome were estimated to be 19~40Mb, which might have been underestimated considering the study of 10 Danish trios [19]. The possibility of these non-reference genomic regions to be the driver mutations for some diseases, especially for those dominated by a certain specific ethnic group, is worth our effort to investigate. reported an African pan-genome [20]. In a subsequent study [2], re-analysis of the 5Mb novel sequences from a Chinese individual showed that 3.7Mb sequences could be aligned to GRCh38 human reference genome. Notably, most of these novel sequences were individual-specific, and only 81Mb sequences were shown in two or more individuals. It contained about 300Mb unique sequences missing in the human reference genome. In a latest paper, Sherman et al. These studies indicated the significance of population-specific genome diversity.

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 molecular weights of the identified differentially expressed proteins ranged from 6.6 kDa to 628.7 kDa; 481 proteins (84.7%) were between 10 kDa and 100 kDa. Gene Ontology (GO) annotation was applied to describe functions of the identified differentially expressed proteins, which were classified into three major categories: cellular component, molecular function, and biological process [24]. Moreover, the isoelectric points ranged from 3.78 to 12.15; 501 proteins (88.2%) were between 4 and 10. To visualize the annotation of gene sets, WEGO was performed to plot the distribution of GO annotation [22] (Figure 3).

The gradient separation was 5% B for 10 min, 5% to 30% B for 30 min, 30% to 60% B for 5 min, 60% to 80% B for 3 min, 80% B for 7 min, 80% to 5% B for 3 min, and 5% B for 7 min. In MS scan, the top 20 most abundant ions in a charged state (+2-+7) were selected for tandem mass spectrometry by HCD fragmentation with normalized collision energy of 28% and an isolation width of 2.0 m/z. For each scan, dynamic exclusion was set to 15 s. A full mass scan was performed in data-dependent mode using a QExactive Mass Spectrometer, with an acquired range of 350-2000 m/z at 70,000 resolution (m/z 200). For subsequent MS2 scans, a resolving power of 17,500 at m/z 200 was used with an AGC target of 1 E+05 and a max ion IT of 100 ms. The automated gain control (AGC) target value was 3.00 E+06 and max ion injection time (IT) was 50 ms. The flow rate used for separation was 400 nl/min. NanoLC-MS/MS analysis was performed on a QExactive Mass Spectrometer (Thermo Fisher Scientific) equipped with an UltiMate 3000 nanoHPLC system (Dionex). The desalted fractions were loaded onto a homemade analytical column [Venusil XBP, C18 (L), 75 um * 150 mm 5 m, 150A, Agela Technologies] and separated using a mobile phase containing buffer A (0.1% formic acid in water) and buffer B (0.1% formic acid in acetonitrile).

About this article

However, the heterogeneity of tumor tissues limits the efficacy of proteomic analysis. LCM enables a very exact selection by isolating single cells, but getting enough material for a valid study consumes considerable time [5]. Proteomics, particularly quantitative proteomics, is a powerful approach developed to identify differentially expressed proteins in response to normal or tumor tissue. Proteomics has the potential to reveal underlying molecular mechanisms of disease. Laser capture microdissection (LCM) technology separates target cells from heterogeneous tumor tissue, which improves the accuracy of proteomic analyses. Manual microdissection is an ideal, cost-efficient method for situations where a clear demarcation between tumor and non-tumor tissues is obvious, as in the lung tumor samples in our study [6]. Many studies have established the compatibility of this method with protein extraction and analysis [3,4]. Proteomic analysis requires a large amount of cells.