Guidebook How To Use HSCGD Online

Homepage of HSCGD. The upper part contains the navigation bar of several main function modules, click to enter the corresponding page. At the bottom of the page, users can choose to browse current single-cell genomic studies by WGS method or by cell type. By clicking on the thumbnail, the corresponding interface will be opened. In addition to the information of the corresponding project, a doughnut chart of statistical data will be provided at the same time.

Users can search single-cell genome data in this module. In the search box, enter the project accession, sample accession or run accession, and then click Search to find detailed relevant information. A table is provided in the lower part of the page, which contains the simple information of all projects currently stored in the database. Users can also directly click the project accession in the table to browse its content.
After entering the detailed information interface, the user can find the corresponding sample and run quantity information in the panel below, and click the project, sample, or run of interest in the table to view their information. The fastq.gz file corresponding to each run cannot be downloaded directly by clicking the ftp address contained in the table, but can be downloaded by copying the address to ftp://. For the search interface of runs, users can also download its alignment information files, including txt documents and pdf reports generated by Qualimap, which contain detailed alignment quality information. By clicking on the advanced search button in the search module, users can navigate to the advanced search page. Here, users can not only search by cell number but also filter the results according to multiple dimensions of information such as year, cancer type, amplification method, and sequencing depth.

Using the navigation tools at the top, users can select different chromosomes and scroll left or right to browse various regions. Additionally, the provided zoom feature can help users to enlarge or reduce the view for detailed examination of specific areas. In this study, the hsa.fa reference genome was imported as the analytical baseline. When BAM files are imported, JBrowse2 can display the alignment of sequencing reads, with blue horizontal bars indicating single nucleotide variations, and other colors denoting insertions, deletions, or other types of variations. Once VCF files are imported, the details of structural variations are displayed in the view. JBrowse2 allows users to directly click on a variation to view its detailed information on the right, including chromosomal position, variation location, reference sequence, the altered base sequence, and quality scores for assessing the reliability of variation detection. By clicking the plus sign on the right, users can add new reference sequences and genome sequencing files for comparative analysis with existing genomic data.

Users can browse the published literatures related to single-cell genomics in this module. In the table, users can see the project accession number corresponding to each article, as well as its brief information and whole genome amplification method, and can directly click the doi address to browse the original article online. Our website also provides a histogram, which data is the number of relevant literatures corresponding to each year, it shows a general view of the development of single-cell genomics landscape.

This module provides single-cell mutation information, including Copy Number Variation (CNV) and Single Nucleotide Variation (SNV). For the CNV module, enter the project accession of interest in the input box and click Check button to get the comprehensive information of the CNV of all cells included in this study, including heatmaps, cluster trees, etc.; click the run accession in the table, you can obtain detailed CNV information of the single cell, including Copy Number Profile and Quality Control information, etc.. All the above calculations are based on Ginkgo (http://qb.cshl.edu) with the window size set to 250kb. For the SNV module, the top of the page displays information on the single nucleotide variations (SNVs) in the cell, including the position of the variation, reference and altered bases, the gene involved, the region of the gene where the variation occurs (such as exons or introns), the function (such as synonymous or non-synonymous mutation), annotations from the COSMIC database (providing information on cancer relevance), and amino acid changes.

Users can compare the amplification uniformity of different single cells in this module. Our website provides two calculation methods, the coefficient of variation and the Lorentz curve. The Lorentz curve is calculated using windows of 250kb in size. In general, the closer the curve to the diagonal dashed line, the higher the degree of uniformity. Use windows of 10k, 25k, 50k, 100k, 175k, 250k, 500k, 1000k, 2500k, 5000k bps to calculate the coefficient of variation. In general, as windows become larger, the coefficient of variation becomes smaller. Enter no more than five run accessions in the input box and click Submit to get figures of CV and Lorentz comparison results. This process may need to wait patiently for about 40s before the page jumps. In addition to the two comparison figures, the results interface after also provides project information corresponding to several cells provided by users, which is convenient for intuitive comparison and analysis.

The analysis presents an overview of single nucleotide variations (SNVs) with charts depicting variant classification, counts, and types. Nonsynonymous and nonsense mutations are highlighted, the former altering amino acids and possibly affecting protein function, and the latter often leading to truncated, non-functional proteins. A bar graph and boxplot summarize these variants, while a list denotes the top ten mutated genes by frequency in samples. The Oncoplot illustrates gene mutations per sample, indicating mutation types and statistical significance. An Interaction Plot reveals gene mutation relationships within samples, such as co-occurrences and exclusivities, suggesting genetic networks or pathways. Transition-transversion ratios and distributions are also visualized, providing insights into data quality. Finally, cell comparison includes base count, sequencing depth, and amplification methods, aiding in deeper genetic variation analysis crucial for understanding diseases and personalizing treatment.

The page is organized into four steps to guide users through the submission process. First, users need to enter a project name, which serves as key information for identifying and managing the dataset. Next, a list of cell numbers must be provided, which can be input line by line into a textbox or by uploading a text file containing the list of runs. Subsequently, users are required to upload CNV analysis files (FQ and BAM) as well as SNV analysis files (VCF and BAM), making sure to select the appropriate bin size and read length parameters from a dropdown menu for CNV files. Lastly, users must provide an email address, so the system can send the results upon completion of the analysis.

This module provides information on single-cell studies in cancer genomics. The displayed statistical table describes the number of projects, samples, and single cells possessed by different types of cancer to facilitate the user to view the current applications of single-cell genomics in the field of tumor research.

3rd-ChimeraMiner https://github.com/dulunar/3rdChimeraMiner
SingleScan https://github.com/victorwang123/SingleScan
SPEED http://speedatlas.net
DISCO https://www.immunesinglecell.org/
scMethBank https://ngdc.cncb.ac.cn/methbank/scm/