Display Settings:

Items per page

Results: 12

1.
Figure 2

Figure 2. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

A flow chart describing the process performed on a sample data.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.
2.
Figure 12

Figure 12. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

The Additional Information interface enables quick transfer to suggested additional databases for further analysis of SNPs.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.
3.
Figure 9

Figure 9. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

The PgSNP interface allows the user to (A) choose a file for conversion to PgSNP format and specify the location where the file will be saved, and (B) instructs the user how to upload the file to UCSC as a Custom Track in five simple steps.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.
4.
Figure 7

Figure 7. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

The Compare Samples interface allows the user to (A) select files for comparison and choose a threshold for minimal SNP frequency and (B) view the results in a bar graph and a corresponding index table.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.
5.
Figure 6

Figure 6. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

The Data Table analysis interface enables the user to (A) select the files for viewing, one at a time and (B) view the data in a clear and familiar MS Spreadsheet environment, allowing easy export to Excel. Multiple files may be shown as separate sheets.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.
6.
Figure 8

Figure 8. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

The SNP-View interface allows the user to (A) select sample files for comparison containing the same chromosome number and (B) view a list of SNPs appearing in the selected samples, in the specified chromosome, with a list of the samples in which each SNP appears. The list may be easily exported to Excel for further analysis.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.
7.
Figure 4

Figure 4. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

The GenomeGems main user interface contains three distinct panels, (A) Upload FIles, (B) Select Files and (C) Analysis. The user may upload an unlimited number of samples and chromosomes that will later be available from each of the analysis tools. More analysis functions may be added to the Analysis panel in the future, as the tool is built in a modular design.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.
8.
Figure 10

Figure 10. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

The UCSC Genome Browser allows the user to view the data uploaded into GenomeGems as a custom track. (A) The user can manipulate the view with options of move, zoom in, and zoom out, (B) the custom track appears at the top of the screen and can be set to hide, dense, squish, pack and full, and (C) when the user moves the mouse control over the specific SNP, the frequency of each allele is shown.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.
9.
Figure 3

Figure 3. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

An illustration of the different analysis functions of GenomeGems in a schematic workflow. The user uploads the SNP files in the pre-determined format and chooses the form of analysis required: translation to a PG-SNP file format for UCSC visualization, visualization via data table, sample comparison via bar graph or table. In addition, more information about investigated SNPs can be obtained by using the suggested links to external databases.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.
10.
Figure 5

Figure 5. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

Example of the input file format required for GenomeGems. The file must contain data from one single sample, and must not contain a heading line. The file may contain one single chromosome or all chromosomes, but in both cases the user must specify the chromosome for analysis. The data in the file must be separated into columns using tabs, and must contain the first 7 columns: chromosome number, SNP position, consensus nucleotide, SNP nucleotide, score of the SNP, number of reads for each nucleotide, as shown in the figure. The file may include any additional data in the following columns, also separated by tabs.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.
11.
Figure 11

Figure 11. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

When the user chooses one of the SNPs appearing in the UCSC visualization interface, a new window opens containing (A) the position of the SNP, in addition to band, genomic size and strand, (B) the frequency and quality score for each allele, and (C) the properties of the changed and original amino acids: polarity, acidity and hydropathy. Notice the alleles are relative to forward strand of reference genome, and the coding sequence changes are relative to the strand of transcript.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.
12.
Figure 1

Figure 1. From: GenomeGems: evaluation of genetic variability from deep sequencing data.

An illustration of a common research process done when investigating a potential genetic disease. This interdisciplinary process normally involves researchers from three distinct disciplines: bio-medical discipline, Deep Sequencing laboratory, and bioinformatics discipline. (1) Researchers from the bio-medical discipline identify a potentially genetic disease. (2) Genomes of afflicted individuals or of whole families are sequenced using Deep Sequencing technology. (3) The sequences acquired are compared with a consensus sequence in order to find SNPs. (4) A list of SNPs and Indels is consequently generated and is filtered. (5) Finally a list of SNPs and Indels is produced which possibly contains the disease causing mutation. The list usually contains either novel or clinically associated SNPs (6) These lists are submitted to the researchers in the bio-medical discipline, for further analysis.

Sharon Ben-Zvi, et al. BMC Res Notes. 2012;5:338-338.

Display Settings:

Items per page

Supplemental Content

Recent activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...
Write to the Help Desk