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Overview

Omicsoft is the leading provider of Next Generation Sequencing, Cancer Genomics, Immunology, and Bioinformatics solutions for Next Generation Sequencing Data and Gene Expression Analysis.

Exciting Updates and Latest News

Keeping you up-to-date with the latest in NGS, Bioinformatics Analysis, and cancer genomics with blogs on Array Suite, OncoLand (TCGA and more), ImmunoLand, and more.

Filtering by Tag: land

[Land Tutorial] DiseaseLand (ImmunoLand And CVMLand) Comparison Views

Vivian Zhang

 DiseaseLand features Comparison Views, allowing users to easily search and visualize statistical contrasts between groups of samples using common queries: Treated vs Control, Disease vs Normal, Responder vs Non-Responder etc. By searching a gene, the user can visualize the association with comparisons across thousands of projects, and narrow down to find interesting projects interactively. (Additional reading: ComparisonLand ). In this article, we will introduce you on how to use comparison views.

Video Tutorial: Comparison

Comparison Distribution by comparison types:

Comparison distribution by comparison type. Statistics from previous Land version. Actually distribution and number of comparison update quarterly. 

Comparison distribution by comparison type. Statistics from previous Land version. Actually distribution and number of comparison update quarterly. 

 

Search gene and view comparisons:

In DiseaseLand, you can search for a gene and view its expression in all samples or a single project, or you can visualize which comparisons detected up- or down-regulation of the gene. This way, you can identify projects of interest, and discover trends in your favorite gene's regulation.

 

Comparison details for Serpinb 7 by treatment vs. control. By selecting the comparisons (dots) of interest, detailed information will pop up.

Comparison details for Serpinb 7 by treatment vs. control. By selecting the comparisons (dots) of interest, detailed information will pop up.

Comparison Details Views:

Omicsoft uses manually curated metadata to generate statistical tests (called comparisons) for each project/study included in DiseaseLand, generally following the comparisons in the original paper. The Comparison collection is useful for finding the common differential expression patterns/signatures between studies, such as between an microarray and NGS study, or to find links between a gene knockout experiment and a compound treatment study.

When searching for project(s), a few views are available:

Here are a couple example views:

Example Volcano Plot of project GSE38713.

Example Volcano Plot of project GSE38713.

Example Venn Diagram of project GSE14905.

Example Venn Diagram of project GSE14905.

Example Significant Genes of project GSE58121, GSE63980 and GSE63980.

Example Significant Genes of project GSE58121, GSE63980 and GSE63980.

[New Feature] Manage Land Sample Clinical Data

Vivian Zhang

Omicsoft has been working diligently over the past few months to both strengthen our ability to incorporate clinical data, as well as  growing our list of curated clinical measurements from public datasets. Currently, there are more than 1000 different clinical measurement variables in total, including sample demographics, survival data, symptoms, treatments and more in OncoLand and DiseaseLand. Moreover, users often have their sets of internal clinical data they wish to add to the system. If you have not started leveraging the power of our clinical data subsystem, please take a look at OncoLand Case Study - Clinical Variables for a 10 mins quick video tutorial on how to utilize clinical data to identify novel associations.

To help users better manage Land clinical data, we recently implemented Manage Sample Clinical Data function in Land. This function can be accessed through:

 

 

This function allows users to add clinical data, manage clinical variable meta data, remove samples and remove clinical vatiables: 

Add Clinical Data

Add Clinical Data

Adding clinical data is straightforward. In addition, "Metadata" for clinical data columns can be controlled by adding a second table. For example, clinical data column grouping can be controlled by a table where the first column contains Clinical Data column names, and the second column contains category:

Add Clinical Variable Metadata

Add Clinical Variable Metadata

 

The function is easy-to-use and straightforward, allowing users to manage their clinical data efficiently and effectively. For more details on the function, please refer to our wiki page

Stay tuned for additional functionality coming at the end of this year, including support for CDISC formatted files, to include time-series measurement data.

[Land Update] Omicsoft Quarterly Land Update Summary

Vivian Zhang

Omicsoft is excited to announce it’s latest Land updates, including OncoLand and DiseaseLand.

Highlights include:

OncoLand:

  • Official release of the B38 Human Lands, including TCGA, CCLE, GTEx, Blueprint, and Sanger
  • Additional samples in the TARGET and Blueprint Lands
  • 5700 new Somatic Mutation samples in the TumorMutation Land
  • 350 new samples and 80 new comparisons in the OncoGeo Land
  • 1750 new expression samples in the ClinicalOutcome Land
  • Updated clinical data and CNVCall data in TCGA
  • New Comparison data (Tumor vs Normal) for 24 tumor types in TCGA




DiseaseLand:

  • 3916 single cell samples from 5 projects to the Single Cell Human Land, including seven new cell types
  • 5466 single cell samples from 10 projects to the Single Cell Human Land, including seven new mouse cell types
  • 840 new RNA-Seq samples in Human DiseaseLand, along with additional comparisons
  • 2402 new RNA-Seq samples in Mouse DiseaseLand, along with additional comparisons
  • DiseaseLand now includes over 67,000 human samples, with 3239 comparison from 1000+ projects and almost 21,000 mouse samples, with 2,248 comparisons from over 650 projects. 


Incorporation with the recently introduced Gene Set Analysis module provides extra value to the release, as we now allow users to query against all of the new Land data as well. 
 

 

Matt Newman, VP of Business Development, will spend 45 minutes on December 12th, at 11:00 am EST, to give an overview of all the new datasets and visualizations that are included with this latest release. Please register here. We will contact users about this release update shortly after our webinar. Please stay tuned. 

[OncoLand Case Study] Summarize per-sample and per-tumor mutations across multiple genes

Vivian Zhang

Summarizing mutation frequencies within a protein complex, members of a pathway, or even across the genome, can give insights into differences between tumors. Combining the power of OncoLand and Array Studio functions, you can explore mutation frequencies. For example, let's take a research example using the Swi/Snf complex, which can regulate chromatin remodeling. 

Swi/Snf complex is multi-subunit ATP-dependent chromatin-remodeling complex. Early studies have suggested that the Swi/Snf complex plays a role in cancer development, likely to be tumor suppressors. ( Nature Reviews Cancer article: The SWI/SNF complex — chromatin and cancer). Mutations in the members of this complex have been linked to various cancers. You can leverage OncoLand to query samples containing those mutations. Please check out the detailed OncoLand case study video tutorials.

 

Identify samples with mutations in the Swi/Snf complex

To find out how often the genes from the Swi/Snf complex are mutated in tumors, you can use Summarize Sample Mutation Count to generate a SampleSet through Analytics tab and use this SampleSet for downstream analysis:

SampleSet results from Summarize Sample Mutation Count analysis by inputing all gene names from Swi/Snf complex as GeneSet and group by Tumor Type. The mutation count is sorted by the number of mutations in each sample.

SampleSet results from Summarize Sample Mutation Count analysis by inputing all gene names from Swi/Snf complex as GeneSet and group by Tumor Type. The mutation count is sorted by the number of mutations in each sample.

 

Visualize differences in Swi/Snf complex mutations using TCGALand Views

There are multiple ways to visualize mutation (frequency) differences in Swi/Snf. Without using land views, we can still achieve this goal in Array Studio. Array Studio empowers users to perform hundreds of different types of analysis with flexibility, and can potentially save biologists the hassle of waiting for a bioinformatician to get back the results for weeks. However, with OncoLand, we can visualize the mutation frequency in minutes. The following analysis pipeline clearly demonstrates the difference of using Array Studio and OncoLand.

OncoLand makes cancer genomics research easy. Again, please check out our case study video tutorials for more details.

[Feature Update] Powerful variant search in GeneticsLand

Vivian Zhang

Since last month's blog post on GeneticsLand:  GeneticsLand: A Turnkey Solution For Genetic Data Storage, Analysis And Annotation, we have continued to rapidly improve the views and functionality of GeneticsLand. The recently improved Search Variants function provides informative details on Variants Annotation, Frequency across populations, GWAS and eQTL Details, Region Association Plot and Reference Links.

Example views:

Variant Annotation View.

Variant Annotation View.

Variant Frequency Across Different Population View.

Variant Frequency Across Different Population View.

GWAS Catalog View. By clicking on, for example, Gene ID, GeneticsLand will link to Variants table of all variants in the displayed gene: 

GWAS Catalog View. By clicking on, for example, Gene ID, GeneticsLand will link to Variants table of all variants in the displayed gene: 

                                  

 

 

 

 

 

 

 

 

 

 

 

 

eQTL Information

eQTL Information

Region Association View. The color represents the correlation with the queried variant.

Region Association View. The color represents the correlation with the queried variant.

Reference Links to public resources including dbSNP. SNPedia, GTEx, Google scholar, Haploreg, RegulomeDB.

Reference Links to public resources including dbSNP. SNPedia, GTEx, Google scholar, Haploreg, RegulomeDB.


[Feature Update] Improved Mutation Annotation

Vivian Zhang

Mutation identification is one of the most important types of genomic research analyses. The genomic position of the identified mutations is a critical factor to assess the importance and functionality of the mutations. Recently, we improved our mutation annotation categorization to help users better research mutation. 

Now, we added a third category, Consequence, beyond the original Type and Location of gene information: 

The new category helps to clarify the effects of the mutation, including the following categories:

  • SYNONYMOUS: change of a single nucleotide in CDS but not causing amino acid change
  • NON_SYNONYMOUS: change of a single nucleotide in CDS and causing amino acid change
  • FRAME_SHIFT: Frameshift (total of NT changes are not 3N) in CDS caused by insertion, deletion or indel
  • STOP_GAIN: mutation creating a stop codon
  • STOP_LOSS: mutation destroying a stop codon
  • NO_CONSEQUENCE: any consequence not described above, such as SUBSTITUTION in the intergeneic regions. It is only a technical (not a biological) definition.