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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.

[Array Studio Video Tutorial] RNA-Seq Analysis Basic functions: Reads Quantification, Exon Junction and Gene Fusion Detection

Vivian Zhang

RNA-Seq has become one of the most popular methods in gene and transcript level genomic research. It could help quantify gene and transcript expression, identify sequence variants and detect gene, transcript or exon level genomic events. Array Studio provides a variety of functions powerful enough for small and large scale genomic research. In this article, we will introduce a few basic and the most commonly used functions, including sequence quantification, gene annotation, exon junction detection and gene fusion detection. 

 

 

ArrayStudio provides a number of modules and options for RNA-Seq quantification at gene, transcript, exon and exon junction levels. Both FPKM and Count tables can be generated. 

Example RNA-seq gene count table and its corresponding design table.

Example RNA-seq gene count table and its corresponding design table.

 

Alternative splicing has been shown to play an important role in a number of human diseases, including cancer, cardiovascular and neurodegenerative diseases. In Omicsoft Array Studio and the Land products, we provide modules and visualization functions that make it easier for users to research splicing. In RNA-Seq analysis, besides gene and transcript counts, Array Studio can report exon junction counts as well. Results can be visualized in Omicsoft's Genome Browser.

Exon junction report and genome browser view.

Exon junction report and genome browser view.

Mutation data allows user to compare mutation frequencies and research individual variants. Users can run the  Summarize Variant Data module to annotate variants. Variants can be annotated in Mutation Reports or VCF files, and visualized directly in the Genome Browser.

Mutation annotation report and example genome browser view of variant V600E. 

Mutation annotation report and example genome browser view of variant V600E. 

 

 

Fusion genes can play an important role in cancer mutations that have multiple effects on a target gene. At Omicsoft, we provide a powerful fusion detection algorithm in FusionMap. FusionMap identifies unmapped reads that span multiple genomic locations, indicating possible gene fusion events:

Map Fusion Reads module will detect fusion genes from fusion junction-spanning reads which can characterize fusion genes at base pair resolution. This works with single end or paired end data. Combined Fusion Analysis will run fusion junction spanning + inter-transcript fusion read pairs detection at the same time. It detects fusion junction spanning reads from unmapped reads in BAM files, and detects inter-transcript fusion read pairs from singletons from BAM alignment entries. It will return a report showing potential fusion genes and counts for each fusion junction  Combined fusion analysis can only be run on paired-end data. 

Fusion report reports fusion count data with fusion annotation information attached.   Fusion genome browser can display sequence information at base pair resolution  . 

Fusion report reports fusion count data with fusion annotation information attached. Fusion genome browser can display sequence information at base pair resolution

 

 

[Land Update] Omicsoft OncoLand 2016 Q2 Update

Vivian Zhang

We've reached the time for our OncoLand Quarterly Update, and we're excited about what we have to tell you about!

In our Q1 2016 release following our kick-off User Group Meeting, we had a major update to the Lands including CCLE_B37, CGCI_B37, , Hematology_B37, ICGC_B37, OncoGEO_B37, TARGET_B37, TCGA_B37, and TumorMutation_B37, and the addition of two new lands, ClinicalOutcome_B37 and expO_B37. In the Q2 update, we provided update for Hematology_B37, ICGC_B37, TCGA_B37 and OncoGEO_B37. 

Here is  the sample statistics for updated Lands. For details, please refer to OncoLand 2016 Q2 Release Whitepaper.

 

Hematology_B37

•    60 samples (two cell lines under different conditions) with RNA-Seq data; based on SRA SRP041036
•    5484 samples with Affymetrix (U133 Plus 2.0) expression data; based on GEO GSE15695, GSE19784, GS6891, GSE12417, GSE13159, GSE17855 and MMGP
•    767 samples with CNV data; based on GEO, MMRC Collection, HMCL69 cell line and Corral2012 study
•    203 samples with DNA-Seq somatic mutation data; based on MMRC Reference Collection
•    68 samples with DNA-Seq mutation data; based on HMCL69 cell line collection

 

ICGC_B37

•    577 samples with RNA-Seq data
•    779 samples with Methylation450 data
•    5587 samples with DNA-Seq Somatic Mutation da
•    2869 samples with CNV data

 

OncoGEO_B37

•    2001 samples with RNA-Seq data
•    4786 samples with expression data

 

TCGA_B37

•    22301 samples with CNV data
•    9677 samples with DNA-Seq Somatic Mutation data
•    2377 samples with Expression Ratio (Agilent) data
•    9793 samples with Methylation450 data
•    11022 samples with miRNA-Seq data
•    7933 samples with RPPA (protein array) data
•    4735 samples with RPPA_RBN (protein array) data
•    11291 samples with RNA-Seq data

 

Most users should have already been contacted about this release update, and if not, we will work with you to update your servers in the near future.

 

[Array Studio Video Tutorial] RNA-Seq Analysis Basics: Getting Started with RNA-Seq Pipeline Analysis and Data QC

Vivian Zhang

Omicsoft Next Generation Sequencing (NGS) analysis includes NGS (next generation sequencing) bioinformatics tools for the entire process, from QC to alignment to post-alignment summarizations and analysis. RNA-Seq data analysis is a critical part of Omicsoft's NGS bioinformatics tools. In this article, we introduce our tutorial on how to get started with RNA-seq pipeline analysis and data QC.

Getting Started with RNA-seq pipeline functions

 

1 Running the RNA-seq pipeline for a new project

A typical RNA-seq analysis project consists steps from data quality control, alignment, aligned data quality control to data quantification, visualization, and statistical inference. In Array Studio, users have the choice of either executing each step of the analysis one-by-one, or can use the RNA-seq pipeline function. It only takes a few clicks to create a new RNA-seq project and run RNA-seq pipeline. 

屏幕截图 2016-07-29 07.02.38.png
RNA-Seq Pipeline. Users have the options to choose to perform analysis steps such as raw data QC, post-alignment data QC, exon junctions, sequence quantification, mutation and fusion detection.

RNA-Seq Pipeline. Users have the options to choose to perform analysis steps such as raw data QC, post-alignment data QC, exon junctions, sequence quantification, mutation and fusion detection.

 

 

2 Raw Data QC

If you choose to perform analysis step by step, before aligning your RNA-seq data, you must first perform quality control (QC) on the raw data, to spot common problems like adapter or barcode sequence contamination, degraded quality at ends of reads, or problematic samples. The Array Studio Raw Data QC Wizard reports a number of useful measures of raw NGS quality, and can be generated as part of the RNA-seq pipeline function. 

Example QC report includes:

  • Base Distribution 
  • Basic Stats 
  • Duplication Level 
  • Kmer Analysis 
  • Overall/Per-sequence Quality Reports 
  • Quality Box plot 
  • Over-represented Sequences 
  • Per-sequence GC report 
  • Sequence Length Report 

 

3 Filtering and Trimming Raw Reads

Array Studio's NGS Filter function can trim low-quality bases from raw NGS data, filter out uniformly low-quality reads, and strip away adapter sequences. The RNA-seq pipeline assumes that input reads are pre-filtered and stripped, so only quality-based trimming and filtering will be performed in the pipeline (no adapter stripping). It is a good idea to run the Filter function on your reads, based on the raw data QC results, before running the RNA-seq pipeline.

 

4 Aligned Data QC

Array Studio automatically generates an Alignment Report after aligning reads to the genome or transcriptome. Additional alignment statistics can be generated by running the Aligned Data QC and RNA-seq 5'->3' Trend modules.

Alignment report is automatically generated after alignment. 

Alignment report is automatically generated after alignment. 

Additional aligned data QC metrics include:   1 Alignment Metrics 2 Flag Metrics 3 Profile Metrics 4 Source Metrics 5 Insert Size Metrics 6 Duplication Metrics 7 Coverage Metrics 8 Strand Metrics 9 Feature Metrics

Additional aligned data QC metrics include: 

1 Alignment Metrics
2 Flag Metrics
3 Profile Metrics
4 Source Metrics
5 Insert Size Metrics
6 Duplication Metrics
7 Coverage Metrics
8 Strand Metrics
9 Feature Metrics

 

The best way to quickly learn how to perform these analysis steps is to watch our short video tutorials Getting Started with RNA-seq pipeline functions. Please stay tuned for more blog articles on RNA-seq analysis.

[Array Studio Analysis] Genome Browser Advanced Analysis of Variants, Fusion and Isoform Expression

Vivian Zhang

Besides basic navigation, visualization and annotation of genome sequence, the Omicsoft Genome Browser also enable users to perform advanced analysis on sequence variants, fusions, isoform expression, and copy number variation data. In this blog, we will introduce Genome Browser Advanced Analysis of Variants, Fusion and Isoform Expression.

Advanced Analysis of Variants, Fusion, and Isoform Expression

 

1 Annotate the Genome Browser with Sequence Variant Information

The Omicsoft Genome Browser can display sequence variants from your .BAM files. You can directly add annotation tracks from .vcf files, or even load .vcf files from Genomemark to quickly navigate your variants of interest.

Variant information and aligned read sequence in .bam coverage track.

Variant information and aligned read sequence in .bam coverage track.

 

2 View Gene Fusion Data in the Genome Browser

The Array Studio Fusion Mapping method identifies putative gene fusion events in DNA-seq and RNA-seq data, outputting both a Fusion Report and a set of .bam files containing only fusion-mapped reads. These reads can be viewed in the Genome Browser, and you can go directly from the Fusion Report to the Genome Browser to view both ends of the fusion in split-pane.  

Fusion tracks for example fusion ID FUS_526235345_526249828(++)

Fusion tracks for example fusion ID FUS_526235345_526249828(++)

 

3 Compare Sample-group Isoform Usage in the Genome Browser

Specific gene isoforms may play an important role in certain body functions. You can use Differentially Expressed Isoforms module in Analysis tab to identify interesting isoforms. In the Genome Browser, you can display relative transcript usage between sample groups to visualize and confirm reports of differential transcript usage.

Example of relative transcript usage between lung and skin tissues. As we can see, transcript us003lvs4 is highly expressed in skin tissue, which is in purple. The other transcript is mainly expressed in lung tissue, which is in blue.

Example of relative transcript usage between lung and skin tissues. As we can see, transcript us003lvs4 is highly expressed in skin tissue, which is in purple. The other transcript is mainly expressed in lung tissue, which is in blue.

 

4 Integrate CNV chip and DNA-seq Visualizations in the Omicsoft Genome Browser

Omicsoft Genome Browser allows user to visualize copy number variation (CNV) data. You can integrate DNA-Seq .bam coverage, SNP chip intensity and segmented CNV calls data for visualization. Please refer to DNA-Seq and copy number variation analysis tutorial to learn how to obtain and generate the data: DNA-seq pipeline .bam and segmented CNV calls, SNP chip intensity and segmented CNV calls.

Copy number variation data from DNA-Seq and SNP array datasets.

Copy number variation data from DNA-Seq and SNP array datasets.

Omicsoft Genome Browser has hundreds of features or options available. The best way to quickly learn to utilize our genome browser is to refer to our short video tutorial clips. Again, please check out our tutorial Genome Browser Advanced Analysis of Variants, Fusion and Isoform Expression.

[Array Studio Analysis] Getting Started with Genome Browser: Basic navigation, visualization and annotation

Vivian Zhang

Omicsoft Genome Browser is a fully-featured genomic data visualization interface. It is fully integrated with Array Suite (Array Viewer, Array Server and Array Studio). Omicsoft Genome Browser empowers Array Suite's visualization capacity on a variety of different genomic data types with hundreds of functions and features. In this blog, we are glad to introduce our video tutorial: Getting Started with the Genome Browser. Starting from the most basic features, we present to you how to navigate, visualize and annotate your genomic data.

1 Getting Started with the Omicsoft Genome Browser

1.1 Basic Navigation in the Genome Browser

Omicsoft's Genome Browser has a very user-friendly user interface with powerful features. Navigation toolbar provides a variety of navigation features, from searching gene, transcript or sequence to jumping through genome region, adjusting, from zooming in/out to hiding, splitting or saving views. By bookmarking to Genomemark, queried genome region will be bookmarked for future usage. Track properties window allows user to fine-tune track display. 

Example Genome Browser interface.

Example Genome Browser interface.

Some of the very basic features introduced in the video tutorial include:

  • Bookmark custom genomic regions 
  • Trim introns to view only exon data 
  • Search by nucleotide sequence 
  • Retrieve DNA, RNA, or protein sequence for a region or feature 
  • Modify track display details
  • Save and share your browser session 
  • Add annotation tracks 

 

1.2 Add .bam Alignment Files to View Genome Coverage

Omicsoft Genome Browser provides multiple options to add tracks. Users can add .bam file to Genome Browser to view genome coverage. Even if you are not sure of your reference genome, you can use BAM Tools | Extract header and refer reference library ID to refer it. Next, simply Add Track From Local File or Server File

Refer reference library ID and add .bam file from server file

Refer reference library ID and add .bam file from server file

 

1.3 Add .bam Alignment Data from Array Studio Analysis

Alternatively, if you have files saved in Analysis project already, you can also right click on data object to create new genome browser, and Add Track from Analysis in Genome Browser.

Add Track from Analysis. Select the data you just created new genome browser in Analysis tab.

Add Track from Analysis. Select the data you just created new genome browser in Analysis tab.

1.4 Advanced Visualization of .bam Alignment Information

BAM alignment files contain nucleotide-level genome coverage, read quality, exon-junction, and sequence variant information. You can directly view all of this information in the Omicsoft Genome Browser. Example queries include:

  • Display exon junction information 
  • Interpreting exon junction information 
  • View the Alignment Profile of your .bam data 
  • Filter reads based on mapping metrics 
  • Display read pair connections
  • Filter displayed reads by quality 
  • View sequence variant data 
  • View nucleotide-level quality information 
Exon Junction Curve marks exon junction positions.

Exon Junction Curve marks exon junction positions.

Exon Junction Details indicates the orientation and the number of sequence mapped to exon junction positions.

Exon Junction Details indicates the orientation and the number of sequence mapped to exon junction positions.

Alignment Profile displays aligned sequence.

Alignment Profile displays aligned sequence.

Show Variant displays variant loci and mutation frequency.

Show Variant displays variant loci and mutation frequency.

2 Annotating Genomic Features and Coverage

The Omicsoft Genome Browser provides several ways to annotate genome features and visualize genome coverage, as well as splicing, read quality, and variation. For example, you can annotate ChIP peak locations and regulatory regions from Browser-Extensible Data (BED) files. Omicsoft provides many popular annotation tracks, or you can add your own. If you created .bedgraph files for viewing NGS coverage in other genome browsers, you can quickly load these into the Omicsoft Genome Browser. BAM Summary (BAS) files retain coverage, exon junction, and sequence variant information from BAM files, but can be up to 63x smaller. 

To learn how to navigate these files and tracks, please check out our tutorial video 2 Annotating Genomic Features and Coverage for details.

[Array Studio Analysis] Getting Started with RT-PCR Analysis

Vivian Zhang

Although RNA-seq has become the invaluable tool to study gene expression, RT-PCR (reverse transcription-polymerase chain reaction) is still the most sensitive method and widely-used for small-scale mRNA expression studies or RNA-seq analysis validation. In this article, we would like to introduce to you how to perform RT-PCR analysis using Array Studio. For more details, please check out our tutorial series: Getting Started with RT-PCR Analysis, which has step-by-step video tutorial clips to help you quickly become RT-PCR analysis expert.

In this tutorial, we provide tutorial video clips on:

 

1. Importing RT-PCR data

Array Studio allows user to import Ct or abundance data from text files or Excel spreadsheets. The Import RT-PCR Wizard function simplifies the data importing and normalizing processes.

Array Studio can process different data formats, no matter its "Tall-skinny" or Matrix data format. 

Array Studio can process different data formats, no matter its "Tall-skinny" or Matrix data format. 

With your data ready to import, Import RT-PCR Wizard offers step-by-step instructions on:

  • Choosing the correct input format
  • Selecting the annotation and data columns 
  • Previewing raw data for missing values 
  • Attaching Annotation and Design metadata 
  • Combining or remove technical replicates
  • Specifying default values for missing data 
  • Transforming Ct data to delta-Ct
  • Normalizing data 
  • Previewing data 

After importing through the RT-PCR Wizard, three data tables are generated: a data table, annotation table and design table. They are standard data formats in Array Studio.

 

2. Downstream Analysis

2.1 Visualizing RT-PCR Data- Adding Views to RT-PCR data

Once the data is in an Array Studio project, a variety of functions are available for downstream analysis. To start with, data visualization provides a good overview of the data. Array Studio has up to 40 different views available for your RT-PCR data. Here are a few commonly used views:

 

2.2 Data Processing-QC and Excluding/Subsetting Data

Sometimes single assay or sample experiment fails and should be removed from downstream analysis to allow for more accurate detection of real differences among groups. These failed experiments can be detected and easily removed from an Array Studio data object. For example, we can use Principal Component Analysis (PCA) to detect and remove outlier samples. To further subset data, we can use hierarchical clustering.

3D PCA plot and hierarchical clustering heatmap. In the upper PCA plot, each dot represents a sample. In the bottom heatmap,   data is clearly separated by source tissue but not so much by group.

3D PCA plot and hierarchical clustering heatmap. In the upper PCA plot, each dot represents a sample. In the bottom heatmap, data is clearly separated by source tissue but not so much by group.

 

 

2.3 Statistical Inference-Two-Way ANOVA of RT-PCR Data

Array Studio has a few different statistical inference modules to identify statistical significant differences between groups, for example, ANOVA and general linear model. Here is an example of Two-Way ANOVA analysis.

Two-Way ANOVA analysis results using source tissue and group as factors. This analysis generates one volcano plot for each test, in addition to the report table. The volcano plot is interactive -- selecting a subset of samples in one plot automatically selects the corresponding samples in another plot. In this example, the three selected genes CDH1, PFN2 and NOTCH2 that are affected in Breast are not similarly affected in Lymphoid, with NOTCH2 affected in the opposite direction.

Two-Way ANOVA analysis results using source tissue and group as factors. This analysis generates one volcano plot for each test, in addition to the report table. The volcano plot is interactive -- selecting a subset of samples in one plot automatically selects the corresponding samples in another plot. In this example, the three selected genes CDH1, PFN2 and NOTCH2 that are affected in Breast are not similarly affected in Lymphoid, with NOTCH2 affected in the opposite direction.

 

2.4 Omic Data Analysis-Integration of RT-PCR and RNA-seq/Microarray Data

RT-PCR data can be compared to other gene or transcript level data, such as from RNA-Seq or microarray, using Microarray-Microarray Integration. Careful data matching is important to ensure proper matching of data. 

Variable view of RT-PCR and RNA-Seq data integration for gene TFPI as an example.

Variable view of RT-PCR and RNA-Seq data integration for gene TFPI as an example.

 

Please check out our tutorial series: Getting Started with RT-PCR Analysis to learn how to perform the above RT-PCA analyses. 

[OncoLand Case Study] Empower OncoLand with Array Studio Analysis: Visualize "mutation burden" in each tumor in TCGALand

Vivian Zhang

One of the common goals in cancer research is identification of genes or samples with mutations that occur during tumor development. The number of identified mutations in cancer samples can vary wildly, but some tumors tend to aggregate widespread alterations. This Nature paper about the mutation landscape and significance across 12 major cancer types (as part of the TCGA Pan-Cancer effort) is a good example. In the very first figure, the authors investigated the mutation frequencies of six transition (Ti) and transversion (Tv) categories for each cancer type:

Figure 1: Mutation frequencies, spectra and contexts across 12 cancer types.   Kandoth, Cyriac, et al. "Mutational landscape and significance across 12 major cancer types."   Nature   502.7471 (2013): 333-339.

Figure 1: Mutation frequencies, spectra and contexts across 12 cancer types. Kandoth, Cyriac, et al. "Mutational landscape and significance across 12 major cancer types." Nature 502.7471 (2013): 333-339.

In another recent Nature paper, Whole-genome mutational burden analysis of three pluripotency induction methods, the authors researched mutational subtypes in each sample:

Figure 2: Characterization of variants caused by reprogramming method.   Bhutani, Kunal, et al. "Whole-genome mutational burden analysis of three pluripotency induction methods."   Nature communications   7 (2016). 

Figure 2: Characterization of variants caused by reprogramming method. Bhutani, Kunal, et al. "Whole-genome mutational burden analysis of three pluripotency induction methods." Nature communications 7 (2016). 

Using OncoLand, you can easily calculate and visualize total mutation burden of every sample or tumor type. Check out this OncoLand case study: Visualize "mutation burden" of each tumor in TCGALand

1. Calculate total mutation burden of every sample in TCGALand.

To calculate the number of total mutations per tumor sample (mutation burden), you can simply use Summarize Sample Mutation Count under Analytics tab in Land. By specifying the individual nucleotide changes, for example "A->C", the result will calculate the total number of mutations (from a selected GeneSet) mutated in each sample (from selected SampleSet).

You can further summarize the data by downloading this TotalMutationBurdenByNTchange table to Array Studio's local analysis. For example, adding a variable view to better visualize mutation burden across samples:

Mutation burden variable view. Y-axis represents mutation number. X-axis represents different samples.

Mutation burden variable view. Y-axis represents mutation number. X-axis represents different samples.

2. Calculate average mutation burden in each tumor in TCGALand

Using local analysis functions, you can further research mutation burden in each tumor in Land data. The Summarize function allows user to calculate the mean mutation number grouped by tumor type, or other preferred grouping options.  

After Stacking the table, you can plot another variable view to visualize the distribution of each type of nucleotide change in each tumor type

                                                                               Stack table by row to generate variable view 

                                                                               Stack table by row to generate variable view 

In this way, we can easily tell which tumor type has the highest mutation burden.

To learn how to exactly perform the above analysis, please watch our OncoLand case study: Visualize "mutation burden" of each tumor in TCGALand.

[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.

[OncoLand Case Study] Find genes that are frequently co-mutated with your gene-of-interest: Co-mutation of TP53 and ATRX when IDH1-R132 is mutated

Vivian Zhang

The IDH1 gene encodes isocitrate dehydrogenase, which is  involved in NADPH production, especially in the brain. Mutations in IDH1 are frequently found in low grade and high grade gliomas (Low grade (grade II), anaplastic (grade III), and glioblastoma (GBM, grade IV).). (Research Article: IDH1 and IDH2 Mutations in Gliomas) These mutations play an important role in gliomagenesis and thus have clinical interest. We can query OncoLand to learn about IDH1 mutations, and other genes frequently co-mutated. For details, please refer to our OncoLand case study wiki:

Identify mutation hotspots in a gene of interest

In several cancers, IDH1 is frequently mutated at arginine 132, which alters the enzyme's active site. We can visualize the frequencies of mutations at different sites in each tumor. As we can see, our data confirms that IDH1 arginine 132 is frequently mutated in low grade gliomas (LGG) and glioblastoma (GBM):

TCGALand DNA-Seq Somatic Mutation Site Distribution View. 

TCGALand DNA-Seq Somatic Mutation Site Distribution View. 

The user can create a SampleSet, for example the one shown below, IDH1_mutaion, from the Analytics | Generate Sample Set | Generate Site Mutation Status SampleSet. 

SampleSet: IDH1_mutation

SampleSet: IDH1_mutation

Identify other genes that are co-mutated with your gene of interest

With the SampleSet, we can identify the gene mutations that are correlated through Analytics | Integration Analysis | Sample Grouping to Mutation. The test may take a few minutes if all genes are queried, and the results will be available from the Analytics | Open Result Set menu. From the results table, we can rank genes with the PValue from the Fisher Exact Test to identify the correlated genes, for instance ARRX and TP53 in LGG and GBM:

Analytics | Integration Analysis | Sample Grouping to Mutation Test results. Rank by PValue, filter by only co-occurring gene in LGG and GBM.

Analytics | Integration Analysis | Sample Grouping to Mutation Test results. Rank by PValue, filter by only co-occurring gene in LGG and GBM.

Visualize Co-mutation patterns with the Alteration Omicprint

There are several ways to visualize co-mutation frequencies of multiple genes. While the "Alteration Distribution" displays the number of samples mutated in any gene of the GeneSet, "Somatic Co-mutation Frequencies" will display the distribution of samples with different mutation loads. The "Alteration Omicprint" efficiently displays per-sample mutation status of one, ten, or even hundreds of genes. You can also generate custom Omicprinst based on custom queries if you want to query mutation status. Please check out our case study tutorial videos to learn how to perform the analysis. 

Alteration Omicprint displays gene alteration status for multiple genes for corresponding samples. Custom quires for IDH1 and TP53 somatic mutation status, and BMP2 RNA-Seq FPKM are created. Next, check out Custom Query Omicprint view. For each custom query, sample status is displayed. As we can see, samples with mutated IDH1 and TP53 frequently over-express BMP2 in GBM. 

Alteration Omicprint displays gene alteration status for multiple genes for corresponding samples. Custom quires for IDH1 and TP53 somatic mutation status, and BMP2 RNA-Seq FPKM are created. Next, check out Custom Query Omicprint view. For each custom query, sample status is displayed. As we can see, samples with mutated IDH1 and TP53 frequently over-express BMP2 in GBM. 

[OncoLand Case Study] MTBP expression and copy number correlates with poor patient survival

Vivian Zhang

A recent paper used TCGA data to show that cancer patients with higher expression/amplified copy number of MTBP had reduced survival. Thus, their data revealed that "MTBP significantly contributes to breast cancer and is a potential novel therapeutic target in the treatment of TNBC". To evaluate MTBP expression, the paper researched mRNA data, copy number variation data and patient survival data from TCGA (The Cancer Genome Atlas) to validate the results. Omicsoft users can easily use OncoLand, and the TCGA Land to verify and explore these results in minutes. We archived a nice OncoLand case study in our wiki:

 

Visualize MTBP expression in tumor vs. normal breast cancer tissue

A typical case starts with visualizing the data. After selecting TCGA2015 Land, users can search for MTBP gene in the search box. Go to Gene FPKM View to visualize gene expressions. Next, filter Tumor Type to breast cancer (BRCA) and select Sample Type for grouping. Thus, you will generate a plot that looks like this:

Categorize breast tumor samples by MTBP expression quartiles

The user can subset by using MTBP expression level to test whether tumors with high expression correlate with other variables, such as survival. You can achieve this by generating a MTBP Gene FPKM custom query and labeling samples according to the query. Only tumor samples express high level of MTBP. 

Next, we can correlate MTBP expression with survival. As we can see, survival of high MTBP expressing samples is significantly worse than low expressing samples:

If you want to explore how amplification or deletion of a gene correlates with expression, mutation, or clinical metadata, you can perform a "custom query" on Copy Number Variation as well (please refer to our OncoLand case study wiki for details). Our results are consistent with the results from the paper, for example, MTBP was significantly elevated in breast cancer samples (Figure 1A) and those patients with elevated MTBP expression exhibited reduced survival (Figure 1B):

 

Create a SampleSet of Triple-Negative Breast Cancer samples

Breast cancers that are estrogen receptor-negative, progesterone receptor-negative and HER2-negative can not be targeted by many common treatments for breast cancer, for instance, common HER2 inhibitors like trastuzumab, however other treatment plans are still available. 

We can use TCGA clinical meta data to identify triple negative samples. By filtering ER, PR and HER2 status from Sample | Clinical Data | Procedure |Genetic Testing filter options, you can select those triple negative data and create a Sampleset using Group Sample Set From Selection: 

Subgroup sample expression data by multiple variables

You can quickly partition a gene's expression by each permutation of multiple clinical variables i.e. triple-negative breast cancer samples in this case. And as we can see, triple-negative samples indeed have higher MTBP expression than any other samples:

To learn how to perform the analyses, please visit our wikipage MTBP expression and copy number correlates with poor patient survival

 

 

 

[Land] Behind the Scenes: Omicsoft Land processing and curation

Vivian Zhang

With Omicsoft continuing to release new and updated Land products to meet the high demand from our clients, we continue to overcome challenges, accumulate unique expertise and establish our leading role as a disease genomics data service and content provider. (If you are not familiar with our Lands, check out our ImmunoLand and CVMLand ). 

Unlike the field of cancer genomics, where there are quite a few institutes or consortiums providing large amounts of data, for example TCGA, CCLE, CGCI, ICGC, TARGET (for details, please check out our OncoLand), immunological, cardiovascular and metabolic disease genomics research have most data of their data scattered in individual research studies. Public data repositories such as GEO (Gene Expression Omnibus) and SRA (Sequence Read Archive) collect data from the research community. Problems easily arise:

  • Data query between studis is time-consuming
  • Data formats vary from study to study, making it difficult to understand the data or to perform cross-comparison and meta-analysis
  • Data accuracy is uncertain, with human error in the process of data uploading, archiving, processing and more
  • Data processing and analysis is not standardized, but instead often the choice of individual investigators

The complexity of the process means that there is a real need for someone to come in and clean up the data that is out there, and to do it properly. At Omicsoft, thanks to our experienced data curation and processing team, we aim to become the leading provider and data hub for public disease genomics research. 

At Omicsoft, we have a team of more than 10 domain experts handling projects manually, from dataset selection and data processing to analysis:

                                                                         Omicsoft Land Data Processing Workflow

                                                                         Omicsoft Land Data Processing Workflow

We carefully select which projects to include, filtering out unrelated projects or any project doesn't pass our curation standards:

 

We use controlled vocabularies and extract sample metadata with standardized fields and input. Very often, our curation team member needs to go back to either the public data repository or author of the primary article to clarify content or report errors, all to ensure the accuracy of our content: 

We perform iterative editing to minimize and eliminate out processing errors:

Our proprietary curation tool is a unique asset that ensures fast, accurate, efficient, and standardized large volumes of curation:

For more details, please refer to our wiki page on Omicsoft DiseaseLand Curation Ppeline

Please contact us is you have questions and suggestions.

Studio on the Cloud: Leveraging the Amazon Cloud in a mixed computing environment | Improvements and Updates

Vivian Zhang

Array Suite empowers medium to large pharmaceutical, biotech companies and research institutes to perform state-of-the-art NGS and OMIC analysis with superior accuracy and speed. However, maintaining a server or HPC cluster may not be a cost effective solution for small organizations or research units that do not have high demand for NGS and OMIC analysis. Even for large pharmaceutical and biotech companies, the computing demand can vary from time to time and many companies have started to leverage cloud solutions for internal data management and analysis.

Omicsoft has a long-term goal to be data location agnostic, allowing a customer to keep data locally, within their firewall, but also stored with a variety of cloud providers.  This makes sense in a world where collaborators each use different platforms and the need to share extremely large datasets safely and efficiently becomes more and more important.

Omicsoft's cloud solutions help both large and small pharma, biotech and research institutes manage their genomic and clinical data faster, more efficiently, and for a lower cost than traditional computing.

Studio on the Cloud allows you to seamlessly run all Array Studio analytics from Amazon, combining the storage of S3 (Amazon Simple Storage Service) with the analytical power of EC2 (Amazon Elastic Compute Cloud).  Easily scale up any number of instances for every analysis., while allowing users of Array Suite to easily intermix local storage-based analytics with cloud-based analytics.  The user can create a standard Server Project, but instead of adding data from their server, seamlessly add data from the cloud instead. Folders in S3 brackets are mapped to the ArrayServer folder structure and to the user this appears seamlessly.

With a cloud configuration, cloud folders appear under the root of Array Server file browser the same as other server folders

With a cloud configuration, cloud folders appear under the root of Array Server file browser the same as other server folders

 

Users can select raw data from a cloud folder or its subfolder. ArrayServer will launch one machine for each sample and run analysis using an optimized EC2 instance. Input files in S3 are copied to EC2 machines where EBS storage is attached. Next, Cloud instances are launched with Omicsoft software installed. The instances receive message from ArraySever and perform the analysis. When a job is finished, all results are uploaded to a S3 output folder. 

Omicsoft has seen an increasing number of clients that implement mixed mode solutions (cloud solution in addition to their SGE/PBS/LSF cluster). Omicsoft integrates cloud and cluster seamlessly so that users can perform jobs either on the cloud or on the local cluster with maximum flexibility, entirely according to their analytical need. For more details on cloud configuration, logic and cost comparisons, please check out our wiki page Example of running 100 CCLE samples on cloud.

With the growing user base, Omicsoft continues to improve the cloud implementation. Recently, we significantly improved the data transfer speed between S3 and EC2 with AWS Command Line Interface (CLI). For example, downloading the reference library now only takes less than 1 minute compared to up to 20 minutes previously. This improvement helps users to reduce analysis time, hence saving money. If you are interested in knowing more technical details, please contact customer support.

Visit our studio-on-the-cloud webpage or contact us at sales@omicsoft.com  for general inquiries. 

 

Bridging Bioinformatics|Genomics|Genetics Research: 2016 Omicsoft User Group Meeting

Vivian Zhang

 

  • Who Attended:
    • More than 30 leading pharmaceutical and biotech companies. 
    • More than 100 attendees who are experts and scientists in the field of bioinformatics/genomics/genetics.
  • What Occurred:
    • Numerous discussions among attendees on the future of biomarker discovery, as well as best practices of data management, visualization and analysis.

 

  

Omicsoft Corporation successfully held our kick-off Omicsoft User Group Meeting in Cambridge, MA on Wednesday May 4, 2016.

We would like to thank all speakers and attendees, all of whom are extremely important in helping build out our platform successfully.  We've received extremely positive feedback from the meeting, and hope to do it again in the future.  Feedback on our software and services help drive our business, and the direct interaction with our customers during the event proved invaluable to us. 

Highlights from the meeting:

  • Introduction of GeneticsLand for management of genetics data
  • Introduction to the future SingleCell Land
  • Overview on curation processes
  • Updates on current data subscription Lands

For more details, please visit our 2016 User Group Meeting webpage.

 

Above is just a glance of some exciting moments at our meeting. If you missed the meeting, we have uploaded our speaker presentations and videos on our 2016 User Group Meeting webpage.

If you have any question with regard to the meeting, please contact us. 

 

Join OmicSoft & Move to NC

Gary Ge

North Carolina is a state filled with beautiful scenery and a moderate climate, spanning from the Atlantic Ocean beaches to the Appalachian Mountains. It has a culture rich in history and the arts, and world-class sports and recreational opportunities. This has helped NC rank as the 6th most-visited state in the US. It is also the 9th largest economy in the country, making North Carolina an exceptional place to live and do business. Combine that with quality health care, top universities, and a low cost of living, and it’s easy to see why most people who live here never want to leave (www.nc.gov).

Why North Carolina?

North Carolina offers one of the best climates in the country, characterized by mild winters, long pleasant periods of spring and fall, and warm, sun-filled summers.

We have beautiful landscapes from the mountains to the coast. From RTP, a short drive west will arrive in the famous Great Smoky Mountains and Blue Ridge National Parks. The view while driving is stunning, especially in the fall, as a lush sea of red leaves envelopes the mountains. Drive about two hours east, and you can reach NC’s white sandy beaches, or a bit further to the Outer Banks and Kitty Hawk, the birth of our “First in Flight” moniker.

This natural beauty will not come at a high cost either. North Carolina’s cost of living is very competitive compared to the rest of the country. From housing and utilities to everyday supplies and groceries, transportation and health care, our cost of living is below the national average based on the ACCRA Cost of Living Index. According to Zillow, the median Wake County home costs just $227,600, which is below the national average for similar metropolitan areas.

North Carolina is proud to have been the first in the nation to simultaneously address learning standards, student tests and school accountability. We have some of the best middle and high schools in the country. NC also has outstanding public and private universities. In the Research Triangle alone, we have three nationally-recognized universities. Duke University (Durham) is consistently ranked in the top 10 best universities in US. University of North Carolina- Chapel Hill, the oldest public university in the US, is ranked in the top 5 public universities in US. North Carolina State University (Raleigh) has one of the best Engineering schools in the US.

If you like sports, we have the 2015 NFC Champions in the Panthers (NFL), a rising NBA team in the Hornets, and a National Hockey League (NHL) team, Carolina Hurricanes, as well as the Charlotte Checkers (AHL). We also have some of the best college sports teams; both Duke and UNC known for their championship-winning basketball teams.

Why RTP?

Research Triangle Park (RTP) is one of the largest research hubs in the world, with some calling it the Silicon Valley of the East Coast. The “Research Triangle” is formed by the three hub cities of Durham, Raleigh and Chapel Hill (and includes the towns of Cary, Morrisville, etc.), and the three major research universities in those cities (Duke, NC State, and UNC-Chapel Hill, respectively). Attracted by this incredible pool of talent, RTP is home to a host of Global Fortune 500 companies such as IBM, Cisco, EMC, GSK, and Lenovo, along with private ones such as SAS. There is an accelerating trend for established companies to relocate here, with Fidelity from New Jersey and MetLife from New York as the latest examples.

This trend has not shown any signs of slowing down. The population in areas around RTP, such as Wake County, grew by 43.5% between 2000 and 2010, which was the highest growth of any metropolitan area in the nation for that period (newgeography.com). The Raleigh-Cary metro region is projected to be the fastest growing region in the U.S. over the coming decade, and expected to DOUBLE in the next two decades (proximityone.com 2011).

All of this growth has led Forbes Magazine to name Raleigh-Cary as one of the Best Places in the US for Business and Careers. Three times out of the past five years, Raleigh has been ranked No. 1, and in the top three for more than a dozen years (Forbes, 07/29/2015). Some other recent accolades for the RTP area include:

  • Raleigh-Cary was ranked as the #1 city in “Ten best cities for Jobs” (CNNMoney, 05/19/15)
  • Raleigh-Cary was ranked in the top three best cities in “American’s 20 best cities for Young Professional in 2016” (Forbes, 3/28/16)
  • Raleigh-Durham was ranked as one of the top four best places to live in “2016 best places to live in the US” (U.S. News, 3/2/2016)

Photo taken in OmicSoft Annual User Meeting (05/04/2016): Daniel, Viv, Chrys, Wenjin, Joe, Rob

Why OmicSoft?

By now, you can see why OmicSoft calls NC home, and we wanted to share some facts about the company as well. In the last eight years, OmicSoft has an amazing average growth rate of more than 50% year over year. OmicSoft has been profitable since day one, so this growth has been no surprise.  As a candidate, you will find that joining OmicSoft is attractive in several ways:

(1)   OmicSoft has the stable financials of a mature company, with the growth opportunity of a start-up. In addition to a highly competitive salary and bonuses, our other benefits (health insurance, 401 K, PTO) are robust as well. In addition, employees will be granted stock options, because we want everyone to buy in and be a part of the OmicSoft success story.

(2)   OmicSoft is still growing quickly. The chance for young employees to take greater responsibilities is significantly higher than in a larger firm. For example, one of our employees was just promoted to Associate Director in less than a year. At OmicSoft, we believe your growth should not be tied just to time in your job, but ability and passion, and we evaluate our employees accordingly.

(3)   OmicSoft is a leading provider of bioinformatics, next generation sequencing, and cancer genomics solutions in the industry. An OmicSoft career provides an enormous opportunity to learn/practice hot topics such as HPC computing, cloud computing, vector databases, software development, NGS analytics, external tool integration, etc.

(4)   OmicSoft’s clients include leading pharmaceutical companies. OmicSoft employees routinely interact with research scientists to optimize analysis pipelines and solve problems, and our software/data solutions evolve to support new customer needs. Your work will directly impact discoveries of new treatments for cancer, immunological diseases, and more.

So, if you are talented and want to move to a beautiful area with great career prospects, come to NC and join us at OmicSoft. We look forward to meeting with you.

 

Omicsoft Moves to New Office

Matt Newman

Omicsoft is pleased to announce the move to our new office at 5001 Weston Parkway, Suite 201, Cary NC.  The Omicsoft team has grown over the past 8+ years, and all of us are excited to have a new building to continue to help our clients with their bioinformatics needs.

[Research] Accelerating Target Identification using GeneticsLand

Vivian Zhang

Over the past decade, genome-wide association studies (GWAS) have been widely used to identify disease associated gene loci. However, the majority of the identified variants are in noncoding regions, making it difficult to identify functional variants and eventually actionable drug target gene/protein. Our newly released GeneticsLand can help to accelerate identification of drug target with its powerful ability to integrate variant annotation, associated eQTL and association study results.

Here is an example of how GeneticsLand quickly identify potential associated genes of noncoding variants  identified in a research article on inflammatory bowel disease (IBD) (1)

In this article, the author investigated 4734 variants from 152 IBD associated GWAS loci and identified 18 prioritized noncoding SNPs that may contribute to IBD by regulating nearby genes. 

Having the list of noncoding SNPs, GeneticsLand can help to quickly research SNP annotation, association, eQTL and more.

1. GeneticsLand allows user access to annotation information from a variety of resources without using each of the individual annotation tools:

2. GeneticsLand integrates phenotype association information from published research articles. It allows users to research other phenotypes associated with the SNP of interest. In this example, SNP rs2231884 is shown to be associated with IBD in a Nature article from 2012, reinforcing the finding in the original article. 

3. GeneticsLand helps user to research eQTL information, which may lead to discovery of new associated genes. For example, in the original article, the author identified CCDC85B, FIBP and FOSL1 as nearby genes and suggests the SNP may contribute to IBD pathogenesis by regulating nearby genes. However, none of these genes was known to be associated with IBD or any immunological related pathways. By using GeneticsLand, we found that SNP rs2231884 has an eQTL association with a couple of genes, including CTSW, which encodes a cysteine proteinase that may have a function regulating T-cells cytolytic activity.

Reference:

(1). Mesbah-Uddin, Md, Ramu Elango, Babajan Banaganapalli, Noor Ahmad Shaik, and Fahad A. Al-Abbasi. "In-Silico Analysis of Inflammatory Bowel Disease (IBD) GWAS Loci to Novel Connections." PloS one 10, no. 3 (2015): e0119420.

[Land Update]GeneticsLand: Data Warehouse for Variant Level Data

Vivian Zhang

 

What is GeneticsLand?


GeneticsLand is a data warehouse for variant level data and provides a turnkey solution to genetic data storage, analysis, and annotation to facilitate a wide range of genetic-based activities in drug discovery and development. 

It allows user to import and export VCF files, array based data, imputed array data, eQTL data, association results, and variant annotated data. GeneticsLand is a subscription, adding value regularly with new content after purchase.. It is a search engine that not only stores millions of samples but also provides fast, easy and accurate search of variants, genes, chromosomal region and phenotype data.


Why use GeneticsLand?

 

Big Data:  GeneticsLand can store up to one million VCF samples per "Land" (database), (assuming 100 million SNPs per sample and essentially unlimited array based files). It can support thousands of association studies. At the same time,  data storage is compressed, significantly reducing IT storage burden.

Fast: GeneticsLand can access 100 trillion data points and perform advanced visualizations and dynamic annotation in real time.  Queries of 10 billion data points can occur in much less than 1 second (0.01 seconds in benchmark testing).

Integrated Solution: GeneticsLand will provide biologist-friendly and high level client for genomic/genetic data integration (including a future web-based client), accelerating target identification and validation using our sophisticated queries and visualization system. Our adjacent products, including OncoLand, ImmunoLand and CVMLand, work together with GeneticsLand to provide comprehensive genomic research tools that empower researchers in all disease fields. 

SNEAK PEEK OF GENETICSLAND

 

Search variant/genes for individual genotypes, SNP allele frequencies (population genetics), and annotations:

Access curated public association data: 

Visualize public and private eQTL data:

Correlate numerical clinical variables to genotypes:

How could GeneticsLand help you with your genetics research?

  • Big Picture - centralized storage and search engine
  • Everyday work
    • Search variant/gene for annotations
    • Search frequencies for a given variant/gene/region cross projects
    • Access individual genotype data (for data managers) cross projects
    • Access public association data (e.g. GRASP2)
    • Access public variant data
    • normalized data from any# of studies through exportinf
    • Instant region/genome plots for association studies

Stay tuned for more information in the coming days and weeks! 

Contact us at: sales@omicsoft.com for any questions or request for demo.

 

[Land Update] CVMLand: Public cardiovascular and metabolic diseases genomics datasets

Vivian Zhang

The increasing number of public genomic datasets and customer demand for integrated database have been driving Omicsoft's Land database development. In the past couple years, our flagship Land, OncoLand, has been an overwhelming success and is now being used by most of our pharma and biotech customers. Encouraged by our OncoLand success, we are in the process of bringing a series of DiseaseLand products to our customers, including ImmunoLand and CVMLand

CVMLand, Cardiovascular and Metabolic Disease Land, is designed for diseases such as cardiovascular diseases, diabetes mellitus, glucose intolerance, islet autoantibody positive, lipid metabolism disorder and nutrition disorders. Currently it has more than 4000 samples with RNA-Seq and Expression data. 

CVMLand Sample Distribution

CVMLand Sample Distribution

CVMLand View Highlight-Comparison View.  All datasets have been curated as belonging to different "Comparison Types", including  Disease vs Normal (shown here). Data have been carefully curated using a set of controlled vocabulary, allowing for visualizations like this one, showing fold changes (sized by p-value) for each disease category.     

CVMLand View Highlight-Comparison View. All datasets have been curated as belonging to different "Comparison Types", including  Disease vs Normal (shown here). Data have been carefully curated using a set of controlled vocabulary, allowing for visualizations like this one, showing fold changes (sized by p-value) for each disease category.  

 

To keep up with the rapidly increasing number of public datasets, CVMLand updates every quarter. We are constantly looking, curating and processing new datasets. Given our customer-driven product development strategy, we welcome all our users and potential users to request their dataset of interest.  

More more details, please check out our CVMLand webpage. Please feel free to register for a CVMLand trial on the webpage.

If you are interested in cardiovascular or metabolic disease genomic and clinical data, please contact us at sales@omicsoft.com.

 

Refresher: Omicsoft's Support Service

Vivian Zhang

Omicsoft has always been proud of our fast and best-in-class customer support. We are highly customer-driven about our product development and support service, and we encourage users to be actively in touch with us for any request, question or problem. In this blog, we would like to briefly introduce all our support services and encourage you to fully utilize our support for your benefit.

As a beginner, we encourage you to start with our comprehensive tutorials that cover each part of our functions or products. Start with the part that you are most interested in or is most relevant to you and take it from there. As we adopt feature-on-demand and agile product development strategy to ensure product quality, we constantly update and improve our functionality and data content. If you find the latest software does not perform or display exactly same way as it is described in tutorials, please feel free to email our support team about your questions (and hopefully we'll get it fixed or update our documentation).

As you get more familiar with our software, our wiki is a great resource to address any detailed questions. We provide rich content that our scientists have been working on for years to address all product and research related questions, knowledge, best practice and so forth. For a specific question, the best way is to start from the search box. For example, if you would like to explore RNA-seq related content, simply search RNA-Seq. 

Omicsoft Wiki Search Box and search results for RNA seq.

Omicsoft Wiki Search Box and search results for RNA seq.

While using our software, you may have urgent questions or things may not work for some reason, so please feel to contact our support team. At Omicsoft, we aim to provide fast and detailed support to every single user. The best way to get your problem solved is to email our support team. We guarantee to get back to you within hours during office hours or the next business day for off office hour emails. If the question is urgent, we offer one-to-one online meetings. Please follow the instructions on the website to contact us. 

In summary, Omicsoft provides beginner tutorials and videos, powerful wiki search, and fast one-to-one support to ensure best support service. Be sure to keep in touch with us and wish you the best with your research projects!

[EVENT] LEARN|NETWORK|IMPACT 2016 OMICSOFT USER GROUP MEETING

Vivian Zhang

Call for attention: Omicsoft Corporation would like to invite you to our first annual Omicsoft User Group Meeting being held in Cambridge, MA on Wednesday May 4, 2016. FREE registration and attendance, limited time only. 

Omicsoft is committed to providing best-in-class products and services that facilitate users in biomarker data management, visualization, and analysis. We believe customer demand has a huge impact on our product design and services. To better understand user needs and questions, and better help users network and learn from each other, we are organizing our first User Group Meeting as the first attempt in building a long-term open network platform.

·      Learn to Use Omicsoft Products More Efficiently
·      Impact Future Product Development
·      Network with Peers and Industry Experts
·      Get One-On-One Help from Experts

Please check out our Press Release. For registration and more details, please directly go to our UGM page.

Learn, network, impact. Come join us and 20 more already committed companies from our customer base (who are our customers?).