Literature: Disease and tissue annotation based on Genomatix literature mining (LitInspector) was updated on 12/19/2017.
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was updated to the release as of 11/29/2017.
With this update we are introducing the ability to share projects with co-workers and consequently you will be able to create additional projects besides the default MyProject.
Projects can either be shared read-only or with write access.
Here is a short introduction to the new sharing feature:
The sharing feature is accessible through the Project Info
To share a project and its results click the button at the bottom of the info view. In the upcoming dialog, you can select the permissions for the receiving user(s): readonly or write. By having write permission, every action is allowed except moving and copying. Specify the receiving users, by inserting their login names into the text field in comma or blank separated form.
The section shared with lists all users the project is shared with. Clicking on one of the users allowes to revoke the project from that specific user. Different colors are used to indicate the permission the project was shared with.
You can also revoke the shared project from all users. For that, simply click the button at the bottom of the info view.
To return a shared project to its owner, use the button at the bottom of the info view, which will be in place of the button.
The allele frequencies from the Exome Aggregation Consortium (ExAC) source are also available as a subset excluding TCGA samples.
In addition the highest allele frequency (maximum) in any one population of ExAC set is available as a separate field. For details see Lek et al. 2016.
PolyPhen scores are added to the effect prediction evaluation and are based on pre-calculated values from Ensembl.
Literature: Disease, tissue and drug annotation based on Genomatix literature mining (LitInspector) was updated on 11/02/2016.
ClinVar: Clinical disease annotation based on the ClinVar database was updated to the November 2016 data release. Details about this release can be found on the ClinVar site: Release notes and Submission statistics
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was updated to the release as of 11/02/2016.
GTR: Genetic Testing Registry (GTR) database was updated to the release as of 11/05/2016.
GO: Gene Ontology (GO) database was updated to the release as of 11/08/2016.
This update is a bug fix release and affects the columns Effect prediction, Regulatory feature, TF binding, Matched binding motif, DNaseI hypersensitivity and Histone modification. Using a filter condition with either does not contain or is not equal excluded variants with entries that have undefined or unknown values in the specified filter field. Please note, that this is limited to the extended filtering mode in combination with an inverse filter.
Here is an example to illustrate the issue:
The filter for effect prediction with is not equal to synonymous returned all the variants except for synonymous variants and variants that had undefined effects (in this case all intergenic, intron, promoter or any other non-coding variants). After the fix the variants with undefined effect are kept and returned as well.
In the following screenshot you can see that the filtered list contains both sets of variants: the non-synonymous and the ones with unknown effects.
A second bug fix resolves issues in the validation process of VCF files with multiple samples. The genotype definition for each sample as defined in the FORMAT column can skip trailing fields with undefined values. This case is fully supported by the VCF file specification but was incorrectly rejected in our import process. A typical scenario where this arises is the concatenation of samples from different variant callers.
With this update you will be able to track variants in your sample that have changed ClinVar annotation. So you won't miss relevant new entries in ClinVar or changes in the clinical significance of a variant and can always reclassify results, if necessary.
As highlighted at the latest ACMG annual meeting in March 2016, on-going variant reclassification is rapidly becoming a requirement for clinical labs to ultimately keep their patients up-to-date with consequential knowledge changes about their condition.
Here are two articles that emphasize the importance of reclassification:
On June 21, 2016 we presented the latest developments around Continuous Annotation.
dbSNP: Known variants annotation based on dbSNP was updated to build 147 from April 2016.
Literature: Disease, tissue and drug annotation based on Genomatix literature mining (LitInspector) was updated on 07/05/2016.
ClinVar: Clinical disease annotation based on the ClinVar database was updated to the June 2016 data release. Details about this release can be found on the ClinVar site: Release notes and Submission statistics
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was updated to the release as of 06/09/2016.
GTR: Genetic Testing Registry (GTR) database was updated to the release as of 06/12/2016.
Panels: Gene lists for popular panels from major vendors were updated in June 2016. These include for instance the TruSight™ Panels from Illumina and the Ion AmpliSeq™ Panels from Thermo Fisher Scientific. As an additional source the list of curated genes was added as panel filter.
The result management is the central place to access, rename and delete samples and comparisons. This release brings a fresh look and feel to the interface making it easier to navigate the results and get an overview of all currently stored data.
The left side divides the results that are imported and owned by yourself from the results that are shared with you like the tutorial for example.
A single click on a row selects a result. A second click on a selected row switches into the edit mode. The name and comment of a sample or comparison analysis can be changed.
A double click on a row opens the result. In order to quickly retrieve results, the list can be filtered by a search term.
Genomatix literature mining (LitInspector) searches for variants in abstracts of biomedical literature. These matches have been available in the column literature citations. In addition, the references are now also included when a variant report is generated. A second source of reference are OMIM articles about genes when they mention specific alleles.
The citations for the example in the tutorial example with the Val9Met mutation on chr1 at position 10,032,156 bp:
The first citation is produced by LitInspector and the source for the second citation is the corresponding OMIM entry.
Variant annotation always has been a snapshot of the existing annotation data at a given time point which provide context to the variants. Such annotation sources are not only the underlying transcript set, but also known variants in databases like dbSNP and ClinVar and the biomedical literature that is used in our automated literature mining. All these sources change over time and may significantly improve by correcting previously erroneous or conflicting entries or simply by adding new data. In the context of variant interpretation it is important to apply the latest available annotation data at hand for the assessment of pathogenicity. Additionally, a reanalysis of previously reported variants can be of advantage when additional data support a different assessment or add additional layers of evidence.
With this release we are introducing the concept of continuous annotation. The variant annotation is constantly updated to the latest releases of major annotation data sources or when new data sources are added.
How does continuous annotation improve the interpretation of my samples?
VCF files based on genome build GRCh38/hg38 are now supported in addition to GRCh37/hg19. Transcript sets for build GRCh37 are frozen and will remain at RefSeq release 105 and Ensembl release 75. Transcript and gene definitions will be updated for GRCh38 and are currently at RefSeq release 107 and Ensembl release 81. Other annotations like dbSNP and ClinVar will be updated for both builds as long as they are provided by the external source.
The samples view provides two additional details tabs for each sample:
The quality control panel gives a brief summary about the data quality of the variants the file. This includes metrics like the percentage of SNVs that were not found in dbSNP also known as dbSNP novelty rate, coverage and genotype quality summaries and others.
The annotation distribution panel gives a summary about the annotation process. It lists the number of variants that were not considered for annotation due to unknown genotype or reference sequence mismatches.
Both details panel can be accessed even if the sample has not been activated. The quality control serves as an initial control to decide if the variant calling step met certain standards in order to continue with variant interpretation. Whereas, the annotation distribution serves as a check point to see if the format of the variants was detected and if the variants could be annotated as expected. The statistics are calculated both genome-wide and per chromosome.
Every month an increasing number of new articles about genetic variants are published which are reported to have an impact on phenotypes. Abstracts of biomedical literature (PubMed) are analyzed and the variants that are mentioned are automatically extracted through Genomatix literature mining (LitInspector). Those articles are listed in the column citations and quickly provide access to available literature for a given genomic position.
Often the number of variants that are reported to have an impact the protein at a molecular level (deleterious) is too broad. An additional column called low confidence allows to remove those variants that might have a higher uncertainty associated with. The criteria for low confidence are:
ExAC: Added ExAC allele frequencies from release 0.3 in January 2015. For details, see Analysis of protein-coding genetic variation in 60,706 humans
dbSNP: Known variants annotation based on dbSNP was updated to build 144 from June 2015.
Literature: Disease, tissue and drug annotation based on Genomatix literature mining (LitInspector) was updated on 10/14/2015.
ClinVar: Clinical disease annotation based on the ClinVar database was updated to the November 2015 data release. Details about this release can be found on the ClinVar site: Release notes and Submission statistics
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was updated to the release as of 11/02/2015.
GTR: Genetic Testing Registry (GTR) database was updated to the release as of 11/05/2015.
Literature: Disease, tissue and small molecule annotation based on Genomatix literature mining (LitInspector) was updated on 04/14/2015.
ClinVar: Clinical disease annotation based on the ClinVar database was updated to the April 2015 data release. Details about this release can be found on the ClinVar site: Release notes and Submission statistics
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was updated to the release as of 04/15/2015.
GTR: Genetic Testing Registry (GTR) database was updated to the release as of 04/11/2015.
Literature: Disease, tissue and small molecule annotation based on Genomatix literature mining (LitInspector) was updated on 01/27/2015.
ClinVar: Clinical disease annotation based on the ClinVar database was updated to the February 2015 data release. Details about this release can be found on the ClinVar site: Release notes and Submission statistics
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was updated to the release as of 02/05/2015.
GTR: Genetic Testing Registry (GTR) database was updated to the release as of 02/11/2015.
For a full reference of all data sources that are currently used: Data Sources
For each genomic position an estimated maximum somatic mutation frequency is given based on somatic mutation databases from COSMIC and TCGA. The maximum frequency is determined across all available cancer samples and can be extracted individually in the details panel somatic mutations below. It allows to filter the list of variants for known somatic mutations. The second column somatic mutation tissues lists the cancer tissues (primary sites) that contain mutated samples at that very specific position sorted by their mutation frequency. The TCGA data source contains a variety of differently processed data sets mainly differentiating automatic pipelines and additional manual curation efforts from participating centers. The list of data sets that were considered from the TCGA source is adapted from the the list of Broad GDAC Firehose. Since the two new columns provide a improved filtering strategy at genomic level the previously available column for COSMIC tissues became obsolete and was removed.
Not every missense and nonsense SNV has a corresponding SIFT prediction score because the pre-calculated SIFT scores do not cover all the existing transcripts or predicted transcripts. We are in progress of extending the availability of SIFT scores to more transcripts in one of the future annotation updates. When filtering for SIFT scores with smaller than only variants are retained in the list that have an actual score and all variants without any score are removed. In most cases this was not the expected behavior and we have changed the filtering mechanism. Variants with empty SIFT scores are kept now after filtering for a SIFT score with smaller than. Effectively this returns variants that either have a score smaller than or equal to the specified value or do not have a score at all (either due to a missing SIFT score prediction for the transcript or variants other than SNV).
All imported samples are categorized into one of the classes small, medium and large depending on number of non-ref variants. A monthly fee is charged for samples that are stored. Today, we are happy to announce a price reduction for this fee by as much as 80%, effective on February 1, 2015:
This fee includes not only the storage of the annotated variants but also includes the storage one BAM file, that is associated to the sample from the VCF file. In addition, every sample gets continuously annotated based on the latest annotation sources in our variant annotation pipeline. This is made possible through our continuous annotation approach which keeps all annotation data for the current retention period seamlessly up to the latest data release.
Back in June last year we added support for the emerging standard of The Genome VCF (gVCF) file format. This was based on file coming from the GATK gVCF pipeline. Now, we also support files from the latest SAMtools release as of version 1.1. For latest information on how to produce gVCF files with SAMtools, please also see their Release notes.
Non-coding variants are annotated by overlapping variant regions with predicted regulatory features like enhancer, promoter and promoter flank. Those regulatory features are defined in the Ensembl Regulatory Build based on segmentation algorithms. In addition, the underlying experimental evidence for such regions is extracted from ChIP-Seq peaks, DNaseI hypersensitive sites and histone modification experiments. Genomatix' MatInspector is applied to find matched motifs within the ChIP-Seq peaks. A combined column for the number of regulatory evidences with 4 classes helps to quickly filter down a variant set without using the evidence columns individually:
Each experimental evidence layer is counted towards the class assignment only if tissue matches among the experiments.
Experimental details about the transcription factor binding sites with optional motif match and overlap with hypersensitive sites or histone modifications are available in the details panel for each region:
Another new column helps to find variants with associated known literature. The citations column lists the number of articles in PubMed that refer to the genomic position of the variant unregarded of the actual type of variation. For instance, the variant of interest in our tutorial in the gene NMNAT1 gives instantly access to the corresponding paper.
Furthermore, multiple data sources that have been available already before are updated in this release.
ClinVar: Clinical disease annotation based on the ClinVar database was updated to the December 2014 data release. Details about this release can be found on the ClinVar site: Release notes and Submission statistics
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was updated to the release as of 12/10/2014.
Literature: Disease, tissue and pathway annotation based on Genomatix literature mining (LitInspector) was updated on 12/02/2014.
GTR: Genetic Testing Registry (GTR) database was updated to the release as of 11/30/2014.
COSMIC: Catalogue Of Somatic Mutations In Cancer (COSMIC) database was update to release 71 from November 2014. Details can be found on the COSMIC site: News
dbSNP: Known variants annotation based on dbSNP was updated to build 142 from October 2014.
1000 Genomes: Allele frequencies based on 1000 Genomes data were updated to phase 3 from November 2014. The Asian super population was split into East Asian (EAS) and South Asians (SAS).
For a full reference of all data sources that are currently used: Data Sources
Gene-based annotation for ClinVar and OMIM are combined together with expert curated annotation from NetPro in a single comprehensive source called Clinical Diseases. This makes it easier to quickly gather concordance among those disease annotation sources without switching multiple details tabs.
Allele frequencies from the Exome Sequencing Project (ESP) are converted from minor allele frequency (MAF) to alternative allele frequency (AAF). Now, the scores can be directly compared to the alternative allele frequencies from the 1000 Genomes project.
All previously imported and activated samples and all generated comparisons automatically contain the updates described above. Please note, that there is no need to annotate or manually upload the source VCF files again. This is made possible through our continuous annotation approach which keeps all annotation data for the current retention period seamlessly up to the latest data release. As always, you are very welcome to let us know what you think about this release or what you are expecting in future updates. Feel free to drop us a line and contact our support team.
ClinVar: Clinical disease annotation based on the ClinVar database was updated to the October 2014 data release. Details about this release can be found on the ClinVar site: Release notes and Submission statistics
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was updated to the release as of 10/06/2014.
Literature: Disease, tissue and pathway annotation based on Genomatix literature mining (LitInspector) was updated on 09/23/2014.
GTR: Genetic Testing Registry (GTR) database was updated to the release as of 10/03/2014.
COSMIC: Catalogue Of Somatic Mutations In Cancer (COSMIC) database was update to release 70 from August 2014. Details can be found on the COSMIC site: News
We always strive to provide a comprehensive and diverse set of prediction algorithms in our annotation pipeline. But at the same it's even more important for us that all scores are based on the most current gene annotation available as of today.
The variant view has provided the optional columns for effect predictions based on MutationTaster and LRT algorithms. Unfortunately, with this release we had to remove both columns because both algorithms are not publicly available to let them run on the latest transcript annotation. For further information on MutationTaster, please refer to: MutationTaster
ClinVar: Clinical disease annotation based on the ClinVar database was updated to the July 2014 data release.
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was updated to the release as of 07/14/2014.
Literature: Disease, tissue and pathway annotation based on Genomatix literature mining (LitInspector) was updated on 07/29/2014.
GTR: Genetic Testing Registry (GTR) database was updated to the release as of 07/16/2014.
COSMIC: Catalogue Of Somatic Mutations In Cancer (COSMIC) database was update to release 69 from June 2014.
GO: Gene Ontology (GO) database was updated to the release as of 07/21/2014.
NetPro: NetPro cuarted diseases annotation database was updated to the release as of 06/30/2014.
This release adds another column for filtering variants. This enables to quickly filter for genomic positions where ClinVar has any information available. The significance summary is given in a basic color coding scheme for pathogenicity annotation from the ClinVar submitters. It's important to know that this information is shown solely based on the genomic position and does not necessarily reflect the actual variant allele. Therefore it is up to the user to compare it to the observed variant alleles in order to draw a conclusion about the variant itself This can be done for instance by using the filter column for effect prediction.
Here is an example for the use of clinical significance filter:
The filled red boxes show that at this specific positions all submitted variants in ClinVar were annotated as pathogenic. If the box is only half-filled it means submitters showed discordance any there was least one who annotated a variant at this position with unknown or other significance.
Another helpful column for asserting a gene to disease association was added based on available diagnostic tests by molecular labs. The source for this annotation is the NCBI GTR (Genetic Testing Registry). It allows to see which disease were associated to specific genes by the molecular lab that developed the test. Usually such associations comprise diseases that are known to have a genetic cause. It is important to note that this association is at gene-level and does not necessarily mean that any observed variant has be associated with one of the listed diseases.
In this example it can be seen that there are 5 diagnostic tests available that have NMNAT1 associated with diseases like Leber congenital amaurosis and cone rod dystrophy.
Filtering variants for a single gene or single disease can be sometimes too stringent when exploring the list of candidate variants. The set of filter rules were limiting the possibilities to find variants and often required exploring many different filter settings.
It is now possible to define a combination of filter rules for the same column. For instance, this allows to search at the same time for up to 10 genes or disease terms in a single filter setting.
Here is an example with multiple genes:
The filter rules are created by simply selecting a column from the add column select box. In this example, the gene column was added three times and the genes NMNAT1, RBM20 and TTN were defined as search criteria. Additionally the coverage column was added two times and the range between 30 and 400 reads was specified. The filter will be executed with the search button. All variants are shown that were annotated to NMNAT1 OR RBM20 OR TTN, while the coverage has to greater than or equal to 30 AND less than or equal to 400.
A common problem when comparing the genotypes for samples is incomplete information for genomic positions. Usually this occurs when the variants for the samples are called separately. It is practically impossible to decide if a given genomic position is reference or no-call if the position is not listed in the VCF file. The Genome VCF (gVCF) is a set of conventions to overcome this limitation. In a gVCF file the non-variant regions are listed in addition to the rows of a traditional variant file. This enables an upstream analysis tool to categorize each genomic position as either variant, reference or no-call. The distinction between reference and no-call is made through filter criteria set during gVCF creation. The filter criteria not only allows to recognize that this position was a no-call, but also to see due to which criteria (e.g. low coverage, low genotype quality score, conflicting predictions, or any other quality control setting) this decision was made.
The comparison analysis takes advantage of these additional information provided in a gVCF file. The interpretation of the supplied filter criteria follows the simple rule set: 1. If all the filter criteria were passed (PASS) the position is considered as reference. 2. Otherwise, if any of the filter steps failed for that region, the position is categorized as a no-call.
For further information of the generation of gVCF files and their usage: gVCF Conventions
The comparison analysis with study type Cancer automatically determines somatic single nucleotide variants (sSNVs). The analysis is performed in paired mode where a sample from the case group has a matched sample in the control group.
The classification criteria have been changed in this release: * Samples that have no genotype information at the compared position (no call) are skipped for somatic detection. No meaningful assumption can be made about the genotype in this case. * Small indels are now included for somatic detection.
Previous results from comparison analyses will remain unaffected.
In addition to the improved somatic detection we updated the COSMIC annotation for somatic mutations to release 68. Two detailed annotation views are available: a position-based and a gene-based annotation details tab at the bottom. The position-based lists only mutations at the current position whereas the gene-based view gives a high-level tissue distribution summary for the gene. Both views are supported by direct links into the COSMIC database for further evaluation.
For a coherent annotation view the same annotation pattern as in COSMIC is applied to ClinVar. A position-based view lists only variants that are reported in ClinVar at the current genomic position. The gene-based view can be used to sense the general disease/phenotype distribution in ClinVar for the specific gene of interest.
The previous gene-based annotation source based on GeneTests is replaced with the data source from NCBI's GTR (Genetic Testing Registry).
The variant report contains also references to dbSNP and ClinVar even if the exact alternative allele does not match. This difference between such a match and an exact match is highlighted in the sentence.
The existing report generator was completely revised and the report style was adapted to better suit established standards and to improve readability.
Some characteristics of the new report include: * Abandoned the table style for full paragraph layout * Followed ACMG guidelines for report style and content presentation * All relevant evidence is concisely compiled for each variant * Sections are sorted by relevance to the reader * Comprehensive glossary with relevant metrics and terms at the end * Report title can be specified
ClinVar: The annotation information based on ClinVar database was updated to the March 2014 data release.
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was updated to the 03/25/2014 release.
Literature: Disease, tissue and pathway annotation based on Genomatix literature mining update to 03/17/2014.
dbSNP: Updated to build 138 (affects only new variant annotation jobs)
We are happy to introduce BAM file support with deep integration into the Genome Browser. In addition to every annotated and activated sample it is now possible to upload one BAM file. This comes at no additional cost to all users!
The samples view which lists all the available samples has a new sidebar block to upload BAM files up to 50GB. The BAM file can be used to view alignments at the read and coverage level in the Genome Browser.
In order to quickly find the uploaded BAM files again each of them needs to be associated with a sample that has already been activated. This association is specified before the upload in the same way as samples are assigned to one of the groups for comparison analyses. A new column associated alignments indicates if a sample has an assigned BAM file. If the same sample is assigned again to a new BAM file the old association is automatically cleared and replaced with the one. Thereby, the previous BAM file will be deleted automatically from the server as well.
Please note, that a BAM file is deleted without further notice in the following events: 1. If the associated sample is deleted manually through the Result Management 2. If the associated sample is purged automatically due to insufficient credits 3. If it is replaced with a new BAM file that is uploaded for currently associated sample 4. If the user account expires
The Genome Browser can be opened directly from the variant view by clicking on the browse link in a variant row. The associated BAM file is now automatically loaded for samples that have associated BAM files.
The browser allows to interactively explore alignments, coverage tracks and annotation data. For more information and advanced usage, please see the documentation.
In contrast, files that are publicly available can be directly accessed through HTTP. This saves the time and bandwidth to upload the files at the beginning because only regions are transferred that are currently viewed by the user.
To access this feature, click on the add/remove tracks symbol in the toolbar of the Genome Browser and enter the URL for the BAM file location:
Here is a very short sample BAM for the region of interest from the tutorial:
The template management allows to export and import previously defined template filters to a file. This view also allows to list all defined templates and quickly discover and access samples or comparisons to which this template filter had been applied. It can be accessed from the main menu and from the template filter sidebar in a sample and comparison view.
The search strategy to detect sample- and allele-specific coverage values in VCF files was refined. Different subfields of the VCF INFO and genotype columns are checked, in particular DP4 and AD subfields used by popular variant callers SAMtools and GATK.
The column differentials between groups has been improved to allow finer-grained filtering for differences between case and control. This applies to comparison analyses of type other study only. Previously the possible values for this column was either 0 for no differences or 1 for any difference in the case group compared to the control group. Now, this column lists the number of samples in the case group that differ from the control group.
Please note, that this calculation is only suitable if the control group is homogeneous otherwise the information is unreliable.
The list of filtered variants in the variant annotation view and the sample comparison view can be exported in two different file formats: TSV file and VCF file. Only the variants that are currently filtered will be exported.
The TSV file is a general tab-separated text file and resembles the current view as seen in the variant table on the right side. The columns that are currently activated in the view are also exported to the output file. No additional information from the details tabs are exported in this case. The number of rows match exactly the number of rows that are seen in the view. A short header is added with a description of the columns that are stored in the file.
The VCF file is the exchange format used by many software applications that need to read and process variant formation. VCF 4.1 is used as output format version. This file contains the following data in addition to the general columns that are required by the format itself: * Genotype information for each sample as additional sample column * Genomic feature based on genome annotation * List of genes that overlap with the variant coordinates * Summarized predicted effects category * Detailed list of effects at transcript level
The number of rows in VCF file does not need to match the number of rows that are shown in the view. Each row in the output VCF file represents a single variant. If more than one gene is annotated for at a specific genomic position multiple rows in the view are combined to a single row in the VCF file. In addition, the VCF header contains version information about the applied annotation sources and filter definitions.
We are happy to announce some new features that had been requested by many of our users.
Already used filter definitions no longer have to be entered repeatedly. This release introduces the filter history in the sidebar for sample and comparison views. GeneGrid assists you with a list of the most recently used filter definitions for a particular result data set. This allows to quickly get back to the filtered results while spending more time looking at your data. The list is limited to 50 filters and includes only filters from the past 30 days. Each filter entry has a list of columns that were used for this filter as well as the number of rows that were returned.
Another new feature introduced with this release is the ability to store frequently used filters as reusable template. Reusable means that the filter setting can be applied instantly to other data sets. The only limitation is that all columns that are used in the template must be available as well in the data set to which the template is applied. The templates are ordered automatically after each filter step and the most frequently used templates appear at the top.
The welcome page shows at a glance the views that were used most recently. Views that are considered for this list include the variant annotation view, the sample comparison view, the genome browser and the pathway analysis view. Only the last 10 activities from the past 30 days are shown.
The optional column Differentials in case group is calculated for all sample comparison analyses with at least 2 samples in the case group.
OMIM: The details tab for OMIM diseases contains an excerpt of the first paragraph from the corresponding OMIM article.
Pathway analysis: The enrichment analysis with the Genomatix Pathway System (GePS) is invoked automatically with all available characterization options (diseases, tissues, pathways, ontology, etc.). The options to select the components individually is removed.
Disease search: Tree-based search for disease terms is available in addition to the free text search functionality. A list with suggested terms automatically shows up while typing search terms in the input field. If a term is selected from the list of suggested terms a tree-based search can be triggered. Tree-based search has the advantage that matching entries are not limited to the exact term. Instead, all subordinate terms of the selected term are included in the filter. There is always a small fraction of entries in each annotation source that has entries which are not mapped to any node in the tree. Therefore the tree-based search will miss entries where submitters are allowed to enter any free text (e.g. ClinVar) or terms were not found in the thesaurus tree. Example for tree-based usage: A search for the term Digestive System Neoplasms includes the terms Liver Neoplasms and Stomach Neoplasms when using tree-based search.
Variant annotation: Performance enhancements for the annotation of variants with large VCF files that have multiple samples.
Report generator: Report is generated in the background with a short download notification appearing in the sidebar.
A RSS feed with the latest release notes and updates is available at:
ClinVar: The annotation information based on ClinVar database was updated to the October 2013 data release.
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was updated to the 10/17/2013 release.
Literature: Disease, tissue and pathway annotation based on Genomatix literature mining update to 10/07/2013.
NetPro: Expert curated disease annotation updated to NetPro June 2013 data release.
Two optional pre-filters are available and can be used to restrict the import process to variants with prior conditions.
Exome filter: Only variants in exonic regions, in a promoter or near a splice-site are considered for annotation and are imported into GeneGrid.
Minimum coverage filter: Variants that have less than the specified coverage value are skipped.
ClinVar: The annotation information based on ClinVar database was updated to the August 2013 data release.
Gene Ontology: New gene-based annotation was added for the GO terms from the domains: biological process, molecular function and cellular component.
OMIM: Online Mendelian Inheritance in Man® (OMIM®) database was added as additional gene-based annotation source for gene to disease associations.
Extended documentation: annotation source information and associated citations are available in a new entry Data Sources in the help menu
Report generation: reports now include extended information on the annotation data used in the analysis, e.g. version numbers for databases are listed
Pathway analysis: up to 10,000 genes can be used to start the Genomatix Pathway System GePS
Sample comparison: multiple types of variation at a single genomic position (with different alternative alleles) are supported, i.e. in rare cases with a SNP and InDel at the same position now both are listed
Improved annotation in the Literature diseases category, based on Genomatix literature mining.
Improved HGVS string output on protein level for insertions and deletions.
Update of the underlying annotation for ENSEMBL transcripts on the anti-sense strand. In rare cases, variants in previous results might have been marked to be in coding sequences of ENSEMBL transcripts when actually situated in the UTR. Here, the details tab will now display the correct annotation.