Release notes

NewVariant Browser (BETA)

Variant Browser is under active development with features being added successively. For any suggestions or bug reports, please contact our support team at support@sbgenomics.com.

Variant Browser is an application for genome analysis and interpretation that will allow researchers and clinicians to quickly annotate and accurately prioritize variants and genes involved in a disease. Variant Browser bridges the gap between sequencing/raw data management and clinical management, providing robust genome interpretation to expedite diagnosis and accelerate the understanding of the genetic basis of disease, drug response, and health.

During the BETA stage, you are able to interpret demo VCF files which are readily available inside the app. The available options include filtering with configurable filtering criteria, directly opening the Genome Browser at the position of the selected variant and displaying general statistics for the currently applied filters. In case you want to interpret your own VCF files please contact us at support@sbgenomics.com.

Future development will include an integrated flow for annotation and interpretation of VCF files using the Variant Browser, with annotated files provided by annotation apps on the Seven Bridges Platform (such as VEP annotation workflow or SnpEff), or uploaded by the users. Additionally, there will be more detailed and elaborate filtering options, along with more comprehensive display options for relevant analysis information.

Try the Variant Browser right now by accessing it in your project’s Interactive Analysis section and read more about it in the documentation.

Variant Browser resources

Several resources that will be used for creation of sqlite databases to be browsed further with Variant Browser are already available on the Seven Bridges Platform. Those are annotation workflows based on VEP (Variant Effect Predictor) and SnpEff. Workflows for annotation and conversion of a single VCF file to an sqlite database are: VEP annotation & DB conversion and SnpEff annotation & DB conversion, as well as Trios: SnpEff annotation & DB conversion or Trios: VEP annotation & DB conversion in case of Trios analysis. The workflows are available in the Variant Browser public project.

ReleaseGenome Browser official release

Genome Browser is no longer in the beta stage and has been officially released. The most important achievement in this release is guaranteed accuracy based on manual review of fields in the standard file compared to the most commonly used genome viewer.

Latest additions to Genome Browser also include a history of the last ten positions within the file, easier insertion of markers that takes less clicks than before and the ability to select a different reference file instead of the one that has been preselected based on the headers in the loaded BAM file.

We have also increased the maximum number of simultaneous BAM tracks, which is now up to twenty instead of the earlier maximum of only three. Higher number of tracks might affect the loading time, but will work properly as long as the browser memory limit is not exceeded.

To try out the new features, access the Genome Browser in your project’s Interactive Analysis section.

NewApp archiving

We have introduced the option to archive apps that you don’t intend to use in a project. Archiving will hide an app from the apps list, workflow editor and other relevant places across the Platform. However, this will not affect reproducibility, as you will be able to rerun existing tasks containing the app.

Archived apps can be displayed by changing the Status filter to Archived in the apps list within a project. To restore an app, click Restore next to the desired archived app.

Find out more about app archiving in our documentation.

Multi-Instance Whole Genome Sequencing GATK4.0 Workflow

We’ve published the Multi-Instance Whole Genome Sequencing BWA/GATK 4.0 workflow. This workflow keeps a similar price, with the improvement in total execution time of up to 3.0 times compared to a single-instance implementation. The Multi-Instance Whole Genome Sequencing workflow processes a 30x whole genome in as little time as 3 hours without any additional computational resource such as GPU or FPGA. The differences in precision, recall and f-score between multi-instance and single-instance workflows are lower than 0.001%, which is expected due to stochastic effects.

See the workflow on the Seven Bridges Platform.

NewSpatial Transcriptomics Pipeline with Spotty

Spatial Transcriptomics is a method that allows visualization and quantitative analysis of the transcriptome in individual tissue sections. Spatial Transcriptomics Pipeline with Spotty is a workflow for processing raw sequence data (paired FASTQ-files) generated using the Spatial Transcriptomics technology. The workflow produces a table of spatially distributed gene counts for downstream analysis.

This workflow consists of two nested workflows:

1. ST Pipeline which performs demultiplexing and decoding of the RNA-Seq reads.
2. The Spotty workflow with post processing, to perform automatic spot identification and pairing between the fluorescent (Cy3) and tissue (HE) image. Spotty is proprietary and available only on the Seven Bridges platform environments.

Read more about the workflow on our blog.

NewData Cruncher Interactive Analyses – Public project

As a part of the effort to grow a comprehensive set of platform features and capabilities, we have developed several Data Cruncher Interactive Analyses as an additional resource that should help users mitigate challenges related to interpretation of data obtained through secondary analysis. These Data Cruncher analyses can be found in the Data Cruncher Interactive Analyses public project.

The project contains five analyses:

• Ballgown Interactive Analysis
• VCF visualization Interactive Analysis
• Structural variation Interactive Analysis
• ChIP-seq Interactive Analysis
• Microbiome Differential Abundance Analysis

Each Interactive Analysis comes with explanations of analysis steps and a corresponding set of files needed for successful execution.

NewSupported instances update

The new generation of AWS EC2 Compute Optimized instances (C5) and General Purpose instances (M5) is now also available in task executions and Data Cruncher analyses on eu.sbgenomics.com. See the full list of supported AWS EU instances in our documentation.

NewAPI bulk actions

Due to popular demand, we have created several new API bulk calls. The calls allow users to perform an operation on up to 100 files within a single API call, while using only one API rate limit. This results in significantly faster completion of operations involving a big number of files.

Currently we have bulk calls implemented for the following operations:

• bulk imports from a volume
• bulk exports to a volume
• bulk get file details (stat files)
• bulk delete files
• bulk edit files

The calls have also been implemented in our Python client, with Java and R client implementation coming up in the near future.

Rabix Composer v1.0 Release

We’ve released the initial version of Rabix Composer (v1.0), which includes significant improvements over the beta version with respect to the development of CWL 1.0 and sbg:draft-2 applications on your local machine.

For more details about the Rabix Composer v1.0 release, please refer to the latest Rabix release note. Also, keep an eye out for further updates and improvements to this initial release.

Key new features in Rabix Composer v1.0:

• We have integrated Rabix Executor into the Composer, so you can now develop and test your CWL app through a single user interface.
• When you edit a tool or workflow on the Seven Bridges Platform, you will see the Edit with Rabix Composer button.
• Rabix Composer is now also available for Windows.

Set suggested files for workflow inputs

We’ve introduced the option to set files from public projects as suggested for a file input in a workflow. This will allow anyone who runs the workflow to use the suggested files as inputs in a single click, and only have to add the remaining input files manually. Please refer to documentation for more details.

Folders on the visual interface

We have introduced support for folders on the Seven Bridges Platform. To complement our existing API functionalities, now you can organize your project files into folders through the Platform’s visual interface, which allows you to have a better overview of your files and manage them more easily. Some of the currently available options related to folders are the ability to copy and move files between folders, use the optional sidebar tree for browsing, search recursively inside the folder structure and use files from folders as task inputs. In the upcoming period we will be working on adding more options to folders, such as the ability to rename a folder, output task results into a folder and many more.

Uploaders have also undergone some changes in order to add folder support. Two major features are introduced:

• uploading items (files and/or folders) into folders;
• preserving folder structure when uploading items.

Both of these features are introduced in CLI and Desktop Uploaders.

Smart Variant Filtering

The variant filtering process consists of selecting highly confident variants and removing the ones that are falsely called. This process used to be mostly left out from deeper testing in secondary genomic DNA analyses, even though it can boost precision of variant calls significantly. After developing graph genome alignment tool and reassembly variant caller, we are now launching the Smart Variant Filtering tool used for filtering germline variants.

Smart Variant Filtering (SVF) uses machine learning algorithms trained on features from the existing Genome In A Bottle (GIAB) variant-called samples (HG001-HG005) to perform variant filtering (classification). The comparison results obtained during deep, three-stage testing demonstrate that it outperforms the solutions currently used within most secondary DNA analyses. Smart Variant Filtering increases the precision of called SNVs (removes false positives) by up to 0.2% points while keeping the overall f-score higher by 0.12-0.27% points than in existing solutions.

Learn more about the Smart Variant Filtering public project from our documentation.