Mosaic: A DNAnexus-Powered Platform to Enable the Advancement of Microbiome Research

Please join us for the Mosaic app building webinar on Thursday, November 30, 2017 at 10am PST.

Mosaic Microbiome PlatformImagine a world where doctors are able to individualize a diagnosis, treatment or even cure a disease based on a patient’s microbiome. We believe that microbiome-based health solutions have the potential to transform human health. However, this requires a new approach involving global collaboration focused on the improvement of methods and standards around microbiome research, thus accelerating the translation of microbiome research to the clinic. Mosaic, a cloud-based microbiome informatics platform, was created to help achieve that vision through the following goals:

  • Mosaic enables users to access and share data, tools, pipelines, and analyses with collaborators or the entire community. Collaborations accelerated by the platform will speed the translation from data to insights, ultimately driving publications, partnerships and new ventures.
  • Accelerate the improvement of methods and promote the adoption of best practices in microbiome research by enabling the benchmarking and improvement of tools and pipelines through the administration of community challenges.

Join The Community
If you already have a DNAnexus account, simply sign in with your credentials. If not, you can get a free Mosaic account now by registering here.

Mosaic Features
Mosaic features make bioinformatic tools accessible to the general microbiome research community, not just expert bioinformaticians. Developers can implement their own or open-source tools as Mosaic Apps using a web interface, and also make them public for the community to use and provide feedback. This enables other users to either run the App to analyze their data, or copy and improve the open-source Apps.

Mosaic apps

Additionally, Mosaic makes collaboration easy by providing the ability to manage and share Workspaces with Groups of users, publish tools and participate in discussions.

Learn How to Build Apps on Mosaic
One of the core features of Mosaic is the ability to implement bioinformatic tools as Mosaic Apps, which users can run on the platform or share with the community. Join our webinar: Building Apps on Mosaic, Thursday, November 30, 2017 at 10am PST to learn more and start building your own apps on Mosaic.

Mosaic Community Challenge: Strains #1
We’re excited about Mosaic’s Community Challenges, which will bring the microbiome community together in order to advance solutions that can be directly integrated into pipelines for translational microbiome science. The first community challenge, Strains #1 launches December 1, 2017. This challenge aims to improve the performance of computational tools in analyzing microbiome shotgun sequencing data, providing better quality profiling of microbiome samples at high resolution.

Comparison of Somatic Variant Calling Pipelines On DNAnexus

The detection of somatic mutations in sequenced cancer samples has become increasingly standard in research and clinical settings, as they provide insights into genomic regions which can be targeted by precision medicine therapies. Due to the heterogeneity of tumors, somatic variant calling is challenging, especially for variants at low allele frequencies. Researchers use common somatic variant call tools, including MuTect, MuSE, Strelka, and Somatic Sniper,  that detect somatic mutations by conducting paired comparisons between sequenced normal and tumorous tissue samples. Each of these variant callers differ in algorithms, filtering strategies, recommendations, and output. Thus we set out to compare how these individual apps perform on the DNAnexus Platform. Each app was evaluated for recall and precision, cost, and time to complete.  

To benchmark some of the common somatic variant calling tools available on the DNAnexus Platform, our team of scientists simulated synthetic cancer datasets at varying sequencing depths. DNA samples from the European Nucleotide Archive were obtained and mapped to the hs37d5 reference with the BWA-mem FASTQ read mapper on DNAnexus.

These samples were then merged into a single BAM file representing the normal sample. To obtain the tumor sample, synthetic variants were inserted into each individual sample with the BAMSurgeon app on DNAnexus. All simulated samples were then merged into one BAM file constituting the tumor sample. Both the synthetic tumor and normal BAM files had approximately 250X sequencing depth.The synthetic tumor BAM file was then downsampled into a range of sequencing depths. With the help of sambamba through the Swiss Army Knife application, these files were reduced to 5X, 10X, 15X, 20X, 30X, 40X, 50X, 60X, 90X, and 120X coverage files. The file representing the normal sample was downsampled into a 30X sequencing depth file.  Once the synthetic cancer dataset was created, the common somatic variant calling tools MuTect, MuSE, Strelka, and Somatic Sniper were run to detect single nucleotide variants. Upon completion, the high quality variants were filtered from each VCF.



MuTect performed the best at classifying correct variants followed by Strelka, MuSE, and Somatic Sniper. This was consistent across allele frequency thresholds of 01, 0.2, 0.3, 0.4, and 0.5.

Coverage and Recall

One interesting finding – for the callers investigated, the ability to recall variants at lower frequencies showed a similar pattern. Each of the callers discovers more of the variants before plateauing at a recall ceiling at a certain coverage. Lower allele frequencies require more coverage before saturating for recall at a caller. 30-fold coverage was required to reach the plateau of 0.5 allele frequency variants, while 40-fold coverage was required for 0.1 allele frequency variants. Reliable detection of lower frequency variants presumably require still more coverage to reach a recall plateu.


All tools performed well at identifying relevant variants (>95% precision) regardless of tumor sequencing depth.

To get a more accurate view of the interplay between precision and recall, the harmonic mean of precision and recall (F-score) was computed for each output VCF by depth. MuTect had the best performance overall, followed by Strelka, and then MuSE, and Somatic Sniper. Runtime & Cost

Out of all the apps, Strelka finished most rapidly for the lowest cost. Compared to MuTect, Strelka did not score as high for precision or recall, but completed the analysis of single nucleotide variants in a fraction of the time.

To get a more detailed comparison between MuTect and Strelka, this 3-way venn diagram compares these tools to the truth set. Note, the false negatives called by MuTect are likely due to noise in the dataset.

To better visualize the differences between the callers, we converted the output of each of the callers into high-dimensional vectors in which each variant call in any of the samples is one of the dimensions. This format allows us to calculate the distances between each of the programs and with the truth set. This also allows us to use standard methods such as Mulitdimensional Scaling to convert these distances into positions in 2-D space (axes units are arbitrary, only relative position matter is the graph below).

Valid variant calling results are crucial as next-generation sequencing data is increasingly applied to the development of targeted cancer therapeutics. Our analysis of MuTect, MuSe, Strelka, and Somatic Sniper found that the best results with respect to precision and recall can be achieved by using MuTect. Strelka was also a top performer, and simultaneously reduced runtime and cost.

Need to detect variants in your dataset? Get started using these tools on DNAnexus today.

This research was performed by Nicholas Hill and Victoria Wang as part of their internship with DNAnexus. The project was supervised by Naina Thangaraj, Arkarachai Fungtammasan, Yih-Chii Hwang, Steve Osazuwa, and Andrew Carroll.

Introducing htsget, a new GA4GH protocol for genomic data delivery

DNAnexus is here in Orlando for the fifth plenary meeting of the Global Alliance for Genomics and Health (GA4GH), the standards-making body advancing interoperability and data sharing for genomic medicine. We’re especially pleased this year to join in launching version 1.0 of htsget, a new protocol for the secure web delivery of large genomic datasets, especially whole-genome sequencing reads which can exceed 100 gigabytes per person. 

Htsget complements the incumbent BAM and CRAM file formats for reads, which GA4GH also stewards, and their ecosystem of tools. It adds a standardized protocol for accessing such data over the web, securely, reliably, efficiently, and even federally when needed. Retrieval with htsget is now built into the ubiquitous samtools via its underlying htslib library, allowing bioinformaticians to leverage htsget with most existing tools via a familiar Unix pipe. At the same time, htsget’s streaming parallelism enables scalable ETL into cluster environments like Apache Spark, providing a gradual transition path from incumbent file-based toolchains toward modern “big data” platforms. Lastly, htsget simplifies data access for interactive genome browsers, by unifying authentication and removing the need for index files.

On the server side, htsget has been deployed at the Sanger Institute and the European Genotype Archive; DNAnexus operates a multi-cloud htsget server indexing data within Amazon S3 and Azure Blob storage, which we call htsnexus; Google Cloud Platform has open-sourced their own implementation. Clients can speak a uniform protocol abstracting the diverse authentication and storage schemes of these service providers.

These groups, and others, have all shaped the htsget specification through the GA4GH’s highly collaborative process. But it started in large part with a contribution from DNAnexus, drawing on our experience optimizing how our systems utilize cloud object stores in the huge genome projects we’ve served, such as CHARGE, 1000 Genomes Project, TCGA, and HiSeq X Series data production. Through htsget and other work streams under the new GA4GH Connect framework announced today, DNAnexus looks forward to further contributing from our experience and network to advance the GA4GH’s essential mission.

For more information about how DNAnexus is working with htsget, please contact us at