Announcing the First Group of Google Open Source Peer Bonus winners in 2021!

Thursday, April 8, 2021


Google Open Source Peer Bonus logo

The Google Open Source Peer Bonus program is designed to reward external open source contributors nominated by Googlers for their exceptional contributions to open source. We are very excited to announce our first group of winners in 2021!

Our current winners have contributed to a wide range of projects including Apache Beam, Kubernetes, Tekton and many others. We reward open source enthusiasts not only for their code contributions, but also community work, documentation, mentorships and other types of engagement.

We have award recipients from 25 countries all over the world: Austria, Canada, China, Cyprus, Denmark, Finland, France, Germany, India, Isle of Man, Italy, Japan, Korea, Netherlands, Norway, Russia, Singapore, Spain, Sweden, Switzerland, Uganda, Taiwan, Ukraine, United Kingdom, and the United States.

Open source encourages innovation through collaboration and our modern world, and technology that we rely on, wouldn’t be the same without you—the contributors, who are in many cases volunteers. We would like to thank you for your hard work and congratulate you on receiving this award!

Below is the list of current winners who gave us permission to thank them publicly:

Kashyap JoisAndroid FHIR SDK
David AllisonAnkiDroid
Chad DombrovaApache Beam
Jeff KlukasApache Beam
Steve NiemitzApache Beam
Yoshiki ObataApache Beam
Jaskirat SinghCHAOSS - Community Health Analytics Open Source Software
Eric AmordeCocoaPods
Subrata Banikcoreboot
Ned & related CPython internals
Matthew BryantCursedChrome
Dmitry GutovEmacs/company-mode
Brian JostFirebase
Joe HinkleFirebase iOS SDK
Lorenzo FiamigoFirebase iOS SDK
Mike GerasymenkoFirebase iOS SDK
Morten Bek DitlevsenFirebase iOS SDK
Angel PonsFlashrom
Ole André Vadla RavnåsFrida
Junegunn Choifzf
Alex SaveauGradle Play Publisher
Nate GrahamKDE
Amit SagtaniKDE Community
Niklas HanssonKubeflow Pipelines
William TeoKubeflow Pipelines
Antonio OjeaKubernetes
Dan MangumKubernetes
Jian ZengKubernetes
Darrell Commanderlibjpeg-turbo
James (purpleidea)mgmt
Kareem ErgawyMLIR
Lily BallardNix / Fish
Eelco DolstraNix, NixOS, Nixpkgs
Samuel Dionne-RielNixOS
Dmitry DemenskyOpen source TypeScript definitions for Google Maps Platform
Kay WilliamsOpenSSF
Hassan Kibirigeplotnine
Henry Schreinerpybind11
Paul MoorePython 'pip' project
Tzu-ping ChungPython 'pip' project
Alex GrönholmPython 'wheel' project
Ramon Santamariaraylib
Alexander Weissrestic
Michael Eischerrestic
Ben Leshrxjs
Takeshi Nakatanis3fs
Daniel Wee Soong LimSymbiFlow
Unai Martinez-CorralSymbiFlow, Surelog, Verible, more
Andrea FrittoliTekton
Priti DesaiTekton
Vincent DemeesterTekton
Chengyu Zhangtestsmt & testsmt/yinyang
Dominik Winterertestsmt & testsmt/yinyang
Tom RiniU-Boot

Thank you for your contributions to open source!

By Maria Tabak — Google Open Source Programs Office

Analyzing genomic data in families with deep learning

Wednesday, April 7, 2021

The Genomics team at Google Health is excited to share our latest expansion to DeepVariant - DeepTrio.

First released in 2017, DeepVariant is an open source tool that enables researchers and clinicians to analyze an individual’s genome sequencing data and identify genetic variants, such as those that may cause disease. Our continued work on DeepVariant has been recognized for its top-of-class accuracy. With DeepTrio, we have expanded DeepVariant to be able to consider the genetic variants in the sequence data of a mother-father-child trio.

Humans are diploid organisms, carrying two copies of the human genome. Every individual inherits one copy of the genome from their mother, and the other from their father. Parental inheritance informs analysis of traits and diseases that follow Mendelian inheritance. DeepTrio learns to use the properties of Mendelian inheritance directly from sequencing data in order to more accurately identify genetic variants in cases when both parent and a child sample can be co-analyzed.

Modifying DeepVariant to analyze trio samples

DeepVariant learns to classify positions in a genome as reference or variant using representations of data similar to the “genome browser” which experts use in analysis. “Improving the Accuracy of Genomic Analysis with DeepVariant 1.0” provides a good overview.

DeepVariant receives data as a window of the genome centered on a candidate variant which it is asked to classify as either reference (no variant), heterozygous (one copy of a variant) or homozygous (both copies are variant). DeepVariant sees the sequence evidence as channels representing features of the data (see: “Looking through DeepVariant’s eyes” for a deeper explanation).

We modified DeepTrio to represent the sequence data from a trio in a single image, with a fixed height for each sample and the child in the middle. Using gold standard samples from NIST Genome in a Bottle for truth labels, we train one model to call variants in the child and another to call variants in the top parent. To call both parents, we flip the position of the parent samples.

An image of 4 of the channels that DeepTrio uses in classification (these, and 4 other channels are shown in a stack.

conceptual schematic of how trio files are used to create examples, which are then called by DeepTrio.

Figure 1. (top) An image of 4 of the channels that DeepTrio uses in classification (these, and 4 other channels are shown in a stack. (bottom) conceptual schematic of how trio files are used to create examples, which are then called by DeepTrio.

Measuring DeepTrio’s improved accuracy

We show that DeepTrio is more accurate than DeepVariant for both parent and child variant detection, with an especially pronounced advantage at lower coverages. This enables researchers to either analyze samples at higher accuracy, or to maintain comparable accuracy at a substantially reduced expense.

To assess the accuracy of DeepTrio, we compare its accuracy to DeepVariant using extensively characterized gold standards made available by NIST Genome in a Bottle. In order to have an evaluation dataset which is never seen in training, we exclude chromosome 20 from training and perform evaluations on chromosome 20.

We train DeepVariant and DeepTrio for sequencing data from two different instruments, Illumina and Pacific Biosciences (PacBio), for more information on the differences between these technologies, please see our previous blog. These sequencers both randomly sample the genome in an error-prone manner. To accurately analyze a genome, the same region needs to be sampled repeatedly. The depth of sampling at a position is called coverage. Sequencing to greater coverage is more expensive in an approximately linear manner. This often forces trade-offs between cost, accuracy, and samples sequenced. As a result, in trios parents are often sequenced at lower depth.

In the charts below, we plot the accuracy of DeepTrio and DeepVariant across a range of coverages.

DeepTrio child accuracy

DeepTrio parent accuracy

Figure 2. F1-score for DeepTrio (solid line) and DeepVariant (dashed line) on a child sample (top) and a parent sample (bottom), sequenced with an Illumina (blue) and PacBio (black) instrument. F1 is measured for all types of small variants on chromosome 20, across samples with a range of sequencing coverage (x-axis).

DeepTrio’s performance on de novo variants

Each individual has roughly 5 million variants relative to the human reference genome. The overwhelming majority of these are inherited from their parents. A small number, around 100, are new (referred to as de novo), due to copying errors during DNA replication. We demonstrate that DeepTrio substantially reduces false positives for de novo variants. For Illumina data, this comes with a smaller decrease in recovery of true positives, while for PacBio data, this trade-off does not occur.

To assess accuracy we analyzed sites where both parents are called as non-variant, but the child is called as heterozygous variant. We observe that DeepTrio is more reluctant to call a variant as de novo, which is similar to how a human would require a higher level of evidence for sites violating Mendelian inheritance. This results in a much lower false positive rate for these de novo variants, but a slightly lower recall rate in DeepTrio Illumina. Usually when this occurs, the child is still called as a variant, but the parents are given “no-call” (the classifier is not confident enough to make a call).

Accuracy on de novo calls (child heterozygous variant, parents reference call) for recall of true de novo events

Accuracy on de novo calls (child heterozygous variant, parents reference call) for recall of false positive de novo events

Figure 3. Accuracy on de novo calls (child heterozygous variant, parents reference call) for recall of true de novo events (top) and false positive de novo events (bottom) for DeepTrio (solid line) and DeepVariant (dashed line) on Illumina (blue) and PacBio (black). Accuracy is measured on chromosome 20, across samples with a range of sequencing coverage (x-axis).

Contributing to rare disease research

By releasing DeepTrio as open source software, we hope to improve analysis of genomic data, by allowing scientists to more accurately analyze samples. We hope this will enable research and clinical pipelines, leading to better resolution of rare disease cases, and improve development of therapeutics.

In addition to the release of DeepTrio’s code as open source, we have also released the sequencing data that we generated in order to train these models. That data is described in our pre-print “An Extensive Sequence Dataset of Gold-Standard Samples for Benchmarking and Development”. By releasing both this production model, and the data required to train models of similar complexity, we hope to contribute to methods development by the genomics community.

By Andrew Carroll, Product Lead Genomics and Howard Yang, Program Manager Genomics — Google Health

Lyra - enabling voice calls for the next billion users

Tuesday, April 6, 2021


Lyra Logo

The past year has shown just how vital online communication is to our lives. Never before has it been more important to clearly understand one another online, regardless of where you are and whatever network conditions are available. That’s why in February we introduced Lyra: a revolutionary new audio codec using machine learning to produce high-quality voice calls.

As part of our efforts to make the best codecs universally available, we are open sourcing Lyra, allowing other developers to power their communications apps and take Lyra in powerful new directions. This release provides the tools needed for developers to encode and decode audio with Lyra, optimized for the 64-bit ARM android platform, with development on Linux. We hope to expand this codebase and develop improvements and support for additional platforms in tandem with the community.

The Lyra Architecture

Lyra’s architecture is separated into two pieces, the encoder and decoder. When someone talks into their phone the encoder captures distinctive attributes from their speech. These speech attributes, also called features, are extracted in chunks of 40ms, then compressed and sent over the network. It is the decoder’s job to convert the features back into an audio waveform that can be played out over the listener’s phone speaker. The features are decoded back into a waveform via a generative model. Generative models are a particular type of machine learning model well suited to recreate a full audio waveform from a limited number of features. The Lyra architecture is very similar to traditional audio codecs, which have formed the backbone of internet communication for decades. Whereas these traditional codecs are based on digital signal processing (DSP) techniques, the key advantage for Lyra comes from the ability of the generative model to reconstruct a high-quality voice signal.

Lyra Architecture Chart

The Impact

While mobile connectivity has steadily increased over the past decade, the explosive growth of on-device compute power has outstripped access to reliable high speed wireless infrastructure. For regions where this contrast exists—in particular developing countries where the next billion internet users are coming online—the promise that technology will enable people to be more connected has remained elusive. Even in areas with highly reliable connections, the emergence of work-from-anywhere and telecommuting have further strained mobile data limits. While Lyra compresses raw audio down to 3kbps for quality that compares favourably to other codecs, such as Opus, it is not aiming to be a complete alternative, but can save meaningful bandwidth in these kinds of scenarios.

These trends provided motivation for Lyra and are the reason our open source library focuses on its potential for real time voice communication. There are also other applications we recognize Lyra may be uniquely well suited for, from archiving large amounts of speech, and saving battery by leveraging the computationally cheap Lyra encoder, to alleviating network congestion in emergency situations where many people are trying to make calls at once. We are excited to see the creativity the open source community is known for applied to Lyra in order to come up with even more unique and impactful applications.

The Open Source Release

The Lyra code is written in C++ for speed, efficiency, and interoperability, using the Bazel build framework with Abseil and the GoogleTest framework for thorough unit testing. The core API provides an interface for encoding and decoding at the file and packet levels. The complete signal processing toolchain is also provided, which includes various filters and transforms. Our example app integrates with the Android NDK to show how to integrate the native Lyra code into a Java-based android app. We also provide the weights and vector quantizers that are necessary to run Lyra.

We are releasing Lyra as a beta version today because we wanted to enable developers and get feedback as soon as possible. As a result, we expect the API and bitstream to change as it is developed. All of the code for running Lyra is open sourced under the Apache license, except for a math kernel, for which a shared library is provided until we can implement a fully open solution over more platforms. We look forward to seeing what people do with Lyra now that it is open sourced. Check out the code and demo on GitHub, let us know what you think, and how you plan to use it!

By Andrew Storus and Michael Chinen – Chrome


The following people helped make the open source release possible:
Hengchin Yeh, Alejandro Luebs, Jamieson Brettle, Tom Denton, Felicia Lim, Bastiaan Kleijn, Jan Skoglund, Yaowu Xu, Matt Frost, Jim Bankoski (Chrome), Chenjie Gu, Zach Gleicher, Tom Walters, Norman Casagrande, Luis Cobo, Erich Elsen (DeepMind).