How do you cite a DeepVariant?

DeepVariant

DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file.

DeepVariant supports germline variant-calling in diploid organisms.

• NGS (Illumina) data for either a whole genome or whole exome.
• PacBio HiFi data, see the PacBio case study.
• Hybrid PacBio HiFi + Illumina WGS, see the hybrid case study.
• Oxford Nanopore long-read data by using PEPPER-DeepVariant.
• GenapSys data, by using a model retrained by GenapSys.

Please also note:

• For somatic data or any other samples where the genotypes go beyond two copies of DNA, DeepVariant will not work out of the box because the only genotypes supported are hom-alt, het, and hom-ref.
• The models included with DeepVariant are only trained on human data. For other organisms, see the blog post on non-human variant-calling for some possible pitfalls and how to handle them.

DeepTrio

DeepTrio is a deep learning-based trio variant caller built on top of DeepVariant. DeepTrio extends DeepVariant's functionality, allowing it to utilize the power of neural networks to predict genomic variants in trios or duos. See this page for more details and instructions on how to run DeepTrio.

DeepTrio supports germline variant-calling in diploid organisms for the following types of input data:

• NGS (Illumina) data for either whole genome or whole exome.
• PacBio HiFi data, see the PacBio case study.

Please also note:

• All DeepTrio models were trained on human data.
• It is possible to use DeepTrio with only 2 samples (child, and one parent).
• External tool GLnexus is used to merge output VCFs.

How to run DeepVariant

We recommend using our Docker solution. The command will look like this:

BIN_VERSION="1.3.0"
docker run \
  -v "YOUR_INPUT_DIR":"/input" \
  -v "YOUR_OUTPUT_DIR:/output" \
  google/deepvariant:"${BIN_VERSION}" \
  /opt/deepvariant/bin/run_deepvariant \
  --model_type=WGS \ **Replace this string with exactly one of the following [WGS,WES,PACBIO,HYBRID_PACBIO_ILLUMINA]**
  --ref=/input/YOUR_REF \
  --reads=/input/YOUR_BAM \
  --output_vcf=/output/YOUR_OUTPUT_VCF \
  --output_gvcf=/output/YOUR_OUTPUT_GVCF \
  --num_shards=$(nproc) \ **This will use all your cores to run make_examples. Feel free to change.**
  --logging_dir=/output/logs \ **Optional. This saves the log output for each stage separately.
  --dry_run=false **Default is false. If set to true, commands will be printed out but not executed.

To see all flags you can use, run: docker run google/deepvariant:"${BIN_VERSION}"

If you're using GPUs, or want to use Singularity instead, see Quick Start for more details or see all the setup options available.

For more information, also see:

• Full documentation list
• Detailed usage guide with more information on the input and output file formats and how to work with them.
• Best practices for multi-sample variant calling with DeepVariant
• (Advanced) Training tutorial
• DeepVariant's Frequently Asked Questions, FAQ

How to cite

If you're using DeepVariant in your work, please cite:

A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology 36, 983–987 (2018).
Ryan Poplin, Pi-Chuan Chang, David Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Dan Newburger, Jojo Dijamco, Nam Nguyen, Pegah T. Afshar, Sam S. Gross, Lizzie Dorfman, Cory Y. McLean, and Mark A. DePristo.
doi: https://doi.org/10.1038/nbt.4235

Additionally, if you are generating multi-sample calls using our DeepVariant and GLnexus Best Practices, please cite:

Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Bioinformatics (2021).
Taedong Yun, Helen Li, Pi-Chuan Chang, Michael F. Lin, Andrew Carroll, and Cory Y. McLean.
doi: https://doi.org/10.1093/bioinformatics/btaa1081

Why Use DeepVariant?

• High accuracy - DeepVariant won 2020 PrecisionFDA Truth Challenge V2 for All Benchmark Regions for ONT, PacBio, and Multiple Technologies categories, and 2016 PrecisionFDA Truth Challenge for best SNP Performance. DeepVariant maintains high accuracy across data from different sequencing technologies, prep methods, and species. For lower coverage, using DeepVariant makes an especially great difference. See metrics for the latest accuracy numbers on each of the sequencing types.
• Flexibility - Out-of-the-box use for PCR-positive samples and low quality sequencing runs, and easy adjustments for different sequencing technologies and non-human species.
• Ease of use - No filtering is needed beyond setting your preferred minimum quality threshold.
• Cost effectiveness - With a single non-preemptible n1-standard-16 machine on Google Cloud, it costs ~$11.8 to call a 30x whole genome and ~$0.89 to call an exome. With preemptible pricing, the cost is $2.84 for a 30x whole genome and $0.21 for whole exome (not considering preemption).
• Speed - See metrics for the runtime of all supported datatypes on a 64-core CPU-only machine. Multiple options for acceleration exist.
• Usage options - DeepVariant can be run via Docker or binaries, using both on-premise hardware or in the cloud, with support for hardware accelerators like GPUs and TPUs.

(1): Time estimates do not include mapping.

How DeepVariant works

For more information on the pileup images and how to read them, please see the "Looking through DeepVariant's Eyes" blog post.

DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework. Nucleus was built with DeepVariant in mind and open-sourced separately so it can be used by anyone in the genomics research community for other projects. See this blog post on Using Nucleus and TensorFlow for DNA Sequencing Error Correction.

DeepVariant Setup

Prerequisites

• Unix-like operating system (cannot run on Windows)
• Python 3.6

Official Solutions

Below are the official solutions provided by the Genomics team in Google Health.

Name	Description
Docker	This is the recommended method.
Build from source	DeepVariant comes with scripts to build it on Ubuntu 20.04. To build and run on other Unix-based systems, you will need to modify these scripts.
Prebuilt Binaries	Available at gs://deepvariant/. These are compiled to use SSE4 and AVX instructions, so you will need a CPU (such as Intel Sandy Bridge) that supports them. You can check the /proc/cpuinfo file on your computer, which lists these features under "flags".

Contribution Guidelines

Please open a pull request if you wish to contribute to DeepVariant. Note, we have not set up the infrastructure to merge pull requests externally. If you agree, we will test and submit the changes internally and mention your contributions in our release notes. We apologize for any inconvenience.

If you have any difficulty using DeepVariant, feel free to open an issue. If you have general questions not specific to DeepVariant, we recommend that you post on a community discussion forum such as BioStars.

Acknowledgements

DeepVariant happily makes use of many open source packages. We would like to specifically call out a few key ones:

• Boost Graph Library
• abseil-cpp and abseil-py
• CLIF
• GNU Parallel
• htslib & samtools
• Nucleus
• numpy
• SSW Library
• TensorFlow and Slim

We thank all of the developers and contributors to these packages for their work.

Disclaimer

This is not an official Google product.

NOTE: the content of this research code repository (i) is not intended to be a medical device; and (ii) is not intended for clinical use of any kind, including but not limited to diagnosis or prognosis.

Author: Google
Source Code: https://github.com/google/deepvariant 
License: BSD-3-Clause License

#machine-learning #deep-learning #python 

What is GEEK

Buddha Community

How do you cite a DeepVariant?

DeepVariant

DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file.

DeepVariant supports germline variant-calling in diploid organisms.

• NGS (Illumina) data for either a whole genome or whole exome.
• PacBio HiFi data, see the PacBio case study.
• Hybrid PacBio HiFi + Illumina WGS, see the hybrid case study.
• Oxford Nanopore long-read data by using PEPPER-DeepVariant.
• GenapSys data, by using a model retrained by GenapSys.

Please also note:

• For somatic data or any other samples where the genotypes go beyond two copies of DNA, DeepVariant will not work out of the box because the only genotypes supported are hom-alt, het, and hom-ref.
• The models included with DeepVariant are only trained on human data. For other organisms, see the blog post on non-human variant-calling for some possible pitfalls and how to handle them.

DeepTrio

DeepTrio is a deep learning-based trio variant caller built on top of DeepVariant. DeepTrio extends DeepVariant's functionality, allowing it to utilize the power of neural networks to predict genomic variants in trios or duos. See this page for more details and instructions on how to run DeepTrio.

DeepTrio supports germline variant-calling in diploid organisms for the following types of input data:

• NGS (Illumina) data for either whole genome or whole exome.
• PacBio HiFi data, see the PacBio case study.

Please also note:

• All DeepTrio models were trained on human data.
• It is possible to use DeepTrio with only 2 samples (child, and one parent).
• External tool GLnexus is used to merge output VCFs.

How to run DeepVariant

We recommend using our Docker solution. The command will look like this:

BIN_VERSION="1.3.0"
docker run \
  -v "YOUR_INPUT_DIR":"/input" \
  -v "YOUR_OUTPUT_DIR:/output" \
  google/deepvariant:"${BIN_VERSION}" \
  /opt/deepvariant/bin/run_deepvariant \
  --model_type=WGS \ **Replace this string with exactly one of the following [WGS,WES,PACBIO,HYBRID_PACBIO_ILLUMINA]**
  --ref=/input/YOUR_REF \
  --reads=/input/YOUR_BAM \
  --output_vcf=/output/YOUR_OUTPUT_VCF \
  --output_gvcf=/output/YOUR_OUTPUT_GVCF \
  --num_shards=$(nproc) \ **This will use all your cores to run make_examples. Feel free to change.**
  --logging_dir=/output/logs \ **Optional. This saves the log output for each stage separately.
  --dry_run=false **Default is false. If set to true, commands will be printed out but not executed.

To see all flags you can use, run: docker run google/deepvariant:"${BIN_VERSION}"

If you're using GPUs, or want to use Singularity instead, see Quick Start for more details or see all the setup options available.

For more information, also see:

• Full documentation list
• Detailed usage guide with more information on the input and output file formats and how to work with them.
• Best practices for multi-sample variant calling with DeepVariant
• (Advanced) Training tutorial
• DeepVariant's Frequently Asked Questions, FAQ

How to cite

If you're using DeepVariant in your work, please cite:

A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology 36, 983–987 (2018).
Ryan Poplin, Pi-Chuan Chang, David Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Dan Newburger, Jojo Dijamco, Nam Nguyen, Pegah T. Afshar, Sam S. Gross, Lizzie Dorfman, Cory Y. McLean, and Mark A. DePristo.
doi: https://doi.org/10.1038/nbt.4235

Additionally, if you are generating multi-sample calls using our DeepVariant and GLnexus Best Practices, please cite:

Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Bioinformatics (2021).
Taedong Yun, Helen Li, Pi-Chuan Chang, Michael F. Lin, Andrew Carroll, and Cory Y. McLean.
doi: https://doi.org/10.1093/bioinformatics/btaa1081

Why Use DeepVariant?

• High accuracy - DeepVariant won 2020 PrecisionFDA Truth Challenge V2 for All Benchmark Regions for ONT, PacBio, and Multiple Technologies categories, and 2016 PrecisionFDA Truth Challenge for best SNP Performance. DeepVariant maintains high accuracy across data from different sequencing technologies, prep methods, and species. For lower coverage, using DeepVariant makes an especially great difference. See metrics for the latest accuracy numbers on each of the sequencing types.
• Flexibility - Out-of-the-box use for PCR-positive samples and low quality sequencing runs, and easy adjustments for different sequencing technologies and non-human species.
• Ease of use - No filtering is needed beyond setting your preferred minimum quality threshold.
• Cost effectiveness - With a single non-preemptible n1-standard-16 machine on Google Cloud, it costs ~$11.8 to call a 30x whole genome and ~$0.89 to call an exome. With preemptible pricing, the cost is $2.84 for a 30x whole genome and $0.21 for whole exome (not considering preemption).
• Speed - See metrics for the runtime of all supported datatypes on a 64-core CPU-only machine. Multiple options for acceleration exist.
• Usage options - DeepVariant can be run via Docker or binaries, using both on-premise hardware or in the cloud, with support for hardware accelerators like GPUs and TPUs.

(1): Time estimates do not include mapping.

How DeepVariant works

For more information on the pileup images and how to read them, please see the "Looking through DeepVariant's Eyes" blog post.

DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework. Nucleus was built with DeepVariant in mind and open-sourced separately so it can be used by anyone in the genomics research community for other projects. See this blog post on Using Nucleus and TensorFlow for DNA Sequencing Error Correction.

DeepVariant Setup

Prerequisites

• Unix-like operating system (cannot run on Windows)
• Python 3.6

Official Solutions

Below are the official solutions provided by the Genomics team in Google Health.

Name	Description
Docker	This is the recommended method.
Build from source	DeepVariant comes with scripts to build it on Ubuntu 20.04. To build and run on other Unix-based systems, you will need to modify these scripts.
Prebuilt Binaries	Available at gs://deepvariant/. These are compiled to use SSE4 and AVX instructions, so you will need a CPU (such as Intel Sandy Bridge) that supports them. You can check the /proc/cpuinfo file on your computer, which lists these features under "flags".

Contribution Guidelines

Please open a pull request if you wish to contribute to DeepVariant. Note, we have not set up the infrastructure to merge pull requests externally. If you agree, we will test and submit the changes internally and mention your contributions in our release notes. We apologize for any inconvenience.

If you have any difficulty using DeepVariant, feel free to open an issue. If you have general questions not specific to DeepVariant, we recommend that you post on a community discussion forum such as BioStars.

Acknowledgements

DeepVariant happily makes use of many open source packages. We would like to specifically call out a few key ones:

• Boost Graph Library
• abseil-cpp and abseil-py
• CLIF
• GNU Parallel
• htslib & samtools
• Nucleus
• numpy
• SSW Library
• TensorFlow and Slim

We thank all of the developers and contributors to these packages for their work.

Disclaimer

This is not an official Google product.

NOTE: the content of this research code repository (i) is not intended to be a medical device; and (ii) is not intended for clinical use of any kind, including but not limited to diagnosis or prognosis.

Author: Google
Source Code: https://github.com/google/deepvariant 
License: BSD-3-Clause License

#machine-learning #deep-learning #python 

How to Cite Literature Review in Text Citations

There are very many sources where students can get free or small essay credit for rewriting known texts. While researching, you are usually supposed to find relevant and recent information about a particular theme. This is not the easiest task to achieve, hence the need to state such facts when writing your references.

Most of the time, even professionals don't have enough time to do all that by themselves. Another thing is the enormous demand by learners to have their documents amended to improve performances and to make them more acceptable. Currently, most learning institutions adopt the APA citation standardization, and these are the necessary guidelines for every research document written.

You may be asking yourself why is this important, and it is true. In simple terms, it simply helps writers describe the topic in detail. It also allows the reader to flow with the material they are reading. What’s great about paraphrase services the methodology is that it gives authors a chance to give any opinion regarding the reference list. If an author doesn’t adhere to the conventions, he is bound to introduce new concepts in the article, and not just stick to the previous ones. Better yet, if the writer finds it hard to change the source, the coach wouldn’t believe him, and it ends up affecting the credibility of the entire paper.

For instance, if the MLA is wrong, then it becomes impossible to change the referencing format from magazine to journal. Besides, different publishers would use various formatting styles, and so forth. That is what confuses a scholar because one is in a position to modify the citations, but the organization has no room to alter the instructions.

Steps to Follow When citing in a Journal Article

Whenever a teacher requires a student to write a journal, it is essential to understand the basic steps that apply.

Useful Resources:

Quotation Marks and Paraphrase Writing

The Best Strategy to Take When Paraphrasing

How to Paraphrase and Alter Your Essay

How Do I Cite My Code? How Do I Give Proper Attribution?

How do I give proper attribution? If I copy code from Stack Overflow, do I need to cite that in some way? How do I make sure I protect my code so that I am not doing the wrong thing or stealing ideas? These are the questions we will tackle in this episode of Dev Questions.

#developer #programming

DeepVariant: an Analysis Pipeline That Uses A Deep Neural Network

DeepVariant

DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file.

DeepVariant supports germline variant-calling in diploid organisms.

• NGS (Illumina) data for either a whole genome or whole exome.
• PacBio HiFi data, see the PacBio case study.
• Hybrid PacBio HiFi + Illumina WGS, see the hybrid case study.
• Oxford Nanopore long-read data by using PEPPER-DeepVariant.
• GenapSys data, by using a model retrained by GenapSys.

Please also note:

• For somatic data or any other samples where the genotypes go beyond two copies of DNA, DeepVariant will not work out of the box because the only genotypes supported are hom-alt, het, and hom-ref.
• The models included with DeepVariant are only trained on human data. For other organisms, see the blog post on non-human variant-calling for some possible pitfalls and how to handle them.

DeepTrio

DeepTrio is a deep learning-based trio variant caller built on top of DeepVariant. DeepTrio extends DeepVariant's functionality, allowing it to utilize the power of neural networks to predict genomic variants in trios or duos. See this page for more details and instructions on how to run DeepTrio.

DeepTrio supports germline variant-calling in diploid organisms for the following types of input data:

• NGS (Illumina) data for either whole genome or whole exome.
• PacBio HiFi data, see the PacBio case study.

Please also note:

• All DeepTrio models were trained on human data.
• It is possible to use DeepTrio with only 2 samples (child, and one parent).
• External tool GLnexus is used to merge output VCFs.

How to run DeepVariant

We recommend using our Docker solution. The command will look like this:

BIN_VERSION="1.4.0"
docker run \
  -v "YOUR_INPUT_DIR":"/input" \
  -v "YOUR_OUTPUT_DIR:/output" \
  google/deepvariant:"${BIN_VERSION}" \
  /opt/deepvariant/bin/run_deepvariant \
  --model_type=WGS \ **Replace this string with exactly one of the following [WGS,WES,PACBIO,HYBRID_PACBIO_ILLUMINA]**
  --ref=/input/YOUR_REF \
  --reads=/input/YOUR_BAM \
  --output_vcf=/output/YOUR_OUTPUT_VCF \
  --output_gvcf=/output/YOUR_OUTPUT_GVCF \
  --num_shards=$(nproc) \ **This will use all your cores to run make_examples. Feel free to change.**
  --logging_dir=/output/logs \ **Optional. This saves the log output for each stage separately.
  --dry_run=false **Default is false. If set to true, commands will be printed out but not executed.

To see all flags you can use, run: docker run google/deepvariant:"${BIN_VERSION}"

If you're using GPUs, or want to use Singularity instead, see Quick Start for more details or see all the setup options available.

For more information, also see:

• Full documentation list
• Detailed usage guide with more information on the input and output file formats and how to work with them.
• Best practices for multi-sample variant calling with DeepVariant
• (Advanced) Training tutorial
• DeepVariant's Frequently Asked Questions, FAQ

How to cite

If you're using DeepVariant in your work, please cite:

A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology 36, 983–987 (2018). 
Ryan Poplin, Pi-Chuan Chang, David Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Dan Newburger, Jojo Dijamco, Nam Nguyen, Pegah T. Afshar, Sam S. Gross, Lizzie Dorfman, Cory Y. McLean, and Mark A. DePristo.
doi: https://doi.org/10.1038/nbt.4235

Additionally, if you are generating multi-sample calls using our DeepVariant and GLnexus Best Practices, please cite:

Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Bioinformatics (2021).
Taedong Yun, Helen Li, Pi-Chuan Chang, Michael F. Lin, Andrew Carroll, and Cory Y. McLean.
doi: https://doi.org/10.1093/bioinformatics/btaa1081

Why Use DeepVariant?

• High accuracy - DeepVariant won 2020 PrecisionFDA Truth Challenge V2 for All Benchmark Regions for ONT, PacBio, and Multiple Technologies categories, and 2016 PrecisionFDA Truth Challenge for best SNP Performance. DeepVariant maintains high accuracy across data from different sequencing technologies, prep methods, and species. For lower coverage, using DeepVariant makes an especially great difference. See metrics for the latest accuracy numbers on each of the sequencing types.
• Flexibility - Out-of-the-box use for PCR-positive samples and low quality sequencing runs, and easy adjustments for different sequencing technologies and non-human species.
• Ease of use - No filtering is needed beyond setting your preferred minimum quality threshold.
• Cost effectiveness - With a single non-preemptible n1-standard-16 machine on Google Cloud, it costs ~$11.8 to call a 30x whole genome and ~$0.89 to call an exome. With preemptible pricing, the cost is $2.84 for a 30x whole genome and $0.21 for whole exome (not considering preemption).
• Speed - See metrics for the runtime of all supported datatypes on a 64-core CPU-only machine. Multiple options for acceleration exist.
• Usage options - DeepVariant can be run via Docker or binaries, using both on-premise hardware or in the cloud, with support for hardware accelerators like GPUs and TPUs.

(1): Time estimates do not include mapping.

How DeepVariant works

For more information on the pileup images and how to read them, please see the "Looking through DeepVariant's Eyes" blog post.

DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework. Nucleus was built with DeepVariant in mind and open-sourced separately so it can be used by anyone in the genomics research community for other projects. See this blog post on Using Nucleus and TensorFlow for DNA Sequencing Error Correction.

DeepVariant Setup

Prerequisites

• Unix-like operating system (cannot run on Windows)
• Python 3.6

Official Solutions

Below are the official solutions provided by the Genomics team in Google Health.

Name	Description
Docker	This is the recommended method.
Build from source	DeepVariant comes with scripts to build it on Ubuntu 20.04. To build and run on other Unix-based systems, you will need to modify these scripts.
Prebuilt Binaries	Available at gs://deepvariant/. These are compiled to use SSE4 and AVX instructions, so you will need a CPU (such as Intel Sandy Bridge) that supports them. You can check the /proc/cpuinfo file on your computer, which lists these features under "flags".

Contribution Guidelines

Please open a pull request if you wish to contribute to DeepVariant. Note, we have not set up the infrastructure to merge pull requests externally. If you agree, we will test and submit the changes internally and mention your contributions in our release notes. We apologize for any inconvenience.

If you have any difficulty using DeepVariant, feel free to open an issue. If you have general questions not specific to DeepVariant, we recommend that you post on a community discussion forum such as BioStars.

Acknowledgements

DeepVariant happily makes use of many open source packages. We would like to specifically call out a few key ones:

• Boost Graph Library
• abseil-cpp and abseil-py
• CLIF
• GNU Parallel
• htslib & samtools
• Nucleus
• numpy
• SSW Library
• TensorFlow and Slim

We thank all of the developers and contributors to these packages for their work.

Disclaimer

This is not an official Google product.

NOTE: the content of this research code repository (i) is not intended to be a medical device; and (ii) is not intended for clinical use of any kind, including but not limited to diagnosis or prognosis.

Download Details:

Author: Google
Source Code: https://github.com/google/deepvariant 
License: BSD-3-Clause license

#science #machinelearning #deeplearning

Lack of Time, Knowledge Cited as Biggest Obstacles in API Production

In Postman’s 2020 State of the API report, it was found that when it came to API production, the number one obstacle was “lack of time”, which was cited by more than half (52.3%) of survey respondents comprising 13,500 developers, testers, and even executives.

This was followed by a “lack of knowledge” with 36.4%.

According to Kin Lane, API Evangelist and co-chair of the OpenAPI Initiative Business Governance Board, the solution to address both the lack of time and lack of knowledge that development teams face is an organizational matter that leadership needs to address.

“Those two things represent a lack of prioritization from leadership of these organizations, lack of prioritization of the API itself, as well as the lack of prioritization of their employees learning these new technologies,” Lane says.

During an episode of Coding Over Cocktails, he explains that educating leadership and business users about APIs should be a priority — which means that it’s often not just some new vendor solution or some new trend that will drive better API production and consumption.

Besides convincing developers to embrace OpenAPI to design and deliver more consistent APIs, Lane says that publishers should also make them publicly available in a machine-readable way by providing access for other users through proper authentication, identity, and access management.

This, he says, would make your organization more agile in the long run.

"…we should have OpenAPI specs for all the services that we need out there — and those shouldn’t be hidden. Those should be publicly available, consistent, and up to date. And if you’re doing them well in a machine-readable way like this, it benefits your company and everyone else’s companies as well.

“And we got to get people over that fact that, if I have a catalog of up-to-date, open APIs for all the top services out there, I somehow benefit my competitors. Sure, you do. But it’s going to benefit you even more.” Lane furthers.

Outcompeting Your Competition
Isn’t opening up your APIs to your competitors counterintuitive? Lane doesn’t think so.

“You’re gonna be more agile, nimble, and flexible. You’re gonna be able to pivot. You’re gonna be able to respond to business changes quicker. If you do APIs well, that lack of time shrinks because you’re quicker, you’re faster, your teams well-educated. They know what to do. You can respond to critical changes. You can outcompete your competition. You could do what you do best as a business rather than just the mundane, repetitive things that we have to face when it comes to continuous deployment and continuous integration.”

Lane adds that most people don’t care about specs, too. They just want the business connection to work.

“And that’s the way it should be across the board.” he ends.

Back to Class
Since 2010, Lane has played an educational role in the API space, having written over 5000 blogs in the span of 10 years as the API Evangelist.

He underscores APIs’ importance by educating developers, publishers, and consumers of the API and other stakeholders and business users.

Additionally, he sees that organizations tackling APIs, such as TORO Cloud and Postman, should see beyond the barriers of competition and see the value of educating the community as a whole.

“We all got to get together… Like for this podcast, I responded to you guys because I’m here as a personality to be on these. But, then how do we help amplify?.. Even if I’m a competitor of you guys, how do I see the value in tweeting your podcast and tweeting it out? And how do we have this kind of shared sense of the API community in a way and enrich it and invest in it, knowing that it’s going to make all of us better even when it comes to competing.” he says.

Lane then shared his vision for the API Specification Toolbox, which is ‘a toolbox for all of the leading API specifications, providing a community catalog of news, services, tooling, extensions, and other resources to support your adoption of leading API specifications.’

Check out our exciting discussion with Kin Lane, where he talks about the OpenAPI specification and the importance of educating business organizations on APIs in this episode of Coding Over Cocktails.

Coding Over Cocktails is a podcast created by TORO Cloud, a company that offers a low-code, API-centric platform for application development and integration.

This podcast series tackles issues faced by enterprises as they manage the process of digital transformation, application integration, low-code application development, data management, and business process automation. It’s available for streaming on most major podcast platforms, including Spotify, Apple, Google Podcasts, SoundCloud, and Stitcher.

#api #openapi #microservices