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README.md

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 # NanoCaller
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 NanoCaller is a computational method that integrates long reads in deep convolutional neural network for the detection of SNPs/indels from long-read sequencing data. NanoCaller uses long-range haplotype structure to generate predictions for each SNP candidate variant site by considering pileup information of other candidate sites sharing reads. Subsequently, it performs read phasing, and carries out local realignment of each set of phased reads and the set of all reads for each indel candidate variant site to generate indel calling, and then creates consensus sequences for indel sequence prediction.
 
+NanoCaller is distributed under the [MIT License by Wang Genomics Lab](https://wglab.mit-license.org/).
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 ## Latest Updates
+_**v1.0.0** (Aug 8 2021)_ : First post-production release with citeable DOI: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5176764.svg)](https://doi.org/10.5281/zenodo.5176764)
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 _**v0.4.1** (Aug 3 2021)_ : Fixed a bug causing slower runtime in whole genome variant calling mode. 
 
 _**v0.4.0** (June 2 2021)_ : Added NanoCaller models trained on ONT reads basecalled with Guppy v4.2.2 and Bonito v0.30, as well as R10.3 reads. Added new NanoCaller models trained with long CCS reads (15-20kb library selection). Improved indel calling with rolling window for candidate selection which helps with indels in low complexity regions.