Update README.md

README.md

@@ -5,11 +5,15 @@ Thank you for your interest in MACIE. MACIE (Multi-dimensional Annotation Class
 
 
 ## Data Availability and Code Reproducibility
-The MACIE scores (and other integrative scores) used in all benchmarking examples are available for download [here](https://drive.google.com/drive/folders/1gzqsfgaO1WCh5pAQUgVlUNsX9HYneO7p?usp=sharing). Precomputed MACIE scores for every possible variant in the human genome are available for download.
+The MACIE scores (and other integrative scores) used in all benchmarking examples are available for download [here](https://drive.google.com/drive/folders/1gzqsfgaO1WCh5pAQUgVlUNsX9HYneO7p?usp=sharing). Precomputed MACIE scores for every possible variant in the human genome are available for download via Zenodo: [Part 1 (Chr1 - Chr3)](https://zenodo.org/record/5755656), [Part 2 (Chr4 - Chr7)](https://zenodo.org/record/5756449), [Part 3 (Chr8 - Chr13)](https://zenodo.org/record/5756479), [Part 4 (Chr14 - Chr22)](https://zenodo.org/deposit/5756563). These are compressed with the bgzip utility, and indexed with tabix, both of which are part of the [Samtools software suite](http:https://www.htslib.org/). In addition, tabix provides a means of efficiently extracting subsets of the data defined by genomic regions. For example, the command line
+
+`tabix MACIE_hg19_noncoding_chr1.tab.bgz 1:20000-30000 > Subset.txt`
+
+extracts all variants on chromosome 1 from position 20,000 through 30,000 and writes them to the file Subset.txt. In this example, the tabix index file, `MACIE_hg19_noncoding_chr1.tab.bgz.tbi`, needs to be in the same directory as the main data file, `MACIE_hg19_noncoding_chr1.tab.bgz`. Samtools, including bgzip and tabix, is available [here](http:https://www.htslib.org/download). 
 
 The code used for training MACIE models are available [here](https://github.com/xihaoli/MACIE/blob/main/code/MACIE.py).
 
 All genomic coordinates are given in NCBI Build 37/UCSC hg19.
 
 ## Reference
-Xihao Li*, Godwin Yung*, Hufeng Zhou, Ryan Sun, Zilin Li, Yaowu Liu, Iuliana Ionita-Laza, and Xihong Lin (2021+) "A Multi-Dimensional Integrative Scoring Framework for Predicting Functional Variants in the Human Genome". *Submitted*.
+Xihao Li*, Godwin Yung*, Hufeng Zhou, Ryan Sun, Zilin Li, Kangcheng Hou, Martin Jinye Zhang, Yaowu Liu, Theodore Arapoglou, Chen Wang, Iuliana Ionita-Laza, and Xihong Lin (2021+) "A Multi-Dimensional Integrative Scoring Framework for Predicting Functional Variants in the Human Genome". *Submitted*.