涉及的领域主要是生物信息学、计算生物学和表观遗传学，同时会关注机器学习、统计、干细胞生物学、发育生物学、遗传学、精准医学等方向。
因为经常去看新的发表的文章或者预印本，所以想着在这里写个帖子，和大家分享下，并谈谈自己的想法和心得。
不一定全是刚出来的文章啦！
特别关注：单细胞、网络生物学、算法、细胞命运决定、基因表达调控等。
2019年3月16日
1. An open resource of structural variation for medical and population genetics
https://www.biorxiv.org/content/10.1101/578674v1 Posted March 14, 2019.
原来的gnomAD数据库是没有Structure Variation相关资料的，这一次的更新是非常大的补充，很有价值，值得关注。
The gnomAD-SV resources have been integrated into the gnomAD browser (https://gnomad.broadinstitute.org), where users can freely explore this dataset without restrictions on reuse, which will have broad utility in population genetics, disease association, and diagnostic screening.
2. Fantom CAGE-seq 数据库更新
http://fantom.gsc.riken.jp/5/datafiles/reprocessed/
hg38_latest/ 15-Mar-2019 18:28
mm10_latest/ 15-Mar-2019 18:31
2019年3月12日
1. Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes
https://www.biorxiv.org/content/10.1101/573378v2 (Posted March 10, 2019)
BioRxiv上更新了gnomAD的一个工作，gnomAD还是很有价值，蛮有意思的啦，好像还没有正式发表的工作，该数据库不仅包括外显子测序数据，还包括全基因组测序数据。详细介绍见于：https://gnomad.broadinstitute.org。今天访问时，发现网站已经改版了。
The data set provided on this website spans 125,748 exome sequences and 15,708 whole-genome sequences from unrelated individuals sequenced as part of various disease-specific and population genetic studies.
2. Interrogation of human hematopoiesis at single-cell and single-variant resolution
https://www.nature.com/articles/s41588-019-0362-6 Published: 11 March 2019
Nature Genetics上一篇分析UK Biobank的文章，有分析enhancer上的variant：
For regulatory variants, we explore patterns of developmental enhancer activity, predict molecular mechanisms, and identify likely target genes.
Our study provides a comprehensive framework for single-variant and single-cell analyses of genetic associations.
2019年3月10日
BioRxiv, Posted March 09, 2019.
Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank
https://www.biorxiv.org/content/10.1101/572347v1
Abstract:
The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had ≥1 predicted loss of function carrier, and most genes (>69%) had ≥10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.
