Wei Li

Professor of Bioinformatics at University of California, Irvine

[Cell @CellCellPress]

Now online! A genome-wide spectrum of tandem repeat expansions in 338,963 humans https://t.co/dSNbb15UBx

[Wei Li @superweili]

Thrilled to share our UCI TR-gnomAD published in Cell today: https://t.co/pzVrBoNTed. UCI TR-gnomAD is the first biobank-scale genetic reference for ~1.0M Tandem Repeat (TR) expansions across 340K humans, facilitating TR-based disease association studies and clinical diagnostics

[Wei Li @superweili]

@Goodell_Lab Congratulations Peggy @Goodell_Lab! I enjoyed our collaborations very much.

[Wei Li @superweili]

Our new paper @NatureComms performed the first Alternative polyadenylation transcriptome-wide association study for 11 brain disorders with 17,300 RNA-seq samples. We identified 354 novel APA-linked risk genes including ATXN3 for ALS.
https://t.co/TnsqVkx0Dp

[Jingyi Jessica Li (李婧翌) @jsb_ucla]

Ultimately, adjusting hidden covariates or not examines different DE effects (conditional vs marginal). But regardless of this difference, I still think no DE genes are expected be found (in the average sense) from permuted samples where conditional labels are randomly shuffled.

[Jingyi Jessica Li (李婧翌) @jsb_ucla]

@borishej @mikelove @KasperDHansen @timtriche @andrewejaffe @lpachter @RodThiebaut @denis_agniel We thank @borishej et al for sending us the draft of their correspondence on May 3. Here is a threshold of key points in our reply sent on that day. 1/ The main message of our article is that using popular methods without a sanity check may lead to inflated FDR.

[Genome Biology @GenomeBiology]

Study of false positives when calling differentially expressed genes, from @YM123411, @ge_xinzhou, @FanglueP, @superweili & @jsb_ucla. With population-scale sample sizes, common methods have inflated FDR. The authors suggest using Wilcoxon rank-sum test https://t.co/TrjCbGPwkZ

[Lei Li @LeiLi_bioinfo]

Population-scale genetic control of alternative polyadenylation and its association with human diseases
https://t.co/HZwt9ZWpb9

[Wei Li @superweili]

Our new scDaPars tool for single-cell alternative polyadenylation is published
@genomeresearch
https://t.co/olNLN3H5tq

[Wei Li @superweili]

@YongshengShi @NeugebauerKarla @TheTracyLab @cshlmeetings looks like a very exciting meeting. Any chance to be in person?

[Nature Genetics @NatureGenet]

💥An atlas of alternative polyadenylation quantitative trait loci contributing to complex trait and disease heritability | by Eric J. Wagner & Wei Li and colleagues
Find it here 👇@NatureGenet
https://t.co/3KbCYdms2n

[Wei Li @superweili]

@kauralasoo @anshulkundaje @Wagnerlab_RNA @LeiLi_bioinfo @eQTLCatalogue Hi Kaur, Thank you for the comments. It would be great if you could run Dapars on all of your eQTL Catalogue datasets.

[Wei Li @superweili]

Super excited to share our recent work on 3′UTR alternative polyadenylation (APA) quantitative trait loci (3′aQTLs), which can explain ~16.1% GWAS SNPs and are largely distinct from eQTLs. Wonderful collaborations with @Wagnerlab_RNA @LeiLi_bioinfo
https://t.co/j7nFyPt3pZ

[Xiaole Shirley Liu @XShirleyLiu]

Congratulations to @mourisl on the great improvement on the TRUST algorithm! https://t.co/YX9V6vHsSK

[Wei Li @superweili]

@YongshengShi Very nice work. Congratulations!

[Wei Li @superweili]

Excited to share our new paper: Cellular Heterogeneity–Adjusted cLonal Methylation (CHALM) improves prediction of gene expression
https://t.co/CZtbj7gXMu

[Wei Li @superweili]

@yuefeng_1 @nature @twang5 @rosshardison Amazing work. Congratulations

[Wei Li @superweili]

Our cancer driver gene prediction paper is finally online. https://t.co/dPkTBlcIWD

[Xiaole Shirley Liu @XShirleyLiu]

Twenty years of Genome Biology. Quite honored that our MACS is one of the top papers published in the last two decades, together with Bowtie, DESeq, TopHat2, SCANPY, DAVID, and BioConductor!
https://t.co/deFxbGxh0h