Fan Zhang

Ph.D. in Bioinformatics & Statistical Genetics | Bioinformatics Scientist @ Illumina Inc. | Views are my own

[Fan Zhang @GriffanVI]

Worrying about sequencing contamination? Check out our newly published tool: VerifyBamID2 on @genomeresearch. https://t.co/djKmB5bOue

[Fan Zhang @GriffanVI]

@hanshunhua @illumina Nice work bro!

[Yann LeCun @ylecun]

The kind of attacks that Dr Anthony Fauci is being subjected to by Republicans is disgraceful and dangerous.
This man is a hero. He helped save *millions* of lives.

Yet, he is vilified by Republican members of congress who are more interested in political expediency and posturing than in the safety of the public,
Some even attack the very concept of science, the scientific method.
This is insanely dangerous.
The undermining of the public's trust in the public health system actually killed people during the pandemic.

I've complained about how some captains of industry treating their scientists, but the mistreatment of Dr Fauci by congressional Republicans tops everything.

[David Lloyd @DJLN18]

@TwistBioscience Clever

[Charlie Johnson @BioMath]

Trying out the new @MSFTCopilot app.

What our new @illumina #DRAGEN dreams of empowering the X+

[Fan Zhang @GriffanVI]

@kundu_ritu Congratulations 🥳

[Rami mehio @mehio_rami]

A huge step by the UK Biobank and its partners for the #500KGenomes project in unlocking the power of the genome;

500k WGS sequenced on Illumina NovaSeq6k, analyzed on #DRAGEN and aggregated using @illumina Connected Analytics #iCA

https://t.co/y75UzTCPo5

[Fan Zhang @GriffanVI]

@kundu_ritu The most recent releases of Conda are working on include Mamba Solver.

[Illumina @illumina]

The wait is over. Take your projects to the next level with 52 billion paired-end reads and 8Tb of output. Learn more about the speed, scale, and power of the 25B flow cell on the #NovaSeqX: https://t.co/SxCu2AqXde

[Abdel Abdellaoui @dr_appie]

The field of human genetics is evolving at a rapid pace. Much of this progress is driven by our recent ability to link common DNA variants to complex traits with genome-wide association studies (GWASs) 🧬

For both newcomers and seasoned geneticists, good review papers can be very helpful in introducing oneself to the field and in keeping up to date with the progress.

Here's an overview of key review papers, arranged chronologically:

2005 in @NatureRevGenet: Genome-wide association studies for common diseases and complex traits
A primer on the approach at the dawn of this revolution: https://t.co/9XZaIOt6QP

2008 in @NatureRevGenet: Genome-wide association studies for complex traits: consensus, uncertainty and challenges
A deep dive into the early statistical procedures of GWASs: https://t.co/znnZxxW0O3

2009 in @NatureRevGenet: Validating, augmenting and refining genome-wide association signals
On refining and replicating GWAS results: https://t.co/L6QqD0HaNO

2010 in @NatureRevGenet: Genotype imputation for genome-wide association studies
All about genotype imputation, enabling interrogation of untyped genetic variation: https://t.co/sTEOCVcHjO

2010 in @NatureRevGenet: New approaches to population stratification in genome-wide association studies
Tackling false positives in GWASs due to ancestry differences: https://t.co/KuUvVZaC20

2016 in @NatureRevGenet: Dissecting the genetics of complex traits using summary association statistics
Delving into downstream analyses of GWAS signals: https://t.co/hdCkMSCoDi

2019 in @NatureRevGenet: Benefits and limitations of genome-wide association studies
Exploring the pros, cons, and controversies surrounding GWAS: https://t.co/O8UCWBS2R2

2021 in @MethodsPrimers: Genome-wide association studies
A comprehensive look at GWAS and subsequent analyses: https://t.co/b1dHIIiBtQ

2021 in @NatureHumBehav: Dissecting polygenic signals from genome-wide association studies on human behaviour
Understanding GWAS signals in human behavioural outcomes: https://t.co/FAoWmyMM88

And finally, a series of reviews charting the progress in the field every five years in @AJHGNews
2012 - 5 years of GWAS discovery: https://t.co/5MeRmy5D0c
2017 - 10 years of GWAS discovery: Biology, Function, and Translation: https://t.co/Q8bwoCF3mp
2022 - 15 years of GWAS discovery: Realizing the Promise: https://t.co/pSBPK2U6Lc

[Jing Yu Koh @kohjingyu]

One piece of advice that I frequently give to prospective ML PhD students is to work at a tech company or startup (ideally doing research) for at least a year before diving into a PhD program. This is invaluable if you've never worked full time before. Some reasons why:

1. You develop skills that make you a more effective researcher. At a full time tech job, you learn important skills that PhD programs don't always explicitly teach. Strong software engineering skills, project and task organization, and communication skills are a few that help a lot with ML research. A bonus is that you might also benefit from experiencing what a "normal job" is like, and learn to appreciate not having a regular 9-5 schedule, or dealing with re-orgs and performance reviews as a PhD student :)

2. You might find out that you don't really want to do a PhD. This is surprisingly common! At Google, I met several AI Residents who were very certain when they first joined that they would do a PhD and be professors after. But after a year, many of them realized they don't actually like research that much, or they don't like the day-to-day work of a researcher. Some people also realized that they can do the kind of work they want without a PhD, and wouldn't personally benefit from earning one. Many switched over to working on product-facing software engineering, and I even knew someone who converted to become a product manager. They seem happier than they would have been if they had joined a 4-6 year PhD program.

3. Financial Stability: While most CS PhD stipends cover living expenses quite comfortably, they still can't compete with tech job salaries. Having extra cash on hand provides financial security and helps with unexpected expenses.

4. Diverse Connections: Working in industry allows you to build a set of connections that are very different from your academic network. This is helpful for when you are looking for summer internships, job openings, or just to get different perspectives on research ideas and career decisions.

I personally felt that I benefited greatly from working in big tech for a few years before starting my PhD. The primary argument against doing so, given the opportunity, is that you waste some time because you could have finished your PhD earlier, but this is not necessarily true. You might complete it even quicker because you become a more capable software engineer and can run experiments more quickly, or develop a concrete research agenda earlier in (or even before) the PhD.

[Yi Ma @YiMaTweets]

The HKU CS department this year has multiple faculty position openings in several areas: https://t.co/PNPgDhJSe2 Also, the HKU Institute of Data Science (IDS) always has openings. Joint appointments between CS and IDS are common. Talented candidates are welcome to apply!

[Jimmie Ye @yimmieg]

1/7 Check out our new preprint on the #CLUEFramework, a method for performing multiplexing single-cell sequencing (mux-seq) without the need for reference genotypes. Lion share of the credit goes to @ghartoularos, a graduate student in the lab. https://t.co/DchD9gdp2Z. 👇

[Fan Zhang @GriffanVI]

@Reuters I would say Apple still shines. Thanks for the donations to the children. @tim_cook

[Fan Zhang @GriffanVI]

Just tried chatGPT plus 😆

[Fan Zhang @GriffanVI]

@nytimes Extraordinary claim requires extraordinary evidence.

[Fan Zhang @GriffanVI]

That's true.

[Elon Musk @elonmusk]

What we need is TruthGPT

[Fan Zhang @GriffanVI]

#拜年# 祝大家春节愉快，阖家欢乐，万事如意！

[Sharing Travel @TripInChina]

Wow, the countryside in Taizhou, Zhejiang Province has started dragon dancing to celebrate the Spring Festival!😃😃😃

[Fan Zhang @GriffanVI]

@steve_hyq @karynotype Yes, VB2 jointly estimates contamination and genetic ancestry, which alleviates biases when estimating each of them separately.

[Fan Zhang @GriffanVI]

@ArthurZhou10 How come?