☢ Welcome to Li Tai Fang's Rudimentary Home Server! ☣

There you are wrong. The graveyards are full of middling swordsmen. Best not to be a swordsman at all than a middling swordsman.

-- a young Augustus in HBO's Rome

Places to go:

My Scientific Publications

Photo Gallery, last updated 4/10/2021

A Collection of Jokes

I was the lead developer of SomaticSeq at Bina Technologies (acquired by Roche). SomaticSeq a machine learning algorithm to detect somatic mutations in cancer sequencing. SomaticSeq has placed Bina No. 1 in INDEL and No. 2 in SNV in the Stage 5 of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. Our paper describing the method was an Editor's pick in Genome Biology (2015).

At Roche, I led the project to establish the high-confidence somatic mutation data and call sets for the pair of tumor-normal reference samples established by the FDA-led MAQC-IV/SEQC2 Consortium. This work was published in Fang LT et al. Nat Biotechnol 2021 / PMID:34504347 / SharedIt Link along with the SEQC2 Collection on Nature Biotechnology. The collection includes a commentary of the SEQC2 Consortium by FDA's then acting commissioner Dr. Janet Woodcock. The following are some of the use cases for this work:

• Use the high-confidence call set as the "ground truth" to investigate how different sample preparations, sequencing library kits, and bioinformatic algorithms affect the accuracy of the somatic mutation pipelines, and develop best practices, e.g., Xiao W et al. Nat Biotechnol 2021.

• Use the high-confidence call set as labeled training data to build more accurate machine learning models for somatic mutation detections, e.g., Sahraeian SME et al. Genome Biol 2022.

• nf-core/sarek 3 used SEQC2 to benchmark somatic mutation calling

• Whole-genome somatic small variant calling: this how-to explores calling variants using Mutect2 in the SEQC2 Consortium Sample by NVIDIA Clara Parabricks

• Benchmarking NVIDIA Clara Parabricks' somatic variant calling pipeline on AWS

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A quick 8-minute video explaining SomaticSeq Fang LT, et al. Genome Biol (2015)

Establishing reference samples and call sets for cancer mutation detection using whole-genome sequencing 1st place presentation award at the 2021 MCBIOS & MAQC Annual Meeting Somatic Mutation Working Group of the SEQC2 Consortium Fang LT, et al. Nat Biotechnol (2021) / PubMed / SharedIt

This is my resume.

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My Web Pages Elsewhere:

I maintain two identical mirrors in two different locations:
  (1) My refurbished desktop with Duo Core AMD A4-5300B with 16GB RAM re-purposed as a low-end home server,
  (2) the UCLA Chemistry Department's server that I have user access to.

Google Scholar Profile / ORCID iD

My Cal Alumni Page

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Email me!

My home server is powered by Apache2 HTTP Server, running on 64-bit Ubuntu Linux Server Edition 20.04.
My home network is routed through a ASUS RT-N66R, with its factory firmware replaced by DD-WRT.
However, my Linux Server is physically attached to another ASUS RT-N66R, also with DD-WRT, set up as a Wireless Repeater Bridge.
If your feel this page loads too slowly, blame my local internet infrastructure. I only have a measly 1.5 Mbps upload speed! I don't get fiber optics here!