@DehuiLin

PhD Student, Nanyang Technological University

Joined April 2022
  • Tweets6
  • Following87
  • Followers25
Dehui Lin retweeted
Mile Sikic
@msikic21 May 2024
HERRO - imagine what you can do with accurate ultra-long nanopore reads biorxiv.org/content/10.1101/… w/ @domstanojevic @DehuiLin @PaolaFlorezdeS and great help from @sergeynurk #nanopore. See you at London Calling 2024!!

Telomere-to-telomere phased genome assembly using error-corrected Simplex nanopore reads

Telomere-to-telomere phased assemblies are standard expectations. To achieve these for diploid and even polyploid genomes, the contemporary approach involves at least two long-read sequencing...

biorxiv.org
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Our new paper is out! We made a lot of progress on our GNN-based de novo genome assembly paradigm and here we present all our findings and progress 🧬🥳 Code: github.com/lbcb-sci/GNNome Paper: biorxiv.org/content/10.1101/… See details of GNNome below 👇🧵

GitHub - lbcb-sci/GNNome: A framework for training graph neural networks to untangle assembly...

A framework for training graph neural networks to untangle assembly graphs obtained from OLC-based de novo genome assemblers. - lbcb-sci/GNNome

github.com
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Dehui Lin retweeted
Mile Sikic
@msikic4 Mar 2024
Happy to present RiNALMO - our RNA large language model arxiv.org/abs/2403.00043 w/ @RJPenic Tin Vlasic @ywan_wan and Roland Huber. RiNALMo is the largest RNA language model to date, with 650 million parameters pre-trained on 36 million non-coding RNA sequences. 1/2
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Dehui Lin retweeted
Mile Sikic
@msikic19 Jan 2024
Are nanopore UL reads only long reads we need? We developed Herro github.com/lbcb-sci/herro AI error correction model that can correct reads to accuracy above Q30 while trying to keep informative positions intact. w/ @domstanojevic @DehuiLin @sergeynurk @PaolaFlorezdeS @nanopore

GitHub - lbcb-sci/herro: HERRO is a highly-accurate, haplotype-aware, deep-learning tool for error...

HERRO is a highly-accurate, haplotype-aware, deep-learning tool for error correction of Nanopore R10.4.1 or R9.4.1 reads (read length of >= 10 kbps is recommended). - lbcb-sci/herro

github.com
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Dehui Lin retweeted
Mile Sikic
@msikic12 Jan 2024
Long reads metagenome benchmark is out. Highlights. 1. In most cases Kraken, 2. minimap2/ram for slightly higher accuracy. 3. The right database is of huge importance 4. Check taxonomy files carefully. bmcbioinformatics.biomedcent… w/@NiranjanTW @KrizanovicK @jmaricb @Sylvain14518009

Comparative analysis of metagenomic classifiers for long-read sequencing datasets

BMC Bioinformatics - Long reads have gained popularity in the analysis of metagenomics data. Therefore, we comprehensively assessed metagenomics classification tools on the species taxonomic level....

link.springer.com
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After many months, I'm proud to share our work on untangling genome assembly graphs with GNNs. Or, GNNome Assembly (sorry not sorry)🧬 Paper: arxiv.org/abs/2206.00668 Code/Data: github.com/lvrcek/GNNome-ass… with @xbresson, T. Laurent, @Martin_fschmitz, and @msikic. Read more in 🧵
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