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UID:133@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20250702T140000
DTEND;TZID=Asia/Kolkata:20250702T150000
DTSTAMP:20250626T143600Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-colloquium-hybrid
 -cds-an-error-correction-algorithm-for-long-read-sequencing/
SUMMARY:M.Tech Research Thesis {Colloquium}: Hybrid: CDS: "An error correct
 ion algorithm for long-read sequencing"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nM.Tech Research 
 Thesis Colloquium\n\n\n\nSpeaker : Mr. Parvesh Barak\nS.R. Number : 06-18-
 01-10-22-23-1-23181\nTitle : "An error correction algorithm for long-read 
 sequencing"\nResearch Supervisor :Dr. Chirag Jain\nDate &amp\; Time : July
  02\, 2025 (Wednesday) at 02:00 PM\nVenue : The Thesis Colloquium will be 
 held on HYBRID Mode\n# 102 CDS Seminar Hall /MICROSOFT TEAMS.\nPlease clic
 k this link to join the Thesis Colloquium:\n\n\n\nABSTRACT\n\nThe latest\,
  third-generation\, long-read sequencing technologies have transformed gen
 omics by generating long and sufficiently accurate sequences that bridge m
 ost repeats of an organism’s genome. A single sequencing run can routine
 ly generate terabytes of data\, but these instruments also introduce error
 s during sequencing. The average sequencing error rate ranges from 0.1%-4%
 \, depending on the choice of instrument and protocol. These errors must b
 e corrected while preserving the correctly sequenced bases to reconstruct 
 the genome sequence. In diploid organisms like humans\, each individual in
 herits two copies of the genome\, one from each parent. These two copies\,
  called haplotypes\, are nearly identical but differ at about 0.1% of geno
 mic positions. Knowing these genetic differences between the two haplotype
 s is important for biological applications. So\, this introduces a challen
 ge of correcting noise (sequencing errors) while preserving the true signa
 l (0.1% genetic variation between the haplotypes). During the last few yea
 rs\, several haplotype-aware error correction methods have been developed.
  However\, existing methods are based on either ad-hoc heuristics or deep 
 learning approaches\, which require substantial computational resources an
 d lack formal guarantees.\n\nThis thesis presents the first rigorous formu
 lation for this problem. Our approach builds on the minimum error correcti
 on framework commonly used in read phasing algorithms. We prove that the p
 roposed formulation for error correction of reads in the de novo setting\,
  i.e.\, without using a reference genome\, is NP-hard. To make our exact a
 lgorithm scale to large datasets\, we design practical heuristics. Experim
 ents using publicly-available sequencing datasets from human and plant gen
 omes show that our method achieves accuracy comparable to state-of-the-art
  tools. A Rust implementation of our algorithm is freely available at http
 s://github.com/at-cg/HALE.\n\n\n\nALL ARE WELCOME
CATEGORIES:MTech Research Thesis Colloquium
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