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UID:147@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20250926T110000
DTEND;TZID=Asia/Kolkata:20250926T120000
DTSTAMP:20250915T152949Z
URL:https://cds.iisc.ac.in/events/mtech-research-thesis-defense-cds-26-sep
 tember-2025-an-error-correction-algorithm-for-long-read-sequencing/
SUMMARY:Mtech Research Thesis Defense: CDS: 26\, September 2025 "An error c
 orrection algorithm for long-read sequencing."
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nMtech Research T
 hesis Defense\n\n\n\nSpeaker : Mr. Parvesh Barak\nS.R. Number : 06-18-01-1
 0-22-23-1-23181\nTitle : An error correction algorithm for long-read seque
 ncing.\nThesis examiner : Prof. Navin Kashyap\, Department of Electrical C
 ommunication Engineering\, IISc\nResearch Supervisor: Dr. Chirag Jain\nDat
 e &amp\; Time : September 26\, 2025 (Friday) at 11:00 AM\nVenue : CDS# 308
 \n\n\n\nABSTRACT\n\nThe latest\, third-generation\, long-read sequencing t
 echnologies have transformed genomics by generating long and sufficiently 
 accurate sequences that bridge most repeats of an organism’s genome. A s
 ingle sequencing run can routinely generate terabytes of data\, but these 
 instruments also introduce errors during sequencing. The average sequencin
 g error rate ranges from 0.1%-4%\, depending on the choice of instrument a
 nd protocol. These errors must be corrected while preserving the correctly
  sequenced bases to reconstruct the genome sequence. In diploid organisms 
 like humans\, each individual inherits two copies of the genome\, one from
  each parent. These two copies\, called haplotypes\, are nearly identical 
 but differ at about 0.1% of genomic positions. Knowing these genetic diffe
 rences between the two haplotypes is important for biological applications
 . So\, this introduces a challenge of correcting noise (sequencing errors)
  while preserving the true signal (0.1% genetic variation between the hapl
 otypes). During the last few years\, several haplotype[1]aware error corre
 ction methods have been developed. However\, existing methods are based on
  either ad-hoc heuristics or deep learning approaches\, which require subs
 tantial computational resources and lack formal guarantees.\n\nThis thesis
  presents the first rigorous formulation for this problem. Our approach bu
 ilds on the minimum error correction framework commonly used in read phasi
 ng algorithms. We prove that the proposed 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 algorithm scale to large datasets\, we de
 sign practical heuristics. Experiments using publicly available sequencing
  datasets from human and plant genomes show that our method achieves accur
 acy comparable to state-of-the-art tools.\n\nA Rust implementation of our 
 algorithm is freely available at https://github.com/at-cg/HALE\n\n\n\nALL 
 ARE WELCOME
CATEGORIES:Events,Thesis Defense
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