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UID:74@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240919T160000
DTEND;TZID=Asia/Kolkata:20240919T170000
DTSTAMP:20240917T153909Z
URL:https://cds.iisc.ac.in/events/seminar-serc-4th-floor-auditorium-19th-s
 eptember-uncovering-the-complexity-of-dna-regulatory-sequence/
SUMMARY:{Seminar} @ SERC : 4th Floor Auditorium: 19th September: " Uncoveri
 ng the complexity of DNA regulatory sequence"
DESCRIPTION:Department of Computational and Data Sciences\nDepartment Semin
 ar\n\n\n\nSPEAKER : Prof. Rahul Siddharthan\, The Institute of Mathematica
 l Sciences\, Chennai.\nTITLE : "Uncovering the complexity of DNA regulator
 y sequence"\nDate &amp\; Time : September 19\, 2024\, 04:00 PM.\nVenue : #
  421\, 4th Floor Auditorium\n\n\n\nABSTRACT\nGenes are regulated by protei
 ns called transcription factors (TFs) which bind DNA and recruit or inhibi
 t the transcriptional machinery. Individual TFs recognize and bind to shor
 t patterns or "motifs" in DNA\, typically 8-15 basepairs long\, and identi
 fying these motifs\, both de novo and from databases of known motifs\, is 
 a longstanding problem. However\, it appears that there are sequence signa
 tures in DNA extending well beyond these "core motifs". We present two app
 roaches to studying this.\nThe first\, THiCweed (NAR 2018)\, an algorithm 
 for analysing ChIP-seq data\, treats it as a clustering problem. We find t
 hat clustering ChIP-seq sequence based on sequence similarity uncovers kno
 wn motifs\, but also many variants of known motifs\, extraneous motifs\, a
 nd sequence signatures extending well beyond the core motif. An extension 
 of this approach to general tabular data\, MMM ("Madras Mixture Model")\, 
 was published in 2024.\n\nA second approach\, SequeCNNs (in preparation)\,
  uses a convolutional neural network to distinguish binding from non-bindi
 ng sequence. It shows high accuracy even when the core motif is removed fr
 om the sequence\, suggesting that there are other strong sequence signatur
 es for TF-binding DNA. It also provides a framework for analysing and visu
 alising the significance of mutations\, individually and in combination\, 
 over hundreds of basepairs surrounding the core motif. Extension of this w
 ork to identifying other functional sequence\, such as TAD boundaries\, is
  in progress\, and a future goal is to generate synthetic sequence that ca
 n perform such functions in vivo.\n\nReferences:\nTHiCweed: A Agrawal\, S 
 Sambare\, L Narlikar\, R Siddharthan\, NAR 2018\nMMM: C Kumari and R SIddh
 arthan\, PLOS One 2024\nSequeCNNs: C Mohan Kumar\, L Narlikar\, R Siddhart
 han\, in preparation\n\nBIOGRAPHY\nDr. Rahul Siddharthan is a Professor at
  The Institute of Mathematical Sciences\, Chennai. He obtained his PhD in 
 physics from the Indian Institute of Science. His interest in biology stem
 s from his second postdoctoral stint at the Rockefeller University\, New Y
 ork. He joined the physics group at IMSc in 2004\, and in 2013 he started 
 a new group\, and new PhD programme\, in computational biology. He is broa
 dly interested in bioinformatic algorithms\, regulatory genomics\, chromat
 in biology\, evolutionary biology\, and\, recently\, machine learning and 
 health/disease/clinical practice. More details about his ongoing work are 
 available at https://www.imsc.res.in/~rsidd/research.html\n\nHost Faculty:
  Dr. Chirag Jain\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Talks
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