Leelavati Narlikar
Leelavati Narlikar
Data Science, IISER Pune
Verified email at - Homepage
Cited by
Cited by
Genome-wide analyses of transcription factor GATA3-mediated gene regulation in distinct T cell types
G Wei, BJ Abraham, R Yagi, R Jothi, K Cui, S Sharma, L Narlikar, ...
Immunity 35 (2), 299-311, 2011
Genome-wide discovery of human heart enhancers
L Narlikar, NJ Sakabe, AA Blanski, FE Arimura, JM Westlund, ...
Genome research 20 (3), 381-392, 2010
Identifying regulatory elements in eukaryotic genomes
L Narlikar, I Ovcharenko
Briefings in functional genomics & proteomics 8 (4), 215-230, 2009
A nucleosome-guided map of transcription factor binding sites in yeast
L Narlikar, R Gordân, AJ Hartemink
PLoS computational biology 3 (11), e215, 2007
Informative priors based on transcription factor structural class improve de novo motif discovery
L Narlikar, R Gordân, U Ohler, AJ Hartemink
Bioinformatics 22 (14), e384-e392, 2006
ChIP-Seq data analysis: identification of Protein–DNA binding sites with SISSRs peak-finder
L Narlikar, R Jothi
Next Generation Microarray Bioinformatics: Methods and Protocols, 305-322, 2012
Sequence features of DNA binding sites reveal structural class of associated transcription factor
L Narlikar, AJ Hartemink
Bioinformatics 22 (2), 157-163, 2006
Finding regulatory DNA motifs using alignment-free evolutionary conservation information
R Gordaˆn, L Narlikar, AJ Hartemink
Nucleic Acids Research 38 (6), e90-e90, 2010
Nucleosome Occupancy Information Improves de novo Motif Discovery
L Narlikar, R Gordân, AJ Hartemink
Research in Computational Molecular Biology: 11th Annual International …, 2007
One size does not fit all: On how Markov model order dictates performance of genomic sequence analyses
L Narlikar, N Mehta, S Galande, M Arjunwadkar
Nucleic acids research 41 (3), 1416-1424, 2013
A fast, alignment-free, conservation-based method for transcription factor binding site discovery
R Gordân, L Narlikar, AJ Hartemink
Annual International Conference on Research in Computational Molecular …, 2008
MuMoD: a Bayesian approach to detect multiple modes of protein–DNA binding from genome-wide ChIP data
L Narlikar
Nucleic acids research 41 (1), 21-32, 2013
Multiple novel promoter-architectures revealed by decoding the hidden heterogeneity within the genome
L Narlikar
Nucleic acids research 42 (20), 12388-12403, 2014
CLARE: Cracking the LAnguage of Regulatory Elements
L Taher, L Narlikar, I Ovcharenko
Bioinformatics 28 (4), 581-583, 2012
DIVERSITY in binding, regulation, and evolution revealed from high-throughput ChIP
S Mitra, A Biswas, L Narlikar
PLoS Comput Biol 14 (4), 2018
Identification and Computational Analysis of Gene Regulatory Elements
L Taher, L Narlikar, I Ovcharenko
Cold Spring Harbor Protocols 2015 (1), pdb. top083642, 2015
No Promoter Left Behind (NPLB): learn de novo promoter architectures from genome-wide transcription start sites
S Mitra, L Narlikar
Bioinformatics 32 (5), 779-781, 2016
Orc4 spatiotemporally stabilizes centromeric chromatin
L Sreekumar, K Kumari, K Guin, A Bakshi, N Varshney, BC Thimmappa, ...
Genome Research 31 (4), 607-621, 2021
Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes
N Periyathambi, D Parkhi, Y Ghebremichael-Weldeselassie, V Patel, ...
PloS one 17 (3), e0264648, 2022
THiCweed: fast, sensitive detection of sequence features by clustering big datasets
A Agrawal, SV Sambare, L Narlikar, R Siddharthan
Nucleic Acids Research, 2017
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