01249nas a2200145 4500008004100000245009600041210006900137260000800206300001100214490000800225520070800233100002400941700001600965856012200981 2012 eng d00aChIP-Seq data analysis: identification of protein-DNA binding sites with SISSRs peak-finder0 aChIPSeq data analysis identification of proteinDNA binding sites cJAN a305-220 v8023 aProtein-DNA interactions play key roles in determining gene-expression programs during cellular development and differentiation. Chromatin immunoprecipitation (ChIP) is the most widely used assay for probing such interactions. With recent advances in sequencing technology, ChIP-Seq, an approach that combines ChIP and next-generation parallel sequencing is fast becoming the method of choice for mapping protein-DNA interactions on a genome-wide scale. Here, we briefly review the ChIP-Seq approach for mapping protein-DNA interactions and describe the use of the SISSRs peak-finder, a software tool for precise identification of protein-DNA binding sites from sequencing data generated using ChIP-Seq.1 aNarlikar, Leelavati1 aJothi, Raja uhttps://library.ncl.res.in/content/chip-seq-data-analysis-identification-protein-dna-binding-sites-sissrs-peak-finder01237nas a2200169 4500008004100000022001400041245005600055210005500111260007200166300001200238490000700250520066300257100001700920700002400937700002100961856008500982 2012 eng d a1367-480300aClare: cracking the language of regulatory elements0 aClare cracking the language of regulatory elements aGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLANDbOXFORD UNIV PRESScFEB a581-5830 v283 a

CLARE is a computational method designed to reveal sequence encryption of tissue- specific regulatory elements. Starting with a set of regulatory elements known to be active in a particular tissue/process, it learns the sequence code of the input set and builds a predictive model from features specific to those elements. The resulting model can then be applied to user-supplied genomic regions to identify novel candidate regulatory elements. CLARE's model also provides a detailed analysis of transcription factors that most likely bind to the elements, making it an invaluable tool for understanding mechanisms of tissue- specific gene regulation.

1 aTaher, Leila1 aNarlikar, Leelavati1 aOvcharenko, Ivan uhttps://library.ncl.res.in/content/clare-cracking-language-regulatory-elements-002123nas a2200145 4500008004100000022001400041245010600055210006900161260007200230300001000302490000700312520150500319100002401824856012901848 2013 eng d a0305-104800aMuMoD: a bayesian approach to detect multiple modes of protein-DNA binding from genome-wide ChIP data0 aMuMoD a bayesian approach to detect multiple modes of proteinDNA aGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLANDbOXFORD UNIV PRESScJAN a21-320 v413 a

High-throughput chromatin immunoprecipitation has become the method of choice for identifying genomic regions bound by a protein. Such regions are then investigated for overrepresented sequence motifs, the assumption being that they must correspond to the binding specificity of the profiled protein. However this approach often fails: many bound regions do not contain the `expected' motif. This is because binding DNA directly at its recognition site is not the only way the protein can cause the region to immunoprecipitate. Its binding specificity can change through association with different co-factors, it can bind DNA indirectly, through intermediaries, or even enforce its function through long-range chromosomal interactions. Conventional motif discovery methods, though largely capable of identifying overrepresented motifs from bound regions, lack the ability to characterize such diverse modes of protein-DNA binding and binding specificities. We present a novel Bayesian method that identifies distinct protein-DNA binding mechanisms without relying on any motif database. The method successfully identifies co-factors of proteins that do not bind DNA directly, such as mediator and p300. It also predicts literature-supported enhancer-promoter interactions. Even for well-studied direct-binding proteins, this method provides compelling evidence for previously uncharacterized dependencies within positions of binding sites, long-range chromosomal interactions and dimerization.

1 aNarlikar, Leelavati uhttps://library.ncl.res.in/content/mumod-bayesian-approach-detect-multiple-modes-protein-dna-binding-genome-wide-chip-data-002189nas a2200181 4500008004100000022001400041245010700055210006900162260007200231300001400303490000700317520147000324100002401794700001701818700002101835700002301856856012801879 2013 eng d a0305-104800aOne size does not fit all: on how markov model order dictates performance of genomic sequence analyses0 aOne size does not fit all on how markov model order dictates per aGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLANDbOXFORD UNIV PRESScFEB a1416-14240 v413 a

The structural simplicity and ability to capture serial correlations make Markov models a popular modeling choice in several genomic analyses, such as identification of motifs, genes and regulatory elements. A critical, yet relatively unexplored, issue is the determination of the order of the Markov model. Most biological applications use a predetermined order for all data sets indiscriminately. Here, we show the vast variation in the performance of such applications with the order. To identify the `optimal' order, we investigated two model selection criteria: Akaike information criterion and Bayesian information criterion (BIC). The BIC optimal order delivers the best performance for mammalian phylogeny reconstruction and motif discovery. Importantly, this order is different from orders typically used by many tools, suggesting that a simple additional step determining this order can significantly improve results. Further, we describe a novel classification approach based on BIC optimal Markov models to predict functionality of tissue-specific promoters. Our classifier discriminates between promoters active across 12 different tissues with remarkable accuracy, yielding 3 times the precision expected by chance. Application to the metagenomics problem of identifying the taxum from a short DNA fragment yields accuracies at least as high as the more complex mainstream methodologies, while retaining conceptual and computational simplicity.

1 aNarlikar, Leelavati1 aMehta, Nidhi1 aGalande, Sanjeev1 aArjunwadkar, Mihir uhttps://library.ncl.res.in/content/one-size-does-not-fit-all-how-markov-model-order-dictates-performance-genomic-sequence-002231nas a2200145 4500008004100000022001400041245010600055210006900161260007200230300001600302490000700318520160400325100002401929856013201953 2014 eng d a0305-104800aMultiple novel promoter-architectures revealed by decoding the hidden heterogeneity within the genome0 aMultiple novel promoterarchitectures revealed by decoding the hi aGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLANDbOXFORD UNIV PRESScNOV a12388-124030 v423 a

An important question in biology is how different promoter-architectures contribute to the diversity in regulation of transcription initiation. A step forward has been the production of genome-wide maps of transcription start sites (TSSs) using high-throughput sequencing. However, the subsequent step of characterizing promoters and their functions is still largely done on the basis of previously established promoter-elements like the TATA-box in eukaryotes or the -10 box in bacteria. Unfortunately, a majority of promoters and their activities cannot be explained by these few elements. Traditional motif discovery methods that identify novel elements also fail here, because TSS neighborhoods are often highly heterogeneous containing no overrepresented motif. We present a new, organism-independent method that explicitly models this heterogeneity while unraveling different promoter-architectures. For example, in five bacteria, we detect the presence of a pyrimidine preceding the TSS under very specific circumstances. In tuberculosis, we show for the first time that the spacing between the bacterial 10-motif and TSS is utilized by the pathogen for dynamic gene-regulation. In eukaryotes, we identify several new elements that are important for development. Identified promoter-architectures show differential patterns of evolution, chromatin structure and TSS spread, suggesting distinct regulatory functions. This work highlights the importance of characterizing heterogeneity within high-throughput genomic data rather than analyzing average patterns of nucleotide composition.

1 aNarlikar, Leelavati uhttps://library.ncl.res.in/content/multiple-novel-promoter-architectures-revealed-decoding-hidden-heterogeneity-within-genome-001604nas a2200145 4500008004100000245007400041210006900115260000800184490000600192520109200198100001701290700002401307700002101331856010601352 2015 eng d00aIdentification and computational analysis of gene regulatory elements0 aIdentification and computational analysis of gene regulatory ele cJAN0 v13 a

Over the last two decades, advances in experimental and computational technologies have greatly facilitated genomic research. Next-generation sequencing technologies have made de novo sequencing of large genomes affordable, and powerful computational approaches have enabled accurate annotations of genomic DNA sequences. Charting functional regions in genomes must account for not only the coding sequences, but also noncoding RNAs, repetitive elements, chromatin states, epigenetic modifications, and gene regulatory elements. A mix of comparative genomics, high-throughput biological experiments, and machine learning approaches has played a major role in this truly global effort. Here we describe some of these approaches and provide an account of our current understanding of the complex landscape of the human genome. We also present overviews of different publicly available, large-scale experimental data sets and computational tools, which we hope will prove beneficial for researchers working with large and complex genomes. © 2015 Cold Spring Harbor Laboratory Press.

1 aTaher, Leila1 aNarlikar, Leelavati1 aOvcharenko, Ivan uhttps://library.ncl.res.in/content/identification-and-computational-analysis-gene-regulatory-elements01215nas a2200157 4500008004100000022001400041245011600055210006900171260007200240300001200312490000700324520056600331100001700897700002400914856011900938 2016 eng d a1367-480300aNo promoter left behind (NPLB): learn de novo promoter architectures from genome-wide transcription start sites0 aNo promoter left behind NPLB learn de novo promoter architecture aGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLANDbOXFORD UNIV PRESScMAR a779-7810 v323 a

Promoters have diverse regulatory architectures and thus activate genes differently. For example, some have a TATA-box, many others do not. Even the ones with it can differ in its position relative to the transcription start site (TSS). No Promoter Left Behind (NPLB) is an efficient, organism-independent method for characterizing such diverse architectures directly from experimentally identified genome-wide TSSs, without relying on known promoter elements. As a test case, we show its application in identifying novel architectures in the fly genome.

1 aMitra, Sneha1 aNarlikar, Leelavati uhttps://library.ncl.res.in/content/no-promoter-left-behind-nplb-learn-de-novo-promoter-architectures-genome-wide-002620nas a2200157 4500008004100000245008700041210006900128260000800197300002900205490000700234520204800241100001702289700002002306700002402326856011202350 2018 eng d00aDiversity in binding, regulation, and evolution revealed from high-throughput ChIP0 aDiversity in binding regulation and evolution revealed from high cAPR aArticle Number: e10060900 v143 a

Genome-wide in vivo protein-DNA interactions are routinely mapped using high-throughput chromatin immunoprecipitation (ChIP). ChIP-reported regions are typically investigated for enriched sequence-motifs, which are likely to model the DNA-binding specificity of the profiled protein and/or of co-occurring proteins. However, simple enrichment analyses can miss insights into the binding-activity of the protein. Note that ChIP reports regions making direct contact with the protein as well as those binding through intermediaries. For example, consider a ChIP experiment targeting protein X, which binds DNA at its cognate sites, but simultaneously interacts with four other proteins. Each of these proteins also binds to its own specific cognate sites along distant parts of the genome, a scenario consistent with the current view of transcriptional hubs and chromatin loops. Since ChIP will pull down all X-associated regions, the final reported data will be a union of five distinct sets of regions, each containing binding sites of one of the five proteins, respectively. Characterizing all five different motifs and the corresponding sets is important to interpret the ChIP experiment and ultimately, the role of X in regulation. We present DIVERSITY which attempts exactly this: it partitions the data so that each partition can be characterized with its own de novo motif. DIVERSITY uses a Bayesian approach to identify the optimal number of motifs and the associated partitions, which together explain the entire dataset. This is in contrast to standard motif finders, which report motifs individually enriched in the data, but do not necessarily explain all reported regions. We show that the different motifs and associated regions identified by DIVERSITY give insights into the various complexes that may be forming along the chromatin, something that has so far not been attempted from ChIP data. Webserver at nci.res.i, if; standalone (Mac OS X/Linux) from from https://github.com/NarlikarLab/DIVERSITY/releases/tag/v1.0.0.

1 aMitra, Sneha1 aBiswas, Anushua1 aNarlikar, Leelavati uhttps://library.ncl.res.in/content/diversity-binding-regulation-and-evolution-revealed-high-throughput-chip02022nas a2200181 4500008004100000022001400041245008800055210006900143260000800212300000800220490000700228520140000235100001901635700002401654700002401678700002301702856011501725 2018 eng d a0305-104800aTHiCweed: fast, sensitive detection of sequence features by clustering big datasets0 aTHiCweed fast sensitive detection of sequence features by cluste cMAR ae290 v463 a

We present THiCweed, a new approach to analyzing transcription factor binding data from high-throughput chromatin immunoprecipitation-sequencing (ChIP-seq) experiments. THiCweed clusters bound regions based on sequence similarity using a divisive hierarchical clustering approach based on sequence similarity within sliding windows, while exploring both strands. ThiCweed is specially geared toward data containing mixtures of motifs, which present a challenge to traditional motif-finders. Our implementation is significantly faster than standard motif-finding programs, able to process 30 000 peaks in 1-2 h, on a single CPU core of a desktop computer. On synthetic data containing mixtures of motifs it is as accurate or more accurate than all other tested programs. THiCweed performs best with large `window' sizes (>50 bp), much longer than typical binding sites (7-15 bp). On real data it successfully recovers literature motifs, but also uncovers complex sequence characteristics in flanking DNA, variant motifs and secondary motifs even when they occur in <5% of the input, all of which appear biologically relevant. We also find recurring sequence patterns across diverse ChIP-seq datasets, possibly related to chromatin architecture and looping. THiCweed thus goes beyond traditional motif finding to give new insights into genomic transcription factor-binding complexity.

1 aAgrawal, Ankit1 aSambare, Snehal, V.1 aNarlikar, Leelavati1 aSiddharthan, Rahul uhttps://library.ncl.res.in/content/thicweed-fast-sensitive-detection-sequence-features-clustering-big-datasets02543nas a2200253 4500008004100000022001400041245005900055210005900114260000800173300001200181490000700193520177800200100002301978700001802001700002102019700001702040700001902057700002602076700002402102700002602126700002302152700002002175856009402195 2021 eng d a1088-905100aOrc4 spatiotemporally stabilizes centromeric chromatin0 aOrc4 spatiotemporally stabilizes centromeric chromatin cAPR a607-6210 v313 a

The establishment of centromeric chromatin and its propagation by the centromere-specific histone CENPA is mediated by epigenetic mechanisms in most eukaryotes. DNA replication origins, origin binding proteins, and replication timing of centromere DNA are important determinants of centromere function. The epigenetically regulated regional centromeres in the budding yeast Candida albicans have unique DNA sequences that replicate earliest in every chromosome and are clustered throughout the cell cycle. In this study, the genome-wide occupancy of the replication initiation protein Orc4 reveals its abundance at all centromeres in C. albicans. Orc4 is associated with four different DNA sequence motifs, one of which coincides with tRNA genes (tDNA) that replicate early and cluster together in space. Hi-C combined with genome-wide replication timing analyses identify that early replicating Orc4-bound regions interact with themselves stronger than with late replicating Orc4-bound regions. We simulate a polymer model of chromosomes of C. albicans and propose that the early replicating and highly enriched Orc4-bound sites preferentially localize around the clustered kinetochores. We also observe that Orc4 is constitutively localized to centromeres, and both Orc4 and the helicase Mcm2 are essential for cell viability and CENPA stability in C. albicans. Finally, we show that new molecules of CENPA are recruited to centromeres during late anaphase/telophase, which coincides with the stage at which the CENPA-specific chaperone Scm3 localizes to the kinetochore. We propose that the spatiotemporal localization of Orc4 within the nucleus, in collaboration with Mcm2 and Scm3, maintains centromeric chromatin stability and CENPA recruitment in C. albicans.

1 aSreekumar, Lakshmi1 aKumari, Kiran1 aGuin, Krishnendu1 aBakshi, Asif1 aVarshney, Neha1 aThimmappa, Bhagya, C.1 aNarlikar, Leelavati1 aPadinhateeri, Ranjith1 aSiddharthan, Rahul1 aSanyal, Kaustuv uhttps://library.ncl.res.in/content/orc4-spatiotemporally-stabilizes-centromeric-chromatin02302nas a2200157 4500008004100000022001400041245008500055210006900140260000800209300001400217490000700231520174700238100002001985700002402005856011502029 2021 eng d a1367-480300aResolving diverse protein-DNA footprints from exonuclease-based ChIP experiments0 aResolving diverse proteinDNA footprints from exonucleasebased Ch cJUL aI367-I3750 v373 aMotivation: High-throughput chromatin immunoprecipitation (ChIP) sequencing-based assays capture genomic regions associated with the profiled transcription factor (TF). ChIP-exo is a modified protocol, which uses lambda exonuclease to digest DNA close to the TF-DNA complex, in order to improve on the positional resolution of the TF-DNA contact. Because the digestion occurs in the 50-30 orientation, the protocol produces directional footprints close to the complex, on both sides of the double stranded DNA. Like all ChIP-based methods, ChIP-exo reports a mixture of different regions associated with the TF: those bound directly to the TF as well as via intermediaries. However, the distribution of footprints are likely to be indicative of the complex forming at the DNA. Results: We present ExoDiversity, which uses a model-based framework to learn a joint distribution over footprints and motifs, thus resolving the mixture of ChIP-exo footprints into diverse binding modes. It uses no prior motif or TF information and automatically learns the number of different modes from the data. We show its application on a wide range of TFs and organisms/cell-types. Because its goal is to explain the complete set of reported regions, it is able to identify co-factor TF motifs that appear in a small fraction of the dataset. Further, ExoDiversity discovers small nucleotide variations within and outside canonical motifs, which co-occur with variations in footprints, suggesting that the TF-DNA structural configuration at those regions is likely to be different. Finally, we show that detected modes have specific DNA shape features and conservation signals, giving insights into the structure and function of the putative TF-DNA complexes.1 aBiswas, Anushua1 aNarlikar, Leelavati uhttps://library.ncl.res.in/content/resolving-diverse-protein-dna-footprints-exonuclease-based-chip-experiments02184nas a2200157 4500008004100000022001400041245008000055210006900135260000800204300001400212490000700226520164300233100002001876700002401896856010601920 2021 eng d a1088-905100aA universal framework for detecting cis-regulatory diversity in DNA regions0 auniversal framework for detecting cisregulatory diversity in DNA cSEP a1646-16620 v313 aHigh-throughput sequencing-based assays measure different biochemical activities pertaining to gene regulation, genomewide. These activities include transcription factor (TF)-DNA binding, enhancer activity, open chromatin, and more. A major goal is to understand underlying sequence components, or motifs, that can explain the measured activity. It is usually not one motif but a combination of motifs bound by cooperatively acting proteins that confers activity to such regions. Furthermore, regions can be diverse, governed by different combinations of TFs/ motifs. Current approaches do not take into account this issue of combinatorial diversity. We present a new statistical framework, cisDIVERSITY, which models regions as diverse modules characterized by combinations of motifs while simultaneously learning the motifs themselves. Because cisDIVERSITY does not rely on knowledge of motifs, modules, cell type, or organism, it is general enough to be applied to regions reported by most high-throughput assays. For example, in enhancer predictions resulting from different assays-GRO-cap, STARR-seq, and those measuring chromatin structure-cisDIVERSITY discovers distinct modules and combinations of TF binding sites, some specific to the assay. From protein-DNA binding data, cisDIVERSITY identifies potential cofactors of the profiled TF, whereas from ATAC-seq data, it identifies tissue-specific regulatory modules. Finally, analysis of single-cell ATAC-seq data suggests that regions open in one cell-state encode information about future states, with certain modules staying open and others closing down in the next time point.1 aBiswas, Anushua1 aNarlikar, Leelavati uhttps://library.ncl.res.in/content/universal-framework-detecting-cis-regulatory-diversity-dna-regions02581nas a2200277 4500008004100000022001400041245008100055210006900136260000800205300001100213490000800224520174600232653000901978653001401987653001502001653001502016653002302031653001202054653001102066100002202077700002602099700002102125700002402146700002002170856011302190 2026 eng d a0006-291X00aRecognition of diverse GATA motifs necessitates multimodal GATA3-DNA binding0 aRecognition of diverse GATA motifs necessitates multimodal GATA3 cJUN a1537180 v8173 a

GATA3 is a pioneer transcription factor that plays a central role in the formation, proliferation, and sustenance of various immune cell types, notably serving as the ``master regulator'' of T helper 2 (Th2) cell differentiation. It regulates gene expression by recognizing the canonical DNA consensus motif (A/T)GATA(A/G) through its highly conserved zinc finger domains. However, beyond this classical binding motif, emerging evidence indicates that GATA3 also recognizes a varied array of noncanonical and palindromic sequence motifs, but the underlying molecular mechanisms are unclear. In this review, we discuss an emerging aspect of GATA3 function that links these diverse DNA sequence motifs to varying structural binding modes and finally to differential chromatin outcomes. We discuss how the alternative sequence motifs may engage the two zinc finger domains differently, leading towards a multitude of binding modes underlying this diverse motif recognition. The spacer length between the GATA motifs has been shown to modulate DNA binding and the geometric constraints imposed may help determine the binding mode. We reanalyzed previous data and show that these diverse motifs, as well as the spacers, modulate nucleosomal outcomes, highlighting the importance of these motifs in GATA3 function. Several disease-associated mutations, such as those implicated in autoimmune diseases and cancer, have been reported for GATA3, and we discuss how these mutations alter the binding of the zinc finger domains. This link between sequence recognition and DNA binding modes represents an underexplored aspect of GATA3 function that is necessary for understanding its multifaceted regulatory role in human health and disease.

10aGATA10aMutations10aNucleosome10aPalindrome10aReverse palindrome10aSpacers10aTandem1 aGharui, Sowmomita1 aPuntambekar, Shraddha1 aSarkar, Ram, Rup1 aNarlikar, Leelavati1 aSengupta, Durba uhttps://library.ncl.res.in/content/recognition-diverse-gata-motifs-necessitates-multimodal-gata3-dna-binding