Biology subjects

Narlikar, L.

Publications and source records attributed to Narlikar, L..

6 recordsLinked to original sources

bioRxiv2022

Transcription factors organize into functional groups on the linear genome and in 3D chromatin

Transcription factors (TFs) and their binding sites have evolved to interact cooperatively or competitively with each other. Here we examine in detail, across multiple cell lines, such cooperation or competition among TFs both in sequential and spatial proximity (using chromatin conformation capture assays) on one hand, and based on both in vivo binding as well as TF binding motifs on the other. We ascertain significantly co-occurring ("attractive") or avoiding ("repulsive") TF pairs using robust randomized models that retain the essential characteristics of the experimental data. Across human cell lines TFs organize into two groups, with intra-group attraction and inter-group repulsion. This is true for both sequential and spatial proximity, as well as for both in vivo binding and motifs. Attractive TF pairs exhibit significantly more physical interactions suggesting an underlying mechanism. The two TF groups differ significantly in their genomic and network properties, as well in their function--while one group regulates housekeeping function, the other potentially regulates lineage-specific functions, that are disrupted in cancer. We also show that weaker binding sites tend to occur in spatially interacting regions of the genome. Our results suggest a complex pattern of spatial cooperativity of TFs that has evolved along with the genome to support housekeeping and lineage-specific functions.

genomics↗

bioRxiv2020

A universal framework for detecting cis-regulatory diversity in DNA regulatory regions

High-throughput sequencing-based assays measure different biochemical activities pertaining to gene regulation, genome-wide. These activities include protein-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, although having a single type of activity, the regions can still be diverse, governed by different combinations of proteins/motifs. Current approaches do not take into account this issue of combinatorial diversity. We present a new statistical framework O_SCPLOWCISC_SCPLOWDO_SCPLOWIVERSITYC_SCPLOW, which models regions as diverse modules characterized by combinations of motifs, while simultaneously learning the motifs themselves. We show that ChIP-seq data for the CTCF protein in fly contains diverse sequence structures, with most direct CTCF-binding sites situated far from promoters, giving insights into its co-factors and potential role in looping. Human CTCF-bound regions, on the other hand, have a different architecture. Because O_SCPLOWCISC_SCPLOWDO_SCPLOWIVERSITYC_SCPLOW 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. Indeed, enhancer predictions resulting from different assays--GRO-cap, STARR-seq, and those measuring chromatin structure--show distinct modules and combinations of TF binding sites, some specific to the assay. No module occurs universally in all enhancer-assays. Finally, analysis of accessible chromatin suggests that regions open in one cell-state encode information about future states, with certain modules staying open and others closing down later. The code is freely available at https://github.com/NarlikarLab/cisDIVERSITY.

genomics↗

bioRxiv2019

MINDFUL: A Method to Identify Novel and Diverse Signals with Fast, Unsupervised Learning

With rapid advances in experimental methods that map transcription start sites (TSSs) at a high resolution, there is a need to characterize the sequence diversity of TSS neighborhoods. Most current techniques scan for previously discovered elements, such as the TATA box, the INR motif, CpG islands, etc. to categorize promoters into different classes. Reliance on such elements hinders the discovery of novel elements. On the other hand, methods that use standard motif discovery to discover de novo promoter elements are also limited by the fact that a motif is picked up only if it is over-represented in the dataset. An element that appears only in a small set of promoters can thus be missed. We previously developed a clustering-based approach that uses no prior knowledge of elements to solve this problem [1]. That method uses Gibbs sampling to learn the model parameters, but is untenable on large datasets. Here we propose a new, fast method called MINDFUL, that uses a greedy k-means-like approach to cluster promoters aligned by TSSs into diverse classes, while also learning the optimal value of k. It is general enough to be used for any data that has categorical variables, and is not restricted to DNA.

bioinformatics↗

bioRxiv2018

DNA replication initiator proteins facilitate CENPA loading on early replicating compact chromatin

Spatiotemporal regulation in DNA replication maintains kinetochore stability. The epigenetically regulated centromeres (CENs) in the budding yeast Candida albicans have unique DNA sequences, replicate early and are clustered throughout the cell cycle. In this study, the genome-wide occupancy of replication initiation protein Orc4 reveals its abundance at all CENs in C. albicans. Orc4 associates with four different DNA motifs, one of which coincides with tRNA genes. Hi-C combined with genome-wide replication timing analyses identify enriched interactions among early or late replicating Orc4-bound regions. A simulated polymer model of chromosomes reveals that early replicating and strongly enriched Orc4-bound sites localize towards the kinetochores. Orc4 is constitutively localized to CENs, and both Orc4 and Mcm2 stabilize CENPA. CENPA chaperone Scm3 localizes at the kinetochore during anaphase, coinciding with the loading time of CENPA. We propose that this spatiotemporal nuclear localization of Orc4, with Mcm2 and Scm3, recruits CENPA and stabilizes centromeric chromatin.

molecular biology↗

bioRxiv2017

DIVERSITY In Binding, Regulation, And Evolution Revealed From High-Throughput ChIP

A high-throughput chromatin immunoprecipitation (ChIP) experiment is like a black-box: it reports all regions that are associated with the profiled protein based on the initial cross-linking step. These regions can be a highly diverse set of DNA sequences, with some making direct contact with the protein, some binding through intermediaries, and some being a result of long-range interactions involving the protein. We present O_SCPLOWDIVERSITYC_SCPLOW, a method that identifies the distinct components of such a mixture, leaving no data behind, while at the same time, using no prior motif knowledge. Using the example of the REST protein, we show that these different components give insights into the various complexes that may be forming along the chromatin and their regulatory functions.\n\nhttp://diversity.ncl.res.in/ (webserver)\n\nhttps://github.com/NarlikarLab/DIVERSITY (standalone for Mac OSX/Linux)

bioinformatics↗

bioRxiv2017

THiCweed: fast, sensitive motif finding by clustering big data sets

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 towards 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 hours, 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.\n\nTHiCweed performs best with large \"window\" sizes ([≥] 50bp), much longer than typical binding sites (7-15 base pairs). 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 data sets, possibly related to chromatin architecture and looping. THiCweed thus goes beyond traditional motif-finding to give new insights into genomic TF binding complexity.

bioinformatics↗