No Promoter Left Behind (NPLB): learn <i>de novo</i> promoter architectures from genome-wide transcription start sites

Mitra, Sneha; Narlikar, Leelavati

doi:10.1093/bioinformatics/btv645

Cited by 7 publications

(12 citation statements)

References 10 publications

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“…We were able to replicate past results of clustering on both these datasets. Our algorithm on the fly dataset achieved a speedup of 14.23x over the best existing algorithm [2], which also uses unsupervised learning with binary search (see table below).…”

Section: Resultsmentioning

confidence: 93%

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

Parulekar

Narlikar²

2019

Preprint

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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.

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Section: Resultsmentioning

confidence: 93%

“…Binary Search Optimization . Instead of iterating over all K cluster candidates as done in findKOptimal , we use binary search, similar to previous work [2]. In each iteration we reduce the search space for k op t by half, depending on W k ( H ) values.…”

Section: Procedures Findkoptimalmentioning

confidence: 99%

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

Parulekar

Narlikar²

2019

Preprint

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“…As a pilot study, we consider the simple task of finding the optimal number of PWMs for each of the 158 JASPAR data sets. We compare the FIC-based learning approach of Disentangler with two other tools that can be used to solve this task, namely DIVERSITY ( 22 ) and NPLB ( 46 ); see Supplementary Section S2 for details about all tools used in the case studies. Results ( Supplementary Section S4.1 ) show that for the majority of data sets all three methods predict more than one PWM to be optimal.…”

Section: Resultsmentioning

confidence: 99%

Disentangling transcription factor binding site complexity

Eggeling

2018

Nucleic Acids Research

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The binding motifs of many transcription factors (TFs) comprise a higher degree of complexity than a single position weight matrix model permits. Additional complexity is typically taken into account either as intra-motif dependencies via more sophisticated probabilistic models or as heterogeneities via multiple weight matrices. However, both orthogonal approaches have limitations when learning from in vivo data where binding sites of other factors in close proximity can interfere with motif discovery for the protein of interest. In this work, we demonstrate how intra-motif complexity can, purely by analyzing the statistical properties of a given set of TF-binding sites, be distinguished from complexity arising from an intermix with motifs of co-binding TFs or other artifacts. In addition, we study the related question whether intra-motif complexity is represented more effectively by dependencies, heterogeneities or variants in between. Benchmarks demonstrate the effectiveness of both methods for their respective tasks and applications on motif discovery output from recent tools detect and correct many undesirable artifacts. These results further suggest that the prevalence of intra-motif dependencies may have been overestimated in previous studies on in vivo data and should thus be reassessed.

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“…This, it turns out, constitutes an effective and fast implementation of an ab initio motif finder on large ChIP-seq datasets, in addition to detecting the variations in motif and sequence context alluded to in the previous point. THiCweed can also be used on sequences that have been previously aligned by a ‘feature’ (motif) to discover additional motifs/complexities, by disabling shifts and reverse complements, similar to the program No Promoter Left Behind ( 20 , 21 ), but we do not discuss this use here.…”

Section: Methodsmentioning

confidence: 99%

“…THiCweed can also be used on sequences that have been previously aligned by a ‘feature’ (motif) to discover additional motifs/complexities, by disabling shifts and reverse complements, similar to the program No Promoter Left Behind ( 20 , 21 ), but we do not discuss this use here.…”

Section: Methodsmentioning

confidence: 99%

THiCweed: fast, sensitive detection of sequence features by clustering big datasets

Agrawal

Sambare

Narlikar

et al. 2017

Nucleic Acids Research

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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.

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No Promoter Left Behind (NPLB): learn de novo promoter architectures from genome-wide transcription start sites

Cited by 7 publications

References 10 publications

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

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

Disentangling transcription factor binding site complexity

THiCweed: fast, sensitive detection of sequence features by clustering big datasets

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