Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA–protein binding sites

Li, Yang Eric; Xiao, Mu; Shi, Binbin; Yang, Yucheng; Wang, Dong; Wang, Fei; Marcia, Marco; Lu, Zhi John

doi:10.1186/s13059-017-1298-8

Cited by 44 publications

(33 citation statements)

References 65 publications

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“…We used Nonnegative Matrix Factorization (NMF) 90 to group cCREs into cis regulatory modules based on their relative accessibility across major clusters. We adapted NMF (Python package: sklearn 91 ) to decompose the cell-by-cCRE matrix V (N×M, N rows: cCRE, M columns: cell clusters) into a coefficient matrix H (R×M, R rows: number of modules) and a basis matrix W (N×R), with a given rank R: …”

Section: Methodsmentioning

confidence: 99%

An Atlas of Gene Regulatory Elements in Adult Mouse Cerebrum

Preissl

Hou

et al. 2020

Preprint

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25The mammalian cerebrum performs high level sensory, motor control and cognitive 26 functions through highly specialized cortical networks and subcortical nuclei. Recent 27 surveys of mouse and human brains with single cell transcriptomics 1-3 and high-28 throughput imaging technologies 4,5 have uncovered hundreds of neuronal cell types and 29 a variety of non-neuronal cell types distributed in different brain regions, but the cell-type-30 specific transcriptional regulatory programs responsible for the unique identity and 31 function of each brain cell type have yet to be elucidated. Here, we probe the accessible 32 chromatin in >800,000 individual nuclei from 45 regions spanning the adult mouse 33 isocortex, olfactory bulb, hippocampus and cerebral nuclei, and use the resulting data to 34 define 491,818 candidate cis regulatory DNA elements in 160 distinct sub-types. We link 35 a significant fraction of them to putative target genes expressed in diverse cerebral cell 36 types and uncover transcriptional regulators involved in a broad spectrum of molecular 37 and cellular pathways in different neuronal and glial cell populations. Our results provide 38 a foundation for comprehensive analysis of gene regulatory programs of the mammalian 39 brain and assist in the interpretation of non-coding risk variants associated with various 40 neurological disease and traits in humans. To facilitate the dissemination of information, 41 we have set up a web portal (http://catlas.org/mousebrain).42 45 functions such as sensory processing, motor control, emotion, and cognition 6 . It is divided 46 into two hemispheres, each consisting of the cerebral cortex and various cerebral nuclei. 47The cerebral cortex is further divided into isocortex and allocortex. Isocortex, 48 characterized by six cortical layers, is a phylogenetically more recent structure that has 49 further expanded greatly in primates. It is responsible for sensory motor integration, 50 decision making, volitional motor command and reasoning. The allocortex, by contrast, is 51 phylogenetically the older structure that features three or four cortical layers. It includes 52 the olfactory bulb responsible for processing the sense of smell and the hippocampus 53 involved in learning, memory and spatial navigation. 54 55 The cerebral cortex and basal ganglia are made up of a vast number of neurons and glial 56 cells. The neurons can be classified into different types of excitatory projection neurons 57 and inhibitory interneurons, defined by the neural transmitters they produce and their 58 connective patterns with other neurons 7-9 . Understanding how the identity and function of 59 each brain cell type is established during development and modified by experience is one 60 of the fundamental challenges in brain research. Recent single cell RNA-seq and high 61 throughput imaging experiments have produced detailed cell atlases for both mouse and 62human brains 3-5,10-15 , leading to a comprehensive view of gene expression patterns in 63 different brain regions, c...

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Section: Methodsmentioning

confidence: 99%

An Atlas of Gene Regulatory Elements in Adult Mouse Cerebrum

Preissl

Hou

et al. 2020

Preprint

Self Cite

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Section: Integrative Analysis Of Clip Data Across Rbpsmentioning

confidence: 99%

“…Non-negative matrix factorisation can then be used to group together RBPs that bind to the same sites to explore co-operativity (83,89) . This does enable more reliable comparison across experiments, but it is best to avoid comparing RBPs for which different peak calling tools were used, or different types of CLIP methods, since this could result in differences that are of technical nature (89) . Ideally, if a comparison is being undertaken using publically available data, the approach taken by POSTAR should be bolstered by first assessing whether the quality of the experiment is sufficient even to proceed with a comparison, and second by using the same peak calling procedure for all the RBPs that are part of the same comparison.…”

Section: Integrative Analysis Of Clip Data Across Rbpsmentioning

confidence: 99%

Data Science Issues in Understanding Protein-RNA Interactions

Haberman

Praznik

et al. 2017

Preprint

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An interplay of experimental and computational methods is required to achieve a comprehensive understanding of protein-RNA interactions. Crosslinking and immunoprecipitation (CLIP) identifies endogenous interactions by sequencing RNA fragments that co-purify with a selected RBP under stringent conditions. Here we focus on approaches for the analysis of resulting data and appraise the methods for peak calling, visualisation, analysis and computational modelling of protein-RNA binding sites. We advocate a combined assessment of cDNA complexity and specificity for data quality control. Moreover, we demonstrate the value of analysing sequence motif enrichment in peaks assigned from CLIP data, and of visualising RNA maps, which examine the positional distribution of peaks around regulated landmarks in transcripts. We use these to assess how variations in CLIP data quality, and in different peak calling methods, affect the insights into regulatory mechanisms. We conclude by discussing future opportunities for the computational analysis of protein-RNA interaction experiments.

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“…One of the most widely used techniques involves immunopurification of specific RNA-binding proteins from cellular extracts followed by a high-throughput analysis of the co-purified RNA species [14]. The coupling of this technique to powerful bioinformatic analysis has led researchers to understand the binding specificity of cis-acting elements [15]. The advent of new technology such as next generation sequencing (NGS) and chemical cross-linking procedures has allowed for finescale mapping of cis-binding motifs as well as for the refinement of RNA-binding proteinbinding sites.…”

Section: Introductionmentioning

confidence: 99%

Mammalian Cis-Acting RNA Sequence Elements

Louis¹,

Sagarsky²

2018

Gene Expression and Regulation in Mammalian Cells - Transcription From General Aspects

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Cis-acting regulatory sequence elements are sequences contained in the 3 0 and 5 0 untranslated region, introns, or coding regions of precursor RNAs and mature mRNAs that are selectively recognized by a complementary set of one or more trans-acting factors to regulate posttranscriptional gene expression. This chapter focuses on mammalian cis-acting regulatory elements that had been recently discovered in different regions: pre-processed and mature. The chapter begins with an overview of two large networks of mRNAs that contain conserved AU-rich elements (AREs) or GU-rich elements (GREs), and their role in mammalian cell physiology. Other, less conserved, cisacting elements and their functional role in different steps of RNA maturation and metabolism will be discussed. The molecular characteristics of pathological cis-acting sequences that rose from gene mutations or transcriptional aberrations are briefly outlined, with the proposed approach to restore normal gene expression. Concise models of the function of posttranscriptional regulatory networks within different cellular compartments conclude this chapter.

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Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA–protein binding sites

Cited by 44 publications

References 65 publications

An Atlas of Gene Regulatory Elements in Adult Mouse Cerebrum

An Atlas of Gene Regulatory Elements in Adult Mouse Cerebrum

Data Science Issues in Understanding Protein-RNA Interactions

Mammalian Cis-Acting RNA Sequence Elements

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