scholarly journals The “Genomic Code”: DNA Pervasively Moulds Chromatin Structures Leaving no Room for “Junk”

Life ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 342
Author(s):  
Giorgio Bernardi
Keyword(s):  
Human Genome ◽  
Dna Sequences ◽  
Interphase Nucleus ◽  
Cpg Islands ◽  
Open Chromatin ◽  
Short Sequence ◽  
Dna Structures ◽  
Chromatin Interactions ◽  
Dna Size

The chromatin of the human genome was analyzed at three DNA size levels. At the first, compartment level, two “gene spaces” were found many years ago: A GC-rich, gene-rich “genome core” and a GC-poor, gene-poor “genome desert”, the former corresponding to open chromatin centrally located in the interphase nucleus, the latter to closed chromatin located peripherally. This bimodality was later confirmed and extended by the discoveries (1) of LADs, the Lamina-Associated Domains, and InterLADs; (2) of two “spatial compartments”, A and B, identified on the basis of chromatin interactions; and (3) of “forests and prairies” characterized by high and low CpG islands densities. Chromatin compartments were shown to be associated with the compositionally different, flat and single- or multi-peak DNA structures of the two, GC-poor and GC-rich, “super-families” of isochores. At the second, sub-compartment, level, chromatin corresponds to flat isochores and to isochore loops (due to compositional DNA gradients) that are susceptible to extrusion. Finally, at the short-sequence level, two sets of sequences, GC-poor and GC-rich, define two different nucleosome spacings, a short one and a long one. In conclusion, chromatin structures are moulded according to a “genomic code” by DNA sequences that pervade the genome and leave no room for “junk”.

scholarly journals Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fan Cao ◽  
Yu Zhang ◽  
Yichao Cai ◽  
Sambhavi Animesh ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Rna Polymerase Ii ◽  
Dna Sequences ◽  
Lymphocytic Leukemia ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Interaction Prediction ◽  
Chromatin Interactions ◽  
Genome Wide

AbstractChromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

scholarly journals Predicting chromatin interactions between open chromatin regions from DNA sequences

2019 ◽  
Author(s):  
Fan Cao ◽  
Ying Zhang ◽  
Yan Ping Loh ◽  
Yichao Cai ◽  
Melissa J. Fullwood
Keyword(s):  
Rna Polymerase Ii ◽  
Dna Sequences ◽  
State Of The Art ◽  
Lymphocytic Leukemia ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Rna Seq ◽  
Chromatin Interactions ◽  
Genome Wide

AbstractChromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is very limited. Various computational methods have been developed to predict chromatin interactions. Most of these methods rely on large collections of ChIP-Seq/RNA-Seq/DNase-Seq datasets and predict only enhancer-promoter interactions. Some of the ‘state-of-the-art’ methods have poor experimental designs, leading to over-exaggerated performances and misleading conclusions. Here we developed a computational method, Chromatin Interaction Neural Network (CHINN), to predict chromatin interactions between open chromatin regions by using only DNA sequences of the interacting open chromatin regions. CHINN is able to predict CTCF- and RNA polymerase II-associated chromatin interactions between open chromatin regions. CHINN also shows good across-sample performances and captures various sequence features that are predictive of chromatin interactions. We applied CHINN to 84 chronic lymphocytic leukemia (CLL) samples and detected systematic differences in the chromatin interactome between IGVH-mutated and IGVH-unmutated CLL samples.

scholarly journals Chromatin Interaction Neural Network (ChINN): A machine learning-based method for predicting chromatin interactions from DNA sequences

2020 ◽  
Author(s):  
Fan Cao ◽  
Yu Zhang ◽  
Yichao Cai ◽  
Sambhavi Animesh ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Rna Polymerase Ii ◽  
Dna Sequences ◽  
Lymphocytic Leukemia ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Clinical Patient ◽  
Chromatin Interactions ◽  
Genome Wide

AbstractChromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. Various computational methods have been developed to predict chromatin interactions. Most of these methods rely on large collections of ChIP-Seq/RNA-Seq/DNase-Seq datasets and predict only enhancer-promoter interactions. Some of the ‘state-of-the-art’ methods have poor experimental designs, leading to over-exaggerated performances and misleading conclusions. Here we developed a computational method, Chromatin Interaction Neural Network (ChINN), to predict chromatin interactions between open chromatin regions by using only DNA sequences of the interacting open chromatin regions. ChINN is able to predict CTCF-, RNA polymerase II- and HiC-associated chromatin interactions between open chromatin regions. ChINN also shows good across-sample performances and captures various sequence features that are predictive of chromatin interactions. To apply our results to clinical patient data, we applied CHINN to predict chromatin interactions in 6 chronic lymphocytic leukemia (CLL) patient samples and a cohort of open chromatin data from 84 CLL samples that was previously published. Our results demonstrated extensive heterogeneity in chromatin interactions in patient samples, and one of the sources of this heterogeneity were the different subtypes of CLL.

scholarly journals CpG islands’ clustering uncovers early development genes in the human genome

2018 ◽  
Vol 15 (2) ◽  
pp. 473-485
Author(s):  
Vladimir Babenko ◽  
Anton Bogomolov ◽  
Roman Babenko ◽  
Elvira Galieva ◽  
Yuriy Orlov
Keyword(s):  
Human Genome ◽  
Early Development ◽  
Cpg Islands ◽  
Replication Timing ◽  
Gene Content ◽  
Embryonic Stage ◽  
Open Chromatin ◽  
Nonhomologous Recombination ◽  
Early Embryonic Stage ◽  
Encoding Genes

We address the problem of the annotation of CpG islands (CGIs) clusters in the human genome. Upon analyzing gene content within CGIs clusters, piRNA, tRNA, and miRNA-encoding genes were found as well as CpG-rich homeobox genes reported previously. Chromosome-wide CGI density is positively correlated with replication timing, confirming that CGIs may serve as open chromatin markers. Early embryonic stage expressed KRAB-ZNF genes abundant at chromosome 19 were found to be interlinked with CGI clusters. We detected that a number of long CGIs and CGI clusters are, in fact, tandem copies with multiple annotated macrosatellites and paralogous genes. This finding implies that tandem expansion of CGIs may serve as a substrate for nonhomologous recombination events.

DNA methylation in satellite repeats disorders

2019 ◽  
Vol 63 (6) ◽  
pp. 757-771 ◽  
Author(s):  
Claire Francastel ◽  
Frédérique Magdinier
Keyword(s):  
Dna Methylation ◽  
Human Genome ◽  
Repetitive Dna ◽  
Dna Sequences ◽  
Satellite Repeats ◽  
Tremendous Progress ◽  
Genes Encoding ◽  
Dna Elements ◽  
Near Future

Abstract Despite the tremendous progress made in recent years in assembling the human genome, tandemly repeated DNA elements remain poorly characterized. These sequences account for the vast majority of methylated sites in the human genome and their methylated state is necessary for this repetitive DNA to function properly and to maintain genome integrity. Furthermore, recent advances highlight the emerging role of these sequences in regulating the functions of the human genome and its variability during evolution, among individuals, or in disease susceptibility. In addition, a number of inherited rare diseases are directly linked to the alteration of some of these repetitive DNA sequences, either through changes in the organization or size of the tandem repeat arrays or through mutations in genes encoding chromatin modifiers involved in the epigenetic regulation of these elements. Although largely overlooked so far in the functional annotation of the human genome, satellite elements play key roles in its architectural and topological organization. This includes functions as boundary elements delimitating functional domains or assembly of repressive nuclear compartments, with local or distal impact on gene expression. Thus, the consideration of satellite repeats organization and their associated epigenetic landmarks, including DNA methylation (DNAme), will become unavoidable in the near future to fully decipher human phenotypes and associated diseases.

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

Non-B DNA structures spatially and sequence-specifically associated with individual centromeres in the human interphase nucleus

2000 ◽  
pp. 57-69 ◽  
Author(s):  
Mizuki Ohno ◽  
Toyoaki Tenzen ◽  
Yoshihisa Watanabe ◽  
Tetsushi Yamagata ◽  
Shigehiko Kanaya ◽  
...  
Keyword(s):  
Interphase Nucleus ◽  
Dna Structures

scholarly journals ABySS 2.0: Resource-Efficient Assembly of Large Genomes using a Bloom Filter

2016 ◽  
Author(s):  
Shaun D Jackman ◽  
Benjamin P Vandervalk ◽  
Hamid Mohamadi ◽  
Justin Chu ◽  
Sarah Yeo ◽  
...  
Keyword(s):  
Human Genome ◽  
Dna Sequences ◽  
Message Passing ◽  
Large Scale ◽  
De Novo ◽  
Bloom Filter ◽  
Genomic Variation ◽  
De Bruijn Graph ◽  
Single Individual ◽  
Probabilistic Data Structure

AbstractThe assembly of DNA sequences de novo is fundamental to genomics research. It is the first of many steps towards elucidating and characterizing whole genomes. Downstream applications, including analysis of genomic variation between species, between or within individuals critically depends on robustly assembled sequences. In the span of a single decade, the sequence throughput of leading DNA sequencing instruments has increased drastically, and coupled with established and planned large-scale, personalized medicine initiatives to sequence genomes in the thousands and even millions, the development of efficient, scalable and accurate bioinformatics tools for producing high-quality reference draft genomes is timely.With ABySS 1.0, we originally showed that assembling the human genome using short 50 bp sequencing reads was possible by aggregating the half terabyte of compute memory needed over several computers using a standardized message-passing system (MPI). We present here its re-design, which departs from MPI and instead implements algorithms that employ a Bloom filter, a probabilistic data structure, to represent a de Bruijn graph and reduce memory requirements.We present assembly benchmarks of human Genome in a Bottle 250 bp Illumina paired-end and 6 kbp mate-pair libraries from a single individual, yielding a NG50 (NGA50) scaffold contiguity of 3.5 (3.0) Mbp using less than 35 GB of RAM, a modest memory requirement by today’s standard that is often available on a single computer. We also investigate the use of BioNano Genomics and 10x Genomics’ Chromium data to further improve the scaffold contiguity of this assembly to 42 (15) Mbp.

scholarly journals Structures and stability of simple DNA repeats from bacteria

2020 ◽  
Vol 477 (2) ◽  
pp. 325-339 ◽  
Author(s):  
Vaclav Brazda ◽  
Miroslav Fojta ◽  
Richard P. Bowater
Keyword(s):  
Repetitive Dna ◽  
Dna Sequences ◽  
Genetic Instability ◽  
Repetitive Sequences ◽  
Biological Significance ◽  
Human Diseases ◽  
Repetitive Dna Sequences ◽  
Dna Repeats ◽  
Diverse Range ◽  
Dna Structures

DNA is a fundamentally important molecule for all cellular organisms due to its biological role as the store of hereditary, genetic information. On the one hand, genomic DNA is very stable, both in chemical and biological contexts, and this assists its genetic functions. On the other hand, it is also a dynamic molecule, and constant changes in its structure and sequence drive many biological processes, including adaptation and evolution of organisms. DNA genomes contain significant amounts of repetitive sequences, which have divergent functions in the complex processes that involve DNA, including replication, recombination, repair, and transcription. Through their involvement in these processes, repetitive DNA sequences influence the genetic instability and evolution of DNA molecules and they are located non-randomly in all genomes. Mechanisms that influence such genetic instability have been studied in many organisms, including within human genomes where they are linked to various human diseases. Here, we review our understanding of short, simple DNA repeats across a diverse range of bacteria, comparing the prevalence of repetitive DNA sequences in different genomes. We describe the range of DNA structures that have been observed in such repeats, focusing on their propensity to form local, non-B-DNA structures. Finally, we discuss the biological significance of such unusual DNA structures and relate this to studies where the impacts of DNA metabolism on genetic stability are linked to human diseases. Overall, we show that simple DNA repeats in bacteria serve as excellent and tractable experimental models for biochemical studies of their cellular functions and influences.

Genes for intermediate filament proteins and the draft sequence of the human genome

2001 ◽  
Vol 114 (14) ◽  
pp. 2569-2575 ◽  
Author(s):  
Michael Hesse ◽  
Thomas M. Magin ◽  
Klaus Weber
Keyword(s):  
Human Genome ◽  
Dna Sequences ◽  
Intermediate Filament ◽  
Muscle Protein ◽  
Gene Families ◽  
Type I ◽  
Type Ii ◽  
Draft Sequence ◽  
Intermediate Filament Proteins ◽  
Processed Pseudogenes

We screened the draft sequence of the human genome for genes that encode intermediate filament (IF) proteins in general, and keratins in particular. The draft covers nearly all previously established IF genes including the recent cDNA and gene additions, such as pancreatic keratin 23, synemin and the novel muscle protein syncoilin. In the draft, seven novel type II keratins were identified, presumably expressed in the hair follicle/epidermal appendages. In summary, 65 IF genes were detected, placing IF among the 100 largest gene families in humans. All functional keratin genes map to the two known keratin clusters on chromosomes 12 (type II plus keratin 18) and 17 (type I), whereas other IF genes are not clustered. Of the 208 keratin-related DNA sequences, only 49 reflect true keratin genes, whereas the majority describe inactive gene fragments and processed pseudogenes. Surprisingly, nearly 90% of these inactive genes relate specifically to the genes of keratins 8 and 18. Other keratin genes, as well as those that encode non-keratin IF proteins, lack either gene fragments/pseudogenes or have only a few derivatives. As parasitic derivatives of mature mRNAs, the processed pseudogenes of keratins 8 and 18 have invaded most chromosomes, often at several positions. We describe the limits of our analysis and discuss the striking unevenness of pseudogene derivation in the IF multigene family. Finally, we propose to extend the nomenclature of Moll and colleagues to any novel keratin.

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