scholarly journals A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Qiongshi Lu ◽  
Yiming Hu ◽  
Jiehuan Sun ◽  
Yuwei Cheng ◽  
Kei-Hoi Cheung ◽  
...  
Keyword(s):  
Human Genome ◽  
Integrated Analysis ◽  
Coding Regions ◽  
Statistical Framework ◽  
Annotation Data

scholarly journals A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data

2015 ◽  
Author(s):  
Qiongshi Lu ◽  
Yiming Hu ◽  
Jiehuan Sun ◽  
Yuwei Cheng ◽  
Kei-Hoi Cheung ◽  
...  
Keyword(s):  
Human Genome ◽  
Genome Annotation ◽  
Human Genetics ◽  
Integrated Analysis ◽  
Whole Genome ◽  
Coding Regions ◽  
Functional Regions ◽  
Statistical Framework ◽  
Annotation Data ◽  
High Throughput Experiments

Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu

scholarly journals Distinctive functional regime of endogenous lncRNAs in dark regions of human genome

2020 ◽  
Author(s):  
Anyou Wang ◽  
Rong Hai
Keyword(s):  
Human Genome ◽  
Rna Processing ◽  
Self Regulation ◽  
Post Translational Modification ◽  
Protein Coding ◽  
Noncoding Regions ◽  
Coding Regions ◽  
Rnaseq Data ◽  
Response To Stress ◽  
Eukaryotic Genomes

AbstractEukaryotic genomes gradually gain noncoding regions when advancing evolution and human genome actively transcribes >90% of its noncoding regions1, suggesting their criticality in evolutionary human genome. Yet <1% of them have been functionally characterized2, leaving most human genome in dark. Here we systematically decode endogenous lncRNAs located in unannotated regions of human genome and decipher a distinctive functional regime of lncRNAs hidden in massive RNAseq data. LncRNAs divergently distribute across chromosomes, independent of protein-coding regions. Their transcriptions barely initiate on promoters through polymerase II, but mostly on enhancers. Yet conventional enhancer activators(e.g. H3K4me1) only account for a small proportion of lncRNA activation, suggesting alternatively unknown mechanisms initiating the majority of lncRNAs. Meanwhile, lncRNA-self regulation also notably contributes to lncRNA activation. LncRNAs trans-regulate broad bioprocesses, including transcription and RNA processing, cell cycle, respiration, response to stress, chromatin organization, post-translational modification, and development. Overall lncRNAs govern their owned regime distinctive from protein’s.

Structural-Statistical Properties of DNA Coding Regions

2015 ◽  
Vol 10 (2) ◽  
pp. 387-397 ◽  
Author(s):  
В.А. Кутыркин ◽  
V.A. Kutyrkin
Keyword(s):  
Human Genome ◽  
Dna Sequences ◽  
Statistical Approach ◽  
Statistical Properties ◽  
Statistical Characteristics ◽  
Dna Coding ◽  
Coding Regions ◽  
Unknown Type ◽  
Triplet Periodicity ◽  
Special Meaning

Structural-statistical characteristics of the coding DNA sequences (CDSs) from human genome are investigated in the frame of spectral-statistical approach (the 2S-approach). Properties of 3-regularity and latent profile periodicity are among of such the characteristics. Special meaning and intrinsic existence of these properties are confirmed by researching the binary recoded CDSs. The only one kind of singular recoding, that identifies complementary nucleotides, serves to persistence of the original CDSs characteristics. Usage of nonsingular binary recoding proves a statement that latent triplet periodicity in the CDSs of human genome belongs to earlier unknown type called as profile periodicity.

scholarly journals Endogenous Retroviruses and Human Evolution

2002 ◽  
Vol 3 (6) ◽  
pp. 494-498 ◽  
Author(s):  
Konstantin Khodosevich ◽  
Yuri Lebedev ◽  
Eugene Sverdlov
Keyword(s):  
Human Genome ◽  
Human Evolution ◽  
Endogenous Retroviruses ◽  
Regulatory Sequences ◽  
Coding Regions ◽  
Regulatory Systems ◽  
Human Genes ◽  
Functional Consequences ◽  
Polyadenylation Signals ◽  
Human Specific

Humans share about 99% of their genomic DNA with chimpanzees and bonobos; thus, the differences between these species are unlikely to be in gene content but could be caused by inherited changes in regulatory systems. Endogenous retroviruses (ERVs) comprise ∼ 5% of the human genome. The LTRs of ERVs contain many regulatory sequences, such as promoters, enhancers, polyadenylation signals and factor-binding sites. Thus, they can influence the expression of nearby human genes. All known human-specific LTRs belong to the HERV-K (human ERV) family, the most active family in the human genome. It is likely that some of these ERVs could have integrated into regulatory regions of the human genome, and therefore could have had an impact on the expression of adjacent genes, which have consequently contributed to human evolution. This review discusses possible functional consequences of ERV integration in active coding regions.

scholarly journals N6-Methyladenine DNA Modification in Human Genome

2017 ◽  
Author(s):  
Chuan-Le Xiao ◽  
Song Zhu ◽  
Minghui He ◽  
De Chen ◽  
Qian Zhang ◽  
...  
Keyword(s):  
Human Genome ◽  
Genomic Dna ◽  
Human Cells ◽  
Human Diseases ◽  
Dna Modification ◽  
Down Regulation ◽  
Coding Regions

SummaryDNA N6-methyladenine (6mA) modification is the most prevalent DNA modification in prokaryotes, but whether it exists in human cells and whether it plays a role in human diseases remain enigmatic. Here, we showed that 6mA is extensively present in human genome, and we cataloged 881,240 6mA sites accounting for ∼0.051% of the total adenines. [G/C]AGG[C/T] was the most significantly associated motif with 6mA modification. 6mA sites were enriched in the coding regions and mark actively transcribed genes in human cells. We further found that DNA N6-methyladenine and N6-demethyladenine modification in human genome were mediated by methyltransferase N6AMT1 and demethylase ALKBH1, respectively. The abundance of 6mA was significantly lower in cancers, accompaning with decreased N6AMT1 and increased ALKBH1 levels, and down-regulation of 6mA modification levels promoted tumorigenesis. Collectively, our results demonstrate that DNA 6mA modification is extensively present in human cells and the decrease of genomic DNA 6mA promotes human tumorigenesis.

scholarly journals Mutation severity spectrum of rare alleles in the human genome is predictive of disease type

2019 ◽  
Author(s):  
Jimin Pei ◽  
Lisa Kinch ◽  
Nick V. Grishin
Keyword(s):  
Human Genome ◽  
Genetic Disorders ◽  
Single Amino Acid ◽  
Missense Mutations ◽  
Single Nucleotide ◽  
Protein Coding ◽  
Coding Regions ◽  
Structural And Functional Properties ◽  
Disease Associations ◽  
Disease Associated Genes

AbstractThe human genome harbors a variety of genetic variations. Single-nucleotide changes that alter amino acids in protein-coding regions are one of the major causes of human phenotypic variation and diseases. These single-amino acid variations (SAVs) are routinely found in whole genome and exome sequencing. Evaluating the functional impact of such genomic alterations is crucial for diagnosis of genetic disorders. We developed DeepSAV, a deep-learning convolutional neural network to differentiate disease-causing and benign SAVs based on a variety of protein sequence, structural and functional properties. Our method outperforms most stand-alone programs and has similar predictive power as some of the best available. We transformed DeepSAV scores of rare SAVs observed in the general population into a mutation severity measure of protein-coding genes. This measure reflects a gene’s tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. Genes implicated in cancer, autism, and viral interaction are found by this measure as intolerant to mutations, while genes associated with a number of other diseases are scored as tolerant. Among known disease-associated genes, those that are mutation-intolerant are likely to function in development and signal transduction pathways, while those that are mutation-tolerant tend to encode metabolic and mitochondrial proteins.

scholarly journals LOGO, a contextualized pre-trained language model of human genome flexibly adapts to various downstream tasks by fine-tuning

2021 ◽  
Author(s):  
Meng Yang ◽  
Haiping Huang ◽  
Lichao Huang ◽  
Nan Zhang ◽  
Jihong Wu ◽  
...  
Keyword(s):  
Human Genome ◽  
Human Genetics ◽  
Language Model ◽  
Fine Tuning ◽  
Human Reference Genome ◽  
Coding Regions ◽  
Conceptual Analogy ◽  
Coding Variants ◽  
Promoter Interaction

Abstract Interpretation of non-coding genome remains an unsolved challenge in human genetics due to impracticality of exhaustively annotate biochemically active elements in all conditions. Deep learning based computational approaches emerge recently to help interpretating non-coding regions. Here we present LOGO (Language of Genome), a self-attention based contextualized pre-trained language model that applies self-supervision techniques to learn bidirectional representations of unlabeled human reference genome and extend to a series of downstream tasks via fine-tuning. We also explore a novel knowledge embedded version of LOGO to incorporate prior human annotations. Experiments show that LOGO achieves 15% absolute improvement for promoter identification and up to 4.5% absolute improvement for enhancer-promoter interaction prediction. LOGO exhibits state-of-the-art predictive power on chromatin features with only 3% parameterization against fully supervised convolutional neural network, DeepSEA. Fine-tuned LOGO also shows outstanding performance in prioritizing non-coding variants associated with human diseases. In addition, we apply LOGO to interpret type 2 diabetes (T2D) GWAS signals and infer underlying regulatory mechanisms. We make a conceptual analogy between natural language and human genome and demonstrate LOGO is an accurate, fast, scalable, and robust framework with powerful adaptability to various tasks without substantial task-specific architecture modifications.

scholarly journals Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape

2014 ◽  
Vol 43 (4) ◽  
pp. e27-e27 ◽  
Author(s):  
Aurélien Griffon ◽  
Quentin Barbier ◽  
Jordi Dalino ◽  
Jacques van Helden ◽  
Salvatore Spicuglia ◽  
...  
Keyword(s):  
Human Genome ◽  
Enrichment Analysis ◽  
Search Space ◽  
Regulatory Elements ◽  
Data Sets ◽  
Analysis Tool ◽  
Coding Regions ◽  
Genome Wide ◽  
Public Data ◽  
Cancer Genomes

Abstract The large collections of ChIP-seq data rapidly accumulating in public data warehouses provide genome-wide binding site maps for hundreds of transcription factors (TFs). However, the extent of the regulatory occupancy space in the human genome has not yet been fully apprehended by integrating public ChIP-seq data sets and combining it with ENCODE TFs map. To enable genome-wide identification of regulatory elements we have collected, analysed and retained 395 available ChIP-seq data sets merged with ENCODE peaks covering a total of 237 TFs. This enhanced repertoire complements and refines current genome-wide occupancy maps by increasing the human genome regulatory search space by 14% compared to ENCODE alone, and also increases the complexity of the regulatory dictionary. As a direct application we used this unified binding repertoire to annotate variant enhancer loci (VELs) from H3K4me1 mark in two cancer cell lines (MCF-7, CRC) and observed enrichments of specific TFs involved in biological key functions to cancer development and proliferation. Those enrichments of TFs within VELs provide a direct annotation of non-coding regions detected in cancer genomes. Finally, full access to this catalogue is available online together with the TFs enrichment analysis tool (http://tagc.univ-mrs.fr/remap/).

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