Predicting Protein Coding Regions by Six-Base Nucleotide Distribution

2015 ◽  
pp. 151-161
Author(s):  
Praveen Kumar Vesapogu ◽  
Changchuan Yin ◽  
Bapi Raju Surampudi
Keyword(s):  
Protein Coding ◽  
Coding Regions

scholarly journals The open targets post-GWAS analysis pipeline

2020 ◽  
Vol 36 (9) ◽  
pp. 2936-2937 ◽  
Author(s):  
Gareth Peat ◽  
William Jones ◽  
Michael Nuhn ◽  
José Carlos Marugán ◽  
William Newell ◽  
...  
Keyword(s):  
Drug Targets ◽  
Gene Expression Regulation ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Protein Coding ◽  
Data Resource ◽  
Coding Regions ◽  
Genome Wide ◽  
Causal Genes ◽  
Interactive Data

Abstract Motivation Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data. Results We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource. Availability and implementation The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.

scholarly journals Novel exon 1 protein‐coding regions N‐terminally extend human KCNE3 and KCNE4

2016 ◽  
Vol 30 (8) ◽  
pp. 2959-2969 ◽  
Author(s):  
Geoffrey W. Abbott
Keyword(s):  
Protein Coding ◽  
Coding Regions ◽  
Exon 1 ◽  
Novel Exon

scholarly journals Protein-coding structured RNAs: A computational survey of conserved RNA secondary structures overlapping coding regions in drosophilids

Biochimie ◽  
2011 ◽  
Vol 93 (11) ◽  
pp. 2019-2023 ◽  
Author(s):  
Sven Findeiß ◽  
Jan Engelhardt ◽  
Sonja J. Prohaska ◽  
Peter F. Stadler
Keyword(s):  
Secondary Structures ◽  
Protein Coding ◽  
Rna Secondary Structures ◽  
Coding Regions

Structure and expression of canary myc family genes

1991 ◽  
Vol 11 (3) ◽  
pp. 1770-1776
Author(s):  
R G Collum ◽  
D F Clayton ◽  
F W Alt
Keyword(s):  
Untranslated Region ◽  
Untranslated Regions ◽  
Coding Region ◽  
Protein Coding ◽  
Coding Regions ◽  
Neuronal Precursors ◽  
Myc Gene ◽  
Mature Neurons

We found that the canary N-myc gene is highly related to mammalian N-myc genes in both the protein-coding region and the long 3' untranslated region. Examined coding regions of the canary c-myc gene were also highly related to their mammalian counterparts, but in contrast to N-myc, the canary and mammalian c-myc genes were quite divergent in their 3' untranslated regions. We readily detected N-myc and c-myc expression in the adult canary brain and found N-myc expression both at sites of proliferating neuronal precursors and in mature neurons.

scholarly journals Sequence and phylogenetic analysis of the non-structural 3A and 3B protein-coding regions of foot-and-mouth disease virus subtype A Iran 05

2010 ◽  
Vol 11 (3) ◽  
pp. 243
Author(s):  
Saber Jelokhani-Niaraki ◽  
Majid Esmaelizad ◽  
Morteza Daliri ◽  
Rasoul Vaez-Torshizi ◽  
Morteza Kamalzadeh ◽  
...  
Keyword(s):  
Phylogenetic Analysis ◽  
Disease Virus ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Protein Coding ◽  
Coding Regions ◽  
Mouth Disease Virus ◽  
Foot And Mouth ◽  
Subtype A ◽  
Virus Subtype

scholarly journals Genome Sequence of Rheinheimera salexigens sp. nov. Isolated from a Fishing Hook off O‘ahu, Hawai‘i

2016 ◽  
Vol 4 (6) ◽  
Author(s):  
Xuehua Wan ◽  
Shaobin Hou ◽  
Kazukuni Hayashi ◽  
James Anderson ◽  
Stuart P. Donachie
Keyword(s):  
Genome Sequence ◽  
Draft Genome ◽  
Draft Genome Sequence ◽  
Protein Coding ◽  
Coding Sequences ◽  
Coding Regions ◽  
Roche 454

Rheinheimera salexigens KH87 T is an obligately halophilic gammaproteobacterium. The strain’s draft genome sequence, generated by the Roche 454 GS FLX+ platform, comprises two scaffolds of ~3.4 Mbp and ~3 kbp, with 3,030 protein-coding sequences and 58 tRNA coding regions. The G+C content is 42 mol%.

scholarly journals Gene prediction by multiple syntenic alignment

2005 ◽  
Vol 2 (1) ◽  
pp. 38-47
Author(s):  
Said S. Adi ◽  
Carlos E. Ferreira
Keyword(s):  
Computer Program ◽  
Genomic Dna ◽  
Gene Prediction ◽  
Genomic Sequences ◽  
Prediction Problem ◽  
Huge Amount ◽  
Protein Coding ◽  
Coding Regions ◽  
Conserved Regions ◽  
Similarity Information

Summary Given the increasing number of available genomic sequences, one now faces the task of identifying their functional parts, like the protein coding regions. The gene prediction problem can be addressed in several ways. One of the most promising methods makes use of similarity information between the genomic DNA and previously annotated sequences (proteins, cDNAs and ESTs). Recently, given the huge amount of newly sequenced genomes, new similarity-based methods are being successfully applied in the task of gene prediction. The so-called comparative-based methods lie in the similarities shared by regions of two evolutionary related genomic sequences. Despite the number of different gene prediction approaches in the literature, this problem remains challenging. In this paper we present a new comparative-based approach to the gene prediction problem. It is based on a syntenic alignment of three or more genomic sequences. With syntenic alignment we mean an alignment that is constructed taking into account the fact that the involved sequences include conserved regions intervened by unconserved ones. We have implemented the proposed algorithm in a computer program and confirm the validity of the approach on a benchmark including triples of human, mouse and rat genomic sequences.

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.

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