scholarly journals Genome level analysis of rice mRNA 3′-end processing signals and alternative polyadenylation

2008 ◽  
Vol 36 (9) ◽  
pp. 3150-3161 ◽  
Author(s):  
Yingjia Shen ◽  
Guoli Ji ◽  
Brian J. Haas ◽  
Xiaohui Wu ◽  
Jianti Zheng ◽  
...  
Keyword(s):  
Alternative Polyadenylation ◽  
Genome Level ◽  
Level Analysis

scholarly journals Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Divneet Kaur ◽  
Rintu Kutum ◽  
Debasis Dash ◽  
Samir K. Brahmachari
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Discovery ◽  
Drug Targets ◽  
Systems Approach ◽  
Data Bank ◽  
Data Intensive ◽  
Synonymous Mutations ◽  
Starting Point ◽  
Genome Level ◽  
Level Analysis

Abstract We report the construction of a novel Systems Biology based virtual drug discovery model for the prediction of non-toxic metabolic targets in Mycobacterium tuberculosis (Mtb). This is based on a data-intensive genome level analysis and the principle of conservation of the evolutionarily important genes. In the 1623 sequenced Mtb strains, 890 metabolic genes identified through a systems approach in Mtb were evaluated for non-synonymous mutations. The 33 genes showed none or one variation in the entire 1623 strains, including 1084 Russian MDR strains. These invariant targets were further evaluated for their experimental and in silico essentiality as well as availability of their crystal structure in Protein Data Bank (PDB). Along with this, targets for the common existing antibiotics and the new Tb drug candidates were also screened for their variation across 1623 strains of Mtb for understanding the drug resistance. We propose that the reduced set of these reported targets could be a more effective starting point for medicinal chemists in generating new chemical leads. This approach has the potential of fueling the dried up Tuberculosis (Tb) drug discovery pipeline.

Genome level analysis of bacteriocins of lactic acid bacteria

2015 ◽  
Vol 56 ◽  
pp. 1-6 ◽  
Author(s):  
Neetigyata Pratap Singh ◽  
Abhay Tiwari ◽  
Ankiti Bansal ◽  
Shruti Thakur ◽  
Garima Sharma ◽  
...  
Keyword(s):  
Lactic Acid ◽  
Lactic Acid Bacteria ◽  
Genome Level ◽  
Level Analysis

scholarly journals Genome-Level Analysis of Selective Constraint without Apparent Sequence Conservation

2013 ◽  
Vol 5 (3) ◽  
pp. 532-541 ◽  
Author(s):  
Olga A. Vakhrusheva ◽  
Georgii A. Bazykin ◽  
Alexey S. Kondrashov
Keyword(s):  
Selective Constraint ◽  
Sequence Conservation ◽  
Genome Level ◽  
Level Analysis

scholarly journals Genome-level analysis of genetic regulation of liver gene expression networks

Hepatology ◽  
2007 ◽  
Vol 46 (2) ◽  
pp. 548-557 ◽  
Author(s):  
Daniel Gatti ◽  
Akira Maki ◽  
Elissa J. Chesler ◽  
Roumyana Kirova ◽  
Oksana Kosyk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Regulation ◽  
Liver Gene ◽  
Genome Level ◽  
Level Analysis

scAPAtrap: identification and quantification of alternative polyadenylation sites from single-cell RNA-seq data

2020 ◽  
Author(s):  
Xiaohui Wu ◽  
Tao Liu ◽  
Congting Ye ◽  
Wenbin Ye ◽  
Guoli Ji
Keyword(s):  
Single Cell ◽  
Genome Annotation ◽  
Gene Expression Regulation ◽  
Rapid Development ◽  
Alternative Polyadenylation ◽  
Cell Types ◽  
Read Coverage ◽  
Mrna Isoforms ◽  
Precise Location ◽  
Genome Level

Abstract Alternative polyadenylation (APA) generates diverse mRNA isoforms, which contributes to transcriptome diversity and gene expression regulation by affecting mRNA stability, translation and localization in cells. The rapid development of 3′ tag-based single-cell RNA-sequencing (scRNA-seq) technologies, such as CEL-seq and 10x Genomics, has led to the emergence of computational methods for identifying APA sites and profiling APA dynamics at single-cell resolution. However, existing methods fail to detect the precise location of poly(A) sites or sites with low read coverage. Moreover, they rely on priori genome annotation and can only detect poly(A) sites located within or near annotated genes. Here we proposed a tool called scAPAtrap for detecting poly(A) sites at the whole genome level in individual cells from 3′ tag-based scRNA-seq data. scAPAtrap incorporates peak identification and poly(A) read anchoring, enabling the identification of the precise location of poly(A) sites, even for sites with low read coverage. Moreover, scAPAtrap can identify poly(A) sites without using priori genome annotation, which helps locate novel poly(A) sites in previously overlooked regions and improve genome annotation. We compared scAPAtrap with two latest methods, scAPA and Sierra, using scRNA-seq data from different experimental technologies and species. Results show that scAPAtrap identified poly(A) sites with higher accuracy and sensitivity than competing methods and could be used to explore APA dynamics among cell types or the heterogeneous APA isoform expression in individual cells. scAPAtrap is available at https://github.com/BMILAB/scAPAtrap.

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