Literature DB >> 10353188

Gene identification in bacterial and organellar genomes using GeneScan.

R Ramakrishna1, R Srinivasan.   

Abstract

The performance of the GeneScan algorithm for gene identification has been improved by incorporation of a directed iterative scanning procedure. Application is made here to the cases of bacterial and organnellar genomes. The sensitivity of gene identification was 100% in Plasmodium falciparum plastid-like genome (35 kb) and in 98% in the Mycoplasma genitalium genome (approximately 580 kb) and the Haemophilus influenzae Rd genome (approximately 1.8 Mb). Sensitivity was found to improve in both the Open Reading Frames (ORFs) which have been identified as genes (by homology or by other methods) and those that are classified as hypothetical. False positive assignments (at the nucleotide level) were 0.25% in H. influenzae genome and 0.3% in M. genitalium. There were no false positive assignments in the plastid-like genome. The agreement between the GeneScan predictions and GeneMark predictions of putative ORFs was 97% in M. genitalium genome and 86% in H. influenzae genome. In terms of an exact match between predicted genes/ORFs and the annotation in the databank, GeneScan performance was evaluated to be between 72% and 90% in different genomes. We predict five putative ORFs that were not annotated earlier in the GenBank files for both M. genitalium and H. influenzae genomes. Our preliminary analysis of the newly sequenced G + C rich genome of Mycobacterium tuberculosis H37Rv also shows comparable sensitivity (99%).

Entities:  

Mesh:

Year:  1999        PMID: 10353188     DOI: 10.1016/s0097-8485(98)00034-5

Source DB:  PubMed          Journal:  Comput Chem        ISSN: 0097-8485


  8 in total

1.  Ab initio gene identification: prokaryote genome annotation with GeneScan and GLIMMER.

Authors:  Gautam Aggarwal; Ramakrishna Ramaswamy
Journal:  J Biosci       Date:  2002-02       Impact factor: 1.826

2.  An automated annotation tool for genomic DNA sequences using GeneScan and BLAST.

Authors:  A M Lynn; C K Jain; K Kosalai; P Barman; N Thakur; H Batra; A Bhattacharya
Journal:  J Genet       Date:  2001-04       Impact factor: 1.166

3.  A computational approach to identify genes for functional RNAs in genomic sequences.

Authors:  R J Carter; I Dubchak; S R Holbrook
Journal:  Nucleic Acids Res       Date:  2001-10-01       Impact factor: 16.971

4.  OMIGA: Optimized Maker-Based Insect Genome Annotation.

Authors:  Jinding Liu; Huamei Xiao; Shuiqing Huang; Fei Li
Journal:  Mol Genet Genomics       Date:  2014-03-09       Impact factor: 3.291

Review 5.  Using DNA microarrays to study host-microbe interactions.

Authors:  C A Cummings; D A Relman
Journal:  Emerg Infect Dis       Date:  2000 Sep-Oct       Impact factor: 6.883

6.  Mining Unknown Porcine Protein Isoforms by Tissue-based Map of Proteome Enhances Pig Genome Annotation.

Authors:  Pengju Zhao; Xianrui Zheng; Ying Yu; Zhuocheng Hou; Chenguang Diao; Haifei Wang; Huimin Kang; Chao Ning; Junhui Li; Wen Feng; Wen Wang; George E Liu; Bugao Li; Jacqueline Smith; Yangzom Chamba; Jian-Feng Liu
Journal:  Genomics Proteomics Bioinformatics       Date:  2021-02-23       Impact factor: 6.409

7.  Chromosome-level genome assembly and population genomic analyses provide insights into adaptive evolution of the red turpentine beetle, Dendroctonus valens.

Authors:  Zhudong Liu; Longsheng Xing; Wanlong Huang; Bo Liu; Fanghao Wan; Kenneth F Raffa; Richard W Hofstetter; Wanqiang Qian; Jianghua Sun
Journal:  BMC Biol       Date:  2022-08-24       Impact factor: 7.364

8.  Hierarchical structure of cascade of primary and secondary periodicities in Fourier power spectrum of alphoid higher order repeats.

Authors:  Vladimir Paar; Nenad Pavin; Ivan Basar; Marija Rosandić; Matko Gluncić; Nils Paar
Journal:  BMC Bioinformatics       Date:  2008-11-03       Impact factor: 3.169

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.