Bioinformatics Approaches for Gene Finding

Gene finding as process of identification of genomic DNA regions encoding proteins , is one of the important scientific research programs and has vast application in structural genomics ,functional genomics ,metabolomics, transcriptomics, proteomics, genome studies and other genetic related studies including genetics disorders detection, treatment and prevention .It is prominent that for study of all above mentioned research programs , identification of fundamental and essential elements of genome such as functional genes, intron, exon, splicing sites, regulatory sites, gene encoding known proteins, motifs, EST, ACR, etc are formed principle basis of the studies and these functions are employed by gene prediction or finding process. So gene finding process plays significant role in the study of genome related programs. Several methods are available for gene finding such as laboratory -based approaches, feature- based approaches homology based approaches, statistical and HMM –based approaches. In this paper, we aim to discuss Insilco approaches for gene prediction in order to make scientist familiar with available bioinformatics tools for gene finding to take benefit from their advantages including low in cost, rapid in time, high in accuracy and large in scale.

[1]  Mahin Ghorbani,et al.  Role of G-Protein Coupled Receptors in Cancer Research and Drug Discovery , 2015 .

[2]  Mahin Ghorbani,et al.  Role of Aquaporins in Diseases and Drug Discovery , 2015 .

[3]  M. Borodovsky,et al.  GeneMark.hmm: new solutions for gene finding. , 1998, Nucleic acids research.

[4]  Neelam Goel,et al.  A comparative analysis of soft computing techniques for gene prediction. , 2013, Analytical biochemistry.

[5]  A. Krogh,et al.  Using database matches with for HMMGene for automated gene detection in Drosophila. , 2000, Genome research.

[6]  Mahin Ghorbani,et al.  Role of Microarray Technology in Diagnosis and Classification of Malignant Tumours , 2015 .

[7]  Ian Korf,et al.  Gene finding in novel genomes , 2004, BMC Bioinformatics.

[8]  Mahin Ghorbani,et al.  Cyclin-Dependent Kinases as valid targets for cancer treatment. , 2015 .

[9]  J. Fickett Recognition of protein coding regions in DNA sequences. , 1982, Nucleic acids research.

[10]  Mahin Ghorbani,et al.  Ion Channels Association with Diseases and their Role as Therapeutic Targets in Drug Discovery , 2015 .

[11]  J. Ishikawa,et al.  FramePlot: a new implementation of the frame analysis for predicting protein-coding regions in bacterial DNA with a high G + C content. , 1999, FEMS microbiology letters.

[12]  Mahin Ghorbani,et al.  Role of Biomarkers in Cancer Research and Drug Development , 2015 .

[13]  S. Salzberg,et al.  Improved microbial gene identification with GLIMMER. , 1999, Nucleic acids research.

[14]  Doug Hyatt,et al.  GrailEXP and Genome Analysis Pipeline for Genome Annotation , 2003, Current protocols in human genetics.

[15]  S. Salzberg,et al.  Microbial gene identification using interpolated Markov models. , 1998, Nucleic acids research.

[16]  P. Rouzé,et al.  Current methods of gene prediction, their strengths and weaknesses. , 2002, Nucleic acids research.

[17]  Parag Rastogi,et al.  Bioinformatics: Methods and Applications: Genomics, Proteomics and Drug Discovery , 2013 .

[18]  D. Searls,et al.  Gene structure prediction by linguistic methods. , 1994, Genomics.

[19]  Mahin Ghorbani,et al.  Ten Bioinformatics Tools for Single Nucleotide Polymorphisms Detection , 2014 .

[20]  Thomas Werner,et al.  MatInspector and beyond: promoter analysis based on transcription factor binding sites , 2005, Bioinform..