Identification Of Protein-coding Genes Research Articles

Possibilities of using information resources In bioremediation

Abstract: Bioremediation using microorganisms has a number of advantages over physical and chemical methods of water, soil and atmosphere purification. Microorganisms have a wide range of metabolic capabilities that enable them to convert, modify and utilize toxic pollutants for energy and biomass production. This article shows their participation in the decomposition of various industrial wastes, such as dyes, hydrocarbons, chlorinated aromatic compounds and pesticides, among others. Although the use of microorganisms is an environmentally friendly and promising way of solving environmental threats, many factors affect the effectiveness of bioremediation, such as the chemical nature of pollutants, their accessibility to microorganisms, the physical and chemical characteristics of the environment, as well as the interaction of the destructive organisms with each other. The search for new effective strains or the creation of superdestructors using genetic and protein engineering methods proves to be crucial under current circumstances. This task can be solved using such “tools” as genomics, proteomics, transcriptomics and metabolomics. These technologies require the integration of a huge amount of data, which cannot be achieved without the use of bioinformatics. Bioinformatics is used in microbial bioremediation in different ways: analysis of genome sequencing data, identification of protein-coding genes, comparative analysis to identify the function of unknown genes, automatic reconstruction and comparison of metabolic pathways, and study of protein–protein and protein–DNA interactions to understand regulatory mechanisms. This review aims to highlight various resources that store information about possible pathways of microbial metabolism involved in the biodegradation of petroleum products. The use of such information resources can become a starting point for many studies in bioremediation.

Dynamic Expression of Long Non-Coding RNAs Throughout Parasite Sexual and Neural Maturation in Schistosoma japonicum.

Schistosoma japonicum is a flatworm that causes schistosomiasis, a neglected tropical disease. S. japonicum RNA-Seq analyses has been previously reported in the literature on females and males obtained during sexual maturation from 14 to 28 days post-infection in mouse, resulting in the identification of protein-coding genes and pathways, whose expression levels were related to sexual development. However, this work did not include an analysis of long non-coding RNAs (lncRNAs). Here, we applied a pipeline to identify and annotate lncRNAs in 66 S. japonicum RNA-Seq publicly available libraries, from different life-cycle stages. We also performed co-expression analyses to find stage-specific lncRNAs possibly related to sexual maturation. We identified 12,291 S. japonicum expressed lncRNAs. Sequence similarity search and synteny conservation indicated that some 14% of S. japonicum intergenic lncRNAs have synteny conservation with S. mansoni intergenic lncRNAs. Co-expression analyses showed that lncRNAs and protein-coding genes in S. japonicum males and females have a dynamic co-expression throughout sexual maturation, showing differential expression between the sexes; the protein-coding genes were related to the nervous system development, lipid and drug metabolism, and overall parasite survival. Co-expression pattern suggests that lncRNAs possibly regulate these processes or are regulated by the same activation program as that of protein-coding genes.

Abstract 3897: Pharmacogenomic landscape of long non-coding RNAs in human cancers

Abstract Extensive efforts in recent large-scale drug screens have enabled identification of protein-coding genes (PCGs) as biomarkers for anti-cancer agent sensitivity, but information on the role of long-non coding RNAs (lncRNAs) in drug response is sparse. Lower abundance, difficulty in expression profiling and limited functional knowledge have so far hindered global analysis of lncRNAs in drug screens. However, lncRNAs represent nearly 60% of the human genome (vs. 3% for PCGs) and contain a vast majority of pharmacogenomic SNPs. Thus, it is critically important to evaluate the role of lncRNAs in drug sensitivity. Here, we perform a comprehensive reanalysis of large-scale drug screens using novel computational methods to create a landscape of pharmacogenomic interactions of lncRNAs in human cancers. We developed an algorithm to accurately impute the lncRNA transcriptome using PCG expression. This approach allowed us to generate lncRNA transcriptome in high throughput drug screening data sets and enabled systematic evaluation of lncRNAs for hundreds of drugs. For example, we applied our method to the Genomics of Drug Sensitivity in Cancer (GDSC) study, where sensitivity to 265 anti-cancer agents were quantified in 963 cancer cell lines. Sparse regression analysis showed lncRNA transcriptome were similar in accuracy at predicting drug response compared to PCGs (P = 0.9), indicating lncRNAs may be equally important biomarkers. Subsequently, we identified drug-specific lncRNA biomarkers using ANOVA while adjusting for tissue type. Our analysis recapitulated literature-verified lncRNAs associated with drug response, e.g. HOTAIR expression with cisplatin resistance (FDR < 0.05). In addition, we identified several compelling novel lncRNA associations for specific drugs along with previously uncharacterized multi-drug response predictors. We next identified lncRNAs that may serve as biomarkers for drug response while controlling for known cancer functional events. As an example, we analyzed EGFR-inhibitors (erlotinib, gefitinib, pelitinib) while controlling for EGFR somatic point mutations and amplification status and identified an anti-sense lncRNA overlapping UGT1A locus that may be a potential regulator of erlotinib metabolism. These findings reveal important lncRNAs that predict response independent of known cancer functional events and are currently being evaluated in non-small cell lung cancer patients. In conclusion, our comprehensive analysis has generated unprecedented insights into the role of lncRNAs in anti-cancer drug response and will be an invaluable resource for researchers studying drug response in various cancer types. Citation Format: Aritro Nath, R. Stephanie Huang. Pharmacogenomic landscape of long non-coding RNAs in human cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3897.

Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi

BackgroundGenome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction.ResultsHere, we present an extension of the gene prediction program GeMoMa that utilizes amino acid sequence conservation, intron position conservation and optionally RNA-seq data for homology-based gene prediction. We show on published benchmark data for plants, animals and fungi that GeMoMa performs better than the gene prediction programs BRAKER1, MAKER2, and CodingQuarry, and purely RNA-seq-based pipelines for transcript identification. In addition, we demonstrate that using multiple reference organisms may help to further improve the performance of GeMoMa. Finally, we apply GeMoMa to four nematode species and to the recently published barley reference genome indicating that current annotations of protein-coding genes may be refined using GeMoMa predictions.ConclusionsGeMoMa might be of great utility for annotating newly sequenced genomes but also for finding homologs of a specific gene or gene family. GeMoMa has been published under GNU GPL3 and is freely available at http://www.jstacs.de/index.php/GeMoMa.

Editorial: Z-curve Applications in Genome Analysis.

Editorial: Z-curve Applications in Genome Analysis.

Malignant transformation of colonic epithelial cells by a colon-derived long noncoding RNA

Recent progress has been made in the identification of protein-coding genes and miRNAs that are expressed in and alter the behavior of colonic epithelia. However, the role of long non-coding RNAs (lncRNAs) in colonic homeostasis is just beginning to be explored. By gene expression profiling of post-mitotic, differentiated tops and proliferative, progenitor-compartment bottoms of microdissected adult mouse colonic crypts, we identified several lncRNAs more highly expressed in crypt bottoms. One identified lncRNA, designated non-coding Nras functional RNA (ncNRFR), resides within the Nras locus but appears to be independent of the Nras coding transcript. Stable overexpression of ncNRFR in non-transformed, conditionally immortalized mouse colonocytes results in malignant transformation, as determined by growth in soft agar and formation of highly invasive tumors in nude mice. Moreover, ncNRFR appears to inhibit the function of the tumor suppressor let-7. These results suggest precise regulation of ncNRFR is necessary for proper cell growth in the colonic crypt, and its misregulation results in neoplastic transformation.

Abstract 794: RNA-Seq and whole transcriptome analyses in ependymomas.

Abstract Ependymoma is a common pediatric central nervous system (CNS) tumor that is believed to originate from ependymal cells located in the lining of ventricular surfaces in the brain. This type of pediatric tumor presents great challenges in treatment despite the advances in neurosurgical techniques and adjuvant therapy. In order to identify possible molecular markers that could improve clinical management and better treatment, we have used RNA-Seq to profile the transcriptome of ependymoma samples with different outcomes. In this study, the ABI SOLiD WT (Whole Transcriptome) technology was utilized to sequence three different ependymoma tumor samples and one normal control brain tissue (enriched for ependymal cells). Whole Transcriptome sequencing produced approximately 10-30 million reads per sample and an average of 30-40% were mapped to the human reference sequence (NCBI Build 36.1) using Novoalign software. After obtaining these mapped locations, Perl scripts were written to overlap these intervals with other annotations and Genespring was used to visualize the reads. Different protein-coding, non-coding and microRNA gene databases were searched for known genes and to calculate the RPKM (Reads Per Kilobase of transcript per Million mapped reads) for each clustered gene. Multiple gene lists were generated corresponding to different RPKM-based comparisons between tumor and control tissues, ultimately enabling the identification of protein-coding genes, non-coding RNAs and microRNAs that are differentially expressed in ependymomas. Protein interaction networks were then built with these genes using the software GeneGo (Thomson Reuters). Thirty genes that are present in these networks are being selected for validation of differential expression in a larger cohort by Real-Time PCR TaqMan customized arrays. In conclusion, we have been able to identify genes differentially expressed in ependymomas, some of which of potential clinical importance. Financial Support: Ann & Robert H Lurie Children's Hospital of Chicago, The Falk Brain Tumor Foundation and The Maeve McNicholas Memorial Foundation. [FF Costa, JM Bischof, and C Hamm contributed equally to this work.] Citation Format: Fabricio F. Costa, Jared M. Bischof, Chris Hamm, Elio F. Vanin, Maria F. Bonaldo, Stephen Iannaccone, Veena Rajaram, Tadanori Tomita, Stewart Goldman, Marcelo B. Soares. RNA-Seq and whole transcriptome analyses in ependymomas. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 794. doi:10.1158/1538-7445.AM2013-794

Bioinformatics methods for the comparative analysis of metazoan mitochondrial genome sequences

In this review we provide an overview of various bioinformatics methods and tools for the analysis of metazoan mitochondrial genomes. We compare available dedicated databases and present current tools for accurate genome annotation, identification of protein coding genes, and determination of tRNA and rRNA models.We also evaluate various tools and models for phylogenetic tree inference using gene order or sequence based data. As for gene order based methods, we compare rearrangement based and gene cluster based methods for gene order rearrangement analysis. As for sequence based methods, we give special emphasis to substitution models or data treatment that reduces certain systematic biases that are typical for metazoan mitogenomes such as within genome and/or among lineage compositional heterogeneity.

MICheck: a web tool for fast checking of syntactic annotations of bacterial genomes

The annotation of newly sequenced bacterial genomes begins with running several automatic analysis methods, with major emphasis on the identification of protein-coding genes. DNA sequences are heterogeneous in local nucleotide composition and this leads sometimes to sequences being annotated as authentic genes when they are not protein-coding genes or are true but uncharacterized protein-coding genes. This first annotation step is generally followed by an expert manual annotation of the predicted genes. The genomic data (sequence and annotations) organized in an appropriate databank file format is subsequently submitted to an entry point of the International Nucleotide Sequence Database. These procedures are inevitably subject to mistakes, and this can lead to unintentional syntactic annotation errors being stored in public databanks. Here, we present a new web program, MICheck (MIcrobial genome Checker), that enables rapid verification of sets of annotated genes and frameshifts in previously published bacterial genomes. The web interface allows one easily to investigate the MICheck results, i.e. inaccurate or missed gene annotations: a graphical representation is drawn, in which the genomic context of a unique coding DNA sequence annotation or a predicted frameshift is given, using information on the coding potential (curves) and annotation of the neighbouring genes. We illustrate some capabilities of the MICheck site through the analysis of 20 bacterial genomes, 9 of which were selected for their ‘Reviewed’ status in the National Center for Biotechnology Information (NCBI) Reference Sequence Project (RefSeq). In the context of the numerous re-annotation projects for microbial genomes, this tool can be seen as a preliminary step before the functional re-annotation step to check quickly for missing or wrongly annotated genes. The MICheck website is accessible at the following address: .

Identification of protein coding genes in genomes with statistical functions based on the circular code

A new statistical approach using functions based on the circular code classifies correctly more than 93% of bases in protein (coding) genes and non-coding genes of human sequences. Based on this statistical study, a research software called ‘Analysis of Coding Genes’ ( acg) has been developed for identifying protein genes in the genomes and for determining their frame. Furthermore, the software acg also allows an evaluation of the length of protein genes, their position in the genome, their relative position between themselves, and the prediction of internal frames in protein genes.

Re-annotation of genome microbial CoDing-Sequences: finding new genes and inaccurately annotated genes

BackgroundAnalysis of any newly sequenced bacterial genome starts with the identification of protein-coding genes. Despite the accumulation of multiple complete genome sequences, which provide useful comparisons with close relatives among other organisms during the annotation process, accurate gene prediction remains quite difficult. A major reason for this situation is that genes are tightly packed in prokaryotes, resulting in frequent overlap. Thus, detection of translation initiation sites and/or selection of the correct coding regions remain difficult unless appropriate biological knowledge (about the structure of a gene) is imbedded in the approach.ResultsWe have developed a new program that automatically identifies biologically significant candidate genes in a bacterial genome. Twenty-six complete prokaryotic genomes were analyzed using this tool, and the accuracy of gene finding was assessed by comparison with existing annotations. This analysis revealed that, despite the enormous effort of genome program annotators, a small but not negligible number of genes annotated within the framework of sequencing projects are likely to be partially inaccurate or plainly wrong. Moreover, the analysis of several putative new genes shows that, as expected, many short genes have escaped annotation. In most cases, these new genes revealed frameshifts that could be either artifacts or genuine frameshifts. Some entirely unexpected new genes have also been identified. This allowed us to get a more complete picture of prokaryotic genomes. The results of this procedure are progressively integrated into the SWISS-PROT reference databank.ConclusionsThe results described in the present study show that our procedure is very satisfactory in terms of gene finding accuracy. Except in few cases, discrepancies between our results and annotations provided by individual authors can be accounted for by the nature of each annotation process or by specific characteristics of some genomes. This stresses that close cooperation between scientists, regular update and curation of the findings in databases are clearly required to reduce the level of errors in genome annotation (and hence in reducing the unfortunate spreading of errors through centralized data libraries).

Combining diverse evidence for gene recognition in completely sequenced bacterial genomes

Analysis of a newly sequenced bacterial genome starts with identification of protein-coding genes. Functional assignment of proteins requires the exact knowledge of protein N-termini. We present a new program ORPHEUS that identifies candidate genes and accurately predicts gene starts. The analysis starts with a database similarity search and identification of reliable gene fragments. The latter are used to derive statistical characteristics of protein-coding regions and ribosome-binding sites and to predict the complete set of genes in the analyzed genome. In a test on Bacillus subtilis and Escherichia coli genomes, the program correctly identified 93.3% (resp. 96.3%) of experimentally annotated genes longer than 100 codons described in the PIR-International database, and for these genes 96.3% (83.9%) of starts were predicted exactly. Furthermore, 98.9% (99.1%) of genes longer than 100 codons annotated in GenBank were found, and 92.9% (75.7%) of predicted starts coincided with the feature table description. Finally, for the complete gene complements of B.subtilis and E.coli, including genes shorter than 100 codons, gene prediction accuracy was 88.9 and 87.1%, respectively, with 94.2 and 76.7% starts coinciding with the existing annotation.

Combining diverse evidence for gene recognition in completely sequenced bacterial genomes.

Analysis of a newly sequenced bacterial genome starts with identification of protein-coding genes. Functional assignment of proteins requires the exact knowledge of protein N-termini. We present a new program ORPHEUS that identifies candidate genes and accurately predicts gene starts. The analysis starts with a database similarity search and identification of reliable gene fragments. The latter are used to derive statistical characteristics of protein-coding regions and ribosome-binding sites and to predict the complete set of genes in the analyzed genome. In a test on Bacillus subtilis and Escherichia coli genomes, the program correctly identified 93.3% (resp. 96.3%) of experimentally annotated genes longer than 100 codons described in the PIR-International database, and for these genes 96.3% (83.9%) of starts were predicted exactly. Furthermore, 98.9% (99.1%) of genes longer than 100 codons annotated in GenBank were found, and 92.9% (75.7%) of predicted starts coincided with the feature table description. Finally, for the complete gene complements of B.subtilis and E.coli , including genes shorter than 100 codons, gene prediction accuracy was 88.9 and 87.1%, respectively, with 94.2 and 76.7% starts coinciding with the existing annotation.

Compilation of Halobacterial Protein Coding Genes, the Halobacterial Codon Usage Table and its Use

Before 1988 only four sequences of halobacterial protein coding genes were known, but in recent years the number has increased dramatically. Today 130 protein coding genes have been sequenced, belonging to a limited set of protein families. Here a compilation of these genes is given. Most of the genes have the same codon preference. A halobacterial codon usage table is presented and its characteristic features are discussed. The genes of the gas vesicle gene clusters exhibit a different codon usage, which is also shown. The use of the halobacterial codon usage table for the identification of protein coding genes as well as its application as a guide in sequencing halobacterial DNA and construct oligonucleotides are exemplified.