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Utilizing Amino Acid Composition and Entropy of Potential Open Reading Frames to Identify Protein-Coding Genes.

ABSTRACT: One of the main steps in gene-finding in prokaryotes is determining which open reading frames encode for a protein, and which occur by chance alone. There are many different methods to differentiate the two; the most prevalent approach is using shared homology with a database of known genes. This method presents many pitfalls, most notably the catch that you only find genes that you have seen before. The four most popular prokaryotic gene-prediction programs (GeneMark, Glimmer, Prodigal, Phanotate) all use a protein-coding training model to predict protein-coding genes, with the latter three allowing for the training model to be created ab initio from the input genome. Different methods are available for creating the training model, and to increase the accuracy of such tools, we present here GOODORFS, a method for identifying protein-coding genes within a set of all possible open reading frames (ORFS). Our workflow begins with taking the amino acid frequencies of each ORF, calculating an entropy density profile (EDP), using KMeans to cluster the EDPs, and then selecting the cluster with the lowest variation as the coding ORFs. To test the efficacy of our method, we ran GOODORFS on 14,179 annotated phage genomes, and compared our results to the initial training-set creation step of four other similar methods (Glimmer, MED2, PHANOTATE, Prodigal). We found that GOODORFS was the most accurate (0.94) and had the best F1-score (0.85), while Glimmer had the highest precision (0.92) and PHANOTATE had the highest recall (0.96).

SUBMITTER: McNair K

PROVIDER: S-EPMC7827183 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Utilizing Amino Acid Composition and Entropy of Potential Open Reading Frames to Identify Protein-Coding Genes.

McNair Katelyn K Ecale Zhou Carol L CL Souza Brian B Malfatti Stephanie S Edwards Robert A RA

Microorganisms 20210108 1

One of the main steps in gene-finding in prokaryotes is determining which open reading frames encode for a protein, and which occur by chance alone. There are many different methods to differentiate the two; the most prevalent approach is using shared homology with a database of known genes. This method presents many pitfalls, most notably the catch that you only find genes that you have seen before. The four most popular prokaryotic gene-prediction programs (GeneMark, Glimmer, Prodigal, Phanota ...[more]

PMID: 33429904

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Project description:Small open reading frames (sORFs) and genes for non-coding RNAs are poorly investigated components of most genomes. Our analysis of 1391 ORFs recently annotated in the soybean symbiont Bradyrhizobium japonicum USDA 110 revealed that 78% of them contain less than 80 codons. Twenty-one of these sORFs are conserved in or outside Alphaproteobacteria and most of them are similar to genes found in transposable elements, in line with their broad distribution. Stabilizing selection was demonstrated for sORFs with proteomic evidence and bll1319_ISGA which is conserved at the nucleotide level in 16 alphaproteobacterial species, 79 species from other taxa and 49 other Proteobacteria. Further we used Northern blot hybridization to validate ten small RNAs (BjsR1 to BjsR10) belonging to new RNA families. We found that BjsR1 and BjsR3 have homologs outside the genus Bradyrhizobium, and BjsR5, BjsR6, BjsR7, and BjsR10 have up to four imperfect copies in Bradyrhizobium genomes. BjsR8, BjsR9, and BjsR10 are present exclusively in nodules, while the other sRNAs are also expressed in liquid cultures. We also found that the level of BjsR4 decreases after exposure to tellurite and iron, and this down-regulation contributes to survival under high iron conditions. Analysis of additional small RNAs overlapping with 3'-UTRs revealed two new repetitive elements named Br-REP1 and Br-REP2. These REP elements may play roles in the genomic plasticity and gene regulation and could be useful for strain identification by PCR-fingerprinting. Furthermore, we studied two potential toxin genes in the symbiotic island and confirmed toxicity of the yhaV homolog bll1687 but not of the newly annotated higB homolog blr0229_ISGA in E. coli. Finally, we revealed transcription interference resulting in an antisense RNA complementary to blr1853, a gene induced in symbiosis. The presented results expand our knowledge on sORFs, non-coding RNAs and repetitive elements in B. japonicum and related bacteria.

Dataset Information

Utilizing Amino Acid Composition and Entropy of Potential Open Reading Frames to Identify Protein-Coding Genes.

Publications

Utilizing Amino Acid Composition and Entropy of Potential Open Reading Frames to Identify Protein-Coding Genes.

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