Protein Coding Genes In Genomes Research Articles

Annotations of four high-quality indigenous chicken genomes identify more than one thousand missing genes in subtelomeric regions and micro-chromosomes with high G/C contents.

Although multiple chicken genomes have been assembled and annotated, the numbers of protein-coding genes in chicken genomes and their variation among breeds are still uncertain due to the low quality of these genome assemblies and limited resources used in their gene annotations. To fill these gaps, we recently assembled genomes of four indigenous chicken breeds with distinct traits at chromosome-level. In this study, we annotated genes in each of these assembled genomes using a combination of RNA-seq- and homology-based approaches. We identified varying numbers (17,497-17,718) of protein-coding genes in the four indigenous chicken genomes, while recovering 51 of the 274 "missing" genes in birds in general, and 36 of the 174 "missing" genes in chickens in particular. Intriguingly, based on deeply sequenced RNA-seq data collected in multiple tissues in the four breeds, we found 571 ~ 627 protein-coding genes in each genome, which were missing in the annotations of the reference chicken genomes (GRCg6a and GRCg7b/w). After removing redundancy, we ended up with a total of 1,420 newly annotated genes (NAGs). The NAGs tend to be found in subtelomeric regions of macro-chromosomes (chr1 to chr5, plus chrZ) and middle chromosomes (chr6 to chr13, plus chrW), as well as in micro-chromosomes (chr14 to chr39) and unplaced contigs, where G/C contents are high. Moreover, the NAGs have elevated quadruplexes G frequencies, while both G/C contents and quadruplexes G frequencies in their surrounding regions are also high. The NAGs showed tissue-specific expression, and we were able to verify 39 (92.9%) of 42 randomly selected ones in various tissues of the four chicken breeds using RT-qPCR experiments. Most of the NAGs were also encoded in the reference chicken genomes, thus, these genomes might harbor more genes than previously thought. The NAGs are widely distributed in wild, indigenous and commercial chickens, and they might play critical roles in chicken physiology. Counting these new genes, chicken genomes harbor more genes than originally thought.

TSEBRA: transcript selector for BRAKER

BackgroundBRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins. The BRAKER suite has so far not been able to reliably exceed the accuracy of BRAKER1 and BRAKER2 when incorporating both types of evidence simultaneously. Currently, for a novel genome project where both RNA-seq and protein data are available, the best option is to run both pipelines independently, and to pick one, likely better output. Therefore, one or another type of the extrinsic evidence would remain unexploited.ResultsWe present TSEBRA, a software that selects gene predictions (transcripts) from the sets generated by BRAKER1 and BRAKER2. TSEBRA uses a set of rules to compare scores of overlapping transcripts based on their support by RNA-seq and homologous protein evidence. We show in computational experiments on genomes of 11 species that TSEBRA achieves higher accuracy than either BRAKER1 or BRAKER2 running alone and that TSEBRA compares favorably with the combiner tool EVidenceModeler.ConclusionTSEBRA is an easy-to-use and fast software tool. It can be used in concert with the BRAKER pipeline to generate a gene prediction set supported by both RNA-seq and homologous protein evidence.

BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database.

The task of eukaryotic genome annotation remains challenging. Only a few genomes could serve as standards of annotation achieved through a tremendous investment of human curation efforts. Still, the correctness of all alternative isoforms, even in the best-annotated genomes, could be a good subject for further investigation. The new BRAKER2 pipeline generates and integrates external protein support into the iterative process of training and gene prediction by GeneMark-EP+ and AUGUSTUS. BRAKER2 continues the line started by BRAKER1 where self-training GeneMark-ET and AUGUSTUS made gene predictions supported by transcriptomic data. Among the challenges addressed by the new pipeline was a generation of reliable hints to protein-coding exon boundaries from likely homologous but evolutionarily distant proteins. In comparison with other pipelines for eukaryotic genome annotation, BRAKER2 is fully automatic. It is favorably compared under equal conditions with other pipelines, e.g. MAKER2, in terms of accuracy and performance. Development of BRAKER2 should facilitate solving the task of harmonization of annotation of protein-coding genes in genomes of different eukaryotic species. However, we fully understand that several more innovations are needed in transcriptomic and proteomic technologies as well as in algorithmic development to reach the goal of highly accurate annotation of eukaryotic genomes.

Editorial: Z-curve Applications in Genome Analysis.

It is my pleasure to be a guest editor for the special issue on Z-curve applications in genome analysis. We are now living in the genomic era and facing an avalanche of sequencing information; two decades ago, however, not a single genome sequence of free-living organisms was available. The progress in DNA sequencing technology, in terms of both speed and cost, has been remarkable. The discovery of the double helix structure of DNA by James Watson and Francis Crick in 1953 marked the birth of molecular biology, and gave rise to a series of landmark findings and inventions that made DNA sequencing possible. In 1995, Venter and colleagues published the first genome sequence of a free-living organism, the bacterium Haemophilus influenzae, which has a genome of 1,830,137 bases. The Human Genome Project, which aimed to sequence the three billion bases in the human genome, was completed in 2003. As of October 2013, the number of sequenced bases deposited in GenBank has reached 1.55 × 1011 from nearly 260,000 formally described species, in addition to 5.35 × 1011 bases from whole genome shotgun projects. Technology that has the capacity to sequence the entire human genome within hours for under $1000, such as the nanopore detection system, is on the horizon. The amount and speed in the accumulation of sequencing data are unprecedented in human history. DNA is a fascinating molecule. The basic form of DNA is extremely simple, as it is composed of only four kinds of chemical bases, adenine, guanine, cytosine and thymine, but the information that it conveys is astonishing; DNA encodes the genetic instructions used in the development and functions of almost all organisms from bacteria to humans. The avalanche of DNA sequence information presents a tremendous challenge on how to decode what is hidden in the DNA sequence to obtain biologically meaningful knowledge. I commenced the research on DNA sequence analysis in the 1980s. In physics, algebraic and geometric methods are often jointly used to address a particular problem. With a background in theoretical physics, one of my aims was to develop a geometrical method, or a curve, that constitutes a one-to-one correspondence with the DNA sequence, so that the study of DNA could be shifted to the study of the curve. Considerable symmetry appears to exist in the DNA molecule, which, in turn, can be represented as a DNA group. Based on this group theory, the one-to-one correspondence to a DNA sequence reveals a three-dimensional curve with a zigzag shape, hence the name Z-curve. In the past two decades, the Z-curve method has found applications in a wide range of areas in genome analysis, including the identification of protein-coding genes, promoters, translation start sites, replication origins, isochores and horizontally-transferred genomic islands in various genomes. The identification of protein-coding genes in a genome represents one of the most classical bioinformatics endeavors. A bacterial genome can have a large number of open reading frames (ORFs), while only a small subset is bona fide protein-coding, and the goal is to identify this subset. According to the Z-curve method, ORF nucleotide compositions are represented uniquely by mapping points in a high-dimensional space, and protein-coding ORFs and non-coding ones usually locate in distinct regions. Discriminant algorithms or other classifiers can then be applied to distinguish the two kinds of mapping points. This method, although straightforward, has been proven to be effective and accurate for gene identification in bacterial, archaeal and eukaryotic genomes. In this special issue, Drs. Guo, Lin and Chen review Z-curve applications in identification of protein-coding genes in bacterial and archaeal genomes. DNA replication, as the basis of biological inheritance, is critical for all organisms. DNA replication starts at a specific location, termed the origin of replication (oriC). Bacterial genomes have a single oriC, whereas archaeal genomes can have either single or multiple oriCs. The RY, MK, AT and GC disparity curves (different Z-curve components) have been successfully used for identification of oriCs in many bacterial and archaeal genomes. Dr. Gao reviews Z-curve applications in oriC identification, as well as the development of oriC finding software and databases. The genomic island is the part of a genome that is acquired through horizontal transfer. Genomic islands can code for many functions, such as those for symbiosis or pathogenesis, and can play a role in bacterial drug resistance. Horizontal gene transfer is increasingly being recognized as a universal event throughout bacterial evolution. Based on the Z-curve method, a windowless method for GC content calculation, the GC profile, has been proposed. In this issue, Drs. Zhang, Ou and colleagues review the use of the GC profile in identifying horizontally-transferred genomic islands in bacterial genomes, and in identifying homogenous domains in eukaryotic genomes. In this special issue, I will deduce the Z-curve method from the aspect of group theory, as a foundation for the Z-curve algorithms. Three included reviews focus on the identification of protein-coding genes, replication origins and genomic islands. Over the past two decades, significant advancements have been achieved in genome analysis using the Z-curve method, and further advances are anticipated. Twenty years ago, before the publication of the first bacterial genome sequence, it was not practical even for the most visionary scientists to anticipate what we know now about genomes, such as the high degree of eukaryotic transcriptome complexity, single-cell genomics, synthetic genomes, and the Neanderthal genome. Thus, it is probably a safe bet that our present genome understanding in another twenty years will seem obsolete. Incorporation of the Z-curve method in future research on genome analysis, therefore, may happen in ways that we cannot currently imagine.

Complete mitochondrial genome of the Japanese snapping shrimp Alpheus japonicus (Crustacea: Decapoda: Caridea): Gene rearrangement and phylogeny within Caridea

The complete sequence of the mitochondrial genome of the Japanese snapping shrimp Alpheus japonicus Miers (Crustacea: Decapoda: Caridea) is presented here. A comparative analysis based on the currently available mitochondrial genomic data revealed many previously unknown characteristics of the mitochondrial genomes of caridean shrimps. The A. japonicus mitochondrial genome is 16487 bp long and contains the typical set of 37 metazoan genes. The gene arrangements in the mitochondrial genomes of four previously studied carideans (Macrobrachium rosenbergii, M. nipponense, M. lanchesteri and Halocaridina rubra) were found to be identical to the pancrustacean ground pattern; thus, it was considered that gene rearrangements probably did not occur in the suborder Caridea. In the present study, a translocation of the trnE gene involving inversion was found in Alpheus mitochondrial genomes. This phenomenon has not been reported in any other crustacean mitochondrial genome that has been studied so far; however, the translocation of one transfer RNA gene (trnP or trnT) was reported in the mitochondrial genome of Exopalaemon carinicauda. When the ratios of the nonsynonymous and synonymous substitutions rates (Ka/Ks) for the 13 protein coding genes from two Alpheus species (A. japonicus and A. distinguendus) and three Macrobrachium species (M. rosenbergii, M. nipponense, M. lanchesteri) were calculated, the Ka/Ks values for all the protein coding genes in Alpheus and Macrobrachium mitochondrial genomes were found to be less than 1 (between 0.0048 and 0.2057), indicating that a strong purification selection had occurred. The phylogenetic tree that was constructed based on the mitochondrial protein coding genes in the genomes of nine related species indicated that Palaemonidae and Alpheidae formed a monophyly and shared a statistically significant relationship, (Palaemonidae+Alpheidae)+Atyidae, at the family level.

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.