Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data

The “dark transcriptome” can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated open reading frames (ORFs) in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 30% of the highly transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins (“orphan-ORFs”); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph (MOG), a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally testable hypotheses about candidate genes.

Accurate detection of m6A RNA modifications in native RNA sequences

The epitranscriptomics field has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here, we show that using direct RNA sequencing, N6-methyladenosine (m6A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m6A-modified and unmodified synthetic sequences, can predict m6A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m6A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these ‘errors’ are typically not observed in yeast ime4-knockout strains, which lack m6A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context.

The translation inhibitor cycloheximide affects ribosome profiling data in a species-specific manner

Ribosome profiling provides genome-wide snapshots of translation dynamics by determining ribosomal positions at sub-codon resolution. To maintain this positional information, the translation inhibitor cycloheximide (CHX) has been widely used to arrest translating ribosomes prior to library preparation. Several studies have reported CHX-induced biases in yeast data casting uncertainty about its continued use and questioning the accuracy of many ribosome profiling studies. However, the presence of these biases has not been investigated comprehensively in organisms other than Saccharomyces cerevisiae. Here, we use a highly standardized and optimized protocol to compare different CHX-treatment conditions in yeast and human cells. Our data suggest that unlike in S. cerevisiae, translating ribosomes in human cells are not susceptible to conformational restrictions by CHX. Furthermore, CHX-induced codon-specific effects on ribosome occupancy are not detectable in human cells nor in other model organisms including Schizosaccharomyces pombe and Candida albicans. In fact, we find that even in S. cerevisiae most biases can be avoided by omitting CHX pre-treatment, indicating that other parameters of library generation contribute to differences between ribosome profiling experiments. Together our findings provide a framework for researchers who plan their own ribosome profiling experiments or who analyze published datasets to draw judicious conclusions.

Landscape of the Dark Transcriptome Revealed through Re-mining Massive RNA-Seq Data

The “dark transcriptome” can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated ORFs in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 48% of the transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins (“orphan-ORFs”); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph, a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally-testable hypotheses about candidate genes.

Penggunaan Khamir Laut Dalam Pakan Anguilla bicolor Terhadap Retensi Lemak dan Daya Cerna Energi [The Effect of Use of Sea Yeast in Feed Anguilla bicolor Against Fat Retention and Energy Di gestion]

AbstrakIkan sidat merupakan ikan konsumsi penting. Khamir laut (marine yeast) merupakan organisme seluler dari golongan jamur, bersifat kemoorganotrof, bereproduksi seksual dengan spora dan aseksual dengan pertunasan atau pembelahan. Percobaan ini menggunakan substitusi (A) 0% khamir laut (B) 5% khamir laut utuh (C) 5% khamir laut dipecah dan (D) 5% khamir laut direduksi. Data dianalisis menggunakan uji sidik ragam dan dilanjutkan dengan Uji Beda Nyata Terkecil (BNT). Hasil terbaik pada perlakuan D menunjukkan retensi lemak 24,88% dan daya cerna energy 69,74%. AbstractEel is an important consumption fish. Marine yeast (cellular yeast) is a cellular organism of the fungal group, which is chemoorganotrophic, reproduces sexually with spores and asexual with spreading or cleavage. This experiment uses substitution (A) 0% sea yeast (B) 5% whole sea yeast (C)5% broken sea yeast and (D) 5% reduced sea yeast. Data were analyzed by using Analysis of Variance (ANOVA) and followed by the Smallest Significantly Difference Test (LSD). The best results on treatment D showed fat retention of 24.88% and energy digestibility of 69.74%.

A Novel Method for Identifying Essential Genes by Fusing Dynamic Protein–Protein Interactive Networks

Essential genes play an indispensable role in supporting the life of an organism. Identification of essential genes helps us to understand the underlying mechanism of cell life. The essential genes of bacteria are potential drug targets of some diseases genes. Recently, several computational methods have been proposed to detect essential genes based on the static protein–protein interactive (PPI) networks. However, these methods have ignored the fact that essential genes play essential roles under certain conditions. In this work, a novel method was proposed for the identification of essential proteins by fusing the dynamic PPI networks of different time points (called by FDP). Firstly, the active PPI networks of each time point were constructed and then they were fused into a final network according to the networks’ similarities. Finally, a novel centrality method was designed to assign each gene in the final network a ranking score, whilst considering its orthologous property and its global and local topological properties in the network. This model was applied on two different yeast data sets. The results showed that the FDP achieved a better performance in essential gene prediction as compared to other existing methods that are based on the static PPI network or that are based on dynamic networks.

Gene-Tree Reconciliation with MUL-Trees to Resolve Polyploidy Events

Polyploidy can have a huge impact on the evolution of species, and it is a common occurrence, especially in plants. The two types of polyploids—autopolyploids and allopolyploids—differ in the level of divergence between the genes that are brought together in the new polyploid lineage. Because allopolyploids are formed via hybridization, the homoeologous copies of genes within them are at least as divergent as orthologs in the parental species that came together to form them. This means that common methods for estimating the parental lineages of allopolyploidy events are not accurate, and can lead to incorrect inferences about the number of gene duplications and losses. Here, we have adapted an algorithm for topology-based gene-tree reconciliation to work with multi-labeled trees (MUL-trees). By definition, MUL-trees have some tips with identical labels, which makes them a natural representation of the genomes of polyploids. Using this new reconciliation algorithm we can: accurately place allopolyploidy events on a phylogeny, identify the parental lineages that hybridized to form allopolyploids, distinguish between allo-, auto-, and (in most cases) no polyploidy, and correctly count the number of duplications and losses in a set of gene trees. We validate our method using gene trees simulated with and without polyploidy, and revisit the history of polyploidy in data from the clades including both baker’s yeast and bread wheat. Our re-analysis of the yeast data confirms the allopolyploid origin and parental lineages previously identified for this group. The method presented here should find wide use in the growing number of genomes from species with a history of polyploidy. [Polyploidy; reconciliation; whole-genome duplication.]

Bayesian Inference of Species Networks from Multilocus Sequence Data

Reticulate species evolution, such as hybridization or introgression, is relatively common in nature. In the presence of reticulation, species relationships can be captured by a rooted phylogenetic network, and orthologous gene evolution can be modeled as bifurcating gene trees embedded in the species network. We present a Bayesian approach to jointly infer species networks and gene trees from multilocus sequence data. A novel birth-hybridization process is used as the prior for the species network, and we assume a multispecies network coalescent (MSNC) prior for the embedded gene trees. We verify the ability of our method to correctly sample from the posterior distribution, and thus to infer a species network, through simulations. To quantify the power of our method, we reanalyze two large datasets of genes from spruces and yeasts. For the three closely related spruces, we verify the previously suggested homoploid hybridization event in this clade; for the yeast data, we find extensive hybridization events. Our method is available within the BEAST 2 add-on SpeciesNetwork, and thus provides an extensible framework for Bayesian inference of reticulate evolution.

Gene-tree reconciliation with MUL-trees to resolve polyploidy events

Polyploidy can have a huge impact on the evolution of species, and it is a common occurrence, especially in plants. The two types of polyploids - autopolyploids and allopolyploids - differ in the level of divergence between the genes that are brought together in the new polyploid lineage. Because allopolyploids are formed via hybridization, the homoeologous copies of genes within them are at least as divergent as orthologs in the parental species that came together to form them. This means that common methods for estimating the parental lineages of allopolyploidy events are not accurate, and can lead to incorrect inferences about the number of gene duplications and losses. Here, we have adapted an algorithm for topology-based gene-tree reconciliation to work with multi-labeled trees (MUL-trees). By definition, MUL-trees have some tips with identical labels, which makes them a natural representation of the genomes of polyploids. Using this new reconciliation algorithm we can: accurately place allopolyploidy events on a phylogeny, identify the parental lineages that hybridized to form allopolyploids, distinguish between allo-, auto-, and (in most cases) no polyploidy, and correctly count the number of duplications and losses in a set of gene trees. We validate our method using gene trees simulated with and without polyploidy, and revisit the history of polyploidy in data from the clades including both baker’s yeast and bread wheat. Our re-analysis of the yeast data confirms the allopolyploid origin and parental lineages previously identified for this group. The method presented here should find wide use in the growing number of genomes from species with a history of polyploidy.