A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists.

BACKGROUND: G-protein coupled receptors (GPCRs) sense and transmit extracellular signals into the intracellular machinery by regulating G proteins. GPCR malfunctions are associated with a variety of signaling-related diseases, including cancer and diabetes; at least a third of the marketed drugs target GPCRs. Thus, characterization of their signaling and regulatory mechanisms is crucial for the development of effective drugs. RESULTS: In this study, we developed a machine learning model to identify GPCR agonists and antagonists. We designed two-step prediction models: the first model identified the ligands binding to GPCRs and the second model classified the ligands as agonists or antagonists. Using 990 selected subset features from 5270 molecular descriptors calculated from 4590 ligands deposited in two drug databases, our model classified non-ligands, agonists, and antagonists of GPCRs, and achieved an area under the ROC curve (AUC) of 0.795, sensitivity of 0.716, specificity of 0.744, and accuracy of 0.733. In addition, we verified that 70% (44 out of 63) of FDA-approved GPCR-targeting drugs were correctly classified into their respective groups. CONCLUSIONS: Studies of ligand-GPCR interaction recognition are important for the characterization of drug action mechanisms. Our GPCR-ligand interaction prediction model can be employed in the pharmaceutical sciences for the efficient virtual screening of putative GPCR-binding agonists and antagonists.

PIC-Me: paralogs and isoforms classifier based on machine-learning approaches.

BACKGROUND: Paralogs formed through gene duplication and isoforms formed through alternative splicing have been important processes for increasing protein diversity and maintaining cellular homeostasis. Despite their recognized importance and the advent of large-scale genomic and transcriptomic analyses, paradoxically, accurate annotations of all gene loci to allow the identification of paralogs and isoforms remain surprisingly incomplete. In particular, the global analysis of the transcriptome of a non-model organism for which there is no reference genome is especially challenging. RESULTS: To reliably discriminate between the paralogs and isoforms in RNA-seq data, we redefined the pre-existing sequence features (sequence similarity, inverse count of consecutive identical or non-identical blocks, and match-mismatch fraction) previously derived from full-length cDNAs and EST sequences and described newly discovered genomic and transcriptomic features (twilight zone of protein sequence alignment and expression level difference). In addition, the effectiveness and relevance of the proposed features were verified with two widely used support vector machine (SVM) and random forest (RF) models. From nine RNA-seq datasets, all AUC (area under the curve) scores of ROC (receiver operating characteristic) curves were over 0.9 in the RF model and significantly higher than those in the SVM model. CONCLUSIONS: In this study, using an RF model with five proposed RNA-seq features, we implemented our method called Paralogs and Isoforms Classifier based on Machine-learning approaches (PIC-Me) and showed that it outperformed an existing method. Finally, we envision that our tool will be a valuable computational resource for the genomics community to help with gene annotation and will aid in comparative transcriptomics and evolutionary genomics studies, especially those on non-model organisms.

Microbial community analysis using high-throughput sequencing technology: a beginner's guide for microbiologists.

Microbial communities present in diverse environments from deep seas to human body niches play significant roles in the complex ecosystem and human health. Characterizing their structural and functional diversities is indispensable, and many approaches, such as microscopic observation, DNA fingerprinting, and PCR-based marker gene analysis, have been successfully applied to identify microorganisms. Since the revolutionary improvement of DNA sequencing technologies, direct and high-throughput analysis of genomic DNA from a whole environmental community without prior cultivation has become the mainstream approach, overcoming the constraints of the classical approaches. Here, we first briefly review the history of environmental DNA analysis applications with a focus on profiling the taxonomic composition and functional potentials of microbial communities. To this end, we aim to introduce the shotgun metagenomic sequencing (SMS) approach, which is used for the untargeted ("shotgun") sequencing of all ("meta") microbial genomes ("genomic") present in a sample. SMS data analyses are performed in silico using various software programs; however, in silico analysis is typically regarded as a burden on wet-lab experimental microbiologists. Therefore, in this review, we present microbiologists who are unfamiliar with in silico analyses with a basic and practical SMS data analysis protocol. This protocol covers all the bioinformatics processes of the SMS analysis in terms of data preprocessing, taxonomic profiling, functional annotation, and visualization.

Conventionally used reference genes are not outstanding for normalization of gene expression in human cancer research.

BACKGROUND: The selection of reference genes is essential for quantifying gene expression. Theoretically they should be expressed stably and not regulated by experimental or pathological conditions. However, identification and validation of reference genes for human cancer research are still being regarded as a critical point, because cancerous tissues often represent genetic instability and heterogeneity. Recent pan-cancer studies have demonstrated the importance of the appropriate selection of reference genes for use as internal controls for the normalization of gene expression; however, no stably expressed, consensus reference genes valid for a range of different human cancers have yet been identified. RESULTS: In the present study, we used large-scale cancer gene expression datasets from The Cancer Genome Atlas (TCGA) database, which contains 10,028 (9,364 cancerous and 664 normal) samples from 32 different cancer types, to confirm that the expression of the most commonly used reference genes is not consistent across a range of cancer types. Furthermore, we identified 38 novel candidate reference genes for the normalization of gene expression, independent of cancer type. These genes were found to be highly expressed and highly connected to relevant gene networks, and to be enriched in transcription-translation regulation processes. The expression stability of the newly identified reference genes across 29 cancerous and matched normal tissues were validated via quantitative reverse transcription PCR (RT-qPCR). CONCLUSIONS: We reveal that most commonly used reference genes in current cancer studies cannot be appropriate to serve as representative control genes for quantifying cancer-related gene expression levels, and propose in this study three potential reference genes (HNRNPL, PCBP1, and RER1) to be the most stably expressed across various cancerous and normal human tissues.

Identification and sequencing of the gene encoding DNA methyltransferase 3 (DNMT3) from sea cucumber, Apostichopus japonicus.

The sea cucumber Apostichopus japonicus is well known as a traditional tonic food and as a commercially important cultured aquatic species. This species produces saponins, and has a high potential to cope with environmental stress, such as aestivation, organ regeneration, and wound healing. Recently, several studies have shown that cellular reprogramming and the physiological responses of the sea cucumber to environmental changes, including aestivation, are potentially mediated by epigenetic DNA methylation. The DNA methyltransferase (DNMT)1 and DNMT3 genes are independent participants in the maintenance and de novo methylation of specific sequences. Sea urchin (Strongylocentrotus purpuratus) and starfish (Asterina pectinifera), which belong to the same phylum as A. japonicus, have both DNMT1 and DNMT3 genes. However, it was previously reported that DNMT1 is present, but DNMT3 is absent, in A. japonicus. In the present study, we sequenced the full-length cDNA of the A. japonicus DNMT3 gene. The newly sequenced DNMT3 gene comprises three major conserved domains (Pro-Trp-Trp-Pro (PWWP), plant homeodomain (PHD), and S-adenosylmethionine-dependent methyltransferase (AdoMet-MTase)), indicating that the DNMT3 possibly has de novo DNA methylation catalytic activity. Gene structure and phylogenetic analysis showed that sea cucumber DNMT3 is evolutionarily conserved in the Echinodermata. Next, we demonstrated the conservation of DNMT3 gene expression in sea cucumber and starfish belong to same phylum, echinoderm. Using reverse transcription-polymerase chain reaction, sea cucumber DNMT3 mRNA was detected in testis tissue, but not in other tissues tested, including the respiratory tree, muscle, tentacle, intestine, and ovary. This is inconsistent with previous reports, which showed the expression of DNMT3 in ovary, but not in testis of the starfish A. pectinifera, indicating the tissue- and species-specific expression of DNMT3 gene. Although further studies are needed to clarify the epigenetic regulatory mechanisms of DNMT3 and its application to the aquaculture industry, our findings may provide insights into the sea cucumber biology.

Population Genomic Analysis Reveals Contrasting Demographic Changes of Two Closely Related Dolphin Species in the Last Glacial.

Population genomic data can be used to infer historical effective population sizes (Ne), which help study the impact of past climate changes on biodiversity. Previous genome sequencing of one individual of the common bottlenose dolphin Tursiops truncatus revealed an unusual, sharp rise in Ne during the last glacial, raising questions about the reliability, generality, underlying cause, and biological implication of this finding. Here we first verify this result by additional sampling of T. truncatus. We then sequence and analyze the genomes of its close relative, the Indo-Pacific bottlenose dolphin T. aduncus. The two species exhibit contrasting demographic changes in the last glacial, likely through actual changes in population size and/or alterations in the level of gene flow among populations. Our findings suggest that even closely related species can have drastically different responses to climatic changes, making predicting the fate of individual species in the ongoing global warming a serious challenge.

The developmental transcriptome atlas of the spoon worm Urechis unicinctus (Echiurida: Annelida).

Background: Echiurida is one of the most intriguing major subgroups of annelida because, unlike most other annelids, echiurids lack metameric body segmentation as adults. For this reason, transcriptome analyses from various developmental stages of echiurid species can be of substantial value for understanding precise expression levels and the complex regulatory networks during early and larval development. Results: A total of 914 million raw RNA-Seq reads were produced from 14 developmental stages of Urechis unicinctus and were de novo assembled into contigs spanning 63,928,225 bp with an N50 length of 2700 bp. The resulting comprehensive transcriptome database of the early developmental stages of U. unicinctus consists of 20,305 representative functional protein-coding transcripts. Approximately 66% of unigenes were assigned to superphylum-level taxa, including Lophotrochozoa (40%). The completeness of the transcriptome assembly was assessed using benchmarking universal single-copy orthologs; 75.7% of the single-copy orthologs were presented in our transcriptome database. We observed 3 distinct patterns of global transcriptome profiles from 14 developmental stages and identified 12,705 genes that showed dynamic regulation patterns during the differentiation and maturation of U. unicinctus cells. Conclusions: We present the first large-scale developmental transcriptome dataset of U. unicinctus and provide a general overview of the dynamics of global gene expression changes during its early developmental stages. The analysis of time-course gene expression data is a first step toward understanding the complex developmental gene regulatory networks in U. unicinctus and will furnish a valuable resource for analyzing the functions of gene repertoires in various developmental phases.

Draft Genome Sequences of Pseudoalteromonas tetraodonis CSB01KR and Pseudoalteromonas lipolytica CSB02KR, Isolated from the Gut of the Sea Cucumber Apostichopus japonicus.

We present here the complete genome sequences of two newly isolated Pseudoalteromonas tetraodonis and Pseudoalteromonas lipolytica strains, isolated from the gut of the sea cucumber Apostichopus japonicus, to provide a useful means for facilitating the study of antibacterial, bacteriolytic, agarolytic, and algicidal activities of marine Pseudoalteromonas species.

Draft genome of the sea cucumber Apostichopus japonicus and genetic polymorphism among color variants.

The Japanese sea cucumber (Apostichopus japonicus Selenka 1867) is an economically important species as a source of seafood and ingredient in traditional medicine. It is mainly found off the coasts of northeast Asia. Recently, substantial exploitation and widespread biotic diseases in A. japonicus have generated increasing conservation concern. However, the genomic knowledge base and resources available for researchers to use in managing this natural resource and to establish genetically based breeding systems for sea cucumber aquaculture are still in a nascent stage. A total of 312 Gb of raw sequences were generated using the Illumina HiSeq 2000 platform and assembled to a final size of 0.66 Gb, which is about 80.5% of the estimated genome size (0.82 Gb). We observed nucleotide-level heterozygosity within the assembled genome to be 0.986%. The resulting draft genome assembly comprising 132 607 scaffolds with an N50 value of 10.5 kb contains a total of 21 771 predicted protein-coding genes. We identified 6.6-14.5 million heterozygous single nucleotide polymorphisms in the assembled genome of the three natural color variants (green, red, and black), resulting in an estimated nucleotide diversity of 0.00146. We report the first draft genome of A. japonicus and provide a general overview of the genetic variation in the three major color variants of A. japonicus. These data will help provide a comprehensive view of the genetic, physiological, and evolutionary relationships among color variants in A. japonicus, and will be invaluable resources for sea cucumber genomic research.