Use and misuse of the gene ontology annotations

A transcriptome comparison of time-matched developing human, mouse and rat neural progenitor cells reveals human uniqueness

It is widely accepted that human brain development has unique features that cannot be represented by rodents. Obvious reasons are the evolutionary distance and divergent physiology. This might lead to false predictions when rodents are used for safety or pharmacological efficacy studies. For a better translation of animal-based research to the human situation, human in vitro systems might be useful. In this study, we characterize developing neural progenitor cells from prenatal human and time-matched rat and mouse brains by analyzing the changes in their transcriptome profile during neural differentiation. Moreover, we identify hub molecules that regulate neurodevelopmental processes like migration and differentiation. Consequences of modulation of three of those hubs on these processes were studied in a species-specific context. We found that although the gene expression profiles of the three species largely differ qualitatively and quantitatively, they cluster in similar GO terms like cell migration, gliogenesis, neurogenesis or development of multicellular organism. Pharmacological modulation of the identified hub molecules triggered species-specific cellular responses. This study underlines the importance of understanding species differences on the molecular level and advocates the use of human based in vitro models for pharmacological and toxicological research.

The accessible chromatin landscape during conversion of human embryonic stem cells to trophoblast by bone morphogenetic protein 4

Human embryonic stem cells (hESCs) exposed to the growth factor bone morphogenetic protein 4 (BMP4) in the absence of FGF2 have been used as a model to study the development of placental development. However, little is known about the cis-regulatory mechanisms underlying this important process. In this study, we used the public available chromatin accessibility data of hESC H1 cells and BMP4-induced trophoblast (TB) cell lines to identify DNase I hypersensitive sites (DHSs) in the two cell lines, as well as the transcription factor (TF) binding sites within the DHSs. By comparing read profiles in H1 and TB, we identified 17 472 TB-specific DHSs. The TB-specific DHSs are enriched in terms of "blood vessel" and "trophectoderm," consisting of TF motifs family: Leucine Zipper, Helix-Loop-Helix, GATA, and ETS. To validate differential expression of the TFs binding to these motifs, we analyzed public available RNA-seq and microarray data in the same context. Finally, by integrating the protein-protein interaction data, we constructed a TF network for placenta development and identified top 20 key TFs through centrality analysis in the network. Our results indicate BMP4-induced TB system provided an invaluable model for the study of TB development and highlighted novel candidate genes in placenta development in human.

Three macrophage subsets are identified in the uterus during early human pregnancy

Macrophages are crucial for a successful pregnancy, and malfunctions of decidual macrophages correlate with adverse pregnancy outcomes, such as spontaneous abortion and preeclampsia. Previously, decidual macrophages were often thought to be a single population. In the present study, we identified three decidual macrophage subsets, CCR2-CD11cLO (CD11c^low, ~80%), CCR2-CD11cHI (CD11c^high, ~5%), and CCR2+CD11cHI (CD11c^high, 10-15%), during the first trimester of human pregnancy by flow cytometry analysis. CCR2-CD11cLO macrophages are widely distributed in the decidua, while CCR2-CD11cHI and CCR2+CD11cHI macrophages are primarily detected close to extravillous trophoblast cells according to immunofluorescence staining. According to RNA sequencing bioinformatics analysis and in vitro functional studies, these three subsets of macrophages have different phagocytic capacities. CCR2+CD11cHI macrophages have pro-inflammatory characteristics, while the CCR2-CD11cHI population is suggested to be anti-oxidative and anti-inflammatory due to its high expression of critical heme metabolism-related genes, suggesting that these two subsets of macrophages maintain an inflammatory balance at the leading edge of trophoblast invasion to facilitate the clearance of pathogen infection as well as maintain the homeostasis of the maternal-fetal interface. The present study physiologically identifies three decidual macrophage subsets. Further clarification of the functions of these subsets will improve our understanding of maternal-fetal crosstalk in the maintenance of a healthy pregnancy.

Selective Constraints on Coding Sequences of Nervous System Genes Are a Major Determinant of Duplicate Gene Retention in Vertebrates

The evolutionary history of vertebrates is marked by three ancient whole-genome duplications: two successive rounds in the ancestor of vertebrates, and a third one specific to teleost fishes. Biased loss of most duplicates enriched the genome for specific genes, such as slow evolving genes, but this selective retention process is not well understood. To understand what drives the long-term preservation of duplicate genes, we characterized duplicated genes in terms of their expression patterns. We used a new method of expression enrichment analysis, TopAnat, applied to in situ hybridization data from thousands of genes from zebrafish and mouse. We showed that the presence of expression in the nervous system is a good predictor of a higher rate of retention of duplicate genes after whole-genome duplication. Further analyses suggest that purifying selection against the toxic effects of misfolded or misinteracting proteins, which is particularly strong in nonrenewing neural tissues, likely constrains the evolution of coding sequences of nervous system genes, leading indirectly to the preservation of duplicate genes after whole-genome duplication. Whole-genome duplications thus greatly contributed to the expansion of the toolkit of genes available for the evolution of profound novelties of the nervous system at the base of the vertebrate radiation.

The Rate of Evolution of Postmating-Prezygotic Reproductive Isolation in Drosophila

Reproductive isolation is an intrinsic aspect of species formation. For that reason, the identification of the precise isolating traits, and the rates at which they evolve, is crucial to understanding how species originate and persist. Previous work has measured the rates of evolution of prezygotic and postzygotic barriers to gene flow, yet no systematic analysis has studied the rates of evolution of postmating-prezygotic (PMPZ) barriers. We measured the magnitude of two barriers to gene flow that act after mating occurs but before fertilization. We also measured the magnitude of a premating barrier (female mating rate in nonchoice experiments) and two postzygotic barriers (hybrid inviability and hybrid sterility) for all pairwise crosses of all nine known extant species within the melanogaster subgroup. Our results indicate that PMPZ isolation evolves faster than hybrid inviability but slower than premating isolation. Next, we partition postzygotic isolation into different components and find that, as expected, hybrid sterility evolves faster than hybrid inviability. These results lend support for the hypothesis that, in Drosophila, reproductive isolation mechanisms (RIMs) that act early in reproduction (or in development) tend to evolve faster than those that act later in the reproductive cycle. Finally, we tested whether there was evidence for reinforcing selection at any RIM. We found no evidence for generalized evolution of reproductive isolation via reinforcement which indicates that there is no pervasive evidence of this evolutionary process. Our results indicate that PMPZ RIMs might have important evolutionary consequences in initiating speciation and in the persistence of new species.

Identification of risk factors for sepsis-associated mortality by gene expression profiling analysis

Sepsis is a common cause of mortality due to systemic infection. Although numerous studies have investigated this life-threatening condition, there remains a lack of suitable markers to evaluate the severity of sepsis. The present study focused on the identification of risk factors for sepsis‑associated mortality by genome‑wide expression profiling. Initially, the GEO2R web tool was used to identify the differentially expressed genes (DEGs) between sepsis survivors and nonsurvivors. It was identified that the upregulated DEGs in the nonsurvivors compared with survivors were highly enriched in the type I interferon (IFN‑I) signaling pathway. Furthermore, the associations of the upregulated genes were analyzed by STRING and the results demonstrated that a set of proteins in IFN‑I signaling pathway closely interacted with each other. To further investigate whether the IFN‑I signaling pathway is dysregulated in a subset of patients with a high risk of mortality due to sepsis, in this case neonates, the DEGs between the cord blood mononuclear cells of neonates and adult peripheral blood mononuclear cells were analyzed. It was identified that DEGs were not enriched in IFN‑I signaling in the blood of untreated neonates and adults; however, IFN‑I signaling was upregulated in the lipopolysaccharide (LPS)‑treated cord blood mononuclear cells of healthy neonates compared with the LPS‑treated peripheral blood mononuclear cells of adults. In addition, these data revealed that the proteins involved in the IFN‑I signaling pathway possessed a higher number of interacting partners. These results indicated that upregulated IFN‑I signaling may be a high-risk factor for mortality due to sepsis.

Gene annotation bias impedes biomedical research

We found tremendous inequality across gene and protein annotation resources. We observed that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate that researchers reduce these biases by pursuing data-driven hypotheses.

ANISEED 2017: extending the integrated ascidian database to the exploration and evolutionary comparison of genome-scale datasets

ANISEED (www.aniseed.cnrs.fr) is the main model organism database for tunicates, the sister-group of vertebrates. This release gives access to annotated genomes, gene expression patterns, and anatomical descriptions for nine ascidian species. It provides increased integration with external molecular and taxonomy databases, better support for epigenomics datasets, in particular RNA-seq, ChIP-seq and SELEX-seq, and features novel interactive interfaces for existing and novel datatypes. In particular, the cross-species navigation and comparison is enhanced through a novel taxonomy section describing each represented species and through the implementation of interactive phylogenetic gene trees for 60% of tunicate genes. The gene expression section displays the results of RNA-seq experiments for the three major model species of solitary ascidians. Gene expression is controlled by the binding of transcription factors to cis-regulatory sequences. A high-resolution description of the DNA-binding specificity for 131 Ciona robusta (formerly C. intestinalis type A) transcription factors by SELEX-seq is provided and used to map candidate binding sites across the Ciona robusta and Phallusia mammillata genomes. Finally, use of a WashU Epigenome browser enhances genome navigation, while a Genomicus server was set up to explore microsynteny relationships within tunicates and with vertebrates, Amphioxus, echinoderms and hemichordates.

Inferring Unknown Biological Function by Integration of GO Annotations and Gene Expression Data

Characterizing genes with semantic information is an important process regarding the description of gene products. In spite that complete genomes of many organisms have been already sequenced, the biological functions of all of their genes are still unknown. Since experimentally studying the functions of those genes, one by one, would be unfeasible, new computational methods for gene functions inference are needed. We present here a novel computational approach for inferring biological function for a set of genes with previously unknown function, given a set of genes with well-known information. This approach is based on the premise that genes with similar behaviour should be grouped together. This is known as the guilt-by-association principle. Thus, it is possible to take advantage of clustering techniques to obtain groups of unknown genes that are co-clustered with genes that have well-known semantic information (GO annotations). Meaningful knowledge to infer unknown semantic information can therefore be provided by these well-known genes. We provide a method to explore the potential function of new genes according to those currently annotated. The results obtained indicate that the proposed approach could be a useful and effective tool when used by biologists to guide the inference of biological functions for recently discovered genes. Our work sets an important landmark in the field of identifying unknown gene functions through clustering, using an external source of biological input. A simple web interface to this proposal can be found at http://fich.unl.edu.ar/sinc/webdemo/gamma-am/.

Indolizine-Based Scaffolds as Efficient and Versatile Tools: Application to the Synthesis of Biotin-Tagged Antiangiogenic Drugs

We describe the design and optimization of polyfunctional scaffolds based on a fluorescent indolizine core derivatized with various orthogonal groups (amines, esters, oximes, alkynes, etc.). To show one application as tools in biology, the scaffold was used to prepare drug-biotin conjugates that were then immobilized onto avidin-agarose for affinity chromatography. More specifically, the antiangiogenic drug COB223, whose mechanism of action remained unclear, was chosen as a proof-of-concept drug. The drug-selective discrimination of proteins observed after elution of the cell lysates through the affinity columns, functionalized either with the biologically active COB223 or a structurally related inactive analogue (COB236), is a clear indication that the presence of the indolizine core does not limit drug-protein interaction and confirms the usefulness of the indolizine scaffold. Furthermore, the separation of COB223-interacting proteins from human placental extracts unveiled unanticipated protein targets belonging to the family of regulatory RNA-binding proteins, which opens the way to new hypotheses on the mode of action of this antiangiogenic drug.

Transcriptome analysis of the threatened snail Ellobium chinense reveals candidate genes for adaptation and identifies SSRs for conservation genetics

Ellobium chinense (Pfeiffer, 1854) is a brackish pulmonate species that inhabits the bases of mangrove trees and is most commonly found in salt grass meadows. Threats to mangrove ecosystems due to habitat degradation and overexploitation have threatened the species with extinction. In South Korea, E. chinense has been assessed as vulnerable, but there are limited data on its population structure and distribution. The nucleotide and protein sequences for this species are not available in databases, which limits the understanding of adaptation-related traits. We sequenced an E. chinense cDNA library using the Illumina platform, and the subsequent bioinformatics analysis yielded 227,032 unigenes. Of these unigenes, 69,088 were annotated to matched protein and nucleotide sequences in databases, for an annotation rate of 30.42%. Among the predominant gene ontology terms, cellular and metabolic processes (under the biological process category), membrane and cell (under the cellular component category), and binding and catalytic activity (under the molecular function category) were noteworthy. In addition, 4850 unigenes were distributed to 15 Kyoto Encyclopaedia of Genes and Genomes based enrichment categories. Among the candidate genes related to adaptation, angiotensin I converting enzyme, adenylate cyclase activating polypeptide, and AMP-activated protein kinase were the most prominent. A total of 15,952 simple sequence repeats (SSRs) were identified in sequences of > 1 kb in length. The di- and trinucleotide repeat motifs were the most common. Among the repeat motif types, AG/CT, AC/GT, and AAC/GTT dominated. Our study provides the first comprehensive genomics dataset for E. chinense, which favors conservation programs for the restoration of the species and provides sufficient evidence for genetic variability among the wild populations.

3D-cultivation of NSCLC cell lines induce gene expression alterations of key cancer-associated pathways and mimic in-vivo conditions

This work evaluated gene expression differences between a hanging-drop 3D NSCLC model and 2D cell cultures and their in-vivo relevance by comparison to patient-derived data from The Cancer Genome Atlas.

Gene expression of 2D and 3D cultures for Colo699 and A549 were assessed using Affymetrix HuGene 1.0 ST gene chips. Biostatistical analyses tested for reproducibility, comparability and significant differences in gene expression profiles between cell lines, experiments and culture methods.

The analyses revealed a high interassay correlation within specific culture systems proving a high validity. 979 genes were altered in A549 and 1106 in Colo699 cells due to 3D cultivation. The overlap of changed genes between the cell lines was small (149), but the involved pathways in the reactome and GO- analyses showed a high overlap with DNA methylation, cell cycle, SIRT1, PKN1 pathway, DNA repair and oxidative stress as well known cancer-associated representatives. Additional specific GSEA-analyses revealed changes in immunologic and endothelial cell proliferation pathways, whereas hypoxic, EMT and angiogenic pathways were downregulated.

Gene enrichment analyses showed 3D-induced gene up-regulations in the cell lines 38 to be represented in in-vivo samples of NSCLC patients using data of The Cancer Genome Atlas.

Thus, our 3D NSCLC model might provide a tool for early drug development and investigation of microenvironment-associated mechanisms. However, this work also highlights the need for further individualization and model adaption to address remaining challenges.

Identification of genetic networks that act in the somatic cells of the testis to mediate the developmental program of spermatogenesis

Spermatogenesis is a dynamic developmental process requiring precisely timed transitions between discrete stages. Specifically, the germline undergoes three transitions: from mitotic spermatogonia to spermatocytes, from meiotic spermatocytes to spermatids, and from morphogenetic spermatids to spermatozoa. The somatic cells of the testis provide essential support to the germline throughout spermatogenesis, but their precise role during these developmental transitions has not been comprehensively explored. Here, we describe the identification and characterization of genes that are required in the somatic cells of the Drosophila melanogaster testis for progress through spermatogenesis. Phenotypic analysis of candidate genes pinpointed the stage of germline development disrupted. Bioinformatic analysis revealed that particular gene classes were associated with specific developmental transitions. Requirement for genes associated with endocytosis, cell polarity, and microtubule-based transport corresponded with the development of spermatogonia, spermatocytes, and spermatids, respectively. Overall, we identify mechanisms that act specifically in the somatic cells of the testis to regulate spermatogenesis.

Sexual reproduction in animals requires the production of male and female gametes, spermatozoa and ova, respectively. Gametes are derived from specialized cells known as the germline through a process called gametogenesis. Gametogenesis typically takes place in a gonad and requires the germ cells to be surrounded by specialized somatic cells that support germline development. While many prior studies have identified germline specific genes required for gametogenesis few have systematically identified genes required in the somatic cells for gametogenesis. To this end we performed an RNAi screen where we disrupted the function of genes specifically in the somatic cyst cells of the Drosophila melanogaster testis. Using fertility assays we identified 281 genes that are required in somatic cyst cells for fertility. To better understand the role of these genes in regulating spermatogenesis we classified the resulting phenotypes by the stage of germline development disrupted. This revealed distinct sets of genes required to support specific stages of spermatogenesis. Our study characterizes the somatic specific defects resulting from disruption of candidate genes and provides insight into their function in the testes. Overall, our findings reveal the mechanisms controlling Drosophila melanogaster spermatogenesis and provide a resource for studying soma-germline interactions more broadly.

Bioinformatics Tools for Proteomics Data Interpretation

Biological systems function via intricate cellular processes and networks in which RNAs, metabolites, proteins and other cellular compounds have a precise role and are exquisitely regulated (Kumar and Mann, FEBS Lett 583(11):1703-1712, 2009). The development of high-throughput technologies, such as the Next Generation DNA Sequencing (NGS) and DNA microarrays for sequencing genomes or metagenomes, have triggered a dramatic increase in the last few years in the amount of information stored in the GenBank and UniProt Knowledgebase (UniProtKB). GenBank release 210, reported in October 2015, contains 202,237,081,559 nucleotides corresponding to 188,372,017 sequences, whilst there are only 1,222,635,267,498 nucleotides corresponding to 309,198,943 sequences from Whole Genome Shotgun (WGS) projects. In the case of UniProKB/Swiss-Prot, release 2015_12 (December 9, 2015) contains 196,219,159 amino acids that correspond to 550,116 entries. Meanwhile, UniProtKB/TrEMBL (release 2015_12 of December 9 2015) contains 1,838,851,8871 amino acids corresponding to 555,270,679 entries. Proteomics has also improved our knowledge of proteins that are being expressed in cells at a certain time of the cell cycle. It has also allowed the identification of molecules forming part of multiprotein complexes and an increasing number of posttranslational modifications (PTMs) that are present in proteins, as well as the variants of proteins expressed.

The porcine translational research database: a manually curated, genomics and proteomics-based research resource

BACKGROUND

The use of swine in biomedical research has increased dramatically in the last decade. Diverse genomic- and proteomic databases have been developed to facilitate research using human and rodent models. Current porcine gene databases, however, lack the robust annotation to study pig models that are relevant to human studies and for comparative evaluation with rodent models. Furthermore, they contain a significant number of errors due to their primary reliance on machine-based annotation. To address these deficiencies, a comprehensive literature-based survey was conducted to identify certain selected genes that have demonstrated function in humans, mice or pigs.

RESULTS

The process identified 13,054 candidate human, bovine, mouse or rat genes/proteins used to select potential porcine homologs by searching multiple online sources of porcine gene information. The data in the Porcine Translational Research Database (( http://www.ars.usda.gov/Services/docs.htm?docid=6065 ) is supported by >5800 references, and contains 65 data fields for each entry, including >9700 full length (5' and 3') unambiguous pig sequences, >2400 real time PCR assays and reactivity information on >1700 antibodies. It also contains gene and/or protein expression data for >2200 genes and identifies and corrects 8187 errors (gene duplications artifacts, mis-assemblies, mis-annotations, and incorrect species assignments) for 5337 porcine genes.

CONCLUSIONS

This database is the largest manually curated database for any single veterinary species and is unique among porcine gene databases in regard to linking gene expression to gene function, identifying related gene pathways, and connecting data with other porcine gene databases. This database provides the first comprehensive description of three major Super-families or functionally related groups of proteins (Cluster of Differentiation (CD) Marker genes, Solute Carrier Superfamily, ATP binding Cassette Superfamily), and a comparative description of porcine microRNAs.

NoGOA: predicting noisy GO annotations using evidences and sparse representation

BACKGROUND

Gene Ontology (GO) is a community effort to represent functional features of gene products. GO annotations (GOA) provide functional associations between GO terms and gene products. Due to resources limitation, only a small portion of annotations are manually checked by curators, and the others are electronically inferred. Although quality control techniques have been applied to ensure the quality of annotations, the community consistently report that there are still considerable noisy (or incorrect) annotations. Given the wide application of annotations, however, how to identify noisy annotations is an important but yet seldom studied open problem.

RESULTS

We introduce a novel approach called NoGOA to predict noisy annotations. NoGOA applies sparse representation on the gene-term association matrix to reduce the impact of noisy annotations, and takes advantage of sparse representation coefficients to measure the semantic similarity between genes. Secondly, it preliminarily predicts noisy annotations of a gene based on aggregated votes from semantic neighborhood genes of that gene. Next, NoGOA estimates the ratio of noisy annotations for each evidence code based on direct annotations in GOA files archived on different periods, and then weights entries of the association matrix via estimated ratios and propagates weights to ancestors of direct annotations using GO hierarchy. Finally, it integrates evidence-weighted association matrix and aggregated votes to predict noisy annotations. Experiments on archived GOA files of six model species (H. sapiens, A. thaliana, S. cerevisiae, G. gallus, B. Taurus and M. musculus) demonstrate that NoGOA achieves significantly better results than other related methods and removing noisy annotations improves the performance of gene function prediction.

CONCLUSIONS

The comparative study justifies the effectiveness of integrating evidence codes with sparse representation for predicting noisy GO annotations. Codes and datasets are available at http://mlda.swu.edu.cn/codes.php?name=NoGOA .

Identifying Significantly Impacted Pathways and Putative Mechanisms with iPathwayGuide

iPathwayGuide is a gene expression analysis tool that provides biological context and inferences from data generated by high-throughput sequencing. iPathwayGuide utilizes a systems biology approach to identify significantly impacted signaling pathways, Gene Ontology terms, disease processes, predicted microRNAs, and putative mechanisms based on the given differential expression signature. By using a novel analytical approach called Impact Analysis, iPathwayGuide considers the role, position, and relationships of each gene within a pathway, which results in a significant reduction in false positives, as well as a better ability to identify the truly impacted pathways and putative mechanisms that can explain all measured gene expression changes. It is a Web-based, user-friendly, interactive tool that does not require prior training in bioinformatics. The protocols in this unit describe how to use iPathwayGuide to analyze a single contrast between two phenotypes (any number of samples), and provide guidance on how to interpret the results obtained from iPathwayGuide. Even though iPathwayGuide has powerful meta-analysis capabilities, these are not covered in this unit. © 2017 by John Wiley & Sons, Inc.

Embryonic lethal abnormal vision proteins and adenine and uridine-rich element mRNAs after global cerebral ischemia and reperfusion in the rat

Prolonged translation arrest correlates with delayed neuronal death of hippocampal CA1 neurons following global cerebral ischemia and reperfusion. Many previous studies investigated ribosome molecular biology, but mRNA regulatory mechanisms after brain ischemia have been less studied. Here we investigated the embryonic lethal abnormal vision/Hu isoforms HuR, HuB, HuC, and HuD, as well as expression of mRNAs containing adenine and rich uridine elements following global ischemia in rat brain. Proteomics of embryonic lethal abnormal vision immunoprecipitations or polysomes isolated from rat hippocampal CA1 and CA3 from controls or following 10 min ischemia plus 8 h of reperfusion showed distinct sets of mRNA-binding proteins, suggesting differential mRNA regulation in each condition. Notably, HuB, HuC, and HuD were undetectable in NIC CA1. At 8 h reperfusion, polysome-associated mRNAs contained 46.1% of ischemia-upregulated mRNAs containing adenine and rich uridine elements in CA3, but only 18.7% in CA1. Since mRNAs containing adenine and rich uridine elements regulation are important to several cellular stress responses, our results suggest CA1 is disadvantaged compared to CA3 in coping with ischemic stress, and this is expected to be an important contributing factor to CA1 selective vulnerability. (Data are available via ProteomeXchange identifier PXD004078 and GEO Series accession number GSE82146).

Translational effects and coding potential of an upstream open reading frame associated with DOPA Responsive Dystonia

Upstream open reading frames (uORFs) have emerged as major post-transcriptional regulatory elements in eukaryotic species. In general, uORFs are initiated by a translation start codon within the 5' untranslated region of a gene (upstream ATG; uATG), and they are negatively correlated with translational efficiency. In addition to their translational regulatory role, some uORFs can code for biologically active short peptides. The importance of uATGs/uORFs is further underscored by human diseases associated with single nucleotide polymorphisms (SNPs), which disrupt existing uORFs or introduce novel uORFs. Although several functional proteins translated from naturally occurring uORFs have been described, the coding potential of uORFs created by SNPs has been ignored because of the a priori assumption that these proteins are short-lived with no likely impact on protein homeostasis. Thus, studies on SNP-created uORFs are limited to their translational effects, leaving unexplored the potential cellular consequences of a SNP/uORF-encoded protein. Here, we investigate functionality of a uATG/uORF introduced by a +142C>T SNP within the GCH1 gene and associated with a familial form of DOPA Responsive Dystonia. We report that the +142C>T SNP represses GCH1 translation, and introduces a short, frame shifted uORF that encodes a 73-amino acid peptide. This peptide is localized within the nucleus and compromises cell viability upon proteasome inhibition. Our work extends the list of uATG/uORF associated diseases and advances research on peptides translated from SNP-introduced uORFs, a neglected component of the proteome.

Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework

Protein functional similarity based on gene ontology (GO) annotations serves as a powerful tool when comparing proteins on a functional level in applications such as protein-protein interaction prediction, gene prioritization, and disease gene discovery. Functional similarity (FS) is usually quantified by combining the GO hierarchy with an annotation corpus that links genes and gene products to GO terms. One large group of algorithms involves calculation of GO term semantic similarity (SS) between all the terms annotating the two proteins, followed by a second step, described as “mixing strategy”, which involves combining the SS values to yield the final FS value. Due to the variability of protein annotation caused e.g. by annotation bias, this value cannot be reliably compared on an absolute scale. We therefore introduce a similarity z-score that takes into account the FS background distribution of each protein. For a selection of popular SS measures and mixing strategies we demonstrate moderate accuracy improvement when using z-scores in a benchmark that aims to separate orthologous cases from random gene pairs and discuss in this context the impact of annotation corpus choice. The approach has been implemented in Frela, a fast high-throughput public web server for protein FS calculation and interpretation.

Primer on the Gene Ontology

The Gene Ontology (GO) project is the largest resource for cataloguing gene function. The combination of solid conceptual underpinnings and a practical set of features have made the GO a widely adopted resource in the research community and an essential resource for data analysis. In this chapter, we provide a concise primer for all users of the GO. We briefly introduce the structure of the ontology and explain how to interpret annotations associated with the GO.

Gene Ontology: Pitfalls, Biases, and Remedies

The Gene Ontology (GO) is a formidable resource, but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding of its structure or how it is developed, which is both a strength and a weakness. In this chapter, we discuss some common misinterpretations of the ontology and the annotations. A better understanding of the pitfalls and the biases in the GO should help users make the most of this very rich resource. We also review some of the misconceptions and misleading assumptions commonly made about GO, including the effect of data incompleteness, the importance of annotation qualifiers, and the transitivity or lack thereof associated with different ontology relations. We also discuss several biases that can confound aggregate analyses such as gene enrichment analyses. For each of these pitfalls and biases, we suggest remedies and best practices.

Interspecies gene function prediction using semantic similarity

BACKGROUND

Gene Ontology (GO) is a collaborative project that maintains and develops controlled vocabulary (or terms) to describe the molecular function, biological roles and cellular location of gene products in a hierarchical ontology. GO also provides GO annotations that associate genes with GO terms. GO consortium independently and collaboratively annotate terms to gene products, mainly from model organisms (or species) they are interested in. Due to experiment ethics, research interests of biologists and resources limitations, homologous genes from different species currently are annotated with different terms. These differences can be more attributed to incomplete annotations of genes than to functional difference between them.

RESULTS

Semantic similarity between genes is derived from GO hierarchy and annotations of genes. It is positively correlated with the similarity derived from various types of biological data and has been applied to predict gene function. In this paper, we investigate whether it is possible to replenish annotations of incompletely annotated genes by using semantic similarity between genes from two species with homology. For this investigation, we utilize three representative semantic similarity metrics to compute similarity between genes from two species. Next, we determine the k nearest neighborhood genes from the two species based on the chosen metric and then use terms annotated to k neighbors of a gene to replenish annotations of that gene. We perform experiments on archived (from Jan-2014 to Jan-2016) GO annotations of four species (Human, Mouse, Danio rerio and Arabidopsis thaliana) to assess the contribution of semantic similarity between genes from different species. The experimental results demonstrate that: (1) semantic similarity between genes from homologous species contributes much more on the improved accuracy (by 53.22%) than genes from single species alone, and genes from two species with low homology; (2) GO annotations of genes from homologous species are complementary to each other.

CONCLUSIONS

Our study shows that semantic similarity based interspecies gene function annotation from homologous species is more prominent than traditional intraspecies approaches. This work can promote more research on semantic similarity based function prediction across species.

Transcriptome Analysis of the Tadpole Shrimp (Triops longicaudatus) by Illumina Paired-End Sequencing: Assembly, Annotation, and Marker Discovery

The tadpole shrimp (Triops longicaudatus) is an aquatic crustacean that helps control pest populations. It inhabits freshwater ponds and pools and has been described as a living fossil. T. longicaudatus was officially declared an endangered species South Korea in 2005; however, through subsequent protection and conservation management, it was removed from the endangered species list in 2012. The limited number of available genetic resources on T. longicaudatus makes it difficult to obtain valuable genetic information for marker-aided selection programs. In this study, whole-transcriptome sequencing of T. longicaudatus generated 39.74 GB of clean data and a total of 269,822 contigs using the Illumina HiSeq 2500 platform. After clustering, a total of 208,813 unigenes with an N₅₀ length of 1089 bp were generated. A total of 95,105 unigenes were successfully annotated against Protostome (PANM), Unigene, Eukaryotic Orthologous Groups (KOG), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases using BLASTX with a cut-off of 1E−5. A total of 57,731 unigenes were assigned to GO terms, and 7247 unigenes were mapped to 129 KEGG pathways. Furthermore, 1595 simple sequence repeats (SSRs) were detected from the unigenes with 1387 potential SSR markers. This is the first report of high-throughput transcriptome analysis of T. longicaudatus, and it provides valuable insights for genetic research and molecular-assisted breeding of this important species.

Expression-level support for gene dosage sensitivity in three Glycine subgenus Glycine polyploids and their diploid progenitors

Retention or loss of paralogs following duplication correlates strongly with the function of the gene and whether the gene was duplicated by whole-genome duplication (WGD) or by small-scale duplication. Selection on relative gene dosage (to maintain proper stoichiometry among interacting proteins) has been invoked to explain these patterns of duplicate gene retention and loss. In order for gene dosage to be visible to natural selection, there must necessarily be a correlation between gene copy number and gene expression level (transcript abundance), but this has rarely been examined. We used RNA-Seq data from seven Glycine subgenus Glycine species (three recently formed allotetraploids and their four diploid progenitors) to determine if expression patterns and gene dosage responses at the level of transcription are consistent with selection on relative gene dosage. As predicted, metabolic pathways and gene ontologies that are putatively dosage-sensitive based on duplication history exhibited reduced expression variance across species, and more coordinated expression responses to recent WGD, relative to putatively dosage-insensitive networks. We conclude that selection on relative dosage has played an important role in shaping gene networks in Glycine.

Cannabidiol Modulates the Immunophenotype and Inhibits the Activation of the Inflammasome in Human Gingival Mesenchymal Stem Cells

Human Gingival Mesenchymal Stem Cells (hGMSCs) are multipotential cells that can expand and differentiate in culture under specific and standardized conditions. In the present study, we have investigated whether in vitro pre-treatment of hGMSCs with Cannabidiol (CBD) can influence their expression profile, improving the therapeutic potential of this cell culture. Following CBD treatment (5 μM) for 24 h, gene expression analysis through Next Generation Sequencing (NGS) has revealed several genes differentially expressed between CBD-treated hGMSCs (CBD-hGMSCs) and control cells (CTR-hGMSCs) that were linked to inflammation and apoptosis. In particular, we have demonstrated that CBD treatment in hGMSCs prevented the activation of the NALP3-inflammasome pathway by suppressing the levels of NALP3, CASP1, and IL18, and in parallel, inhibited apoptosis, as demonstrated by the suppression of Bax. CBD treatment was also able to modulate the expression of the well-known mesenchymal stem cell markers (CD13, CD29, CD73, CD44, CD90, and CD166), and other surface antigens. Specifically, CBD led to the downregulation of genes codifying for antigens involved in the activation of the immune system (CD109, CD151, CD40, CD46, CD59, CD68, CD81, CD82, CD99), while it led to the upregulation of those implicated in the inhibition of the immune responses (CD47, CD55, CD276). In conclusion, the present study will provide a new simple and reproducible method for preconditioning hGMSCs with CBD, before transplantation, as an interesting strategy for improving the hGMSCs molecular phenotype, reducing the risk of immune or inflammatory reactions in the host, and in parallel, for increasing their survival and thus, their long-term therapeutic efficacy.

BgeeDB, an R package for retrieval of curated expression datasets and for gene list expression localization enrichment tests

BgeeDB is a collection of functions to import into R re-annotated, quality-controlled and reprocessed expression data available in the Bgee database. This includes data from thousands of wild-type healthy samples of multiple animal species, generated with different gene expression technologies (RNA-seq, Affymetrix microarrays, expressed sequence tags, and in situ hybridizations). BgeeDB facilitates downstream analyses, such as gene expression analyses with other Bioconductor packages. Moreover, BgeeDB includes a new gene set enrichment test for preferred localization of expression of genes in anatomical structures ("TopAnat"). Along with the classical Gene Ontology enrichment test, this test provides a complementary way to interpret gene lists. Availability: http://www.bioconductor.org/packages/BgeeDB/.

BgeeDB, an R package for retrieval of curated expression datasets and for gene list expression localization enrichment tests

BgeeDB is a collection of functions to import into R re-annotated, quality-controlled and re-processed expression data available in the Bgee database. This includes data from thousands of wild-type healthy samples of multiple animal species, generated with different gene expression technologies (RNA-seq, Affymetrix microarrays, expressed sequence tags, and in situ hybridizations). BgeeDB facilitates downstream analyses, such as gene expression analyses with other Bioconductor packages. Moreover, BgeeDB includes a new gene set enrichment test for preferred localization of expression of genes in anatomical structures ("TopAnat"). Along with the classical Gene Ontology enrichment test, this test provides a complementary way to interpret gene lists. Availability: https://www.bioconductor.org/packages/BgeeDB/.

Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this "guilt-by-association" (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies.

Sequencing and de novo assembly of visceral mass transcriptome of the critically endangered land snail Satsuma myomphala: Annotation and SSR discovery

Satsuma myomphala is critically endangered through loss of natural habitats, predation by natural enemies, and indiscriminate collection. It is a protected species in Korea but lacks genomic resources for an understanding of varied functional processes attributable to evolutionary success under natural habitats. For assessing the genetic information of S. myomphala, we performed for the first time, de novo transcriptome sequencing and functional annotation of expressed sequences using Illumina Next-Generation Sequencing (NGS) platform and bioinformatics analysis. We identified 103,774 unigenes of which 37,959, 12,890, and 17,699 were annotated in the PANM (Protostome DB), Unigene, and COG (Clusters of Orthologous Groups) databases, respectively. In addition, 14,451 unigenes were predicted under Gene Ontology functional categories, with 4581 assigned to a single category. Furthermore, 3369 sequences with 646 having Enzyme Commission (EC) numbers were mapped to 122 pathways in the Kyoto Encyclopedia of Genes and Genomes Pathway database. The prominent protein domains included the Zinc finger (C2H2-like), Reverse Transcriptase, Thioredoxin-like fold, and RNA recognition motif domain. Many unigenes with homology to immunity, defense, and reproduction-related genes were screened in the transcriptome. We also detected 3120 putative simple sequence repeats (SSRs) encompassing dinucleotide to hexanucleotide repeat motifs from >1kb unigene sequences. A list of PCR primers of SSR loci have been identified to study the genetic polymorphisms. The transcriptome data represents a valuable resource for further investigations on the species genome structure and biology. The unigenes information and microsatellites would provide an indispensable tool for conservation of the species in natural and adaptive environments.

In silico analysis of the transportome in human pancreatic ductal adenocarcinoma

The altered expression and/or activity of ion channels and transporters (transportome) have been associated with malignant behavior of cancer cells and were proposed to be a hallmark of cancer. However, the impact of altered transportome in epithelial cancers, such as pancreatic ductal adenocarcinoma (PDAC), as well as its pathophysiological consequences, still remains unclear. Here, we report the in silico analysis of 840 transportome genes in PDAC patients' tissues. Our study was focused on the transportome changes and their correlation with functional and behavioral responses in PDAC tumor and stromal compartments. The dysregulated gene expression datasets were filtered using a cut-off of fold-change values ≤-2 or ≥2 (adjusted p value ≤0.05). The dysregulated transportome genes were clearly associated with impaired physiological secretory mechanisms and/or pH regulation, control of cell volume, and cell polarity. Additionally, some down-regulated transportome genes were found to be closely linked to epithelial cell differentiation. Furthermore, the observed decrease in genes coding for calcium and chloride transport might be a mechanism for evasion of apoptosis. In conclusion, the current work provides a comprehensive overview of the altered transportome expression and its association with predicted PDAC malignancy with special focus on the epithelial compartment.

Big Data in Caenorhabditis elegans: quo vadis?

A clear definition of what constitutes “Big Data” is difficult to identify, but we find it most useful to define Big Data as a data collection that is complete. By this criterion, researchers on Caenorhabditis elegans have a long history of collecting Big Data, since the organism was selected with the idea of obtaining a complete biological description and understanding of development. The complete wiring diagram of the nervous system, the complete cell lineage, and the complete genome sequence provide a framework to phrase and test hypotheses. Given this history, it might be surprising that the number of “complete” data sets for this organism is actually rather small—not because of lack of effort, but because most types of biological experiments are not currently amenable to complete large-scale data collection. Many are also not inherently limited, so that it becomes difficult to even define completeness. At present, we only have partial data on mutated genes and their phenotypes, gene expression, and protein–protein interaction—important data for many biological questions. Big Data can point toward unexpected correlations, and these unexpected correlations can lead to novel investigations; however, Big Data cannot establish causation. As a result, there is much excitement about Big Data, but there is also a discussion on just what Big Data contributes to solving a biological problem. Because of its relative simplicity, C. elegans is an ideal test bed to explore this issue and at the same time determine what is necessary to build a multicellular organism from a single cell.

NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity

Gene Ontology (GO) provides GO annotations (GOA) that associate gene products with GO terms that summarize their cellular, molecular and functional aspects in the context of biological pathways. GO Consortium (GOC) resorts to various quality assurances to ensure the correctness of annotations. Due to resources limitations, only a small portion of annotations are manually added/checked by GO curators, and a large portion of available annotations are computationally inferred. While computationally inferred annotations provide greater coverage of known genes, they may also introduce annotation errors (noise) that could mislead the interpretation of the gene functions and their roles in cellular and biological processes. In this paper, we investigate how to identify noisy annotations, a rarely addressed problem, and propose a novel approach called NoisyGOA. NoisyGOA first measures taxonomic similarity between ontological terms using the GO hierarchy and semantic similarity between genes. Next, it leverages the taxonomic similarity and semantic similarity to predict noisy annotations. We compare NoisyGOA with other alternative methods on identifying noisy annotations under different simulated cases of noisy annotations, and on archived GO annotations. NoisyGOA achieved higher accuracy than other alternative methods in comparison. These results demonstrated both taxonomic similarity and semantic similarity contribute to the identification of noisy annotations. Our study shows that annotation errors are predictable and removing noisy annotations improves the performance of gene function prediction. This study can prompt the community to study methods for removing inaccurate annotations, a critical step for annotating gene and pathway functions.

Gene signatures associated with adaptive humoral immunity following seasonal influenza A/H1N1 vaccination

This study aimed to identify gene expression markers shared between both influenza hemagglutination-inhibition (HAI) and virus-neutralization antibody (VNA) responses. We enrolled 158 older subjects who received the 2010–2011 trivalent inactivated influenza vaccine (TIV). Influenza-specific HAI and VNA titers, and mRNA-sequencing were performed using blood samples obtained at Days 0, 3 and 28 post-vaccination. For antibody response at Day 28 vs Day 0, several genesets were identified as significant in predictive models for HAI (n=7) and VNA (n=35) responses. Five genesets (comprising the genes MAZ, TTF, GSTM, RABGGTA, SMS, CA, IFNG, and DOPEY) were in common for both HAI and VNA. For response at Day 28 vs Day 3, many genesets were identified in predictive models for HAI (n=13) and VNA (n=41). Ten genesets (comprising biologically related genes, such as MAN1B1, POLL, CEBPG, FOXP3, IL12A, TLR3, TLR7, and others) were shared between HAI and VNA. These identified genesets demonstrated a high degree of network interactions and likelihood for functional relationships. Influenza-specific HAI and VNA responses demonstrated a remarkable degree of similarity. Although unique geneset signatures were identified for each humoral outcome, several genesets were determined to be in common with both HAI and VNA response to influenza vaccine.

Iron Content Affects Lipogenic Gene Expression in the Muscle of Nelore Beef Cattle

Iron (Fe) is an essential mineral for metabolism and plays a central role in a range of biochemical processes. Therefore, this study aimed to identify differentially expressed (DE) genes and metabolic pathways in Longissimus dorsi (LD) muscle from cattle with divergent iron content, as well as to investigate the likely role of these DE genes in biological processes underlying beef quality parameters. Samples for RNA extraction for sequencing and iron, copper, manganese, and zinc determination were collected from LD muscles at slaughter. Eight Nelore steers, with extreme genomic estimated breeding values for iron content (Fe-GEBV), were selected from a reference population of 373 animals. From the 49 annotated DE genes (FDR<0.05) found between the two groups, 18 were up-regulated and 31 down-regulated for the animals in the low Fe-GEBV group. The functional enrichment analyses identified several biological processes, such as lipid transport and metabolism, and cell growth. Lipid metabolism was the main pathway observed in the analysis of metabolic and canonical signaling pathways for the genes identified as DE, including the genes FASN, FABP4, and THRSP, which are functional candidates for beef quality, suggesting reduced lipogenic activities with lower iron content. Our results indicate metabolic pathways that are partially influenced by iron, contributing to a better understanding of its participation in skeletal muscle physiology.

Whole Transcriptome Profiling Identifies CD93 and Other Plasma Cell Survival Factor Genes Associated with Measles-Specific Antibody Response after Vaccination

Background

There are insufficient system-wide transcriptomic (or other) data that help explain the observed inter-individual variability in antibody titers after measles vaccination in otherwise healthy individuals.

Methods

We performed a transcriptome(mRNA-Seq)-profiling study after in vitro viral stimulation of PBMCs from 30 measles vaccine recipients, selected from a cohort of 764 schoolchildren, based on the highest and lowest antibody titers. We used regression and network biology modeling to define markers associated with neutralizing antibody response.

Results

We identified 39 differentially expressed genes that demonstrate significant differences between the high and low antibody responder groups (p-value≤0.0002, q-value≤0.092), including the top gene CD93 (p<1.0E^-13, q<1.0E^-09), encoding a receptor required for antigen-driven B-cell differentiation, maintenance of immunoglobulin production and preservation of plasma cells in the bone marrow. Network biology modeling highlighted plasma cell survival (CD93, IL6, CXCL12), chemokine/cytokine activity and cell-cell communication/adhesion/migration as biological processes associated with the observed differential response in the two responder groups.

Conclusion

We identified genes and pathways that explain in part, and are associated with, neutralizing antibody titers after measles vaccination. This new knowledge could assist in the identification of biomarkers and predictive signatures of protective immunity that may be useful in the design of new vaccine candidates and in clinical studies.

Transcriptome sequencing and de novo characterization of Korean endemic land snail, Koreanohadra kurodana for functional transcripts and SSR markers

The Korean endemic land snail Koreanohadra kurodana (Gastropoda: Bradybaenidae) found in humid areas of broadleaf forests and shrubs have been considered vulnerable as the number of individuals are declining in recent years. The species is poorly characterized at the genomic level that limits the understanding of functions at the molecular and genetics level. In the present study, we performed de novo transcriptome sequencing to produce a comprehensive transcript dataset of visceral mass tissue of K. kurodana by the Illumina paired-end sequencing technology. Over 234 million quality reads were assembled to a total of 315,924 contigs and 191,071 unigenes, with an average and N50 length of 585.6 and 715 bp and 678 and 927 bp, respectively. Overall, 36.32 % of the unigenes found matches to known protein/nucleotide sequences in the public databases. The direction of the unigenes to functional categories was determined using COG, GO, KEGG, and InterProScan protein domain search. The GO analysis search resulted in 22,967 unigenes (12.02 %) being categorized into 40 functional groups. The KEGG annotation revealed that metabolism pathway genes were enriched. The most prominent protein motifs include the zinc finger, ribonuclease H, reverse transcriptase, and ankyrin repeat domains. The simple sequence repeats (SSRs) identified from >1 kb length of unigenes show a dominancy of dinucleotide repeat motifs followed with tri- and tetranucleotide motifs. A number of unigenes were putatively assessed to belong to adaptation and defense mechanisms including heat shock proteins 70, Toll-like receptor 4, AMP-activated protein kinase, aquaporin-2, etc. Our data provide a rich source for the identification and functional characterization of new genes and candidate polymorphic SSR markers in K. kurodana. The availability of transcriptome information ( http://bioinfo.sch.ac.kr/submission/ ) would promote the utilization of the resources for phylogenetics study and genetic diversity assessment.

Transcriptional signatures of influenza A/H1N1-specific IgG memory-like B cell response in older individuals

BACKGROUND

Studies suggest that the recall-based humoral immune responses to influenza A/H1N1 originates from activated memory B cells. The aim of this study was to identify baseline, early and late blood transcriptional signatures (in peripheral blood mononuclear cells/PBMCs) associated with memory B cell response following influenza vaccination.

METHODS

We used pre- and post-vaccination mRNA-Seq transcriptional profiling on samples from 159 subjects (50-74years old) following receipt of seasonal trivalent influenza vaccine containing the A/California/7/2009/H1N1-like virus, and penalized regression modeling to identify associations with influenza A/H1N1-specific memory B cell ELISPOT response after vaccination.

RESULTS

Genesets and genes (p-value range 7.92E(-08) to 0.00018, q-value range 0.00019-0.039) demonstrating significant associations (of gene expression levels) with memory B cell response suggest the importance of metabolic (cholesterol and lipid metabolism-related), cell migration/adhesion, MAP kinase, NF-kB cell signaling (chemokine/cytokine signaling) and transcriptional regulation gene signatures in the development of memory B cell response after influenza vaccination.

CONCLUSION

Through an unbiased transcriptome-wide profiling approach, our study identified signatures of memory B cell response following influenza vaccination, highlighting the underappreciated role of metabolic changes (among the other immune function-related events) in the regulation of influenza vaccine-induced immune memory.

Transcriptomic Analysis of the Endangered Neritid Species Clithon retropictus: De Novo Assembly, Functional Annotation, and Marker Discovery

An aquatic gastropod belonging to the family Neritidae, Clithon retropictus is listed as an endangered class II species in South Korea. The lack of information on its genomic background limits the ability to obtain functional data resources and inhibits informed conservation planning for this species. In the present study, the transcriptomic sequencing and de novo assembly of C. retropictus generated a total of 241,696,750 high-quality reads. These assembled to 282,838 unigenes with mean and N50 lengths of 736.9 and 1201 base pairs, respectively. Of these, 125,616 unigenes were subjected to annotation analysis with known proteins in Protostome DB, COG, GO, and KEGG protein databases (BLASTX; E ≤ 0.00001) and with known nucleotides in the Unigene database (BLASTN; E ≤ 0.00001). The GO analysis indicated that cellular process, cell, and catalytic activity are the predominant GO terms in the biological process, cellular component, and molecular function categories, respectively. In addition, 2093 unigenes were distributed in 107 different KEGG pathways. Furthermore, 49,280 simple sequence repeats were identified in the unigenes (>1 kilobase sequences). This is the first report on the identification of transcriptomic and microsatellite resources for C. retropictus, which opens up the possibility of exploring traits related to the adaptation and acclimatization of this species.

De Novo Assembly and Developmental Transcriptome Analysis of the Small White Butterfly Pieris rapae

The small white butterfly Pieris rapae is one of the most destructive pests of Brassicaceae. Yet little is understood about its genes involved in development. To facilitate research on P. rapae, we sequenced the transcriptome of P. rapae during six developmental stages, including the egg, three larval stages, the pupa, and the adult. In total, 240 million high-quality reads were obtained. De novo assembly generated 96,069 unigenes with an average length of 1353 nt. Of these, 31,629 unigenes had homologs as determined by a blastx search against the NR database with a cut-off e-value of 10⁻⁵. Clusters of Orthologous Groups of proteins (COG), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to functionally annotate those genes. Then, 849 genes involved in seven canonical development signaling pathway were identified, including dozens of key genes such as Hippo, Notch, and JAK2. A total of 21,883 differentially expressed (cut-off of 2-fold) unigenes were detected across the developmental stages, most of which were found between the egg and first larval stages. Interestingly, only 34 differentially expressed unigenes, most of which are cuticle protein related genes, were detected with a cut-off of 2¹⁰-fold. Furthermore, we identified 32 heat shock protein (Hsp) genes that were expressed with complete open reading frames. Based on phylogenetic trees of the Hsp genes, we found that Hsp genes with close evolutionary relationships had similar expression pattern. Additionally, partial pattern recognition receptors genes were found to be developmental regulated. This study provides comprehensive sequence resources for P. rapae and numerous differential expressed genes, and these findings will lay the foundation for future functional genomics studies on this species.

NegGOA: negative GO annotations selection using ontology structure

MOTIVATION

Predicting the biological functions of proteins is one of the key challenges in the post-genomic era. Computational models have demonstrated the utility of applying machine learning methods to predict protein function. Most prediction methods explicitly require a set of negative examples-proteins that are known not carrying out a particular function. However, Gene Ontology (GO) almost always only provides the knowledge that proteins carry out a particular function, and functional annotations of proteins are incomplete. GO structurally organizes more than tens of thousands GO terms and a protein is annotated with several (or dozens) of these terms. For these reasons, the negative examples of a protein can greatly help distinguishing true positive examples of the protein from such a large candidate GO space.

RESULTS

In this paper, we present a novel approach (called NegGOA) to select negative examples. Specifically, NegGOA takes advantage of the ontology structure, available annotations and potentiality of additional annotations of a protein to choose negative examples of the protein. We compare NegGOA with other negative examples selection algorithms and find that NegGOA produces much fewer false negatives than them. We incorporate the selected negative examples into an efficient function prediction model to predict the functions of proteins in Yeast, Human, Mouse and Fly. NegGOA also demonstrates improved accuracy than these comparing algorithms across various evaluation metrics. In addition, NegGOA is less suffered from incomplete annotations of proteins than these comparing methods.

AVAILABILITY AND IMPLEMENTATION

The Matlab and R codes are available at https://sites.google.com/site/guoxian85/neggoa

CONTACT

gxyu@swu.edu.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Using biological networks to integrate, visualize and analyze genomics data

Network biology is a rapidly developing area of biomedical research and reflects the current view that complex phenotypes, such as disease susceptibility, are not the result of single gene mutations that act in isolation but are rather due to the perturbation of a gene’s network context. Understanding the topology of these molecular interaction networks and identifying the molecules that play central roles in their structure and regulation is a key to understanding complex systems. The falling cost of next-generation sequencing is now enabling researchers to routinely catalogue the molecular components of these networks at a genome-wide scale and over a large number of different conditions. In this review, we describe how to use publicly available bioinformatics tools to integrate genome-wide ‘omics’ data into a network of experimentally-supported molecular interactions. In addition, we describe how to visualize and analyze these networks to identify topological features of likely functional relevance, including network hubs, bottlenecks and modules. We show that network biology provides a powerful conceptual approach to integrate and find patterns in genome-wide genomic data but we also discuss the limitations and caveats of these methods, of which researchers adopting these methods must remain aware.

Molecular Profile of Mitochondrial Dysfunction in Kidney Transplant Biopsies Is Associated With Poor Allograft Outcome

BACKGROUND

In kidney transplantation (KT), progression of chronic histological damage with subclinical inflammation is associated with poor long-term allograft survival. The role of nonimmunological pathways in chronic allograft injury has not been fully assessed.

METHODS

We analyzed a public microarray dataset that used 1-year protocol kidney transplant biopsy specimens to investigate whether nonimmunological genes and pathways might influence long-term allograft outcome. The selected microarray dataset included 3 patient/sample groups based on their histological findings: normal histology (n = 25), interstitial fibrosis alone (IF alone, n = 24), and interstitial fibrosis with inflammation (IF+i, n = 16). The IF+i group had lower death-censored graft survival and renal function in patients with a mean follow-up of 4 years. We performed statistical analysis comparing gene expression patterns in the 3 group samples.

RESULTS

Gene cluster enrichment and group-specific expression patterns demonstrated a divergent pattern between mitochondrial and immune response genes, with downregulation of mitochondrial genes in the IF+i group. Gene ontological analysis of the downregulated mitochondrial genes identified generation of precursor metabolite and energy, and response to oxidative stress as the most significant biological processes. The transcription regulation pathway analysis of downregulated gene cluster demonstrated transcription factors involved in mitochondrial biogenesis.

CONCLUSIONS

The molecular signature of mitochondrial dysfunction reflects mitochondrial energetic insufficiency, and inadequate antioxidant response involved in mitochondria biogenesis pathways is associated with IF+i and worse long-term allograft survival. Thus, mitochondria function impairment appears to be an important nonimmune factor involved in chronic allograft injury.

GO annotation in InterPro: why stability does not indicate accuracy in a sea of changing annotations

The removal of annotation from biological databases is often perceived as an indicator of erroneous annotation. As a corollary, annotation stability is considered to be a measure of reliability. However, diverse data-driven events can affect the stability of annotations in both primary protein sequence databases and the protein family databases that are built upon the sequence databases and used to help annotate them. Here, we describe some of these events and their consequences for the InterPro database, and demonstrate that annotation removal or reassignment is not always linked to incorrect annotation by the curator.

Database URL:http://www.ebi.ac.uk/interpro

De novo Transcriptome Generation and Annotation for Two Korean Endemic Land Snails, Aegista chejuensis and Aegista quelpartensis, Using Illumina Paired-End Sequencing Technology

Aegista chejuensis and Aegista quelpartensis (Family-Bradybaenidae) are endemic to Korea, and are considered vulnerable due to declines in their population. The limited genetic resources for these species restricts the ability to prioritize conservation efforts. We sequenced the transcriptomes of these species using Illumina paired-end technology. Approximately 257 and 240 million reads were obtained and assembled into 198,531 and 230,497 unigenes for A. chejuensis and A. quelpartensis, respectively. The average and N50 unigene lengths were 735.4 and 1073 bp, respectively, for A. chejuensis, and 705.6 and 1001 bp, respectively, for A. quelpartensis. In total, 68,484 (34.5%) and 77,745 (33.73%) unigenes for A. chejuensis and A. quelpartensis, respectively, were annotated to databases. Gene Ontology terms were assigned to 23,778 (11.98%) and 26,396 (11.45) unigenes, for A. chejuensis and A. quelpartensis, respectively, while 5050 and 5838 unigenes were mapped to 117 and 124 pathways in the Kyoto Encyclopedia of Genes and Genomes database. In addition, we identified and annotated 9542 and 10,395 putative simple sequence repeats (SSRs) in unigenes from A. chejuensis and A. quelpartensis, respectively. We designed a list of PCR primers flanking the putative SSR regions. These microsatellites may be utilized for future phylogenetics and conservation initiatives.

Dynamic interplay between locus-specific DNA methylation and hydroxymethylation regulates distinct biological pathways in prostate carcinogenesis

BACKGROUND

Despite the significant global loss of DNA hydroxymethylation marks in prostate cancer tissues, the locus-specific role of hydroxymethylation in prostate tumorigenesis is unknown. We characterized hydroxymethylation and methylation marks by performing whole-genome next-generation sequencing in representative normal and prostate cancer-derived cell lines in order to determine functional pathways and key genes regulated by these epigenomic modifications in cancer.

RESULTS

Our cell line model shows disruption of hydroxymethylation distribution in cancer, with global loss and highly specific gain in promoter and CpG island regions. Significantly, we observed locus-specific retention of hydroxymethylation marks in specific intronic and intergenic regions which may play a novel role in the regulation of gene expression in critical functional pathways, such as BARD1 signaling and steroid hormone receptor signaling in cancer. We confirm a modest correlation of hydroxymethylation with expression in intragenic regions in prostate cancer, while identifying an original role for intergenic hydroxymethylation in differentially expressed regulatory pathways in cancer. We also demonstrate a successful strategy for the identification and validation of key candidate genes from differentially regulated biological pathways in prostate cancer.

CONCLUSIONS

Our results indicate a distinct function for aberrant hydroxymethylation within each genomic feature in cancer, suggesting a specific and complex role for the deregulation of hydroxymethylation in tumorigenesis, similar to methylation. Subsequently, our characterization of key cellular pathways exhibiting dynamic enrichment patterns for methylation and hydroxymethylation marks may allow us to identify differentially epigenetically modified target genes implicated in prostate cancer tumorigenesis.

ChlamyNET: a Chlamydomonas gene co-expression network reveals global properties of the transcriptome and the early setup of key co-expression patterns in the green lineage

BACKGROUND

Chlamydomonas reinhardtii is the model organism that serves as a reference for studies in algal genomics and physiology. It is of special interest in the study of the evolution of regulatory pathways from algae to higher plants. Additionally, it has recently gained attention as a potential source for bio-fuel and bio-hydrogen production. The genome of Chlamydomonas is available, facilitating the analysis of its transcriptome by RNA-seq data. This has produced a massive amount of data that remains fragmented making necessary the application of integrative approaches based on molecular systems biology.

RESULTS

We constructed a gene co-expression network based on RNA-seq data and developed a web-based tool, ChlamyNET, for the exploration of the Chlamydomonas transcriptome. ChlamyNET exhibits a scale-free and small world topology. Applying clustering techniques, we identified nine gene clusters that capture the structure of the transcriptome under the analyzed conditions. One of the most central clusters was shown to be involved in carbon/nitrogen metabolism and signalling, whereas one of the most peripheral clusters was involved in DNA replication and cell cycle regulation. The transcription factors and regulators in the Chlamydomonas genome have been identified in ChlamyNET. The biological processes potentially regulated by them as well as their putative transcription factor binding sites were determined. The putative light regulated transcription factors and regulators in the Chlamydomonas genome were analyzed in order to provide a case study on the use of ChlamyNET. Finally, we used an independent data set to cross-validate the predictive power of ChlamyNET.

CONCLUSIONS

The topological properties of ChlamyNET suggest that the Chlamydomonas transcriptome posseses important characteristics related to error tolerance, vulnerability and information propagation. The central part of ChlamyNET constitutes the core of the transcriptome where most authoritative hub genes are located interconnecting key biological processes such as light response with carbon and nitrogen metabolism. Our study reveals that key elements in the regulation of carbon and nitrogen metabolism, light response and cell cycle identified in higher plants were already established in Chlamydomonas. These conserved elements are not only limited to transcription factors, regulators and their targets, but also include the cis-regulatory elements recognized by them.

GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data

Background

Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors.

Results

We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples.

Conclusions

GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0971-3) contains supplementary material, which is available to authorized users.