GOTA: GO term annotation of biomedical literature

How Oratosquilla oratoria compound eye response to the polarization of light: In the perspective of vision genes and related proteins

The special rhabdom structure of the mid-band ommatidium in compound eye contributes to the mantis shrimp being the only animal species known to science that can recognize circularly polarized light (CPL). Although the number of mid-band ommatidium of Oratosquilla oratoria is reduced, the mid-band ommatidium still has orthogonal geometric interleaved rhabdom and short oval distal rhabdom, which may mean that the O. oratoria has weakened circular polarized light vision (CPLV). Here we explored the molecular mechanisms of how O. oratoria response to the polarization of light. Based on the specific expression patterns of vision-related functional genes and proteins, we suggest that the order of light response by O. oratoria compound eye was first natural light, then left-circularly polarized light (LCPL), linearly polarized light, right-circularly polarized light (RCPL) and dark. Meanwhile, we found that the expression levels of vision-related functional genes and proteins in O. oratoria compound eye under RCPL were not significantly different from those in DL, which may imply that O. oratoria cannot respond to RCPL. Furthermore, the response of LCPL is likely facilitated by the differential expression of opsin and microvilli - related functional genes and proteins (arrestin and sodium-coupled neutral amino acid transporter). In conclusion, this study systematically illustrated for the first time how O. oratoria compound eye response to the polarization of light at the genetic level, and it can improve the visual ecological theory behind polarized light vision evolution.

Efficient Self-Supervised Metric Information Retrieval: A Bibliography Based Method Applied to COVID Literature

The literature on coronaviruses counts more than 300,000 publications. Finding relevant papers concerning arbitrary queries is essential to discovery helpful knowledge. Current best information retrieval (IR) use deep learning approaches and need supervised training sets with labeled data, namely to know a priori the queries and their corresponding relevant papers. Creating such labeled datasets is time-expensive and requires prominent experts' efforts, resources insufficiently available under a pandemic time pressure. We present a new self-supervised solution, called SUBLIMER, that does not require labels to learn to search on corpora of scientific papers for most relevant against arbitrary queries. SUBLIMER is a novel efficient IR engine trained on the unsupervised COVID-19 Open Research Dataset (CORD19), using deep metric learning. The core point of our self-supervised approach is that it uses no labels, but exploits the bibliography citations from papers to create a latent space where their spatial proximity is a metric of semantic similarity; for this reason, it can also be applied to other domains of papers corpora. SUBLIMER, despite is self-supervised, outperforms the Precision@5 (P@5) and Bpref of the state-of-the-art competitors on CORD19, which, differently from our approach, require both labeled datasets and a number of trainable parameters that is an order of magnitude higher than our.

Accelerating biological insight for understudied genes

The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The vast majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.

Gene function finding through cross-organism ensemble learning

Background

Structured biological information about genes and proteins is a valuable resource to improve discovery and understanding of complex biological processes via machine learning algorithms. Gene Ontology (GO) controlled annotations describe, in a structured form, features and functions of genes and proteins of many organisms. However, such valuable annotations are not always reliable and sometimes are incomplete, especially for rarely studied organisms. Here, we present GeFF (Gene Function Finder), a novel cross-organism ensemble learning method able to reliably predict new GO annotations of a target organism from GO annotations of another source organism evolutionarily related and better studied.

Results

Using a supervised method, GeFF predicts unknown annotations from random perturbations of existing annotations. The perturbation consists in randomly deleting a fraction of known annotations in order to produce a reduced annotation set. The key idea is to train a supervised machine learning algorithm with the reduced annotation set to predict, namely to rebuild, the original annotations. The resulting prediction model, in addition to accurately rebuilding the original known annotations for an organism from their perturbed version, also effectively predicts new unknown annotations for the organism. Moreover, the prediction model is also able to discover new unknown annotations in different target organisms without retraining.We combined our novel method with different ensemble learning approaches and compared them to each other and to an equivalent single model technique. We tested the method with five different organisms using their GO annotations: Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum. The outcomes demonstrate the effectiveness of the cross-organism ensemble approach, which can be customized with a trade-off between the desired number of predicted new annotations and their precision.A Web application to browse both input annotations used and predicted ones, choosing the ensemble prediction method to use, is publicly available at http://tiny.cc/geff/.

Conclusions

Our novel cross-organism ensemble learning method provides reliable predicted novel gene annotations, i.e., functions, ranked according to an associated likelihood value. They are very valuable both to speed the annotation curation, focusing it on the prioritized new annotations predicted, and to complement known annotations available.

Quantitative Proteomic Analysis of Porcine Intestinal Epithelial Cells Infected with Porcine Deltacoronavirus Using iTRAQ-Coupled LC-MS/MS

Porcine deltacoronavirus (PDCoV) is an emergent enteropathogenic coronavirus associated with swine diarrhea. Porcine small intestinal epithelial cells (IPEC) are the primary target cells of PDCoV infection in vivo. Here, isobaric tags for relative and absolute quantification (iTRAQ) labeling coupled to liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to quantitatively identify differentially expressed proteins (DEPs) in PDCoV-infected IPEC-J2 cells. A total of 78 DEPs, including 23 upregulated and 55 downregulated proteins, were identified at 24 h postinfection. The data are available via ProteomeXchange with identifier PXD019975. To ensure reliability of the proteomics data, two randomly selected DEPs, the downregulated anaphase-promoting complex subunit 7 (ANAPC7) and upregulated interferon-induced protein with tetratricopeptide repeats 1 (IFIT1), were verified by real-time PCR and Western blot, and the results of which indicate that the proteomics data were reliable and valid. Bioinformatics analyses, including GO, COG, KEGG, and STRING, further demonstrated that a majority of the DEPs are involved in numerous crucial biological processes and signaling pathways, such as immune system, digestive system, signal transduction, RIG-I-like receptor, mTOR, PI3K-AKT, autophagy, and cell cycle signaling pathways. Altogether, this is the first study on proteomes of PDCoV-infected host cells, which shall provide valuable clues for further investigation of PDCoV pathogenesis.

Effect of salinity fluctuation on the transcriptome of the Japanese mantis shrimp Oratosquilla oratoria

Salinity fluctuation may detrimentally affect the composition and biological processes of crustaceans. As a euryhaline crustacean, Oratosquilla oratoria can survive at salinities ranging from 20 psu to 40 psu. Therefore, we designed five salinity gradients (20, 25, 30, 35, and 40 psu) and 66.39 Gb clean transcriptome data were obtained after O. oratorias were exposed to each gradient for 24 h. All clean data were spliced into 50,482 unigenes and 17,035 unigenes were annotated in at least one database. Compared with 30 psu, 1010, 851, 1733 and 2188 differentially expressed genes were obtained at 20, 25, 35 and 40 psu, respectively. Results also showed that the osmoregulation of O. oratoria is primarily regulated by lipid and amino acid metabolism, amongst others. No significant up-regulated pathways were enriched at 25 psu and 35 psu, although more significant down-regulated pathways were obtained at 35 psu. Therefore, we assumed that the optimum survival salinity of O. oratoria may range from 25 psu to 35 psu. However, 35 psu may be more suitable for O. oratoria. In addition, 55 unigenes that encode putative inorganic ion exchanges were identified. This study aims to provide fundamental information for understanding the osmoregulation mechanisms of crustaceans.

De Novo assembly, characterization and development of EST-SSRs from Bletilla striata transcriptomes profiled throughout the whole growing period

Bletilla striata is an endangered orchid that has been used for millennia as a medicinal herb, in cosmetics and as a horticultural plant. To construct the first nucleotide database for this species and to develop abundant EST-SSR markers for facilitating further studies, various tissues and organs of plants in the main developmental stages were harvested for mRNA isolation and subsequent RNA sequencing. A total of 106,054,784 clean reads were generated by using Illumina paired-end sequencing technology. The reads were assembled into 127,261 unigenes by the Trinity package; the unigenes had an average length of 612 bp and an N50 of 957 bp. Of these unigenes, 67,494 (51.86%) were annotated in a series of databases. Of these annotated unigenes, 41,818 and 24,615 were assigned to gene ontology categories and clusters of orthologous groups, respectively. Additionally, 20,764 (15.96%) unigenes were mapped onto 275 pathways using the KEGG database. In addition, 25,935 high-quality EST-SSR primer pairs were developed from the 15,433 unigenes by MISA mining. To validate the accuracy of the newly designed markers, 87 of 100 randomly selected primers were effectively amplified; 63 of those yielded PCR products of the expected size, and 25 yielded products with significant amounts of polymorphism among the 4 landraces. Furthermore, the transferability test of the 25 polymorphic markers was performed in 6 individuals of two closely related genus Phalaenopsis and dendrobium. Which results showed a total of 5 markers can successfully amplified among these populations. This research provides a comprehensive nucleotide database and lays a solid foundation for functional gene mining and genomic research in B. striata. The developed EST-SSR primers could facilitate phylogenetic studies and breeding.

De novo assembly and annotation of the whole transcriptome of Oratosquilla oratoria

As the representative species of Stomatopoda, Oratosquilla oratoria can tolerate the complex benthic environment. In this study, our goal was to develop the transcriptomic resource of O. oratoria that would support adaptation mechanism studies. We generated the whole transcriptome of O. oratoria from combined tissues (eyestalk, muscle, sexual and viscus) using Hiseq technology. A total of 51,305,284 high-quality clean reads were assembled to produce 59,054 non-redundant transcripts with a mean length of 987nt using Trinity and CORSET software. Among the predictable unigenes, a total of 32,451 unigenes were annotated based on protein databases. Finally, we predicted the coding sequences of 31,822 unigenes and obtained 19,057 SSRs in the present study. The present study will provide an important resource and foundational understanding for future genomic research of O. oratoria.

Comparative transcriptome and potential antiviral signaling pathways analysis of the gills in the red swamp crayfish, Procambarus clarkii infected with White Spot Syndrome Virus (WSSV)

Red swamp crayfish is an important model organism for research of the invertebrate innate immunity mechanism. Its excellent disease resistance against bacteria, fungi, and viruses is well-known. However, the antiviral mechanisms of crayfish remain unclear. In this study, we obtained high-quality sequence reads from normal and white spot syndrome virus (WSSV)-challenged crayfish gills. For group normal (GN), 39,390,280 high-quality clean reads were randomly assembled to produce 172,591 contigs; whereas, 34,011,488 high-quality clean reads were randomly assembled to produce 182,176 contigs for group WSSV-challenged (GW). After GO annotations analysis, a total of 35,539 (90.01%), 14,931 (37.82%), 28,221 (71.48%), 25,290 (64.05%), 15,595 (39.50%), and 13,848 (35.07%) unigenes had significant matches with sequences in the Nr, Nt, Swiss-Prot, KEGG, COG and GO databases, respectively. Through the comparative analysis between GN and GW, 12,868 genes were identified as differentially up-regulated DEGs, and 9,194 genes were identified as differentially down-regulated DEGs. Ultimately, these DEGs were mapped into different signaling pathways, including three important signaling pathways related to innate immunity responses. These results could provide new insights into crayfish antiviral immunity mechanism.

Text Mining to Support Gene Ontology Curation and Vice Versa

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

In-depth comparative transcriptome analysis of intestines of red swamp crayfish, Procambarus clarkii, infected with WSSV

Crayfish has become one of the most important farmed aquatic species in China due to its excellent disease resistance against bacteria and viruses. However, the antiviral mechanism of crayfish is still not very clear. In the present study, many high-quality sequence reads from crayfish intestine were obtained using Illumina-based transcriptome sequencing. For the normal group (GN), 44,600,142 high-quality clean reads were randomly assembled to produce 125,394 contigs. For the WSSV-challenged group (GW), 47,790,746 high-quality clean reads were randomly assembled to produce 148,983 contigs. After GO annotation, 39,482 unigenes were annotated into three ontologies: biological processes, cellular components, and molecular functions. In addition, 15,959 unigenes were mapped to 25 different COG categories. Moreover, 7,000 DEGs were screened out after a comparative analysis between the GN and GW samples, which were mapped into 250 KEGG pathways. Among these pathways, 36 were obviously changed (P-values < 0.05) and 28 pathways were extremely significantly changed (P-values < 0.01). Finally, five key DEGs involved in the JAK-STAT signaling pathway were chosen for qRT-PCR. The results showed that these five DEGs were obviously up-regulated at 36 h post WSSV infection in crayfish intestine. These results provide new insight into crayfish antiviral immunity mechanisms.

Cross-organism learning method to discover new gene functionalities

BACKGROUND

Knowledge of gene and protein functions is paramount for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. Analyses for biomedical knowledge discovery greatly benefit from the availability of gene and protein functional feature descriptions expressed through controlled terminologies and ontologies, i.e., of gene and protein biomedical controlled annotations. In the last years, several databases of such annotations have become available; yet, these valuable annotations are incomplete, include errors and only some of them represent highly reliable human curated information. Computational techniques able to reliably predict new gene or protein annotations with an associated likelihood value are thus paramount.

METHODS

Here, we propose a novel cross-organisms learning approach to reliably predict new functionalities for the genes of an organism based on the known controlled annotations of the genes of another, evolutionarily related and better studied, organism. We leverage a new representation of the annotation discovery problem and a random perturbation of the available controlled annotations to allow the application of supervised algorithms to predict with good accuracy unknown gene annotations. Taking advantage of the numerous gene annotations available for a well-studied organism, our cross-organisms learning method creates and trains better prediction models, which can then be applied to predict new gene annotations of a target organism.

RESULTS

We tested and compared our method with the equivalent single organism approach on different gene annotation datasets of five evolutionarily related organisms (Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum). Results show both the usefulness of the perturbation method of available annotations for better prediction model training and a great improvement of the cross-organism models with respect to the single-organism ones, without influence of the evolutionary distance between the considered organisms. The generated ranked lists of reliably predicted annotations, which describe novel gene functionalities and have an associated likelihood value, are very valuable both to complement available annotations, for better coverage in biomedical knowledge discovery analyses, and to quicken the annotation curation process, by focusing it on the prioritized novel annotations predicted.