Current Annotation Research Articles

Arabidopsis bioinformatics resources: The current state, challenges, and priorities for the future.

Effective research, education, and outreach efforts by the Arabidopsis thaliana community, as well as other scientific communities that depend on Arabidopsis resources, depend vitally on easily available and publicly‐shared resources. These resources include reference genome sequence data and an ever‐increasing number of diverse data sets and data types. TAIR (The Arabidopsis Information Resource) and Araport (originally named the Arabidopsis Information Portal) are community informatics resources that provide tools, data, and applications to the more than 30,000 researchers worldwide that use in their work either Arabidopsis as a primary system of study or data derived from Arabidopsis. Four years after Araport's establishment, the IAIC held another workshop to evaluate the current status of Arabidopsis Informatics and chart a course for future research and development. The workshop focused on several challenges, including the need for reliable and current annotation, community‐defined common standards for data and metadata, and accessible and user‐friendly repositories/tools/methods for data integration and visualization. Solutions envisioned included (a) a centralized annotation authority to coalesce annotation from new groups, establish a consistent naming scheme, distribute this format regularly and frequently, and encourage and enforce its adoption. (b) Standards for data and metadata formats, which are essential, but challenging when comparing across diverse genotypes and in areas with less‐established standards (e.g., phenomics, metabolomics). Community‐established guidelines need to be developed. (c) A searchable, central repository for analysis and visualization tools. Improved versioning and user access would make tools more accessible. Workshop participants proposed a “one‐stop shop” website, an Arabidopsis “Super‐Portal” to link tools, data resources, programmatic standards, and best practice descriptions for each data type. This must have community buy‐in and participation in its establishment and development to encourage adoption.

No lanthanides-based catalysis in eukaryotes.

In the "Hypothesis" paper entitled "Lanthanides-Based Catalysis in Eukaryotes" (IUBMB Life 2018 Nov;70(11):1067-1075), we analyzed the possibility that Ce3+ -dependent methanol dehydrogenases (MDHs) could be found not only in archaea and bacteria but also in eukaryotic organisms. This hypothesis was based on the observation that MDHs protein sequences carrying the signature of Ce3+ -based active sites could be found in genome-derived proteomes of the eukaryotes Plasmodium yoelii yoelii, Nephila clavipes, Hyalella azteca, Pantholops hodgsonii, and Homo sapiens. Data were analyzed following standard procedures employed in the study of phylogenetic relationships among members of protein families and their occurrence in diverse organisms. Furthermore, the study relied on current annotations of protein sequences in the nonredundant protein sequences database, which we did not have any element to doubt about. After the publication of this hypothesis, following analyses carried out by Prof. Huub Op den Camp (Department of Microbiology, Faculty of Science, Radboud University, 6500 GL Nijmegen, The Netherlands), evidence has emerged that the sequences of the putative eukaryotic homologs of bacterial lanthanide-dependent MDHs, identified in our work, either derive from wrong annotation in GenBank or from undetected and pervasive bacterial contamination of the corresponding genomes. Thus, even though our study was technically correct, we were induced to support the initial hypothesis due to annotation errors and undetected bacterial contamination of the relevant genomes in the nucleotide sequences database. Therefore, at present, the hypothesis put forward in our article is not backed up by the currently available data. On a different note, this issue calls for a much higher attention on the integrity/correctness of the data deposited in the sequence databases, a need already highlighted in the literature also for the opposite problem, that is, human contamination of genomic data of other organisms. © 2018 IUBMB Life, 71(3):398-399, 2019.

The Transcriptomic Landscape of Yaks Reveals Molecular Pathways for High Altitude Adaptation.

Yak is one of the largest native mammalian species at the Himalayas, the highest plateau area in the world with an average elevation of >4,000 m above the sea level. Yak is well adapted to high altitude environment with a set of physiological features for a more efficient blood flow for oxygen delivery under hypobaric hypoxia. Yet, the genetic mechanism underlying its adaptation remains elusive. We conducted a cross-tissue, cross-altitude, and cross-species study to characterize the transcriptomic landscape of domestic yaks. The generated multi-tissue transcriptomic data greatly improved the current yak genome annotation by identifying tens of thousands novel transcripts. We found that among the eight tested tissues (lung, heart, kidney, liver, spleen, muscle, testis, and brain), lung and heart are two key organs showing adaptive transcriptional changes and >90% of the cross-altitude differentially expressed genes in lung display a nonlinear regulation. Pathways related to cell survival and proliferation are enriched, including PI3K-Akt, HIF-1, focal adhesion, and ECM–receptor interaction. These findings, in combination with the comprehensive transcriptome data set, are valuable to understanding the genetic mechanism of hypoxic adaptation in yak.

Using Whole Genome Sequencing (WGS) of 3256 Patients with Hematologic Malignancies to Determine Genome Instability

Background:Genome instability is a hallmark of cancer. Mutations in DNA repair pathway genes are frequent in a number of solid tumors. Defects in DNA repair or damage response can weaken response to conventional chemotherapy and are frequently regarded as poor prognostic markers. However, a high tumor mutation burden (TMB, number of somatic mutations per mega base) was recently found to correlate with better response to immune checkpoint inhibitors e.g. in colon cancer. Patients with defects in the DNA mismatch repair (MMR) pathway in solid tumors are among the cases with the highest TMB. Hematological malignancies are generally expected on the lower end of the TMB spectrum. We used whole genome sequencing (WGS) for 3256 patients with hematological malignancies (lymphatic and myeloid) to determine factors of genetic instability across all entities.Aim:To determine the number of known mutations in genes from the DNA repair pathwayTo estimate TMB using WGS and identify cases with high TMB in hematologic malignanciesMethods:We investigated a cohort of 3256 patients with hematological malignancies, who were analyzed according to WHO diagnostic gold standards for routine purposes (incl. 584 acute myeloid leukemia [AML] and 635 myelodysplastic syndromes [MDS] samples). We performed amplification-free library preparation and sequencing on HiseqX and NovaSeq 6000 with a median coverage of 106x. Mapping and variant calling was performed with standard pipelines via BaseSpace (all Illumina, San Diego, CA). A pool of gender-matched genomic DNA (Promega, Madison, WI) was used for a tumor-unmatched normal variant calling. (a) In detail we evaluated 180 genes involved in DNA repair. We filtered on (likely) pathogenic variants from ClinVar and for TP53 on protein-truncating variants and (likely, possibly) pathogenic variants from the IARC database. (b) For TBM calculation we determined protein-altering changes and then subtracted all gnomAD listed variants in order to eliminate most germline variants.Results:We found 479 of 3256 (15%) patients with at least one pathogenic variant according to current database annotations in DNA repair or damage response genes. Most pathogenic variants were found in TP53 (330/3256; 10%) and ATM (25/3256, 1%), however, this is probably the effect of the already available systematic database annotation for both genes. For routine diagnostic purposes TP53 mutation status had been analyzed for 1184 patients with Sanger sequencing (7%) or amplicon next-generation sequencing (93%). A 98% and 99% concordance of the pathogenic and non-pathogenic TP53 status was found in comparison to WGS. Mutations in genes from the DNA double-strand break repair (and/or homologous DNA pairing and strand exchange) pathway were found in 93 patients (3%). Pathogenic and potentially germline MMR gene mutations were found in only 3 patients (0.1%, 2 MLH1, 1 MSH6), which equals the expected frequency in the Western population (0.05-0.3%).Next, we calculated TMB. The average was 2.4 [range: 0.4-39.2]. Only samples above the 95th percentile were defined as “TMBhi” (TMB ≥5). TMB was lowest in chronic myeloid leukemia (CML) and essential thrombocythemia (ET) (< 2) and no ET or CML patient was found among the TMBhi. We then focused on MDS, which is our largest subcohort: 56 of 635 (9%) patients were in TMBhi. Furthermore, among MDS patients, a significantly higher TMB was found in MDS-EB-2 (average 3.3 vs. 2.3 for non EB-2, p<.001) and a significantly lower TMB in MDS with isolated del(5q) (1.7 vs. 2.6 in all other MDS, p<.001).There was no association of high TMB and annotated DNA repair gene mutations. The three MMR deficient patients had an average TMB (1.8-2.2) in the hematologic sample. However, TMB was positively correlated with age (Spearman's rank correlation, p<.001) and average TMB was lower in patients under 60 years (2.3 vs. 2.5, p=0.007, t-test).Conclusions:High TMB is rare in hematological neoplasms, and our data across all entities suggest that the acquisition of mutations over age should be a major contributing factor.While TP53 and a few other factors are well studied, using genome-wide datasets in the near future will allow to deeply understand the broad patterns or signatures of genome stability (incl. copy number or structural variants) and their prognostic or predictive value. DisclosuresBaer:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Höllein:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

Harmonizing semantic annotations for computational models in biology.

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Long-Read Annotation: Automated Eukaryotic Genome Annotation Based on Long-Read cDNA Sequencing.

Single-molecule full-length complementary DNA (cDNA) sequencing can aid genome annotation by revealing transcript structure and alternative splice forms, yet current annotation pipelines do not incorporate such information. Here we present long-read annotation (LoReAn) software, an automated annotation pipeline utilizing short- and long-read cDNA sequencing, protein evidence, and ab initio prediction to generate accurate genome annotations. Based on annotations of two fungal genomes (Verticillium dahliae and Plicaturopsis crispa) and two plant genomes (Arabidopsis [Arabidopsis thaliana] and Oryza sativa), we show that LoReAn outperforms popular annotation pipelines by integrating single-molecule cDNA-sequencing data generated from either the Pacific Biosciences or MinION sequencing platforms, correctly predicting gene structure, and capturing genes missed by other annotation pipelines.

Whole transcriptome analysis for identification of exercise-related markers in thoroughbred racing horse

Thoroughbred is the main horse breed in the horse racing industry. The horses which record excellent performance are valuable in the aspects of economics and industry. Because of the limitation of the conventional breeding methods, it is needed to develop a reliable system for the selection of stallion stud based on molecular breeding. Therefore we conducted high throughput analysis to discover exercise-related single nucleotide variations (SNVs) and differentially expressed genes (DEGs). We sequenced the whole mRNA from the blood and muscle tissues of six thoroughbred horses before and after exercise. By comparing current horse genome annotation, we identified 32,361 unigene clusters and 189,973 SNVs from the sequence aligned against the horse reference genome. We also identified genes regulated by exercise: 62 up-regulated and 80 down-regulated genes in the blood, and 878 up-regulated and 285 down-regulated genes in the muscle, respectively. In addition to DEGs, we also found differentially expressed alternative splicing forms of AXL, DYNC1, PLEKHG1, and COBLL1 before and after exercise. Overall, our results indicated that exercise stress induces massive gene expression change. Further studies should be focused on the mechanism of exercise-induced change and their relation in excellent performance in racing horses. Our data would be valuable information to develop exercise-related markers in horses and ultimately applied to establish a reliable selection system for producing a better stallion.

A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling

BackgroundBiochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism and one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires the reconstruction of biochemical and regulatory pathways at the community level in a given environment. In order to understand how environmental factors affect the genetic material and the dynamics of the expression from one environment to another, we want to evaluate the quantity of gene protein sequences or transcripts associated to a given pathway by precisely estimating the abundance of protein domains, their weak presence or absence in environmental samples.ResultsMetaCLADE is a novel profile-based domain annotation pipeline based on a multi-source domain annotation strategy. It applies directly to reads and improves identification of the catalog of functions in microbiomes. MetaCLADE is applied to simulated data and to more than ten metagenomic and metatranscriptomic datasets from different environments where it outperforms InterProScan in the number of annotated domains. It is compared to the state-of-the-art non-profile-based and profile-based methods, UProC and HMM-GRASPx, showing complementary predictions to UProC. A combination of MetaCLADE and UProC improves even further the functional annotation of environmental samples.ConclusionsLearning about the functional activity of environmental microbial communities is a crucial step to understand microbial interactions and large-scale environmental impact. MetaCLADE has been explicitly designed for metagenomic and metatranscriptomic data and allows for the discovery of patterns in divergent sequences, thanks to its multi-source strategy. MetaCLADE highly improves current domain annotation methods and reaches a fine degree of accuracy in annotation of very different environments such as soil and marine ecosystems, ancient metagenomes and human tissues.

High resolution annotation of zebrafish transcriptome using long-read sequencing.

With the emergence of zebrafish as an important model organism, a concerted effort has been made to study its transcriptome. This effort is limited, however, by gaps in zebrafish annotation, which are especially pronounced concerning transcripts dynamically expressed during zygotic genome activation (ZGA). To date, short-read sequencing has been the principal technology for zebrafish transcriptome annotation. In part because these sequence reads are too short for assembly methods to resolve the full complexity of the transcriptome, the current annotation is rudimentary. By providing direct observation of full-length transcripts, recently refined long-read sequencing platforms can dramatically improve annotation coverage and accuracy. Here, we leveraged the SMRT platform to study the transcriptome of zebrafish embryos before and after ZGA. Our analysis revealed additional novelty and complexity in the zebrafish transcriptome, identifying 2539 high-confidence novel transcripts that originated from previously unannotated loci and 1835 high-confidence new isoforms in previously annotated genes. We validated these findings using a suite of computational approaches including structural prediction, sequence homology, and functional conservation analyses, as well as by confirmatory transcript quantification with short-read sequencing data. Our analyses provided insight into new homologs and paralogs of functionally important proteins and noncoding RNAs, isoform switching occurrences, and different classes of novel splicing events. Several novel isoforms representing distinct splicing events were validated through PCR experiments, including the discovery and validation of a novel 8-kb transcript spanning multiple mir-430 elements, an important driver of early development. Our study provides a significantly improved zebrafish transcriptome annotation resource.

The 'TranSeq' 3'-end sequencing method for high-throughput transcriptomics and gene space refinement in plant genomes.

High-throughput RNA sequencing has proven invaluable not only to explore gene expression but also for both gene prediction and genome annotation. However, RNA sequencing, carried out on tens or even hundreds of samples, requires easy and cost-effective sample preparation methods using minute RNA amounts. Here, we present TranSeq, a high-throughput 3'-end sequencing procedure that requires 10- to 20-fold fewer sequence reads than the current transcriptomics procedures. TranSeq significantly reduces costs and allows a greater increase in size of sample sets analyzed in a single experiment. Moreover, in comparison with other 3'-end sequencing methods reported to date, we demonstrate here the reliability and immediate applicability of TranSeq and show that it not only provides accurate transcriptome profiles but also produces precise expression measurements of specific gene family members possessing high sequence similarity. This is difficult to achieve in standard RNA-seq methods, in which sequence reads cover the entire transcript. Furthermore, mapping TranSeq reads to the reference tomato genome facilitated the annotation of new transcripts improving >45% of the existing gene models. Hence, we anticipate that using TranSeq will boost large-scale transcriptome assays and increase the spatial and temporal resolution of gene expression data, in both model and non-model plant species. Moreover, as already performed for tomato (ITAG3.0; www.solgenomics.net), we strongly advocate its integration into current and future genome annotations.

Proteogenomic analysis of Mycobacterium tuberculosis Beijing B0/W148 cluster strains

Nowadays proteomics is one of the major instruments for editing and correcting annotation of genomic information. The correct genome annotation is necessary for omics studies of clinically relevant pathogens like Mycobacterium tuberculosis as well as for the progress in drug design and in silico biology. Here, we focused on the proteogenomic analysis of W-148 strain belonging to the Beijing B0/W148 cluster. This cluster, also known as a “successful” clone possesses unique pathogenic properties and has a unique genome organization. Taking into account high similarity of cluster strains at the genomic level we analyzed MS/MS dataset obtained for 63 clinical isolates of Beijing B0/W148. Based on H37Rv and W-148 annotations we identified 2546 proteins representing more than 60% of total proteome. A set of peptides (n = 404) specific for W-148 was found when compared with H37Rv. Start sites for 32 genes were corrected based on the combination of LC-MS/MS proteomic data with genomic six-frame translation. Additionally, we have shown the presence of peptides related to 10 genes earlier known as “pseudogenes”. SignificanceMycobacterium tuberculosis is one of the most dangerous pathogens. Phylogenetically, it may be divided into major lineages and among them, lineage 2 (predominantly Beijing genotype) one of the most successful lineages with an increasing prevalence in the global population. At the same time, strains of the Beijing B0/W148 cluster, a “successful” clone of Mycobacterium tuberculosis possess even more interesting features. Only one complete genome of this cluster, W-148, present in the NCBI database (CP012090.1) and it demonstrates a number of significant differences from the well-known reference genome H37Rv. For the W-148 strain many genes are annotated as “pseudo” and no attempts were made to correct this. Thereby, in this study, we have conducted a proteomic analysis of the cluster strains and corrected current genome annotation. We hope that the data obtained will help to increase the quality of identifications in proteomic and transcriptomic analysis of M. tuberculosis Beijing B0/W148 cluster strain in subsequent studies.

Deep neural models for extracting entities and relationships in the new RDD corpus relating disabilities and rare diseases

Background and objective: There is a huge amount of rare diseases, many of which have associated important disabilities. It is paramount to know in advance the evolution of the disease in order to limit and prevent the appearance of disabilities and to prepare the patient to manage the future difficulties. Rare disease associations are making an effort to manually collect this information, but it is a long process. A lot of information about the consequences of rare diseases is published in scientific papers, and our goal is to automatically extract disabilities associated with diseases from them. MethodsThis work presents a new corpus of abstracts from scientific papers related to rare diseases, which has been manually annotated with disabilities. This corpus allows to train machine and deep learning systems that can automatically process other papers, thus extracting new information about the relations between rare diseases and disabilities. The corpus is also annotated with negation and speculation when they appear affecting disabilities. The corpus has been made publicly accessible. ResultsWe have devised some experiments using deep learning techniques to show the usefulness of the developed corpus. Specifically, we have designed a long short-term memory based architecture for disabilities identification, as well as a convolutional neural network for detecting their relationships to diseases. The systems designed do not need any preprocessing of the data, but only low dimensional vectors representing the words. ConclusionsThe developed corpus will allow to train systems to identify disabilities in biomedical documents, which the current annotation systems are not able to detect. The system could also be trained to detect relationships between them and diseases, as well as negation and speculation, that can change the meaning of the language. The deep learning models designed for identifying disabilities and their relationships to diseases in new documents show that the corpus allows obtaining an F-measure of around 81% for the disability recognition and 75% for relation extraction.

Emerging roles of long non-coding RNA in cancer.

Since comprehensive analysis of the mammalian genome revealed that the majority of genomic products are transcribed in long non‐coding RNA (lncRNA), increasing attention has been paid to these transcripts. The applied next‐generation sequencing technologies have provided accumulating evidence of dysregulated lncRNA in cancer. The implication of this finding can be seen in many forms and at multiple levels. With impacts ranging from integrating chromatin remodeling complexes to regulating transcription and post‐transcriptional processes, aberrant expression of lncRNA may have repercussions in cell proliferation, tumor progression or metastasis. lncRNA may act as enhancers, scaffolds or decoys by physically interacting with other RNA species or proteins, resulting in a direct impact on cell signaling cascades. Even though their functional classification is well‐established in the context of cancer, clearer characterization in terms of their phenotypic outputs is needed to optimize and identify suitable candidates that enable the development of new therapeutic strategies and the design of novel diagnostic approaches. The present article aims to outline different cancer‐associated lncRNA according to their contribution to tumor suppression or tumor promotion based on their most current functional annotations.

Revealing the transcriptomic complexity of switchgrass by PacBio long-read sequencing

BackgroundSwitchgrass (Panicum virgatum L.) is an important bioenergy crop widely used for lignocellulosic research. While extensive transcriptomic analyses have been conducted on this species using short read-based sequencing techniques, very little has been reliably derived regarding alternatively spliced (AS) transcripts.ResultsWe present an analysis of transcriptomes of six switchgrass tissue types pooled together, sequenced using Pacific Biosciences (PacBio) single-molecular long-read technology. Our analysis identified 105,419 unique transcripts covering 43,570 known genes and 8795 previously unknown genes. 45,168 are novel transcripts of known genes. A total of 60,096 AS transcripts are identified, 45,628 being novel. We have also predicted 1549 transcripts of genes involved in cell wall construction and remodeling, 639 being novel transcripts of known cell wall genes. Most of the predicted transcripts are validated against Illumina-based short reads. Specifically, 96% of the splice junction sites in all the unique transcripts are validated by at least five Illumina reads. Comparisons between genes derived from our identified transcripts and the current genome annotation revealed that among the gene set predicted by both analyses, 16,640 have different exon–intron structures.ConclusionsOverall, substantial amount of new information is derived from the PacBio RNA data regarding both the transcriptome and the genome of switchgrass.

Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi

BackgroundGenome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction.ResultsHere, we present an extension of the gene prediction program GeMoMa that utilizes amino acid sequence conservation, intron position conservation and optionally RNA-seq data for homology-based gene prediction. We show on published benchmark data for plants, animals and fungi that GeMoMa performs better than the gene prediction programs BRAKER1, MAKER2, and CodingQuarry, and purely RNA-seq-based pipelines for transcript identification. In addition, we demonstrate that using multiple reference organisms may help to further improve the performance of GeMoMa. Finally, we apply GeMoMa to four nematode species and to the recently published barley reference genome indicating that current annotations of protein-coding genes may be refined using GeMoMa predictions.ConclusionsGeMoMa might be of great utility for annotating newly sequenced genomes but also for finding homologs of a specific gene or gene family. GeMoMa has been published under GNU GPL3 and is freely available at http://www.jstacs.de/index.php/GeMoMa.

Three-Dimensional Surface Point Cloud Ultrasound for Better Understanding and Transmission of Ultrasound Scan Information.

Ultrasound is notoriously plagued by high user dependence. There is a steep drop-off in information in going from what the sonographer sees during image acquisition and what the interpreting radiologist is able to view at the reading station. One countermeasure is probe localization and tracking. Current implementations are too difficult and expensive to use and/or do not provide adequate detail and perspective. The aim of this work was to demonstrate that a protocol combining surface three-dimensional photographic imaging with traditional ultrasound images may be a solution to the problem of probe localization, this approach being termed surface point cloud ultrasound (SPC-US). Ultrasound images were obtained of major vessels in an ultrasound training phantom, while simultaneously obtaining surface point cloud (SPC) 3D photographic images, with additional scanning performed on the right forearm soft tissues, kidneys, chest, and pelvis. The resulting sets of grayscale/color Doppler ultrasound and SPC images are juxtaposed and displayed for interpretation in a manner analogous to current text-based annotation or computer-generated stick figure probe position illustrations. Clearly demonstrated is that SPC-US better communicates information of probe position and orientation. Overall, it is shown that SPC-US provides much richer image representations of probe position on the patients than the current prevailing schemes. SPC-US turns out to be a rather general technique with many anticipated future applications, though only a few sample applications are illustrated in the present work.

Towards a complete map of the human long non-coding RNA transcriptome.

Gene maps, or annotations, enable us to navigate the functional landscape of our genome. They are a resource upon which virtually all studies depend, from single-gene to genome-wide scales and from basic molecular biology to medical genetics. Yet present-day annotations suffer from trade-offs between quality and size, with serious but often unappreciated consequences for downstream studies. This is particularly true for long non-coding RNAs (lncRNAs), which are poorly characterized compared to protein-coding genes. Long-read sequencing technologies promise to improve current annotations, paving the way towards a complete annotation of lncRNAs expressed throughout a human lifetime.

Genome-wide analysis of ionotropic receptor gene repertoire in Lepidoptera with an emphasis on its functions of Helicoverpa armigera

The functions of the Ionotropic Receptor (IR) family have been well studied in Drosophila melanogaster, but only limited information is available in Lepidoptera. Here, we conducted a large-scale genome-wide analysis of the IR gene repertoire in 13 moths and 16 butterflies. Combining a homology-based approach and manual efforts, totally 996 IR candidates are identified including 31 pseudogenes and 825 full-length sequences, representing the most current comprehensive annotation in lepidopteran species. The phylogeny, expression and sequence characteristics classify Lepidoptera IRs into three sub-families: antennal IRs (A-IRs), divergent IRs (D-IRs) and Lepidoptera-specific IRs (LS-IRs), which is distinct from the case of Drosophila IRs. In comparison to LS-IRs and D-IRs, A-IRs members share a higher degree of protein identity and are distinguished into 16 orthologous groups in the phylogeny, showing conservation of gene structure. Analysis of selective forces on 27 orthologous groups reveals that these lepidopteran IRs have evolved under strong purifying selection (dN/dS≪1). Most notably, lineage-specific gene duplications that contribute primarily to gene number variations across Lepidoptera not only exist in D-IRs, but are present in the two other sub-families including members of IR41a, 76b, 87a, 100a and 100b. Expression profiling analysis reveals that over 80% (21/26) of Helicoverpa armigera A-IRs are expressed more highly in antennae of adults or larvae than other tissues, consistent with its proposed function in olfaction. However, some are also detected in taste organs like proboscises and legs. These results suggest that some A-IRs in H. armigera likely bear a dual function with their involvement in olfaction and gustation. Results from mating experiments show that two HarmIRs (IR1.2 and IR75d) expression is significantly up-regulated in antennae of mated female moths. However, no expression difference is observed between unmated female and male adults, suggesting an association with female host-searching behaviors. Our current study has greatly extended the IR gene repertoire resource in Lepidoptera, and more importantly, identifies potential IR candidates for olfactory, gustatory and oviposition behaviors in the cotton bollworm.

EzTag: tagging biomedical concepts via interactive learning.

Recently, advanced text-mining techniques have been shown to speed up manual data curation by providing human annotators with automated pre-annotations generated by rules or machine learning models. Due to the limited training data available, however, current annotation systems primarily focus only on common concept types such as genes or diseases. To support annotating a wide variety of biological concepts with or without pre-existing training data, we developed ezTag, a web-based annotation tool that allows curators to perform annotation and provide training data with humans in the loop. ezTag supports both abstracts in PubMed and full-text articles in PubMed Central. It also provides lexicon-based concept tagging as well as the state-of-the-art pre-trained taggers such as TaggerOne, GNormPlus and tmVar. ezTag is freely available at http://eztag.bioqrator.org.

Towards a map of cis-regulatory sequences in the human genome.

Accumulating evidence indicates that transcription factor (TF) binding sites, or cis-regulatory elements (CREs), and their clusters termed cis-regulatory modules (CRMs) play a more important role than do gene-coding sequences in specifying complex traits in humans, including the susceptibility to common complex diseases. To fully characterize their roles in deriving the complex traits/diseases, it is necessary to annotate all CREs and CRMs encoded in the human genome. However, the current annotations of CREs and CRMs in the human genome are still very limited and mostly coarse-grained, as they often lack the detailed information of CREs in CRMs. Here, we integrated 620 TF ChIP-seq datasets produced by the ENCODE project for 168 TFs in 79 different cell/tissue types and predicted an unprecedentedly completely map of CREs in CRMs in the human genome at single nucleotide resolution. The map includes 305 912 CRMs containing a total of 1 178 913 CREs belonging to 736 unique TF binding motifs. The predicted CREs and CRMs tend to be subject to either purifying selection or positive selection, thus are likely to be functional. Based on the results, we also examined the status of available ChIP-seq datasets for predicting the entire regulatory genome of humans.