Current Annotation Research Articles

Improving Gene Annotation of the Peanut Genome by Integrated Proteogenomics Workflow.

Peanut (Arachis hypogaea L.) is a staple crop in semiarid tropical and subtropical regions. Although the genome of peanut has been fully sequenced, the current gene annotations are still incomplete. New technologies in genomics and proteomics have resulted in the emergence of proteogenomics, which can integrate genomic, transcriptomic, and proteomic data for improving gene annotation. In the present study, we collected RNA-seq and proteomic data from multiple tissues such as seed, shell, and gynophore of peanut and utilized a proteogenomic approach to improve the gene annotation of peanut based on these data. A total of 1 935 655 904 RNA-seq reads and 7 490 280 MS/MS spectra were collected. Ultimately, 13 767 annotated genes were found with evidence at the protein level, and seven novel protein-coding genes were found with both RNA-seq and proteomics evidence. In addition, 35 gene models were updated based on proteomics data. Proteogenomic approaches improved the gene annotation in certain aspects by integrating both RNA-seq and proteomic data. We expect that these approaches could help improve existing genome annotations of other species.

A benchmark study of ab initio gene prediction methods in diverse eukaryotic organisms

BackgroundThe draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality, complex gene structures, or a lack of suitable sequences for evidence-based annotations.ResultsWe describe the construction of a new benchmark, called G3PO (benchmark for Gene and Protein Prediction PrOgrams), designed to represent many of the typical challenges faced by current genome annotation projects. The benchmark is based on a carefully validated and curated set of real eukaryotic genes from 147 phylogenetically disperse organisms, and a number of test sets are defined to evaluate the effects of different features, including genome sequence quality, gene structure complexity, protein length, etc. We used the benchmark to perform an independent comparative analysis of the most widely used ab initio gene prediction programs and identified the main strengths and weaknesses of the programs. More importantly, we highlight a number of features that could be exploited in order to improve the accuracy of current prediction tools.ConclusionsThe experiments showed that ab initio gene structure prediction is a very challenging task, which should be further investigated. We believe that the baseline results associated with the complex gene test sets in G3PO provide useful guidelines for future studies.

Estudio de bases de datos para el reconocimiento automático de lenguas de signos

Automatic sign language recognition (ASLR) is quite a complex task, not only for the difficulty of dealing with very dynamic video information, but also because almost every sign language (SL) can be considered as an under-resourced language when it comes to language technology. Spanish sign language (LSE) is one of those under-resourced languages. Developing technology for SSL implies a number of technical challenges that must be tackled down in a structured and sequential manner. In this paper, some problems of machine-learning- based ASLR are addressed. A review of publicly available datasets is given and a new one is presented. It is also discussed the current annotations methods and annotation programs. In our review of existing datasets, our main conclusion is that there is a need for more with high-quality data and annotations.

Estudio de bases de datos para el reconocimiento automático de lenguas de signos

Automatic sign language recognition (ASLR) is quite a complex task, not only for the difficulty of dealing with very dynamic video information, but also because almost every sign language (SL) can be considered as an under-resourced language when it comes to language technology. Spanish sign language (LSE) is one of those under-resourced languages. Developing technology for SSL implies a number of technical challenges that must be tackled down in a structured and sequential manner. In this paper, some problems of machine-learning- based ASLR are addressed. A review of publicly available datasets is given and a new one is presented. It is also discussed the current annotations methods and annotation programs. In our review of existing datasets, our main conclusion is that there is a need for more with high-quality data and annotations.

The full-length transcriptome of C. elegans using direct RNA sequencing.

Current transcriptome annotations have largely relied on short read lengths intrinsic to the most widely used high-throughput cDNA sequencing technologies. For example, in the annotation of the Caenorhabditis elegans transcriptome, more than half of the transcript isoforms lack full-length support and instead rely on inference from short reads that do not span the full length of the isoform. We applied nanopore-based direct RNA sequencing to characterize the developmental polyadenylated transcriptome of C. elegans. Taking advantage of long reads spanning the full length of mRNA transcripts, we provide support for 23,865 splice isoforms across 14,611 genes, without the need for computational reconstruction of gene models. Of the isoforms identified, 3452 are novel splice isoforms not present in the WormBase WS265 annotation. Furthermore, we identified 16,342 isoforms in the 3′ untranslated region (3′ UTR), 2640 of which are novel and do not fall within 10 bp of existing 3′-UTR data sets and annotations. Combining 3′ UTRs and splice isoforms, we identified 28,858 full-length transcript isoforms. We also determined that poly(A) tail lengths of transcripts vary across development, as do the strengths of previously reported correlations between poly(A) tail length and expression level, and poly(A) tail length and 3′-UTR length. Finally, we have formatted this data as a publicly accessible track hub, enabling researchers to explore this data set easily in a genome browser.

PacBio single-molecule long-read sequencing shed new light on the transcripts and splice isoforms of the perennial ryegrass.

Perennial ryegrass (Lolium perenne), one of the most widely used forage and cool-season turfgrass worldwide, has a breeding history of more than 100years. However, the current draft genome annotation and transcriptome characterization are incomplete mainly because of the enormous difficulty in obtaining full-length transcripts. To explore the complete structure of the mRNA and improve the current draft genome, we performed PacBio single-molecule long-read sequencing for full-length transcriptome sequencing in perennial ryegrass. We generated 29,175 high-confidence non-redundant transcripts from 15,893 genetic loci, among which more than 66.88% of transcripts and 24.99% of genetic loci were not previously annotated in the current reference genome. The re-annotated 18,327 transcripts enriched the reference transcriptome. Particularly, 6709 alternative splicing events and 23,789 alternative polyadenylation sites were detected, providing a comprehensive landscape of the post-transcriptional regulation network. Furthermore, we identified 218 long non-coding RNAs and 478 fusion genes. Finally, the transcriptional regulation mechanism of perennial ryegrass in response to drought stress based on the newly updated reference transcriptome sequences was explored, providing new information on the underlying transcriptional regulation network. Taken together, we analyzed the full-length transcriptome of perennial ryegrass by PacBio single-molecule long-read sequencing. These results improve our understanding of the perennial ryegrass transcriptomes and refined the annotation of the reference genome.

Full-length transcriptome sequencing from multiple tissues of duck, Anas platyrhynchos

Duck (Anas platyrhynchos), one of the most economically important waterfowl, is an ideal model for studying the immune protection mechanism of birds. An incomplete duck reference genome and very limited availability of full-length cDNAs has hindered the identification of alternatively spliced transcripts and slowed down many basic studies in ducks. We applied PacBio Iso-Seq technologies to multiple tissues from duck for use in transcriptome sequencing. We obtained 199,993 full-length transcripts and comprehensively annotated these transcripts. 23,755 lncRNAs were predicted from all identified transcripts and 35,031 alternative splicing events, which divided into 5 models, were accurately predicted from 3,346 genes. Our data constitute a large increase in the known number of both lncRNA, and alternatively spliced transcripts of duck and plays an important role in improving current genome annotation. In addition, the data will be extremely useful for functional studies in other birds.

Historical chemical annotations of Cinchona bark collections are comparable to results from current day high-pressure liquid chromatography technologies.

Species of the genus Cinchona (Rubiaceae) have been used in traditional medicine, and as a source for quinine since its discovery as an effective medicine against malaria in the 17th century. Despite being the sole cure of malaria for almost 350 years, little is known about the chemical diversity between and within species of the antimalarial alkaloids found in the bark. Extensive historical Cinchona bark collections housed at the Royal Botanic Gardens, Kew, UK, and in other museums may shed new light on the alkaloid chemistry of the Cinchona genus and the history of the quest for the most effective Cinchona barks. We used High-Pressure Liquid Chromatography (HPLC) coupled with fluorescence detection (FLD) to reanalyze a set of Cinchona barks originally annotated for the four major quinine alkaloids by John Eliot Howard and others more than 150 years ago. We performed an archival search on the Cinchona bark collections in the Economic Botany Collection housed in Kew, focusing on those with historical alkaloid content information. Then, we performed HPLC analysis of the bark samples to separate and quantify the four major quinine alkaloids and the total alkaloid content using fluorescence detection. Correlations between historic and current annotations were calculated using Spearman's rank correlation coefficient, before paired comparisons were performed using Wilcox rank sum tests. The effects of source were explored using generalized linear modelling (GLM), before the significance of each parameter in predicting alkaloid concentrations were assessed using chi-square tests as likelihood ratio testing (LRT) models. The total alkaloid content estimation obtained by our HPLC analysis was comparatively similar to the historical chemical annotations made by Howard. Additionally, the quantity of two of the major alkaloids, quinine and cinchonine, and the total content of the four alkaloids obtained were significantly similar between the historical and current day analysis using linear regression. This study demonstrates that the historical chemical analysis by Howard and current day HPLC alkaloid content estimations are comparable. Current day HPLC analysis thus provide a realistic estimate of the alkaloid contents in the historical bark samples at the time of sampling more than 150 years ago. Museum collections provide a powerful but underused source of material for understanding early use and collecting history as well as for comparative analyses with current day samples.

PlanExp: intuitive integration of complex RNA-seq datasets with planarian omics resources.

There is an increasing amount of transcriptomic and genomic data available for planarians with the advent of both traditional and single-cell RNA sequencing technologies. Therefore, exploring, visualizing and making sense of all these data in order to understand planarian regeneration and development can be challenging. In this work, we present PlanExp, a web-application to explore and visualize gene expression data from different RNA-seq experiments (both traditional and single-cell RNA-seq) for the planaria Schmidtea mediterranea. PlanExp provides tools for creating different interactive plots, such as heatmaps, scatterplots, etc. and links them with the current sequence annotations both at the genome and the transcript level thanks to its integration with the PlanNET web application. PlanExp also provides a full gene/protein network editor, a prediction of genetic interactions from single-cell RNA-seq data, and a network expression mapper that will help researchers to close the gap between systems biology and planarian regeneration. PlanExp is freely available at https://compgen.bio.ub.edu/PlanNET/planexp. The source code is available at https://compgen.bio.ub.edu/PlanNET/downloads. Supplementary data are available at Bioinformatics online.

Overlapping protein-coding genes in human genome and their coincidental expression in tissues

The completion of human genome sequences and the advancement of next-generation sequencing technologies have engendered a clear understanding of all human genes. Overlapping genes are usually observed in compact genomes, such as those of bacteria and viruses. Notably, overlapping protein-coding genes do exist in human genome sequences. Accordingly, we used the current Ensembl gene annotations to identify overlapping human protein-coding genes. We analysed 19,200 well-annotated protein-coding genes and determined that 4,951 protein-coding genes overlapped with their adjacent genes. Approximately a quarter of all human protein-coding genes were overlapping genes. We observed different clusters of overlapping protein-coding genes, ranging from two genes (paired overlapping genes) to 22 genes. We also divided the paired overlapping protein-coding gene groups into four subtypes. We found that the divergent overlapping gene subtype had a stronger expression association than did the subtypes of 5ʹ-tandem overlapping and 3ʹ-tandem overlapping genes. The majority of paired overlapping genes exhibited comparable coincidental tissue expression profiles; however, a few overlapping gene pairs displayed distinctive tissue expression association patterns. In summary, we have carefully examined the genomic features and distributions about human overlapping protein-coding genes and found coincidental expression in tissues for most overlapping protein-coding genes.

ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity

The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. ViSEAGO is publicly available on https://bioconductor.org/packages/ViSEAGO .

Limits to a classic paradigm: most transcription factors in E. coli regulate genes involved in multiple biological processes.

Transcription factors (TFs) are important drivers of cellular decision-making. When bacteria encounter a change in the environment, TFs alter the expression of a defined set of genes in order to adequately respond. It is commonly assumed that genes regulated by the same TF are involved in the same biological process. Examples of this are methods that rely on coregulation to infer function of not-yet-annotated genes. We have previously shown that only 21% of TFs involved in metabolism regulate functionally homogeneous genes, based on the proximity of the gene products’ catalyzed reactions in the metabolic network. Here, we provide more evidence to support the claim that a 1-TF/1-process relationship is not a general property. We show that the observed functional heterogeneity of regulons is not a result of the quality of the annotation of regulatory interactions, nor the absence of protein–metabolite interactions, and that it is also present when function is defined by Gene Ontology terms. Furthermore, the observed functional heterogeneity is different from the one expected by chance, supporting the notion that it is a biological property. To further explore the relationship between transcriptional regulation and metabolism, we analyzed five other types of regulatory groups and identified complex regulons (i.e. genes regulated by the same combination of TFs) as the most functionally homogeneous, and this is supported by coexpression data. Whether higher levels of related functions exist beyond metabolism and current functional annotations remains an open question.

Roles of membrane transporters: connecting the dots from sequence to phenotype.

Plant membrane transporters are involved in diverse cellular processes underpinning plant physiology, such as nutrient acquisition, hormone movement, resource allocation, exclusion or sequestration of various solutes from cells and tissues, and environmental and developmental signalling. A comprehensive characterization of transporter function is therefore key to understanding and improving plant performance. In this review, we focus on the complexities involved in characterizing transporter function and the impact that this has on current genomic annotations. Specific examples are provided that demonstrate why sequence homology alone cannot be relied upon to annotate and classify transporter function, and to show how even single amino acid residue variations can influence transporter activity and specificity. Misleading nomenclature of transporters is often a source of confusion in transporter characterization, especially for people new to or outside the field. Here, to aid researchers dealing with interpretation of large data sets that include transporter proteins, we provide examples of transporters that have been assigned names that misrepresent their cellular functions. Finally, we discuss the challenges in connecting transporter function at the molecular level with physiological data, and propose a solution through the creation of new databases. Further fundamental in-depth research on specific transport (and other) proteins is still required; without it, significant deficiencies in large-scale data sets and systems biology approaches will persist. Reliable characterization of transporter function requires integration of data at multiple levels, from amino acid residue sequence annotation to more in-depth biochemical, structural and physiological studies.

Improved annotation of the domestic pig genome through integration of Iso-Seq and RNA-seq data

BackgroundOur understanding of the pig transcriptome is limited. RNA transcript diversity among nine tissues was assessed using poly(A) selected single-molecule long-read isoform sequencing (Iso-seq) and Illumina RNA sequencing (RNA-seq) from a single White cross-bred pig.ResultsAcross tissues, a total of 67,746 unique transcripts were observed, including 60.5% predicted protein-coding, 36.2% long non-coding RNA and 3.3% nonsense-mediated decay transcripts. On average, 90% of the splice junctions were supported by RNA-seq within tissue. A large proportion (80%) represented novel transcripts, mostly produced by known protein-coding genes (70%), while 17% corresponded to novel genes. On average, four transcripts per known gene (tpg) were identified; an increase over current EBI (1.9 tpg) and NCBI (2.9 tpg) annotations and closer to the number reported in human genome (4.2 tpg). Our new pig genome annotation extended more than 6000 known gene borders (5′ end extension, 3′ end extension, or both) compared to EBI or NCBI annotations. We validated a large proportion of these extensions by independent pig poly(A) selected 3′-RNA-seq data, or human FANTOM5 Cap Analysis of Gene Expression data. Further, we detected 10,465 novel genes (81% non-coding) not reported in current pig genome annotations. More than 80% of these novel genes had transcripts detected in > 1 tissue. In addition, more than 80% of novel intergenic genes with at least one transcript detected in liver tissue had H3K4me3 or H3K36me3 peaks mapping to their promoter and gene body, respectively, in independent liver chromatin immunoprecipitation data.ConclusionsThese validated results show significant improvement over current pig genome annotations.

High-density genetic map construction and QTL mapping of first flower node in pepper (Capsicum annuum L.)

BackgroundFirst flower node (FFN) is an important trait for evaluating fruit earliness in pepper (Capsicum annuum L.). The trait is controlled by quantitative trait loci (QTL); however, studies have been limited on QTL mapping and genes contributing to the trait.ResultsIn this study, we developed a high density genetic map using specific-locus amplified fragment sequencing (SLAF-seq), a high-throughput strategy for de novo single nucleotide polymorphism discovery, based on 146 recombinant inbred lines (RILs) derived from an intraspecific cross between PM702 and FS871. The map contained 9328 SLAF markers on 12 linkage groups (LGs), and spanned a total genetic distance of 2009.69 centimorgan (cM) with an average distance of 0.22 cM. The sequencing depth for the map was 72.39-fold in the male parent, 57.04-fold in the female parent, and 15.65-fold in offspring. Using the genetic map, two major QTLs, named Ffn2.1 and Ffn2.2, identified on LG02 were strongly associated with FFN, with a phenotypic variance explanation of 28.62 and 19.56%, respectively. On the basis of the current annotation of C. annuum cv. Criollo de Morelos (CM334), 59 candidate genes were found within the Ffn2.1 and Ffn2.2 region, but only 3 of 59 genes were differentially expressed according to the RNA-seq results. Eventually we identified one gene associated with the FFN based on the function through GO, KEGG, and Swiss-prot analysis.ConclusionsOur research showed that the construction of high-density genetic map using SLAF-seq is a valuable tool for fine QTL mapping. The map we constructed is by far the most saturated complete genetic map of pepper, and using it we conducted fine QTL mapping for the important trait, FFN. QTLs and candidate genes obtained in this study lay a good foundation for the further research on FFN-related genes and other genetic applications in pepper.

Heritage image annotation via collective knowledge

The automatic image annotation can provide semantic illustrations to understand image contents, and builds a foundation to develop algorithms that can search images within a large database. However, most current methods focus on solving the annotation problem by modeling the image visual content and tag semantic information, which overlooks the additional information, such as scene descriptions and locations. Moreover, the majority of current annotation datasets are visually consistent and only annotated by common visual objects and attributes, which makes the classic methods vulnerable to handle the more diverse image annotation. To address above issues, we propose to annotate images via collective knowledge, that is, we uncover relationships between the image and its neighbors by measuring similarities among metadata and conduct the metric learning to obtain the representations of image contents, we also generate semantic representations for images given collective semantic information from their neighbors. Two representations from different paradigms are embedded together to train an annotation model. We ground our model on the heritage image collection we collected from the library online open data. Annotations on the heritage image collection are not limited to common visual objects, and are highly relevant to historical events, and the diversity of the heritage image content is much larger than the current datasets, which makes it more suitable for this task. Comprehensive experimental results on the benchmark dataset indicate that the proposed model achieves the best performance compared to baselines and state-of-the-art methods.

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames.

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame (ORF) annotation impose two arbitrary criteria: a minimum length of 100 codons and a single ORF per transcript. However, a growing number of studies report expression of proteins from allegedly non-coding regions, challenging the accuracy of current genome annotations. These novel proteins were found encoded either within non-coding RNAs, 5' or 3' untranslated regions (UTRs) of mRNAs, or overlapping a known coding sequence (CDS) in an alternative ORF. OpenProt is the first database that enforces a polycistronic model for eukaryotic genomes, allowing annotation of multiple ORFs per transcript. OpenProt is freely accessible and offers custom downloads of protein sequences across 10 species. Using OpenProt database for proteomic experiments enables novel proteins discovery and highlights the polycistronic nature of eukaryotic genes. The size of OpenProt database (all predicted proteins) is substantial and need be taken in account for the analysis. However, with appropriate false discovery rate (FDR) settings or the use of a restricted OpenProt database, users will gain a more realistic view of the proteomic landscape. Overall, OpenProt is a freely available tool that will foster proteomic discoveries.

TIR-Learner, a New Ensemble Method for TIR Transposable Element Annotation, Provides Evidence for Abundant New Transposable Elements in the Maize Genome

Transposable elements (TEs) make up a large and rapidly evolving proportion of plant genomes. Among Class II DNA TEs, TIR elements are flanked by characteristic terminal inverted repeat sequences (TIRs). TIR TEs may play important roles in genome evolution, including generating allelic diversity, inducing structural variation, and regulating gene expression. However, TIR TE identification and annotation has been hampered by the lack of effective tools, resulting in erroneous TE annotations and a significant underestimation of the proportion of TIR elements in the maize genome. This problem has largely limited our understanding of the impact of TIR elements on plant genome structure and evolution. In this paper, we propose a new method of TIR element detection and annotation. This new pipeline combines the advantages of current homology-based annotation methods with powerful de novo machine-learning approaches, resulting in greatly increased efficiency and accuracy of TIR element annotation. The results show that the copy number and genome proportion of TIR elements in maize is much larger than that of current annotations. In addition, the distribution of some TIR superfamily elements is reduced in centromeric and pericentromeric positions, while others do not show a similar bias. Finally, the incorporation of machine-learning techniques has enabled the identification of large numbers of new DTA (hAT) family elements, which have all the hallmarks of bona fide TEs yet which lack high homology with currently known DTA elements. Together, these results provide new tools for TE research and new insight into the impact of TIR elements on maize genome diversity.

Contrasting patterns of gene expression indicate differing pyrethroid resistance mechanisms across the range of the New World malaria vector Anopheles albimanus.

Decades of unmanaged insecticide use and routine exposure to agrochemicals have left many populations of malaria vectors in the Americas resistant to multiple classes of insecticides, including pyrethroids. The molecular basis of pyrethroid resistance is relatively uncharacterised in American malaria vectors, preventing the design of suitable resistance management strategies. Using whole transcriptome sequencing, we characterized the mechanisms of pyrethroid resistance in Anopheles albimanus from Peru and Guatemala. An. albimanus were phenotyped as either deltamethrin or alpha-cypermethrin resistant. RNA from 1) resistant, 2) unexposed, and 3) a susceptible laboratory strain of An. albimanus was sequenced and analyzed using RNA-Seq. Expression profiles of the three groups were compared based on the current annotation of the An. albimanus reference genome. Several candidate genes associated with pyrethroid resistance in other malaria vectors were found to be overexpressed in resistant An. albimanus. In addition, gene ontology terms related to serine-type endopeptidase activity, extracellular activity and chitin metabolic process were also commonly overexpressed in the field caught resistant and unexposed samples from both Peru and Guatemala when compared to the susceptible strain. The cytochrome P450 CYP9K1 was overexpressed 14x in deltamethrin and 8x in alpha-cypermethrin-resistant samples from Peru and 2x in deltamethrin-resistant samples from Guatemala, relative to the susceptible laboratory strain. CYP6P5 was overexpressed 68x in deltamethrin-resistant samples from Peru but not in deltamethrin-resistant samples from Guatemala. When comparing overexpressed genes between deltamethrin-resistant and alpha-cypermethrin-resistant samples from Peru, a single P450 gene, CYP4C26, was overexpressed 9.8x (p<0.05) in alpha-cypermethrin-resistant samples. In Peruvian deltamethrin-resistant samples, the knockdown resistance mutation (kdr) variant alleles at position 1014 were rare, with approximately 5% frequency, but in the alpha-cypermethrin-resistant samples, the frequency of these alleles was approximately 15–30%. Functional validation of the candidate genes and the kdr mutation as a resistance marker for alpha-cypermethrin will confirm the role of these mechanisms in conferring pyrethroid resistance.

Insight into novel RNA-binding activities via large-scale analysis of lncRNA-bound proteome and IDH1-bound transcriptome.

RNA-binding proteins (RBPs) play pivotal roles in directing RNA fate and function. Yet the current annotation of RBPs is largely limited to proteins carrying known RNA-binding domains. To systematically reveal dynamic RNA–protein interactions, we surveyed the human proteome by a protein array-based approach and identified 671 proteins with RNA-binding activity. Among these proteins, 525 lack annotated RNA-binding domains and are enriched in transcriptional and epigenetic regulators, metabolic enzymes, and small GTPases. Using an improved CLIP (crosslinking and immunoprecipitation) method, we performed genome-wide target profiling of isocitrate dehydrogenase 1 (IDH1), a novel RBP. IDH1 binds to thousands of RNA transcripts with enriched functions in transcription and chromatin regulation, cell cycle and RNA processing. Purified IDH1, but not an oncogenic mutant, binds directly to GA- or AU-rich RNA that are also enriched in IDH1 CLIP targets. Our study provides useful resources of unconventional RBPs and IDH1-bound transcriptome, and convincingly illustrates, for the first time, the in vivo and in vitro RNA targets and binding preferences of IDH1, revealing an unanticipated complexity of RNA regulation in diverse cellular processes.