Haitao Luo

Large-scale prediction of long non-coding RNA functions in a coding–non-coding gene co-expression network

Although accumulating evidence has provided insight into the various functions of long-non-coding RNAs (lncRNAs), the exact functions of the majority of such transcripts are still unknown. Here, we report the first computational annotation of lncRNA functions based on public microarray expression profiles. A coding–non-coding gene co-expression (CNC) network was constructed from re-annotated Affymetrix Mouse Genome Array data. Probable functions for altogether 340 lncRNAs were predicted based on topological or other network characteristics, such as module sharing, association with network hubs and combinations of co-expression and genomic adjacency. The functions annotated to the lncRNAs mainly involve organ or tissue development (e.g. neuron, eye and muscle development), cellular transport (e.g. neuronal transport and sodium ion, acid or lipid transport) or metabolic processes (e.g. involving macromolecules, phosphocreatine and tyrosine).

NONCODE v3.0: integrative annotation of long noncoding RNAs

Facilitated by the rapid progress of high-throughput sequencing technology, a large number of long noncoding RNAs (lncRNAs) have been identified in mammalian transcriptomes over the past few years. LncRNAs have been shown to play key roles in various biological processes such as imprinting control, circuitry controlling pluripotency and differentiation, immune responses and chromosome dynamics. Notably, a growing number of lncRNAs have been implicated in disease etiology. With the increasing number of published lncRNA studies, the experimental data on lncRNAs (e.g. expression profiles, molecular features and biological functions) have accumulated rapidly. In order to enable a systematic compilation and integration of this information, we have updated the NONCODE database (http://www.noncode.org) to version 3.0 to include the first integrated collection of expression and functional lncRNA data obtained from re-annotated microarray studies in a single database. NONCODE has a user-friendly interface with a variety of search or browse options, a local Genome Browser for visualization and a BLAST server for sequence-alignment search. In addition, NONCODE provides a platform for the ongoing collation of ncRNAs reported in the literature. All data in NONCODE are open to users, and can be downloaded through the website or obtained through the SOAP API and DAS services.

Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts

It is a challenge to classify protein-coding or non-coding transcripts, especially those re-constructed from high-throughput sequencing data of poorly annotated species. This study developed and evaluated a powerful signature tool, Coding-Non-Coding Index (CNCI), by profiling adjoining nucleotide triplets to effectively distinguish protein-coding and non-coding sequences independent of known annotations. CNCI is effective for classifying incomplete transcripts and sense–antisense pairs. The implementation of CNCI offered highly accurate classification of transcripts assembled from whole-transcriptome sequencing data in a cross-species manner, that demonstrated gene evolutionary divergence between vertebrates, and invertebrates, or between plants, and provided a long non-coding RNA catalog of orangutan. CNCI software is available at http://www.bioinfo.org/software/cnci.

Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks

More and more evidences demonstrate that the long non-coding RNAs (lncRNAs) play many key roles in diverse biological processes. There is a critical need to annotate the functions of increasing available lncRNAs. In this article, we try to apply a global network-based strategy to tackle this issue for the first time. We develop a bi-colored network based global function predictor, long non-coding RNA global function predictor (‘lnc-GFP’), to predict probable functions for lncRNAs at large scale by integrating gene expression data and protein interaction data. The performance of lnc-GFP is evaluated on protein-coding and lncRNA genes. Cross-validation tests on protein-coding genes with known function annotations indicate that our method can achieve a precision up to 95%, with a suitable parameter setting. Among the 1713 lncRNAs in the bi-colored network, the 1625 (94.9%) lncRNAs in the maximum connected component are all functionally characterized. For the lncRNAs expressed in mouse embryo stem cells and neuronal cells, the inferred putative functions by our method highly match those in the known literature.

ncFANs: a web server for functional annotation of long non-coding RNAs

Recent interest in the non-coding transcriptome has resulted in the identification of large numbers of long non-coding RNAs (lncRNAs) in mammalian genomes, most of which have not been functionally characterized. Computational exploration of the potential functions of these lncRNAs will therefore facilitate further work in this field of research. We have developed a practical and user-friendly web interface called ncFANs (non-coding RNA Function ANnotation server), which is the first web service for functional annotation of human and mouse lncRNAs. On the basis of the re-annotated Affymetrix microarray data, ncFANs provides two alternative strategies for lncRNA functional annotation: one utilizing three aspects of a coding-non-coding gene co-expression (CNC) network, the other identifying condition-related differentially expressed lncRNAs. ncFANs introduces a highly efficient way of re-using the abundant pre-existing microarray data. The present version of ncFANs includes re-annotated CDF files for 10 human and mouse Affymetrix microarrays, and the server will be continuously updated with more re-annotated microarray platforms and lncRNA data. ncFANs is freely accessible at http://www.ebiomed.org/ncFANs/ or http://www.noncode.org/ncFANs/.

Identification of prognostic biomarkers in hepatitis B virus-related hepatocellular carcinoma and stratification by integrative multi-omics analysis

BACKGROUND & AIMS The differentiation of distinct multifocal hepatocellular carcinoma (HCC): multicentric disease vs. intrahepatic metastases, in which the management and prognosis varies substantively, remains problematic. We aim to stratify multifocal HCC and identify novel diagnostic and prognostic biomarkers by performing whole genome and transcriptome sequencing, as part of a multi-omics strategy. METHODS A complete collection of tumour and somatic specimens (intrahepatic HCC lesions, matched non-cancerous liver tissue and blood) were obtained from representative patients with multifocal HCC exhibiting two distinct postsurgical courses. Whole-genome and transcriptome sequencing with genotyping were performed for each tissue specimen to contrast genomic alterations, including hepatitis B virus integrations, somatic mutations, copy number variations, and structural variations. We then constructed a phylogenetic tree to visualise individual tumour evolution and performed functional enrichment analyses on select differentially expressed genes to elucidate biological processes involved in multifocal HCC development. Multi-omics data were integrated with detailed clinicopathological information to identify HCC biomarkers, which were further validated using a large cohort of HCC patients (n = 174). RESULTS The multi-omics profiling and tumour biomarkers could successfully distinguish the two multifocal HCC types, while accurately predicting clonality and aggressiveness. The dual-specificity protein kinase TTK, which is a key mitotic checkpoint regulator with links to p53 signaling, was further shown to be a promising overall prognostic marker for HCC in the large patient cohort. CONCLUSIONS Comprehensive multi-omics characterisation of multifocal tumour evolution may improve clinical decision-making, facilitate personalised medicine, and expedite identification of novel biomarkers and therapeutic targets in HCC.

Comprehensive Characterization of 10,571 Mouse Large Intergenic Noncoding RNAs from Whole Transcriptome Sequencing

Large intergenic noncoding RNAs (lincRNAs) have been recognized in recent years to constitute a significant portion of the mammalian transcriptome, yet their biological functions remain largely elusive. This is partly due to an incomplete annotation of tissue-specific lincRNAs in essential model organisms, particularly in mice, which has hindered the genetic annotation and functional characterization of these novel transcripts. In this report, we performed ab initio assembly of 1.9 billion tissue-specific RNA-sequencing reads across six tissue types, and identified 3,965 novel expressed lincRNAs in mice. Combining these with 6,606 documented lincRNAs, we established a comprehensive catalog of 10,571 transcribed lincRNAs. We then systemically analyzed all mouse lincRNAs to reveal that some of them are evolutionally conserved and that they exhibit striking tissue-specific expression patterns. We also discovered that mouse lincRNAs carry unique genomic signatures, and that their expression level is correlated with that of neighboring protein-coding transcripts. Finally, we predicted that a large portion of tissue-specific lincRNAs are functionally associated with essential biological processes including the cell cycle and cell development, and that they could play a key role in regulating tissue development and functionality. Our analyses provide a framework for continued discovery and annotation of tissue-specific lincRNAs in model organisms, and our transcribed mouse lincRNA catalog will serve as a roadmap for functional analyses of lincRNAs in genetic mouse models.

Systematic study of human long intergenic non-coding RNAs and their impact on cancer

The functional impact of several long intergenic non-coding RNAs (lincRNAs) has been characterized in previous studies. However, it is difficult to identify lincRNAs on a large-scale and to ascertain their functions or predict their structures in laboratory experiments because of the diversity, lack of knowledge and specificity of expression of lincRNAs. Furthermore, although there are a few well-characterized examples of lincRNAs associated with cancers, these are just the tip of the iceberg owing to the complexity of cancer. Here, by combining RNA-Seq data from several kinds of human cell lines with chromatin-state maps and human expressed sequence tags, we successfully identified more than 3000 human lincRNAs, most of which were new ones. Subsequently, we predicted the functions of 105 lincRNAs based on a coding-non-coding gene co-expression network. Finally, we propose a genetic mediator and key regulator model to unveil the subtle relationships between lincRNAs and lung cancer. Twelve lincRNAs may be principal players in lung tumorigenesis. The present study combines large-scale identification and functional prediction of human lincRNAs, and is a pioneering work in characterizing cancer-associated lincRNAs by bioinformatics.

Genome-Wide Identification and Characterization of Long Non-Coding RNAs from Mulberry (Morus notabilis) RNA-seq Data

Numerous sources of evidence suggest that most of the eukaryotic genome is transcribed into protein-coding mRNAs and also into a large number of non-coding RNAs (ncRNAs). Long ncRNAs (lncRNAs), a group consisting of ncRNAs longer than 200 nucleotides, have been found to play critical roles in transcriptional, post-transcriptional, and epigenetic gene regulation across all kingdoms of life. However, lncRNAs and their regulatory roles remain poorly characterized in plants, especially in woody plants. In this paper, we used a computational approach to identify novel lncRNAs from a published RNA-seq data set and analyzed their sequences and expression patterns. In total, 1133 novel lncRNAs were identified in mulberry, and 106 of these lncRNAs displayed a predominant tissue-specific expression in the five major tissues investigated. Additionally, functional predictions revealed that tissue-specific lncRNAs adjacent to protein-coding genes might play important regulatory roles in the development of floral organ and root in mulberry. The pipeline used in this study would be useful for the identification of lncRNAs obtained from other deep sequencing data. Furthermore, the predicted lncRNAs would be beneficial towards an understanding of the variations in gene expression in plants.

Evolutionary annotation of conserved long non-coding RNAs in major mammalian species.

Mammalian genomes contain tens of thousands of long non-coding RNAs (lncRNAs) that have been implicated in diverse biological processes. However, the lncRNA transcriptomes of most mammalian species have not been established, limiting the evolutionary annotation of these novel transcripts. Based on RNA sequencing data from six tissues of nine species, we built comprehensive lncRNA catalogs (4,142–42,558 lncRNAs) covering the major mammalian species. Compared to protein- coding RNAs, expression of lncRNAs exhibits striking lineage specificity. Notably, although 30%–99% human lncRNAs are conserved across different species on DNA locus level, only 20%–27% of these conserved lncRNA loci are detected to transcription, which represents a stark contrast to the proportion of conserved protein-coding genes (48%–80%). This finding provides a valuable resource for experimental scientists to study the mechanisms of lncRNAs. Moreover, we constructed lncRNA expression phylogenetic trees across nine mammals and demonstrated that lncRNA expression profiles can reliably determine phylogenic placement in a manner similar to their coding counterparts. Our data also reveal that the evolutionary rate of lncRNA expression varies among tissues and is significantly higher than those for protein-coding genes. To streamline the processes of browsing lncRNAs and detecting their evolutionary statuses, we integrate all the data produced in this study into a database named PhyloNONCODE (http://www.bioinfo.org/phyloNoncode). Our work starts to place mammalian lncRNAs in an evolutionary context and represent a rich resource for comparative and functional analyses of this critical layer of genome.