ncRNA Analysis Research Articles

Integrated analysis of ncRNA in hepatocellular carcinoma with CTNNB1 mutations reveals miR-205-5p and miR-3940-3p Axes

BackgroundCatenin beta 1 (CTNNB1) mutations are one of the most common mutations involved in hepatocellular carcinoma (HCC) progression. However, the association between CTNNB1 mutations and HCC remains controversial. MethodsFive tumor samples with wild-type CTNNB1 and three tumor samples with CTNNB1 mutations were collected from patients with HCC for whole transcriptome sequencing. Selected ncRNAs and mRNAs were validated by qPCR in 48 HCC tumors. Selected ncRNA regulatory axes were verified in HCC cells by transfecting mimics and inhibitors of miRNA. ResultsA network of differentially expressed (DE) lncRNA/circRNA–miRNA–mRNA was constructed to explore the effects of CTNNB1 mutations on ncRNA regulation. TXNRD1, CES1, MATN2, SERPINA5, lncRNA STAT4-210, hsa_circ_0007824, hsa_circ_0008234, hsa-miR-205-5p and hsa-miR-199a-5p were verified at the RNA expression level to validate the sequencing results. The down-up-down axes GLIS3-209/circ_0085440–miR-205-5p–GHRHR and WNK2-213–miR-3940-3p–LY6E were verified at the expression level, and proved to inhibit and promote cell proliferation, respectively. ConclusionThis study demonstrated CTNNB1 mutations associated ncRNA regulatory axes playing different roles in HCC cell proliferation, providing novel insights into the controversial role of CTNNB1 in HCC.

NcRNAs-mediated TIMELESS overexpression in lung adenocarcinoma correlates with reduced tumor immune cell infiltration and poor prognosis.

Lung adenocarcinoma (LUAD) has a poor prognosis. Circadian genes such as TIMELESS have been associated with several pathologies, including cancer. The expression of TIMELESS and the relationship between TIMELESS, infiltration of tumors and prognosis in LUAD requires further investigation. In this study, we investigated the expression of TIMELESS and its association with survival across several types of human cancer using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Program. Noncoding RNAs (ncRNAs) regulating overexpression of TIMELESS in lung adenocarcinoma (LUAD) were explored with expression, correlation, and survival analyses. Immune cell infiltration and biomarkers were analyzed between different TIMELESS expression levels. The relationship between TIMELESS expression and immunophenoscores, which were used to predict response to immunotherapy, was evaluated. TIMELESS was identified as a potential oncogene in LUAD. NcRNA analysis showed MIR4435-2HG/hsa-miR-1-3p may interact with TIMELESS in a competitive endogenous RNA network in LUAD tumor tissues. Most immune cells were significantly decreased in TCGA LUAD tumor tissues with high TIMELESS expression except for CD4+T cells and Th2 cells. TIMELESS expression in LUAD tumor tissues was significantly negatively correlated with neutrophil biomarkers, dendritic cell biomarkers (HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, CD1C) and an immunophenoscore that predicted outcomes associated with the use of immune checkpoint inhibitors. These findings imply that ncRNAs-mediated TIMELESS overexpression in LUAD tumor tissues correlated with poor prognosis, reduced immune cell infiltration in the tumor microenvironment, and poor response to immune checkpoint inhibitors.

Regulation of cashmere fineness traits by noncoding RNA in Jiangnan cashmere goats

BackgroundCashmere has long been used as the raw material for wool textiles. The diameter of the cashmere fibre determines its quality and economic value. However, the regulatory role of noncoding RNAs (ncRNAs) in cashmere fineness remains unclear, especially regarding the interaction between ncRNAs and coding RNAs.ResultsTranscriptome sequencing was used to identify the expression profiles of long noncoding RNAs (lncRNAs), circular RNAs (circRNAs) and microRNAs (miRNAs) in the skin tissues of Jiangnan cashmere goats with different cashmere fineness levels. Integration analysis of ncRNA and coding RNA was performed in combination with previous research results. The results showed that 16,437 lncRNAs, 2234 circRNAs, and 1322 miRNAs were identified in 8 skin samples of cashmere goats. A total of 403 differentially expressed (DE) lncRNAs, 62 DE circRNAs and 30 DE miRNAs were identified in the skin tissues of the fine groups (Fe) and coarse groups (Ce). We predicted the target gene of DE lncRNA, the target gene of DE miRNA and the host gene of DE circRNA. Based on functional annotation and enrichment analysis of target genes, we found that DE lncRNAs could be involved in regulating the fineness traits of cashmere. The most potential lncRNAs were MSTRG.42054.1, MSTRG.18602.3, and MSTRG.2199.13.ConclusionsThe data from this study enriched the cashmere goat noncoding RNA database and helped to supplement the annotation of the goat genome. The results provided a new direction for the breeding of cashmere characters.

Developmental programming: adverse sexually dimorphic transcriptional programming of gestational testosterone excess in cardiac left ventricle of fetal sheep

Adverse in-utero insults during fetal life alters offspring’s developmental trajectory, including that of the cardiovascular system. Gestational hyperandrogenism is once such adverse in-utero insult. Gestational testosterone (T)-treatment, an environment of gestational hyperandrogenism, manifests as hypertension and pathological left ventricular (LV) remodeling in adult ovine offspring. Furthermore, sexual dimorphism is noted in cardiomyocyte number and morphology in fetal life and at birth. This study investigated transcriptional changes and potential biomarkers of prenatal T excess-induced adverse cardiac programming. Genome-wide coding and non-coding (nc) RNA expression were compared between prenatal T-treated (T propionate 100 mg intramuscular twice weekly from days 30 to 90 of gestation; Term: 147 days) and control ovine LV at day 90 fetus in both sexes. Prenatal T induced differential expression of mRNAs in the LV of female (2 down, 5 up) and male (3 down, 1 up) (FDR < 0.05, absolute log2 fold change > 0.5); pathways analysis demonstrated 205 pathways unique to the female, 382 unique to the male and 23 common pathways. In the male, analysis of ncRNA showed differential regulation of 15 lncRNAs (14 down, 1 up) and 27 snoRNAs (26 down and 1 up). These findings suggest sexual dimorphic modulation of cardiac coding and ncRNA with gestational T excess.

Transcriptomic analysis of ncRNA and mRNA interactions during leaf senescence in tomato

An increasing number of non-coding RNAs (ncRNAs) have been discovered recently with the advance of RNA-seq. Nevertheless, the function of ncRNAs in leaf senescence was not fully elucidated. In this study, the whole transcriptome sequencing was employed to characterize the expression profiles of mRNA, lncRNA and miRNA during leaf senescence. A total of 2774 mRNAs, 160 lncRNAs and 117 miRNAs were identified to be significantly differentially expressed between the senescent and the young leaves. Co-expression analysis showed that 160 differential expressing (DE) lncRNAs potentially regulated 946 protein-coding genes in trans, but only 32 targeted protein-coding genes were predicated to be regulated by 30 lncRNAs in cis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of these trans- and cis-target genes revealed that the DE lncRNAs participated in pathways, such as photosynthesis, transporters and circadian rhythm. Furthermore, virus-induced gene silencing (VIGS) was employed to illuminate the role of lncRNAs. The silence of MSTRG.16920 and MSTRG.7613, two intergenic lncRNAs, significantly inhibited leaf senescence induced by darkness, presumably attributed to the downregulated expression of their corresponding target genes Solyc02g069960 and Solyc06g050440, respectively. Notably, Solyc02g069960 and Solyc06g050440 encoding senescence-related NAC transcription factor and reactive oxygen species (ROS)-related peroxidase, respectively, served as positive regulators of leaf senescence. Collectively, our study provides a comprehensive expression profile of lncRNAs in senescent leaf with the concurrent integrated expression of mRNAs and miRNAs.

Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants.

Plant transcriptomes encompass a large number of functional non-coding RNAs (ncRNAs), only some of which have protein-coding capacity. Since their initial discovery, ncRNAs have been classified into two broad categories based on their biogenesis and mechanisms of action, housekeeping ncRNAs and regulatory ncRNAs. With advances in RNA sequencing technology and computational methods, bioinformatics resources continue to emerge and update rapidly, including workflow for in silico ncRNA analysis, up-to-date platforms, databases, and tools dedicated to ncRNA identification and functional annotation. In this review, we aim to describe the biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs (miRNA, siRNA, tsRNA, circRNA, lncRNA) in plants. Then, we systematically summarize tools for analysis and prediction of plant ncRNAs, as well as databases. Furthermore, we discuss the silico analysis process of these ncRNAs and present a protocol for step-by-step computational analysis of ncRNAs. In general, this review will help researchers better understand the world of ncRNAs at multiple levels.

Revisiting the Glass Treatment for Single-Molecule Analysis of ncRNA Function.

Single-molecule imaging is a powerful method for unveiling precise molecular mechanisms. Particularly, single-molecule analysis with total internal reflection fluorescence (TIRF ) microscopy has been successfully applied to the characterization of molecular mechanisms in ncRNA studies. Tracing interactions at the single-molecule level have elucidated the intermediate states of the reaction, which are hidden by ensemble averaging in combinational biochemical approaches, and clarified the key steps of the interaction. However, applying a single-molecule technique to ncRNA analysis still remains a challenge, requiring laborious trial and error to identify a suitable glass surface passivation method. In this chapter, we revisit the major glass surface passivation methods using polyethylene glycol (PEG) treatment and summarize a detailed protocol for single-molecule analysis of the dicing process of Dcr-2, which may apply piRNA studies in the future.

Integrated Analysis of Coding Genes and Non-coding RNAs Associated with Shell Color in the Pacific Oyster (Crassostrea gigas)

Molluscan shell color polymorphism is important in genetic breeding, while the molecular information mechanism for shell coloring is unclear. Here, high-throughput RNA sequencing was used to compare expression profiles of coding and non-coding RNAs (ncRNAs) from Pacific oyster Crassostrea gigas with orange and black shell, which were from an F2 family constructed by crossing an orange shell male with a black shell female. First, 458, 13, and 8 differentially expressed genes (DEGs), lncRNAs (DELs), and miRNAs (DEMs) were identified, respectively. Functional analysis suggested that the DEGs were significantly enriched in 9 pathways including tyrosine metabolism and oxidative phosphorylation pathways. Several genes related to melanin synthesis and biomineralization expressed higher whereas genes associated with carotenoid pigmentation or metabolism expressed lower in orange shell oyster. Then, based on the ncRNA analysis, 163 and 20 genes were targeted by 13 and 8 differentially expressed lncRNAs (DELs) and miRNAs (DEMs), severally. Potential DELs-DEMs-DEGs interactions were also examined. Seven DEMs-DEGs pairs were detected, in which tyrosinase-like protein 1 was targeted by lgi-miR-133-3p and lgi-miR-252a and cytochrome P450 was targeted by dme-miRNA-1-3p. These results revealed that melanin synthesis-related genes and miRNAs-mRNA interactions functioned on orange shell coloration, which shed light on the molecular regulation of shell coloration in marine shellfish.

FexRNA: Exploratory Data Analysis and Feature Selection of Non-Coding RNA.

Non-coding RNA (ncRNA) is involved in many biological processes and diseases in all species. Many ncRNA datasets exist that provide ncRNA data in FASTA format which is well suited for biomedical purposes. However, for ncRNA analysis and classification, statistical learning methods require hidden numerical features from the data. Furthermore, in the literature, a wealth of sequence intrinsic features has been proposed for ncRNA identification. The extraction of hidden features, their analysis, and usage of a suitable set of features is crucial for the performance of any statistical learning method. To alleviate the posed challenges, we generated 96 feature datasets from ncRNA widely used features. The feature datasets are based on RNACentral and consist of species, ncRNA types, and expert databases that are available on the FexRNA platform. Additionally, the feature datasets are explored and analysed to provide statistical information, univariate, and bivariate analysis. We sought to determine which of these 17 features would be most appropriate to use in developing ncRNA classification approaches. For feature selection (FS), a two-phase hierarchical FS framework based on correlation and majority voting is proposed and evaluated on 5 species. The FexRNA platform provides information about ncRNA feature analysis and selection.

Comprehensive Evaluation of Endocytosis-Associated Protein SCAMP3 in Hepatocellular Carcinoma.

BackgroundSecretory carrier membrane proteins 3 (SCAMP3) is an endocytosis-associated protein involved in regulating endosomal pathways and the trafficking of vital signaling receptors. This study aimed to comprehensively assess the role of SCAMP3 in hepatocellular carcinoma (HCC) by integrated bioinformatics analysis.MethodsIn this study, bioinformatics databases were used to explore the differential expression status and prognostic value of SCAMP3 gene in HCC, and bioinformatics analyses of survival data and interactors of SCAMP3 were conducted to predict the prognostic value of SCAMP3 in HCC.ResultsUsing the TCGA data, our data shows that SCAMP3 mRNA expression is most significantly different between liver and hepatocellular carcinoma tissues and higher expression of SCAMP3 has unfavorable prognostic significance in HCC. Tumor grade, stage, and gender also showed a significant relevance with SCAMP3 expression. High SCAMP3 expression of males revealed significantly poorer survival and progression compared with low SCAMP3 expression of males. BioGRID statistics explores 79 unique interactions with SCAMP3 and multiple post translational modifications. Further analysis finds that SOCS2 may negatively correlate with SCAMP3, while GBA, MX1, and DDOST positively correlate with SCAMP3. Moreover, ncRNA analysis shows that SCAMP3 gene expression is positively associated with lncRNA SBF2-AS1 and negatively related with Has-miR-145. The expressions of SBF2-AS1 and Has-miR-145 are also significantly related with survival in HCC.DiscussionSCAMP3 expression can be affected by multiple genes or ncRNAs expression that are associated with survival, thus suggesting that SCAMP3 can be used as a clinical diagnosis and prognostic biomarker in HCC.

SMARCA4 promotes benign skin malignant transformation into melanoma through Adherens junction signal transduction.

Melanoma is a malignant skin tumor, and its incidence is rising. To explore the specific differences in benign and malignant melanoma at the genetic level, we performed a series of bioinformatics analyses, including differential gene analysis, co-expression analysis, enrichment analysis, and regulatory prediction. The microarray data of benign and malignant melanocytes were downloaded from GEO, and 1917 differential genes were obtained by differential analysis (p < 0.05). Weighted gene co-expression network analysis obtained three functional barrier modules. The essential genes of each module are SMARTA4, HECA, and C1R. The results of the enrichment analysis showed that the dysfunctional module gene was mainly associated with RNA splicing and Adherens junction. Through the pivotal analysis of ncRNA, it was found that miR-448, miR-152-3p, and miR-302b-3p essentially regulate three modules, which we consider to be critical regulators. In the pivot analysis of TF, more control modules include ARID3A, E2F1, E2F3, and E2F8. We believe that the regulator (miR-448, miR-152-3p, miR-302b-3p) regulates the expression of the core gene SMARCA4, which in turn affects the signal transduction of the Adherens junction. It eventually leads to the deterioration of benign skin spasms into melanoma.

Transcriptomic Analysis Reveals the Involvement of lncRNA-miRNA-mRNA Networks in Hair Follicle Induction in Aohan Fine Wool Sheep Skin.

Long non-coding RNAs (lncRNA) and microRNAs (miRNA) are new found classes of non-coding RNAs (ncRNAs) that are not translated into proteins but regulate various cellular and biological processes. In this study, we conducted a transcriptomic analysis of ncRNA and mRNA expression in Aohan fine wool sheep (AFWS) at different growth stages (embryonic day 90, embryonic day 120, and the day of birth), and explored their relationship with wool follicle growth. In total, 461 lncRNAs, 106 miRNAs, and 1,009 mRNAs were found to be differentially expressed during the three stages of wool follicle development. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to clarify the roles of the differentially expressed lncRNA, miRNA, and mRNA in the different stages of wool follicle development. Quantitative real-time PCR (qRT-PCR) was used to validate the results of RNA-seq analysis. lncRNA (MSTRG.223165) was found to act as a competing endogenous RNA (ceRNA) and may participate in wool follicle development by acting as an miR-21 sponge. Network prediction implicated the MSTRG.223165-miR-21-SOX6 axis in the wool follicle development. The targeting relationships of miR-21 with SOX6 and MSTRG.223165 were validated in dual-luciferase assays. This is the first report indicating the association of the lncRNA–miRNA–mRNA network with wool follicle development in AFWS. This study provides new insights into the regulation of the wool follicle growth and represents a solid foundation for wool sheep breeding programs.

Targeted regulation of miR-17-5p on TMOD1 promotes the development of cardia cancer.

Cardia cancer is a common type of gastric cancer. Most clinical prevention and prognosis focus on surgical resection, but the efficacy is not satisfactory. Studying the molecular mechanism of pathogenesis of cardia cancer helps us intervene in prognosis and treatment. a total of 134 normal cases related to cardia cancer and 62 cases of cardia cancer samples from the Gene Expression Omnibus (GEO) database were collected. A series of bioinformatics analyses, including differential gene analysis, co-expression analysis, enrichment analysis, regulator prediction, and (Protein-protein interaction) PPI analysis validation were performed. Differential analysis highlighted 10882 differential genes (p<0.05). Weighted gene co-expression network analysis indicated 6 functional disorder modules. TMOD1, JAM2, SPARC, ST18, NOS1 were key genes of each module. Enrichment analysis showed the dysfunctional module genes were mainly related to the proteinaceous extracellular matrix and neuroactive ligand-receptor interaction. Pivotal analysis of ncRNA demonstrated miR-17-5p significantly regulates modular genes including m1, m3, and m5. Target genes were backtracked according to the key regulators. Then, the Module_target gene_ncRNA interaction network diagram was constructed. The network shows m1 has the strongest regulation effect in the network. PPI showed that the core gene TMOD1 (Tropomodulin1) of m1 was at TOP10 in the algorithm. In other words, PPI indicated the importance of TMOD1 in the interaction network. We believe that targeted regulation of miR-17-5p on TMOD1 gene affects the neuroactive ligand-receptor interaction pathway, and it promotes proliferation and apoptosis of cardia cancer cells.

Versatile interactions and bioinformatics analysis of noncoding RNAs.

Advances in RNA sequencing technologies and computational methodologies have provided a huge impetus to noncoding RNA (ncRNA) study. Once regarded as inconsequential results of transcriptional promiscuity, ncRNAs were later found to exert great roles in various aspects of biological functions. They are emerging as key players in gene regulatory networks by interacting with other biomolecules (DNA, RNA or protein). Here, we provide an overview of ncRNA repertoire and highlight recent discoveries of their versatile interactions. To better investigate the ncRNA-mediated regulation, it is necessary to make full use of innovative sequencing techniques and computational tools. We further describe a comprehensive workflow for in silico ncRNA analysis, providing up-to-date platforms, databases and tools dedicated to ncRNA identification and functional annotation.

Large-scale profiling of noncoding RNA function in yeast.

Noncoding RNAs (ncRNAs) are emerging as key regulators of cellular function. We have exploited the recently developed barcoded ncRNA gene deletion strain collections in the yeast Saccharomyces cerevisiae to investigate the numerous ncRNAs in yeast with no known function. The ncRNA deletion collection contains deletions of tRNAs, snoRNAs, snRNAs, stable unannotated transcripts (SUTs), cryptic unstable transcripts (CUTs) and other annotated ncRNAs encompassing 532 different individual ncRNA deletions. We have profiled the fitness of the diploid heterozygous ncRNA deletion strain collection in six conditions using batch and continuous liquid culture, as well as the haploid ncRNA deletion strain collections arrayed individually onto solid rich media. These analyses revealed many novel environmental-specific haplo-insufficient and haplo-proficient phenotypes providing key information on the importance of each specific ncRNA in every condition. Co-fitness analysis using fitness data from the heterozygous ncRNA deletion strain collection identified two ncRNA groups required for growth during heat stress and nutrient deprivation. The extensive fitness data for each ncRNA deletion strain has been compiled into an easy to navigate database called Yeast ncRNA Analysis (YNCA). By expanding the original ncRNA deletion strain collection we identified four novel essential ncRNAs; SUT527, SUT075, SUT367 and SUT259/691. We defined the effects of each new essential ncRNA on adjacent gene expression in the heterozygote background identifying both repression and induction of nearby genes. Additionally, we discovered a function for SUT527 in the expression, 3’ end formation and localization of SEC4, an essential protein coding mRNA. Finally, using plasmid complementation we rescued the SUT075 lethal phenotype revealing that this ncRNA acts in trans. Overall, our findings provide important new insights into the function of ncRNAs.

Computational Approaches for the Analysis of ncRNA through Deep Sequencing Techniques.

The majority of the human transcriptome is defined as non-coding RNA (ncRNA), since only a small fraction of human DNA encodes for proteins, as reported by the ENCODE project. Several distinct classes of ncRNAs, such as transfer RNA, microRNA, and long non-coding RNA, have been classified, each with its own three-dimensional folding and specific function. As ncRNAs are highly abundant in living organisms and have been discovered to play important roles in many biological processes, there has been an ever increasing need to investigate the entire ncRNAome in further unbiased detail. Recently, the advent of next-generation sequencing (NGS) technologies has substantially increased the throughput of transcriptome studies, allowing an unprecedented investigation of ncRNAs, as regulatory pathways and novel functions involving ncRNAs are now also emerging. The huge amount of transcript data produced by NGS has progressively required the development and implementation of suitable bioinformatics workflows, complemented by knowledge-based approaches, to identify, classify, and evaluate the expression of hundreds of ncRNAs in normal and pathological conditions, such as cancer. In this mini-review, we present and discuss current bioinformatics advances in the development of such computational approaches to analyze and classify the ncRNA component of human transcriptome sequence data obtained from NGS technologies.

Identification of ncRNAs as potential therapeutic targets in multiple sclerosis through differential ncRNA - mRNA network analysis.

BackgroundSeveral studies have revealed a potential role for both small nucleolar RNAs (snoRNAs) and microRNAs (miRNAs) in the physiopathology of relapsing-remitting multiple sclerosis (RRMS). This potential implication has been mainly described through differential expression studies. However, it has been suggested that, in order to extract additional information from large-scale expression experiments, differential expression studies must be complemented with differential network studies. Thus, the present work is aimed at the identification of potential therapeutic ncRNA targets for RRMS through differential network analysis of ncRNA – mRNA coexpression networks. ncRNA – mRNA coexpression networks have been constructed from both selected ncRNA (specifically miRNAs, snoRNAs and sdRNAs) and mRNA large-scale expression data obtained from 22 patients in relapse, the same 22 patients in remission and 22 healthy controls. Condition-specific (relapse, remission and healthy) networks have been built and compared to identify the parts of the system most affected by perturbation and aid the identification of potential therapeutic targets among the ncRNAs.ResultsAll the coexpression networks we built present a scale-free topology and many snoRNAs are shown to have a prominent role in their architecture. The differential network analysis (relapse vs. remission vs. controls’ networks) has revealed that, although both network topology and the majority of the genes are maintained, few ncRNA – mRNA links appear in more than one network. We have selected as potential therapeutic targets the ncRNAs that appear in the disease-specific network and were found to be differentially expressed in a previous study.ConclusionsOur results suggest that the diseased state of RRMS has a strong impact on the ncRNA – mRNA network of peripheral blood leukocytes, as a massive rewiring of the network happens between conditions. Our findings also indicate that the role snoRNAs have in targeted gene silencing is a widespread phenomenon. Finally, among the potential therapeutic target ncRNAs, SNORA40 seems to be the most promising candidate.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1396-5) contains supplementary material, which is available to authorized users.

Advances in non-coding RNA profiling for neurological diseases

Advances in non-coding RNA profiling for neurological diseases

NcPRO-seq: a tool for annotation and profiling of ncRNAs in sRNA-seq data

Non-coding RNA (ncRNA) PROfiling in small RNA (sRNA)-seq (ncPRO-seq) is a stand-alone, comprehensive and flexible ncRNA analysis pipeline. It can interrogate and perform detailed profiling analysis on sRNAs derived from annotated non-coding regions in miRBase, Rfam and RepeatMasker, as well as specific regions defined by users. The ncPRO-seq pipeline performs both gene-based and family-based analyses of sRNAs. It also has a module to identify regions significantly enriched with short reads, which cannot be classified under known ncRNA families, thus enabling the discovery of previously unknown ncRNA- or small interfering RNA (siRNA)-producing regions. The ncPRO-seq pipeline supports input read sequences in fastq, fasta and color space format, as well as alignment results in BAM format, meaning that sRNA raw data from the three current major platforms (Roche-454, Illumina-Solexa and Life technologies-SOLiD) can be analyzed with this pipeline. The ncPRO-seq pipeline can be used to analyze read and alignment data, based on any sequenced genome, including mammals and plants. Source code, annotation files, manual and online version are available at http://ncpro.curie.fr/. bioinfo.ncproseq@curie.fr or cciaudo@ethz.ch Supplementary data are available at Bioinformatics online.

Novel insight into the non-coding repertoire through deep sequencing analysis

Non-coding RNAs (ncRNA) account for a large portion of the transcribed genomic output. This diverse family of untranslated RNA molecules play a crucial role in cellular function. The use of ‘deep sequencing’ technology (also known as ‘next generation sequencing’) to infer transcript expression levels in general, and ncRNA specifically, is becoming increasingly common in molecular and clinical laboratories. We developed a software termed ‘RandA’ (which stands for ncRNA Read-and-Analyze) that performs comprehensive ncRNA profiling and differential expression analysis on deep sequencing generated data through a graphical user interface running on a local personal computer. Using RandA, we reveal the complexity of the ncRNA repertoire in a given cell population. We further demonstrate the relevance of such an extensive ncRNA analysis by elucidating a multitude of characterizing features in pathogen infected mammalian cells. RandA is available for download at http://ibis.tau.ac.il/RandA.