Genome-wide analysis of lncRNA stability in human.

Transcript stability is associated with many biological processes, and the factors affecting mRNA stability have been extensively studied. However, little is known about the features related to human long noncoding RNA (lncRNA) stability. By inhibiting transcription and collecting samples in 10 time points, genome-wide RNA-seq studies was performed in human lung adenocarcinoma cells (A549) and RNA half-life datasets were constructed. The following observations were obtained. First, the half-life distributions of both lncRNAs and messanger RNAs (mRNAs) with one exon (lnc-human1 and m-human1) were significantly different from those of both lncRNAs and mRNAs with more than one exon (lnc-human2 and m-human2). Furthermore, some factors such as full-length transcript secondary structures played a contrary role in lnc-human1 and m-human2. Second, through the half-life comparisons of nucleus- and cytoplasm-specific and common lncRNAs and mRNAs, lncRNAs (mRNAs) in the nucleus were found to be less stable than those in the cytoplasm, which was derived from transcripts themselves rather than cellular location. Third, kmers-based protein-RNA or RNA-RNA interactions promoted lncRNA stability from lnc-human1 and decreased mRNA stability from m-human2 with high probability. Finally, through applying deep learning-based regression, a non-linear relationship was found to exist between the half-lives of lncRNAs (mRNAs) and related factors. The present study established lncRNA and mRNA half-life regulation networks in the A549 cell line and shed new light on the degradation behaviors of both lncRNAs and mRNAs.

Dynamically monitoring cellular γ-H2AX reveals the potential of carcinogenicity evaluation for genotoxic compounds.

Amongst all toxicological endpoints, carcinogenicity might pose the greatest concern. Genetic damage has been considered an important underlying mechanism for the carcinogenicity of chemical substances. The demand for in vitro genotoxic tests as alternative approaches is growing rapidly with the implementation of new regulations for compounds. However, currently available in vitro genotoxicity tests are often limited by relatively high false positive rates. Moreover, few studies have explored carcinogenicity potential by in vitro genotoxicity testing due to the shortage of suitable toxicological biomarkers to link gene damage with cancer risk. γ-H2AX is a recently acknowledged attractive endpoint (biomarker) for evaluating DNA damage and can simultaneously reflect the DNA damage response and repair of cells. We previously reported an ultrasensitive and reliable method, namely stable-isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS), for detecting cellular γ-H2AX and evaluating genotoxic chemicals. More importantly, our method can dynamically monitor the specific processes of genotoxic compounds affecting DNA damage and repair reflected by the amount of γ-H2AX. To clarify the possibility of using this method to assess the potential carcinogenicity of genotoxic chemicals, we applied it to a set of 69 model compounds recommended by the European Center for the Validation of Alternative Methods (ECVAM), with already-characterized genotoxic potential. Compared to conventional in vitro genotoxicity assays, including the Ames test, the γ-H2AX assay by MS has high accuracy (94-96%) due to high sensitivity and specificity (88% and 100%, respectively). The dynamic profiles of model compounds after exposure in HepG2 cells were explored, and a mathematical approach was employed to simulate and quantitatively model the DNA repair kinetics of genotoxic carcinogens (GCs) based on γ-H2AX time-effect curves up to 8 h. Two crucial parameters, i.e., k (rate of γ-H2AX decay) and t50 (time required for γ-H2AX from maximum decrease to half) estimated by the least squares method, were achieved. An open web server to help researchers calculate these two key parameters and profile simulated curves of the tested compound is available online ( http://ccb1.bmi.ac.cn:81/shiny-server/sample-apps/prediction1/ ). We detected a positive association between carcinogenic levels and k and t50 values of γ-H2AX in tested GCs, validating the potential of using this MS-based γ-H2AX in vitro assay to help preliminarily evaluate carcinogenicity and assess genotoxicity. This approach may be used alone or integrated into an existing battery of in vitro genetic toxicity tests.

Up-regulation of FAM64A promotes epithelial-to-mesenchymal transition and enhances stemness features in breast cancer cells.

FAM64A was found to be markedly up-regulated in tumor samples and associated with worse overall survival in multiple cancer types, including breast cancer. However, the functional significance of FAM64A in breast cancer remains largely unknown. In this study, we systematically investigated the expression of FAM64A in multiple public breast cancer datasets. We found that FAM64A is significantly positively correlated with tumor stemness index in breast cancer samples, corresponding with an advanced clinical grade, metastasis and unfavorable prognosis. In vitro experiments further showed an up-regulation of stemness genes after over-expressing FAM64A in breast cancer cells. FAM64A overexpression also promoted breast cancer cell proliferation, migration, accompanied by the activation of epithelial-to-mesenchymal transition (EMT). Besides, we identified a strong association of FAM64A expression with TP53 mutations in TCGA and three additional breast cancer datasets. In summary, our study revealed a novel function of FAM64A in promoting breast cancer stemness and EMT, suggesting that targeting of FAM64A may have therapeutic values in advanced breast cancer.

Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model.

BACKGROUND: Compared with clinically functioning pituitary adenoma (FPA), clinically non-functioning pituitary adenoma (NFPA) lacks of detectable hypersecreting serum hormones and related symptoms which make it difficult to predict the prognosis and monitoring for postoperative tumour regrowth. We aim to investigate whether the expression of selected tumour-related proteins and clinical features could be used as tumour markers to effectively predict the regrowth of NFPA. METHOD: Tumour samples were collected from 295 patients with NFPA from Beijing Tiantan Hospital. The expression levels of 41 tumour-associated proteins were assessed using tissue microarray analyses. Clinical characteristics were analysed via univariate and multivariate logistic regression analyses. Logistic regression algorithm was applied to build a prediction model based on the expression levels of selected proteins and clinical signatures, which was then assessed in the testing set. RESULTS: Three proteins and two clinical signatures were confirmed to be significantly related to the regrowth of NFPA, including cyclin-dependent kinase inhibitor 2A (CDKN2A/p16), WNT inhibitory factor 1 (WIF1), tumour growth factor beta (TGF-ß), age and tumour volume. A prediction model was generated on the training set, which achieved a fivefold predictive accuracy of 81.2%. The prediction ability was validated on the testing set with an accuracy of 83.9%. The area under the receiver operating characteristic curves (AUC) for the signatures were 0.895 and 0.881 in the training and testing sets, respectively. CONCLUSION: The prediction model could effectively predict the regrowth of NFPA, which may facilitate the prognostic evaluation and guide early interventions.

sRNATarBase 3.0: an updated database for sRNA-target interactions in bacteria.

Bacterial sRNAs are a class of small regulatory RNAs of about 40-500 nt in length; they play multiple biological roles through binding to their target mRNAs or proteins. Therefore, elucidating sRNA targets is very important. However, only targets of a few sRNAs have been described. To facilitate sRNA functional studies such as developing sRNA target prediction models, we updated the sRNATarBase database, which was initially developed in 2010. The new version (recently moved to http://ccb1.bmi.ac.cn/srnatarbase/) contains 771 sRNA-target entries manually collected from 213 papers, and 23 290 and 11 750 predicted targets from sRNATarget and sTarPicker, respectively. Among the 771 entries, 475 and 17 were involved in validated sRNA-mRNA and sRNA-protein interactions, respectively, while 279 had no reported interactions. We also presented detailed information for 316 binding regions of sRNA-target mRNA interactions and related mutation experiments, as well as new features, including NCBI sequence viewer, sRNA regulatory network, target prediction-based GO and pathway annotations, and error report system. The new version provides a comprehensive annotation of validated sRNA-target interactions, and will be a useful resource for bacterial sRNA studies.

Detecting RNA-RNA interactions in E. coli using a modified CLASH method.

BACKGROUND: Bacterial small regulatory RNAs (sRNAs) play important roles in sensing environment changes through sRNA-target mRNA interactions. However, the current strategy for detecting sRNA-mRNA interactions usually combines bioinformatics prediction and experimental verification, which is hampered by low prediction accuracy and low-throughput. Additionally, among the 4736 sequenced bacterial genomes, only about 2164 sRNAs from 319 strains have been described. Furthermore, target mRNAs of only 157 sRNAs have been uncovered. Obviously, highly efficient methods were required to detect sRNA-mRNA interactions in the sequenced genomes. This study aimed to apply a modified CLASH (cross-linking, ligation and sequencing hybrids) method to detect RNA-RNA interactions in E. coli, a model bacterial organism. RESULTS: Statistically significant interactions were detected in 29 transcript pairs. To the best of our knowledge, 24 pairs were reported for the first time and were novel RNA interactions, including tRNA-tRNA, tRNA-ncRNA (non-coding RNA), tRNA-rRNA, rRNA-mRNA, rRNA-ncRNA, rRNA-rRNA, rRNA-IGT (intergenic transcript), and tRNA-IGT interactions. CONCLUSIONS: Discovery of novel RNA-RNA interactions in the present study demonstrates that RNA-RNA interactions might be far more complicated than ever expected. New methods may be required to help discover more novel RNA-RNA interactions. The present work describes a high-throughput protocol not only for discovering new RNA interactions, but also directly obtaining base-pairing sequences, which should be useful in assessing RNA structure and interactions.

Altered superficial amygdala-cortical functional link in resting state after 36 hours of total sleep deprivation.

The superficial amygdala (SFA) is important in human emotion/affective processing via its strong connection with other limbic and cerebral cortex for receptive and expressive emotion processing. Few studies have investigated the functional connectivity changes of the SFA under extreme conditions, such as prolonged sleep loss, although the SFA showed a distinct functional connectivity pattern throughout the brain. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) was employed to investigate the changes of SFA-cortical functional connectivity after 36 hr of total sleep deprivation (TSD). Fourteen healthy male volunteers aged 25.9 ± 2.3 years (range 18-28 years) enrolled in this within-subject crossover study. We found that the right SFA showed increased functional connectivity with the right medial prefrontal cortex (mPFC) and decreased functional connectivity with the right dorsal posterior cingulate cortex (dPCC) in the resting brain after TSD compared with that during rested wakefulness. For the left SFA, decreased connectivity with the right dorsal anterior cingulate cortex (dACC) and right dPCC was found. Further regression analysis indicated that the functional link between mPFC and SFA significantly correlated with the Profile of Mood State scores. Our results suggest that the amygdala cannot be treated as a single unit in human neuroimaging studies and that TSD may alter the functional connectivity pattern of the SFA, which in turn disrupts emotional regulation.

Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.

Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we present Protein Mutational Effect Predictor (ProMEP), a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects. A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from ~160 million proteins. ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering. Specifically, ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA, and successfully guides the development of high-performance gene-editing tools with their engineered variants. The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-to-G conversion frequency of up to 77.27% (vs 69.80% for ABE8e, a previous TadA-based adenine base editor) with significantly reduced bystander and off-target effects compared to ABE8e. ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering. Therefore, ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins, thereby advancing studies in biomedicine and synthetic biology.

Structural annotation, semi-quantification and toxicity prediction of pyrrolizidine alkaloids from functional food: In silico and molecular networking strategy.

Many traditional Chinese herbs contain pyrrolizidine alkaloids (PAs), which have been reported to be toxic to livestock and humans. However, the lack of PAs standards makes it difficult to effectively conduct a risk assessment in the varied components of traditional Chinese medicine. It is necessary to propose a suitable strategy to obtain the representative occurrence data of PAs in complex systems. A comprehensive approach for annotating the structures, concentration, and mutagenicity of PAs in three Chinese herbs has been proposed in this article. First, feature-based molecular networking (FBMN) combined with network annotation propagation (NAP) on the Global Natural Products Social Molecular Networking web platform speeds up the process of annotating PAs found in Chinese herbs. Second, a semi-quantitative prediction model based on the quantitative structure and ionization intensity relationship (QSIIR) is used to forecast the amounts of PAs in complex substrates. Finally, the T.E.S.T. was used to provide predictions regarding the mutagenicity of annotated PAs. The goal of this study was to develop a strategy for combining the results of several computer models for PA screening to conduct a comprehensive analysis of PAs, which is a crucial step in risk assessment of unknown PAs in traditional Chinese herbal preparations.

sRNATarBase: a comprehensive database of bacterial sRNA targets verified by experiments.

Bacterial sRNAs are an emerging class of small regulatory RNAs, 40-500 nt in length, which play a variety of important roles in many biological processes through binding to their mRNA or protein targets. A comprehensive database of experimentally confirmed sRNA targets would be helpful in understanding sRNA functions systematically and provide support for developing prediction models. Here we report on such a database--sRNATarBase. The database holds 138 sRNA-target interactions and 252 noninteraction entries, which were manually collected from peer-reviewed papers. The detailed information for each entry, such as supporting experimental protocols, BLAST-based phylogenetic analysis of sRNA-mRNA target interaction in closely related bacteria, predicted secondary structures for both sRNAs and their targets, and available binding regions, is provided as accurately as possible. This database also provides hyperlinks to other databases including GenBank, SWISS-PROT, and MPIDB. The database is available from the web page http://ccb.bmi.ac.cn/srnatarbase/.

A modified visual loop-mediated isothermal amplification method for diagnosis and differentiation of main pathogens from Mycobacterium tuberculosis complex.

This study was aimed to rapidly identify and differentiate two main pathogens of the Mycobacterium tuberculosis complex: Mycobacterium tuberculosis subsp. tuberculosis and Mycobacterium bovis by a modified loop-mediated isothermal amplification (LAMP) assay. The reaction results could be evaluated by naked eye with two optimized closed tube detection methods as follows: adding the modified fluorescence dye in advance into the reaction mix so as to observe the color changes or putting a tinfoil in the tube and adding the SYBR Green I dye on it, then making the dye drop into the bottom of the tube by centrifuge after reaction. The results showed that the two groups of primers used jointly in this assay could successfully identify and differentiate Mycobacterium tuberculosis subsp. tuberculosis and Mycobacterium tuberculosis bovis. Sensitivity test displayed that the modified LAMP assay with the closed tube system could determine the minimal template concentration of 1 copy/µl, which was more sensitive than that of routine PCR. The advantages of this LAMP method for detection of the Mycobacterium tuberculosis complex included high specificity, high sensitivity, simplicity, and superiority in avoidance of aerosol contamination. The modified LAMP assay would provide a potential for clinical diagnosis and therapy of tuberculosis in the developing countries and the resource-limited areas.

Deep learning methods improve linear B-cell epitope prediction.

BACKGROUND: B-cell epitopes play important roles in vaccine design, clinical diagnosis, and antibody production. Although some models have been developed to predict linear or conformational B-cell epitopes, their performance is still unsatisfactory. Hundreds of thousands of linear B-cell epitope data have accumulated in the Immune Epitope Database (IEDB). These data can be explored using the deep learning methods, in order to create better predictive models for linear B-cell epitopes. RESULTS: After data cleaning, we obtained 240,563 peptide samples with experimental evidence from the IEDB database, including 25,884 linear B-cell epitopes and 214,679 non-epitopes. Based on the peptide center, we adapted each peptide to the same length by trimming or extending. A random portion of the data, with the same amount of epitopes and non-epitopes, were set aside as test dataset. Then a same number of epitopes and non-epitopes were randomly selected from the remaining data to build a classifier with the feedforward deep neural network. We built eleven classifiers to form an ensemble prediction model. The model will report a peptide as an epitope if it was classified as epitope by all eleven classifiers. Then we used the test data set to evaluate the performance of the model using the area value under the receiver operating characteristic (ROC) curve (AUC) as an indicator. We established 40 models to predict linear B-cell epitopes of length from 11 to 50 separately, and found that the AUC value increased with the length and tended to be stable when the length was 38. Repeated results showed that the models constructed by this method were robust. Tested on our and two public test datasets, our models outperformed current major models available. CONCLUSIONS: We applied the feedforward deep neural network to the large amount of linear B-cell epitope data with experimental evidence in the IEDB database, and constructed ensemble prediction models with better performance than the current major models available. We named the models as DLBEpitope and provided web services using the models at http://ccb1.bmi.ac.cn:81/dlbepitope/.

BioSunMS: a plug-in-based software for the management of patients information and the analysis of peptide profiles from mass spectrometry.

BACKGROUND: With wide applications of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), statistical comparison of serum peptide profiles and management of patients information play an important role in clinical studies, such as early diagnosis, personalized medicine and biomarker discovery. However, current available software tools mainly focused on data analysis rather than providing a flexible platform for both the management of patients information and mass spectrometry (MS) data analysis. RESULTS: Here we presented a plug-in-based software, BioSunMS, for both the management of patients information and serum peptide profiles-based statistical analysis. By integrating all functions into a user-friendly desktop application, BioSunMS provided a comprehensive solution for clinical researchers without any knowledge in programming, as well as a plug-in architecture platform with the possibility for developers to add or modify functions without need to recompile the entire application. CONCLUSION: BioSunMS provides a plug-in-based solution for managing, analyzing, and sharing high volumes of MALDI-TOF or SELDI-TOF MS data. The software is freely distributed under GNU General Public License (GPL) and can be downloaded from http://sourceforge.net/projects/biosunms/.

[Research progress of prediction of bacterial sRNA genes and their targets--a review].

Bacterial sRNAs are a class of non-coding RNAs with 40-500 nucleotides in length. Most of them function as posttranscriptional regulation of gene expression through binding to the translation initiation region of their target mRNAs. In view that prediction of sRNAs and their targets provides support for experimental identification, some prediction methods have been developed for both of them in recent years. In this review, we firstly gave an overview of methods for prediction of sRNA genes, which are classified into three categories, namely, comparative genomics-based, transcription units-based and machine learning-based prediction methods. Secondly, the methods for sRNA target prediction are classified into two types, which are sequence alignment-based method and prediction of RNA secondary structure-based method, respectively. Finally, the principles, advantages and limitations of each kind of method are discussed, and perspectives for prediction methods of sRNA and their targets is pointed out.

Immune receptor repertoires in pediatric and adult acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML), caused by the abnormal proliferation of immature myeloid cells in the blood or bone marrow, is one of the most common hematologic malignancies. Currently, the interactions between malignant myeloid cells and the immune microenvironment, especially T cells and B cells, remain poorly characterized. METHODS: In this study, we systematically analyzed the T cell receptor and B cell receptor (TCR and BCR) repertoires from the RNA-seq data of 145 pediatric and 151 adult AML samples as well as 73 non-tumor peripheral blood samples. RESULTS: We inferred over 225,000 complementarity-determining region 3 (CDR3) sequences in TCR α, ß, γ, and δ chains and 1,210,000 CDR3 sequences in B cell immunoglobulin (Ig) heavy and light chains. We found higher clonal expansion of both T cells and B cells in the AML microenvironment and observed many differences between pediatric and adult AML. Most notably, adult AML samples have significantly higher level of B cell activation and more secondary Ig class switch events than pediatric AML or non-tumor samples. Furthermore, adult AML with highly expanded IgA2 B cells, which might represent an immunosuppressive microenvironment, are associated with regulatory T cells and worse overall survival. CONCLUSIONS: Our comprehensive characterization of the AML immune receptor repertoires improved our understanding of T cell and B cell immunity in AML, which may provide insights into immunotherapies in hematological malignancies.

Construction of two mathematical models for prediction of bacterial sRNA targets.

Accurate prediction of sRNA targets plays a key role in determining sRNA functions. Here we introduced two mathematical models, sRNATargetNB and sRNATargetSVM, for prediction of sRNA targets using Nai ve Bayes method and support vector machines (SVM), respectively. The training dataset was composed of 46 positive samples (real sRNA-targets interaction) and 86 negative samples (no interaction between sRNA and targets). The leave-one-out cross-validation (LOOCV) classification accuracy was 91.67% for sRNATargetNB, and 100.00% for sRNATargetSVM. To evaluate the performance of the models, an independent test dataset was used, which contained 22 positive samples and 1700 randomly generated negative samples. The results showed that the classification accuracy, sensitivity, and specificity were 93.03%, 40.90%, and 93.71% for sRNATargetNB and 80.55%, 72.73%, and 80.65% for sRNATargetSVM, respectively. Therefore, the presented models provide support for experimental identification of sRNA targets. The related software and supplementary materials can be downloaded from webpage http://www.biosun.org.cn/srnatarget/.

[Computational approaches to microRNA discovery].

microRNAs (miRNAs) are endogenous non-coding RNAs of ~21 nucleotides in length discovered in recent years. They are involved in diverse pathways and play an important role in gene regulation in plants and animals. There are two main groups of approaches to miRNA discovery, which are cDNA cloning and computational identification. Since some miRNAs are expressed at a low level and the expression of many miRNAs has spatio-temporal specificity, it is difficult to find them through cDNA cloning. However, computational approaches can predict the miRNAs specifically expressed or with low abundance, which is complement to cDNA cloning. Computational approaches have hence gained wide attention. In this review, the computational approaches to miRNA discovery were summarized. According to their intrinsic characteristics, computational approaches were categorized into five classes: (1) homology search; (2) prediction based on comparative genomics; (3) scoring candidates using the sequence and structure characteristics; (4) prediction combined with targets; and (5) prediction with machine learning. The principles of each class of the approaches and their advantages and limitations in miRNA discovery were discussed. Finally, the future direction in miRNA discovery was pointed out.

[Effect of hypoxia on the gene profile of human bone marrow-derived mesenchymal stem cells].

Our previous study demonstrates that hypoxia promotes human bone marrow-derived mesenchymal stem cell (hMSC) proliferation. The aim of the present study was to investigate the gene profile involved in this process by using cDNA microarray. Cultured hMSCs were treated with hypoxia (3% O(2)) for 4 h, 12 h, 24 h, 36 h, 48 h and 72 h, respectively. Then these cells were collected to prepare total RNA. Hypoxia-induced gene expression profile was examined and analyzed by GenePix Pro 4.0 software. Some of cDNA microarray results were confirmed by RT-PCR. Microarray analysis identified that 282 genes expressed differentially, of which most were involved in metabolism. The number of differentially expressed genes at different hypoxia time points was different, and most genes were regulated after 24-hour hypoxia. Among the 282 differentially expressed genes, 4 hypoxia-inducible factor 1 (HIF-1) targeted genes and 10 genes that changed at 3 continuous time points were found. The results obtained indicated that 4 HIF-1 targeted genes, i.e., transforming growth factor beta3 (TGFbeta3), phospho-glycerate kinase 1 (PGK1), insulin-like growth factor binding protein 3 (IGFBP3) and BCL2/adenovirus E1B 19 kDa interacting protein 3 (BNIP3), displayed up-regulated pattern at 36 h under hypoxia. BNIP3 displayed a dynamically up-regulated pattern at 12, 36 and 72 h under hypoxia. However, TGFbeta3 and PGK1 were down-regulated at 72 h. In addition, the gene expressions of adenylate kinase 3-like 1 (HAC), neurofilament light polypeptide 68 kDa (NEFL), N-myc downstream regultated gene 1 (NDRG1), discoidin domain receptor family member 1 (DDR1), tribbles homolog 3 (TRIB3), nucleoprotein (AHNAK) and eukaryotic elongation factor selenocyteine-tRNA-specific (EESTS) were up-regulated. Moreover, the gene expressions of EESTS, NEFL were up-regulated at 5 different time points under hypoxia. Furthermore, it was found that the gene expressions of histone cluster 1 (HIS1) and transferring receptor (TFRC) were down-regulated. These results suggest that the proliferation of hMSCs induced by hypoxia is a complex process in which a number of genes may be involved.

Mprobe 2.0: computer-aided probe design for oligonucleotide microarray.

UNLABELLED: DNA chips have proven to be effective tools in detecting gene expression levels. Compared with DNA chips using complementary DNA as probes, oligonucleotide microarrays using oligonucleotides as probes have attracted great attention because of their well known advantages. The design of gene-specific probes for each target is essential to the development of oligonucleotide microarrays. We have previously reported the development of a probe design software termed Mprobe 1.0. Here, we present a new version of this software, termed Mprobe 2.0. Several new features are included in Mprobe 2.0. Firstly, a paradox-based sequence database management system has been developed and integrated into the software, which consequently allows interoperability with sequences in GenBank, EMBL, and FASTA formats. Secondly, in contrast to setting a fixed threshold for the secondary structure of probes in Mprobe 1.0 and other related software, Mprobe 2.0 employs a different method. After parameters such as GC type, probe melting temperature and GC contents have been evaluated, candidate probes are sorted by the free energy from high to low value, followed by specificity analysis. Thirdly, Mprobe 2.0 provides users with substantial parameter options in the visual mode. Mprobe 2.0 possesses an easier interface for users to manage sequences annotated in different formats and design the optimal probes for oligonucleotide microarrays and other applications. AVAILABILITY: The program is free for non-commercial users and can be downloaded from the web page http://www.biosun.org.cn/mprobe/ CONTACT: Wuju Li (wujuli@yahoo.com or liwj@nic.bmi.ac.cn).

RDfolder: a web server for prediction of RNA secondary structure.

Prediction of RNA secondary structure is important in the functional analysis of RNA molecules. The RDfolder web server described in this paper provides two methods for prediction of RNA secondary structure: random stacking of helical regions and helical regions distribution. The random stacking method predicts secondary structure by Monte Carlo simulations. The method of helical regions distribution predicts secondary structure based on the helices that appear most frequently in the set of structures, which are generated by the random stacking method. The RDfolder web server can be accessed at http://rna.cbi.pku.edu.cn.