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Gene transcription and protein translation are two key steps of the 'central dogma.' It is still a major challenge to quantitatively deconvolute factors contributing to the coding ability of transcripts in mammals. Here, we propose ribosome calculator (RiboCalc) for quantitatively modeling the coding ability of RNAs in human genome. In addition to effectively predicting the experimentally confirmed coding abundance via sequence and transcription features with high accuracy, RiboCalc provides interpretable parameters with biological information. Large-scale analysis further revealed a number of transcripts with a variety of coding ability for distinct types of cells (i.e. context-dependent coding transcripts), suggesting that, contrary to conventional wisdom, a transcript's coding ability should be modeled as a continuous spectrum with a context-dependent nature. [ABSTRACT FROM AUTHOR]

While more than a hundred thousand long noncoding RNAs (lncRNAs) have been identified in human genome, their biological functions and regulation are largely elusive. Here we present AnnoLnc, a one-stop online annotation portal for human lncRNAs ( http://annolnc1.gao-lab.org/ ). As the first (and the most comprehensive) Web server to provide on-the-fly annotation for novel human lncRNAs, AnnoLnc exploits more than 700 data sources to annotate inputted lncRNA systematically, spanning genomic location, secondary structure, expression patterns, coexpression-based functional annotation, transcriptional regulation, miRNA interaction, protein interaction, genetic association, and evolution. Moreover, in addition to a user-friendly Web interface, AnnoLnc can also be integrated into existing pipelines by either a set of JSON-based web service APIs or a stand-alone version for Linux server.

With the goal of providing a comprehensive, high-quality resource for both plant transcription factors (TFs) and their regulatory interactions with target genes, we upgraded plant TF database PlantTFDB to version 4.0 (http://planttfdb.cbi.pku.edu.cn/). In the new version, we identified 320 370 TFs from 165 species, presenting a more comprehensive genomic TF repertoires of green plants. Besides updating the pre-existing abundant functional and evolutionary annotation for identified TFs, we generated three new types of annotation which provide more directly clues to investigate functional mechanisms underlying: (i) a set of high-quality, non-redundant TF binding motifs derived from experiments; (ii) multiple types of regulatory elements identified from high-throughput sequencing data; (iii) regulatory interactions curated from literature and inferred by combining TF binding motifs and regulatory elements. In addition, we upgraded previous TF prediction server, and set up four novel tools for regulation prediction and functional enrichment analyses. Finally, we set up a novel companion portal PlantRegMap (http://plantregmap.cbi.pku.edu.cn) for users to access the regulation resource and analysis tools conveniently., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Objective: To explore the application of serum surface-enhanced laser desorption/ionization (SELDI) marker patterns in distinguishing non-small cell lung cancer patients from healthy people by protein chip technology., Methods: One hundred and sixty-three serum samples (123 patients with lung cancer and 40 healthy persons), were randomly divided into a training set [94 cases, 53 non-small cell lung cancer (NSCLC), 21 small cell lung cancer and 20 healthy persons] and a blinded test set (69 cases), were included for analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Five protein peaks at 11,493, 6,429, 8,245, 5,336 and 2,536 were automatically chosen for the system training and the development of a decision classification tree model (marker pattern). The accuracy of the model was tested with the blinded test set (an independent set of masked serum samples from 49 patients with NSCLC and 20 healthy persons)., Results: The model differentiated the patients with NSCLC from the healthy people with a sensitivity of 95.9% (71/74) and a specificity of 90.0% (18/20) in the training set and a sensitivity of 83.7%, and a specificity of 80.0% in the blinded set respectively., Conclusion: SELDI-TOF-MS technique can correctly distinguish NSCLC patients from healthy people, and it has the potential for the development of a screening test for the detection of NSCLC.