Bioinformatics is a fast-growing interdisciplinary field in which the demand for quality education exceeds the supply, especially in developing regions and countries.A massive open online course (MOOC) is a new model for education that delivers videotaped lectures and other course materials over the Internet for all interested persons around the globe to learn for free.Here we present our MOOC ''Bioinformatics: Introduction and Methods,'' which is the second bioinformatics MOOC in the world and one of the first batch of seven MOOCs from China.In the first two runs of this bilingual MOOC, more than 30,000 students with diverse backgrounds registered from 110 countries and regions.In this manuscript, we present the content design of the MOOC, the demographic profiles and learning patterns of the students, the requirement for English support, and feedback from on-campus students.We offer a few suggestions to other scientists who may be interested in creating a MOOC.We also remember the S* course, a successful open online bioinformatics course that ran from 2001 to 2007, long before the current wave of MOOCs.We believe that MOOC education has great potential to enhance global bioinformatics education.

To understand whether any human-specific new genes may be associated with human brain functions, we computationally screened the genetic vulnerable factors identified through Genome-Wide Association Studies and linkage analyses of nicotine addiction and found one human-specific de novo protein-coding gene, FLJ33706 (alternative gene symbol C20orf203). Cross-species analysis revealed interesting evolutionary paths of how this gene had originated from noncoding DNA sequences: insertion of repeat elements especially Alu contributed to the formation of the first coding exon and six standard splice junctions on the branch leading to humans and chimpanzees, and two subsequent substitutions in the human lineage escaped two stop codons and created an open reading frame of 194 amino acids. We experimentally verified FLJ33706's mRNA and protein expression in the brain. Real-Time PCR in multiple tissues demonstrated that FLJ33706 was most abundantly expressed in brain. Human polymorphism data suggested that FLJ33706 encodes a protein under purifying selection. A specifically designed antibody detected its protein expression across human cortex, cerebellum and midbrain. Immunohistochemistry study in normal human brain cortex revealed the localization of FLJ33706 protein in neurons. Elevated expressions of FLJ33706 were detected in Alzheimer's brain samples, suggesting the role of this novel gene in human-specific pathogenesis of Alzheimer's disease. FLJ33706 provided the strongest evidence so far that human-specific de novo genes can have protein-coding potential and differential protein expression, and be involved in human brain functions.

Drug addiction is a serious worldwide problem with strong genetic and environmental influences. Different technologies have revealed a variety of genes and pathways underlying addiction; however, each individual technology can be biased and incomplete. We integrated 2,343 items of evidence from peer-reviewed publications between 1976 and 2006 linking genes and chromosome regions to addiction by single-gene strategies, microrray, proteomics, or genetic studies. We identified 1,500 human addiction-related genes and developed KARG (http://karg.cbi.pku.edu.cn), the first molecular database for addiction-related genes with extensive annotations and a friendly Web interface. We then performed a meta-analysis of 396 genes that were supported by two or more independent items of evidence to identify 18 molecular pathways that were statistically significantly enriched, covering both upstream signaling events and downstream effects. Five molecular pathways significantly enriched for all four different types of addictive drugs were identified as common pathways which may underlie shared rewarding and addictive actions, including two new ones, GnRH signaling pathway and gap junction. We connected the common pathways into a hypothetical common molecular network for addiction. We observed that fast and slow positive feedback loops were interlinked through CAMKII, which may provide clues to explain some of the irreversible features of addiction.

In this personal perspective, we recall the history of bioinformatics and computational biology in China, review current research and education, and discuss future prospects and challenges.The field of bioinformatics in China has grown significantly in the past decade despite a delayed and patchy start at the end of the 1980s by a few scientists from other disciplines, most noticeably physics and mathematics, where China's traditional strength has been.In the late 1990s and early 2000s, rapid expansion of the field was fueled by the Internet boom and genomics boom worldwide and in China.Today bioinformatics research in China is characterized by a great variety of biological questions addressed and the close collaborative efforts between computational scientists and biologists, with a full spectrum of focuses ranging from database building and algorithm development to hypothesis generation and biological discoveries.Although challenges remain, the future of bioinformatics in China is promising thanks to advances in both computing infrastructure and experimental biology research, a steady increase of governmental funding, and most importantly a critical mass of bioinformatics scientists consisting of not only converts from other disciplines but also formally trained overseas returnees and a new generation of domestically trained bioinformatics Ph.D.s.

How to process the information from different systems is an important aspect in the cooperative design environment. In 3D industrial, three dimension models are the main carrier of information. This paper proposed a model based architecture in the 3D solid industrial collaborative design. And, a model based transferring function was presented to implement information exchange in the heterogeneous and homogeneous systems. These were evaluated in the cooperative design of different piping design software.

MOTIVATION: The rapid accumulation of single amino acid polymorphisms (SAPs), also known as non-synonymous single nucleotide polymorphisms (nsSNPs), brings the opportunities and needs to understand and predict their disease association. Currently published attributes are limited, the detailed mechanisms governing the disease association of a SAP remain unclear and thus, further investigation of new attributes and improvement of the prediction are desired. RESULTS: A SAP dataset was compiled from the Swiss-Prot variant pages. We extracted and demonstrated the effectiveness of several new biologically informative attributes including the structural neighbor profiles that describe the SAP's microenvironment, nearby functional sites that measure the structure-based and sequence-based distances between the SAP site and its nearby functional sites, aggregation properties that measure the likelihood of protein aggregation and disordered regions that consider whether the SAP is located in structurally disordered regions. The new attributes provided insights into the mechanisms of the disease association of SAPs. We built a support vector machines (SVMs) classifier employing a carefully selected set of new and previously published attributes. Through a strict protein-level 5-fold cross-validation, we attained an overall accuracy of 82.61%, and an MCC of 0.60. Moreover, a web server was developed to provide a user-friendly interface for biologists. AVAILABILITY: The web server is available at http://sapred.cbi.pku.edu.cn/

SUMMARY: DRTF contains 2025 putative transcription factors (TFs) in Oryza sativa L. ssp. indica and 2384 in ssp. japonica, distributed in 63 families, identified by computational prediction and manual curation. It includes detailed annotations of each TF including sequence features, functional domains, Gene Ontology assignment, chromosomal localization, EST and microarray expression information, as well as multiple sequence alignment of the DNA-binding domains for each TF family. The database can be browsed and searched with a user-friendly web interface. AVAILABILITY: DRTF is available at http://drtf.cbi.pku.edu.cn

BACKGROUND: Automated comparison of complete sets of genes encoded in two genomes can provide insight on the genetic basis of differences in biological traits between species. Gene ontology (GO) is used as a common vocabulary to annotate genes for comparison. Current approaches calculate the fold of unweighted or weighted differences between two species at the high-level GO functional categories. However, to ensure the reliability of the differences detected, it is important to evaluate their statistical significance. It is also useful to search for differences at all levels of GO. RESULTS: We propose a statistical approach to find reliable differences between the complete sets of genes encoded in two genomes at all levels of GO. The genes are first assigned GO terms from BLAST searches against genes with known GO assignments, and for each GO term the abundance of genes in the two genomes is compared using a chi-squared test followed by false discovery rate (FDR) correction. We applied this method to find statistically significant differences between two cyanobacteria, Synechocystis sp. PCC6803 and Anabaena sp. PCC7120. We then studied how the set of identified differences vary when different BLAST cutoffs are used. We also studied how the results vary when only subsets of the genes were used in the comparison of human vs. mouse and that of Saccharomyces cerevisiae vs. Schizosaccharomyces pombe. CONCLUSION: There is a surprising lack of statistical approaches for comparing complete genomes at all levels of GO. With the rapid increase of the number of sequenced genomes, we hope that the approach we proposed and tested can make valuable contribution to comparative genomics.

UNLABELLED: We have probably developed the most comprehensive database of Arabidopsis transcription factors (DATF). The DATF contains known and predicted Arabidopsis transcription factors (1827 genes in 56 families) with the unique information of 1177 cloned sequences and many other features including 3D structure templates, EST expression information, transcription factor binding sites and nuclear location signals. AVAILABILITY: DATF is freely available at http://datf.cbi.pku.edu.cn

MOTIVATION: High-throughput technologies such as DNA sequencing and microarrays have created the need for automated annotation of large sets of genes, including whole genomes, and automated identification of pathways. Ontologies, such as the popular Gene Ontology (GO), provide a common controlled vocabulary for these types of automated analysis. Yet, while GO offers tremendous value, it also has certain limitations such as the lack of direct association with pathways. RESULTS: We demonstrated the use of the KEGG Orthology (KO), part of the KEGG suite of resources, as an alternative controlled vocabulary for automated annotation and pathway identification. We developed a KO-Based Annotation System (KOBAS) that can automatically annotate a set of sequences with KO terms and identify both the most frequent and the statistically significantly enriched pathways. Results from both whole genome and microarray gene cluster annotations with KOBAS are comparable and complementary to known annotations. KOBAS is a freely available stand-alone Python program that can contribute significantly to genome annotation and microarray analysis.

MOTIVATION: More and more often, a gene is epitomized by a large number of sequences in GenBank. This high redundancy makes it very difficult to identify a unique best match for a query sequence from its BLAST results. We developed a novel program UniBLAST that filters out uninformative hits, clusters the redundant hits, groups the hits by LocusLink, and graphically displays the results. We also implemented a scoring function in UniBLAST to assign a unique gene name to a query sequence. UniBLAST significantly increases the efficiency of gene annotation. AVAILABILITY: The program is available at http://south.genomics.org.cn/software/uniblast/index.html CONTACT: [email protected]; [email protected]