Large-scale Sequencing Projects Research Articles

Microarray‐based DNA profiling to study genomic aberrations

High throughput microarrays were initially developed to analyse the expression of many RNA transcripts in parallel. The technology has since been adapted to a variety of applications, one of which is the analysis of the genome to study DNA dosage and sequence content. Advances in microarray fabrication and completion of large-scale genome sequencing projects have enabled the rapid development of affordable array-based methods for high-resolution genome-wide assessment of DNA alterations. This review will describe the evolution of microarray assays to study genomic aberrations and will highlight how they have enabled researchers to gain insight into the biology of human diseases and how they will benefit research in the future.

Phylogeny vs genome reshuffling: horizontal gene transfer.

The evolutionary events in organisms can be tracked to the transfer of genetic material. The inheritance of genetic material among closely related organisms is a slow evolutionary process. On the other hand, the movement of genes among distantly related species can account for rapid evolution. The later process has been quite evident in the appearance of antibiotic resistance genes among human and animal pathogens. Phylogenetic trees based on such genes and those involved in metabolic activities reflect the incongruencies in comparison to the 16S rDNA gene, generally used for taxonomic relationships. Such discrepancies in gene inheritance have been termed as horizontal gene transfer (HGT) events. In the post-genomic era, the explosion of known sequences through large-scale sequencing projects has unraveled the weakness of traditional 16S rDNA gene tree based evolutionary model. Various methods to scrutinize HGT events include atypical composition, abnormal sequence similarity, anomalous phylogenetic distribution, unusual phyletic patterns, etc. Since HGT generates greater genetic diversity, it is likely to increase resource use and ecosystem resilience.

1000 Genomes: on the Road to Personalized Medicine

The recently announced 1000 Genomes Project is an international collaboration to sequence 1000 individuals in an effort to produce the most complete catalog of human genetic variation to date. Building on the International HapMap Project, the 1000 Genomes Project will utilize new sequencing technologies to catalog genetic variants that are present in the human population across most of the genome at a rate of 1 percent or greater frequency. Investigators will not only look for single letter changes in the genome (called single nucleotide polymorphisms or SNPs), but will also look for differences in structural variants in the genome (segments of the genome that have been rearranged, deleted, or duplicated) (1). The first phase of the Project will involve three pilot studies. The first will sequence the genomes of two nuclear families (an adult child and both parents) at deep coverage (20x). The second will sequence the genomes of 180 people at low coverage (2x). The third will sequence the coding regions (exons) of about 1000 – 2000 gene regions in 1000 people at deep coverage (20x) (2). The 1000 Genomes Project represents a major step forward on the road to personalized genomic medicine. By creating an important scientific resource, the Project will help advance understanding of the complex relationship between genetic variation and human health and disease. The promise of personalized genomics is not new; it was almost twenty years ago that the New York Times published an article that predicted: “In the not-so-distant future, we can expect to walk into a physician's office for an annual physical and walk out with a blueprint of our genetic inheritance - and with the knowledge of the most likely cause of our own death.” (3) In the past year, individual genome sequencing has become possible (4–5) but it is still a long way from becoming a routine part of medical care. In order to achieve its fullest potential, the 1000 Genomes Project, like other large scale sequencing projects that came before it, will have to be carefully designed to ensure adequate protection and respectful treatment of research participants. In addition to addressing the research ethics issues involved, however, this new initiative creates an opportunity to reflect on the larger health system challenges that will have to be addressed before the knowledge that is gained through genome research can be integrated into routine clinical care.

Recent developments in the MAFFT multiple sequence alignment program

The accuracy and scalability of multiple sequence alignment (MSA) of DNAs and proteins have long been and are still important issues in bioinformatics. To rapidly construct a reasonable MSA, we developed the initial version of the MAFFT program in 2002. MSA software is now facing greater challenges in both scalability and accuracy than those of 5 years ago. As increasing amounts of sequence data are being generated by large-scale sequencing projects, scalability is now critical in many situations. The requirement of accuracy has also entered a new stage since the discovery of functional noncoding RNAs (ncRNAs); the secondary structure should be considered for constructing a high-quality alignment of distantly related ncRNAs. To deal with these problems, in 2007, we updated MAFFT to Version 6 with two new techniques: the PartTree algorithm and the Four-way consistency objective function. The former improved the scalability of progressive alignment and the latter improved the accuracy of ncRNA alignment. We review these and other techniques that MAFFT uses and suggest possible future directions of MSA software as a basis of comparative analyses. MAFFT is available at http://align.bmr.kyushu-u.ac.jp/mafft/software/.

Home-built integrated microarray system (IMAS). A three-laser confocal fluorescence scanner coupled with a microarray printer

Microarray technology covers the urgent need to exploit the accumulated genetic information from large-scale sequencing projects and facilitate investigations on a genome-wide scale. Although most applications focus on DNA microarrays, the technology has expanded to microarrays of proteins, peptides, carbohydrates, and small molecules aiming either at detection/quantification of biomolecules or investigation of biomolecular interactions in a massively parallel manner. Microarray experiments require two specialized instruments: An arrayer (or printer), for construction of microarrays, and a readout instrument (scanner). We have designed, constructed, and characterized the first integrated microarray system (IMAS) that combines the functions of a microarrayer and a three-laser confocal fluorescence scanner into a single instrument and provides excellent flexibility for the researcher. The three-axis robotic system that moves the printing head carrying multiple pins for arraying is also used for moving the microarray slide in front of a stationary optical system during scanning. Since the translation stages are the most expensive and crucial components of microarray printers and scanners, the proposed design reduces considerably the cost of the instrument and enhances remarkably its operative flexibility. Experiments were carried out at resolutions of 2.5, 5, 10, and 20 microm. The scanner detects 0.128 nmol L(-1) carboxyfluorescein (spots with diameters of 70 microm) corresponding to 1.8 molecules microm(-2). The linear range extends over 3.5 orders of magnitude (R(2) = 0.997) and the dynamic range covers almost five orders of magnitude. DNA microarray model experiments were carried out, including staining with SYBR Green I and hybridization with oligonucleotides labeled with the fluorescent dyes Alexa 488, Alexa 594, and Alexa 633.

GenBank

GenBank (R) is a comprehensive database that contains publicly available nucleotide sequences for more than 260 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the European Molecular Biology Laboratory Nucleotide Sequence Database in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage: www.ncbi.nlm.nih.gov

Viral Evolution in the Genomic Age

Genome sequence data will undoubtedly deliver much to the study of viral pathogens and their diseases. A prominent example of this new genomic perspective is influenza A virus, for which a large-scale genome sequencing project begun in the year 2005 has, to date, generated around 2,500 complete viral genomes [1]. While this alone is newsworthy, the rise of rapid, high-throughput genome “pyrosequencing” promises to take the production of viral genomes to a level once unimaginable [2].

Dual-specificity splice sites function alternatively as 5′ and 3′ splice sites

As a result of large-scale sequencing projects and recent splicing-microarray studies, estimates of mammalian genes expressing multiple transcripts continue to increase. This expansion of transcript information makes it possible to better characterize alternative splicing events and gain insights into splicing mechanisms and regulation. Here, we describe a class of splice sites that we call dual-specificity splice sites, which we identified through genome-wide, high-quality alignment of mRNA/EST and genome sequences and experimentally verified by RT-PCR. These splice sites can be alternatively recognized as either 5' or 3' splice sites, and the dual splicing is conceptually similar to a pair of mutually exclusive exons separated by a zero-length intron. The dual-splice-site sequences are essentially a composite of canonical 5' and 3' splice-site consensus sequences, with a CAG|GURAG core. The relative use of a dual site as a 5' or 3' splice site can be accurately predicted by assuming competition for specific binding between spliceosomal components involved in recognition of 5' and 3' splice sites, respectively. Dual-specificity splice sites exist in human and mouse, and possibly in other vertebrate species, although most sites are not conserved, suggesting that their origin is recent. We discuss the implications of this unusual splicing pattern for the diverse mechanisms of exon recognition and for gene evolution.

CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine

Recent transcriptome studies have revealed that a large number of transcripts in mammals and other organisms do not encode proteins but function as noncoding RNAs (ncRNAs) instead. As millions of transcripts are generated by large-scale cDNA and EST sequencing projects every year, there is a need for automatic methods to distinguish protein-coding RNAs from noncoding RNAs accurately and quickly. We developed a support vector machine-based classifier, named Coding Potential Calculator (CPC), to assess the protein-coding potential of a transcript based on six biologically meaningful sequence features. Tenfold cross-validation on the training dataset and further testing on several large datasets showed that CPC can discriminate coding from noncoding transcripts with high accuracy. Furthermore, CPC also runs an order-of-magnitude faster than a previous state-of-the-art tool and has higher accuracy. We developed a user-friendly web-based interface of CPC at http://cpc.cbi.pku.edu.cn. In addition to predicting the coding potential of the input transcripts, the CPC web server also graphically displays detailed sequence features and additional annotations of the transcript that may facilitate users’ further investigation.

SNP mining porcine ESTs with MAVIANT, a novel tool for SNP evaluation and annotation

Single nucleotide polymorphisms (SNPs) analysis is an important means to study genetic variation. A fast and cost-efficient approach to identify large numbers of novel candidates is the SNP mining of large scale sequencing projects. The increasing availability of sequence trace data in public repositories makes it feasible to evaluate SNP predictions on the DNA chromatogram level. MAVIANT, a platform-independent Multipurpose Alignment VIewing and Annotation Tool, provides DNA chromatogram and alignment views and facilitates evaluation of predictions. In addition, it supports direct manual annotation, which is immediately accessible and can be easily shared with external collaborators. Large-scale SNP mining of polymorphisms bases on porcine EST sequences yielded more than 7900 candidate SNPs in coding regions (cSNPs), which were annotated relative to the human genome. Non-synonymous SNPs were analyzed for their potential effect on the protein structure/function using the PolyPhen and SIFT prediction programs. Predicted SNPs and annotations are stored in a web-based database. Using MAVIANT SNPs can visually be verified based on the DNA sequencing traces. A subset of candidate SNPs was selected for experimental validation by resequencing and genotyping. This study provides a web-based DNA chromatogram and contig browser that facilitates the evaluation and selection of candidate SNPs, which can be applied as genetic markers for genome wide genetic studies. The stand-alone version of MAVIANT program for local use is freely available under GPL license terms at http://snp.agrsci.dk/maviant. Supplementary data are available at Bioinformatics online.

PIP: a database of potential intron polymorphism markers

With the recent progress made in large-scale plant functional genome sequencing projects, a great amount of EST (express sequence tag) data is becoming available. With the help of complete genomic sequence information of model plants (rice and Arabidopsis), it is possible to predict the joints between adjacent exons after splicing (or termed 'intron positions' for short) in homologous ESTs of other plants. This would allow developing potential intron polymorphism (PIP) markers in these plants by designing primers in exons flanking the target intron. We have extracted a total of 57,658 PIP markers in 59 plant species and created a web-based database platform named PIP to provide detailed information of these PIP markers and homologous relationships among PIP markers from different species. The platform also provides a function of online designing of PIP markers based on cDNA/EST sequences submitted by users. With evaluations performed in silico, we have found that the intron position prediction is highly reliable and the polymorphism level of PIP markers is high enough for practical need. http://ibi.zju.edu.cn/pgl/pip/. Supplementary data are available at Bioinformatics online.

Bioinformatics: genomics and post-genomics

Chapter 1. Genome sequencing. 1.1 Automatic sequencing. 1.2 Sequencing strategies. 1.3 Fragmentation strategies. 1.4 Sequence assembly. 1.5 Filling gaps. 1.6 Obstacles to reconstruction. 1.7 Utilizing a complementary 'large' clone library. 1.8 The first large-scale sequencing project: The Haemophilus influenzae genome. 1.9 cDNA and EST. Chapter 2. Sequence comparisons. 2.1 Introduction: Comparison as a sequence prediction method. 2.2 A sample molecule: the human and rosterone receptor. 2.3 Sequence homologies - functional homologies. 2.4 Comparison matrices. 2.5 The problem of insertions and deletions. 2.6 Optimal alignment: the dynamic programming method. 2.7 Fast heuristic methods. 2.8 Sensitivity, specificity, and confidence level. 2.9 Multiple alignments. Chapter 3. Comparative genomics. 3.1 General properties of genomes. 3.2 Genome comparisons. 3.3 Gene evolution and phylogeny: applications to annotation. Chapter 4. Genetic information and biological sequences. 4.1 Introduction: Coding levels. 4.2 Genes and the genetic code. 4.3 Expression signals. 4.4 Specific sites. 4.5 Sites located on DNA. 4.6 Sites present on RNA. 4.7 Pattern detection methods. Chapter 5. Statistics and sequences. 5.1 Introduction. 5.2 Nucleotide base and amino acid distribution. 5.3 The biological basis of codon bias. 5.4 Using statistical bias for prediction. 5.5 Modeling DNA sequences. 5.6 Complex models. 5.7 Sequencing errors and hidden Markov models. 5.8 Hidden Markov processes: a general sequence analysis tool. 5.9 The search for genes - a difficult art. Chapter 6. Structure prediction. 6.1 The structure of RNA. 6.2 Properties of the RNA molecule. 6.3 Secondary RNA structures. 6.4 Thermodynamic stability of RNA structures. 6.5 Finding the most stable structure. 6.6 Validation of predicted secondary structures. 6.7 Using chemical and enzymatic probing to analyze folding. 6.8 Long-distance interactions and three-dimensional structure prediction. 6.9 Protein structure. 6.10 Secondary structure prediction. 6.11 Three-dimensional modeling based on homologous protein structure. 6.12 Predicting folding. Chapter 7. Transcriptome and proteome: macromolecular networks. 7.1 Introduction. 7.2 Post-genomic methods. 7.3 Macromolecular networks. 7.4 Topology of macromolecular networks. 7.5 Modularity and dynamics of macromolecular networks. 7.6 Inference of regulatory networks. Chapter 8. Simulation of Biological Processes in the Genome Context. 8.1 Types of simulations. 8.2 Prediction and explanation. 8.3 Simulation of molecular networks. 8.4 Generic post-genomic simulators. Index.

Needles galore, but no place to sew

Large-scale sequencing projects to identify mutations related to cancer and psychiatric disorders will generate a lot of data. But the usefulness of these results will be limited unless we have good models to test their contribution to disease.

A large number of novel coding small open reading frames in the intergenic regions of the Arabidopsis thaliana genome are transcribed and/or under purifying selection

Large-scale cDNA sequencing projects and tiling array studies have revealed the presence of many unannotated genes. For protein coding genes, small coding sequences may not be identified by gene finders because of the conservative nature of prediction algorithms. In this study, we identified small open reading frames (sORFs) with high coding potential by a simple gene finding method (Coding Index, CI) based on the nucleotide composition bias found in most coding sequences. Applying this method to 18 Arabidopsis thaliana and 84 yeast sORF genes with evidence of expression at the protein level gives 100% accurate prediction. In the A. thaliana genome, we identified 7159 sORFs that are likely coding sequences (coding sORFs) with the CI measure at the 1% false-positive rate. To determine if these coding sORFs are parts of functional genes, we evaluated each coding sORF for evidence of transcription or evolutionary conservation. At the 5% false-positive rate, we found that 2996 coding sORFs are likely expressed in at least one experimental condition of the A. thaliana tiling array data. In addition, the evolutionary conservation of each A. thaliana sORF was examined within A. thaliana or between A. thaliana and five plants with complete or partial genome sequences. In 3997 coding sORFs with readily identifiable homologous sequences, 2376 are subject to purifying selection at the 1% false-positive rate. After eliminating coding sORFs with similarity to known transposable elements and those that are likely missing exons of known genes, the remaining 3241 coding sORFs with either evidence of transcription or purifying selection likely belong to novel coding genes in the A. thaliana genome.

“Smad”eningly Erratic: Target Gene Methylation Determines Whether TGFβ Promotes or Suppresses Malignant Glioma

TGFbeta functions as a tumor suppressor in some contexts and a tumor promoter in others. In a recent issue of Cancer Cell, Bruna et al. (2007) shed light on an epigenetic mechanism that underlies this schizophrenic behavior in malignant glioma. Their findings highlight a stem cell/cancer link...and a potential blind spot in large-scale cancer genome sequencing projects.

Exploring nervous system transcriptomes during embryogenesis and metamorphosis in Xenopus tropicalis using EST analysis

BackgroundThe western African clawed frog Xenopus tropicalis is an anuran amphibian species now used as model in vertebrate comparative genomics. It provides the same advantages as Xenopus laevis but is diploid and has a smaller genome of 1.7 Gbp. Therefore X. tropicalis is more amenable to systematic transcriptome surveys. We initiated a large-scale partial cDNA sequencing project to provide a functional genomics resource on genes expressed in the nervous system during early embryogenesis and metamorphosis in X. tropicalis.ResultsA gene index was defined and analysed after the collection of over 48,785 high quality sequences. These partial cDNA sequences were obtained from an embryonic head and retina library (30,272 sequences) and from a metamorphic brain and spinal cord library (27,602 sequences). These ESTs are estimated to represent 9,693 transcripts derived from an estimated 6,000 genes. Comparison of these cDNA sequences with protein databases indicates that 46% contain their start codon. Further annotation included Gene Ontology functional classification, InterPro domain analysis, alternative splicing and non-coding RNA identification. Gene expression profiles were derived from EST counts and used to define transcripts specific to metamorphic stages of development. Moreover, these ESTs allowed identification of a set of 225 polymorphic microsatellites that can be used as genetic markers.ConclusionThese cDNA sequences permit in silico cloning of numerous genes and will facilitate studies aimed at deciphering the roles of cognate genes expressed in the nervous system during neural development and metamorphosis. The genomic resources developed to study X. tropicalis biology will accelerate exploration of amphibian physiology and genetics. In particular, the model will facilitate analysis of key questions related to anuran embryogenesis and metamorphosis and its associated regulatory processes.

Abundance and functional diversity of riboswitches in microbial communities.

BackgroundSeveral recently completed large-scale enviromental sequencing projects produced a large amount of genetic information about microbial communities ('metagenomes') which is not biased towards cultured organisms. It is a good source for estimation of the abundance of genes and regulatory structures in both known and unknown members of microbial communities. In this study we consider the distribution of RNA regulatory structures, riboswitches, in the Sargasso Sea, Minnesota Soil and Whale Falls metagenomes.ResultsOver three hundred riboswitches were found in about 2 Gbp metagenome DNA sequences. The abundabce of riboswitches in metagenomes was highest for the TPP, B12 and GCVT riboswitches; the S-box, RFN, YKKC/YXKD, YYBP/YKOY regulatory elements showed lower but significant abundance, while the LYS, G-box, GLMS and YKOK riboswitches were rare. Regions downstream of identified riboswitches were scanned for open reading frames. Comparative analysis of identified ORFs revealed new riboswitch-regulated functions for several classes of riboswitches. In particular, we have observed phosphoserine aminotransferase serC (COG1932) and malate synthase glcB (COG2225) to be regulated by the glycine (GCVT) riboswitch; fatty acid desaturase ole1 (COG1398), by the cobalamin (B12) riboswitch; 5-methylthioribose-1-phosphate isomerase ykrS (COG0182), by the SAM-riboswitch. We also identified conserved riboswitches upstream of genes of unknown function: thiamine (TPP), cobalamine (B12), and glycine (GCVT, upstream of genes from COG4198).ConclusionThis study demonstrates applicability of bioinformatics to the analysis of RNA regulatory structures in metagenomes.

SURF’s UP! — Protein classification by surface comparisons

Large-scale genome sequencing and structural genomics projects generate numerous sequences and structures for 'hypothetical' proteins without functional characterizations. Detection of homology to experimentally characterized proteins can provide functional clues, but the accuracy of homology-based predictions is limited by the paucity of tools for quantitative comparison of diverging residues responsible for the functional divergence. SURF'S UP! is a web server for analysis of functional relationships in protein families, as inferred from protein surface maps comparison according to the algorithm. It assigns a numerical score to the similarity between patterns of physicochemical features(charge, hydrophobicity) on compared protein surfaces. It allows recognizing clusters of proteins that have similar surfaces, hence presumably similar functions. The server takes as an input a set of protein coordinates and returns files with "spherical coordinates" of proteins in a PDB format and their graphical presentation, a matrix with values of mutual similarities between the surfaces, and the unrooted tree that represents the clustering of similar surfaces, calculated by the neighbor-joining method. SURF'S UP! facilitates the comparative analysis of physicochemical features of the surface, which are the key determinants of the protein function. By concentrating on coarse surface features, SURF'S UP! can work with models obtained from comparative modelling. Although it is designed to analyse the conservation among homologs, it can also be used to compare surfaces of non-homologous proteins with different three-dimensional folds, as long as a functionally meaningful structural superposition is supplied by the user. Another valuable characteristic of our method is the lack of initial assumptions about the functional features to be compared. SURF'S UP! is freely available for academic researchers at http://asia.genesilico.pl/surfs_up/.

Statistical Genetics Concepts and Approaches in Schizophrenia and Related Neuropsychiatric Research

Statistical genetics is a research field that focuses on mathematical models and statistical inference methodologies that relate genetic variations (ie, naturally occurring human DNA sequence variations or "polymorphisms") to particular traits or diseases (phenotypes) usually from data collected on large samples of families or individuals. The ultimate goal of such analysis is the identification of genes and genetic variations that influence disease susceptibility. Although of extreme interest and importance, the fact that many genes and environmental factors contribute to neuropsychiatric diseases of public health importance (eg, schizophrenia, bipolar disorder, and depression) complicates relevant studies and suggests that very sophisticated mathematical and statistical modeling may be required. In addition, large-scale contemporary human DNA sequencing and related projects, such as the Human Genome Project and the International HapMap Project, as well as the development of high-throughput DNA sequencing and genotyping technologies have provided statistical geneticists with a great deal of very relevant and appropriate information and resources. Unfortunately, the use of these resources and their interpretation are not straightforward when applied to complex, multifactorial diseases such as schizophrenia. In this brief and largely nonmathematical review of the field of statistical genetics, we describe many of the main concepts, definitions, and issues that motivate contemporary research. We also provide a discussion of the most pressing contemporary problems that demand further research if progress is to be made in the identification of genes and genetic variations that predispose to complex neuropsychiatric diseases.

EMBL Nucleotide Sequence Database in 2006

The EMBL Nucleotide Sequence Database () at the EMBL European Bioinformatics Institute, UK, offers a large and freely accessible collection of nucleotide sequences and accompanying annotation. The database is maintained in collaboration with DDBJ and GenBank. Data are exchanged between the collaborating databases on a daily basis to achieve optimal synchrony. Webin is the preferred tool for individual submissions of nucleotide sequences, including Third Party Annotation, alignments and bulk data. Automated procedures are provided for submissions from large-scale sequencing projects and data from the European Patent Office. In 2006, the volume of data has continued to grow exponentially. Access to the data is provided via SRS, ftp and variety of other methods. Extensive external and internal cross-references enable users to search for related information across other databases and within the database. All available resources can be accessed via the EBI home page at . Changes over the past year include changes to the file format, further development of the EMBLCDS dataset and developments to the XML format.