Transcriptomic variation of pharmacogenes in multiple human tissues and lymphoblastoid cell lines.

Variation in the expression level and activity of genes involved in drug disposition and action ('pharmacogenes') can affect drug response and toxicity, especially when in tissues of pharmacological importance. Previous studies have relied primarily on microarrays to understand gene expression differences, or have focused on a single tissue or small number of samples. The goal of this study was to use RNA-sequencing (RNA-seq) to determine the expression levels and alternative splicing of 389 Pharmacogenomics Research Network pharmacogenes across four tissues (liver, kidney, heart and adipose) and lymphoblastoid cell lines, which are used widely in pharmacogenomics studies. Analysis of RNA-seq data from 139 different individuals across the 5 tissues (20-45 individuals per tissue type) revealed substantial variation in both expression levels and splicing across samples and tissue types. Comparison with GTEx data yielded a consistent picture. This in-depth exploration also revealed 183 splicing events in pharmacogenes that were previously not annotated. Overall, this study serves as a rich resource for the research community to inform biomarker and drug discovery and use.

The transcriptional landscape of the mammalian genome.

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

A tour of structural genomics.

Structural genomics projects aim to provide an experimental or computational three-dimensional model structure for all of the tractable macromolecules that are encoded by complete genomes. To this end, pilot centres worldwide are now exploring the feasibility of large-scale structure determination. Their experimental structures and computational models are expected to yield insight into the molecular function and mechanism of thousands of proteins. The pervasiveness of this information is likely to change the use of structure in molecular biology and biochemistry.

Computational prediction of membrane-tethered transcription factors.

BACKGROUND: Sequestration of transcription factors in the membrane is emerging as an important mechanism for the regulation of gene expression. A handful of membrane-spanning transcription factors has been previously identified whose access to the nucleus is regulated by proteolytic cleavage from the membrane. To investigate the existence of other transmembrane transcription factors, we analyzed computationally all proteins in SWISS-PROT/TrEMBL for the combined presence of a DNA-binding domain and a transmembrane segment. RESULTS: Using Pfam hidden Markov models and four transmembrane-prediction programs, we identified with high confidence 76 membrane-spanning transcription factors in SWISS-PROT/TrEMBL. Analysis of the distribution of two proteins predicted by our method, MTJ1 and DMRT2, confirmed their localization to intracellular membrane compartments. Furthermore, elimination of the predicted transmembrane segment led to nuclear localization for each of these proteins. CONCLUSIONS: Our analysis uncovered a wealth of predicted membrane-spanning transcription factors that are structurally and taxonomically diverse, 56 of which lack experimental annotation. Seventy-five of the proteins are modular in structure, suggesting that a single proteolysis may be sufficient to liberate a DNA-binding domain from the membrane. This study provides grounds for investigations into the stimuli and mechanisms that release this intriguing class of transcription factors from membranes.

Target selection for structural genomics.

Structural genomics aims to use high-throughput structure determination and computational analysis to provide three-dimensional models of every tractable protein. The process of choosing proteins for experimental structure characterization is known as target selection. In this nomenclature, the targets are regions of proteins to be studied by crystallography or NMR. Selection of the targets is principally a computational process of restricting candidate proteins to those that are tractable and of unknown structure, and prioritizing according to expected interest and accessibility.

Expectations from structural genomics.

Structural genomics projects aim to provide an experimental structure or a good model for every protein in all completed genomes. Most of the experimental work for these projects will be directed toward proteins whose fold cannot be readily recognized by simple sequence comparison with proteins of known structure. Based on the history of proteins classified in the SCOP structure database, we expect that only about a quarter of the early structural genomics targets will have a new fold. Among the remaining ones, about half are likely to be evolutionarily related to proteins of known structure, even though the homology could not be readily detected by sequence analysis.

The ASTRAL compendium for protein structure and sequence analysis.

The ASTRAL compendium provides several databases and tools to aid in the analysis of protein structures, particularly through the use of their sequences. The SPACI scores included in the system summarize the overall characteristics of a protein structure. A structural alignments database indicates residue equivalencies in superimposed protein domain structures. The PDB sequence-map files provide a linkage between the amino acid sequence of the molecule studied (SEQRES records in a database entry) and the sequence of the atoms experimentally observed in the structure (ATOM records). These maps are combined with information in the SCOPdatabase to provide sequences of protein domains. Selected subsets of the domain database, with varying degrees of similarity measured in several different ways, are also available. ASTRALmay be accessed at http://astral.stanford.edu/

SCOP: a structural classification of proteins database.

The Structural Classification of Proteins (SCOP) database provides a detailed and comprehensive description of the relationships of known protein structures. The classification is on hierarchical levels: the first two levels, family and superfamily, describe near and distant evolutionary relationships; the third, fold, describes geometrical relationships. The distinction between evolutionary relationships and those that arise from the physics and chemistry of proteins is a feature that is unique to this database so far. The sequences of proteins in SCOP provide the basis of the ASTRAL sequence libraries that can be used as a source of data to calibrate sequence search algorithms and for the generation of statistics on, or selections of, protein structures. Links can be made from SCOP to PDB-ISL: a library containing sequences homologous to proteins of known structure. Sequences of proteins of unknown structure can be matched to distantly related proteins of known structure by using pairwise sequence comparison methods to find homologues in PDB-ISL. The database and its associated files are freely accessible from a number of WWW sites mirrored from URL http://scop.mrc-lmb.cam.ac.uk/scop/

Distribution of protein folds in the three superkingdoms of life.

A sensitive protein-fold recognition procedure was developed on the basis of iterative database search using the PSI-BLAST program. A collection of 1193 position-dependent weight matrices that can be used as fold identifiers was produced. In the completely sequenced genomes, folds could be automatically identified for 20%-30% of the proteins, with 3%-6% more detectable by additional analysis of conserved motifs. The distribution of the most common folds is very similar in bacteria and archaea but distinct in eukaryotes. Within the bacteria, this distribution differs between parasitic and free-living species. In all analyzed genomes, the P-loop NTPases are the most abundant fold. In bacteria and archaea, the next most common folds are ferredoxin-like domains, TIM-barrels, and methyltransferases, whereas in eukaryotes, the second to fourth places belong to protein kinases, beta-propellers and TIM-barrels. The observed diversity of protein folds in different proteomes is approximately twice as high as it would be expected from a simple stochastic model describing a proteome as a finite sample from an infinite pool of proteins with an exponential distribution of the fold fractions. Distribution of the number of domains with different folds in one protein fits the geometric model, which is compatible with the evolution of multidomain proteins by random combination of domains. [Fold predictions for proteins from 14 proteomes are available on the World Wide Web at. The FIDs are available by anonymous ftp at the same location.]

The PRESAGE database for structural genomics.

The PRESAGE database is a collaborative resource for structural genomics. It provides a database of proteins to which researchers add annotations indicating current experimental status, structural predictions and suggestions. The database is intended to enhance communication among structural genomics researchers and aid dissemination of their results. The PRESAGE database may be accessed at http://presage.stanford.edu/

SCOP: a Structural Classification of Proteins database.

The Structural Classification of Proteins (SCOP) database provides a detailed and comprehensive description of the relationships of all known proteins structures. The classification is on hierarchical levels: the first two levels, family and superfamily, describe near and far evolutionary relationships; the third, fold, describes geometrical relationships. The distinction between evolutionary relationships and those that arise from the physics and chemistry of proteins is a feature that is unique to this database, so far. The database can be used as a source of data to calibrate sequence search algorithms and for the generation of population statistics on protein structures. The database and its associated files are freely accessible from a number of WWW sites mirrored from URL http://scop. mrc-lmb.cam.ac.uk/scop/

Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships.

Pairwise sequence comparison methods have been assessed using proteins whose relationships are known reliably from their structures and functions, as described in the SCOP database [Murzin, A. G., Brenner, S. E., Hubbard, T. & Chothia C. (1995) J. Mol. Biol. 247, 536-540]. The evaluation tested the programs BLAST [Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. (1990). J. Mol. Biol. 215, 403-410], WU-BLAST2 [Altschul, S. F. & Gish, W. (1996) Methods Enzymol. 266, 460-480], FASTA [Pearson, W. R. & Lipman, D. J. (1988) Proc. Natl. Acad. Sci. USA 85, 2444-2448], and SSEARCH [Smith, T. F. & Waterman, M. S. (1981) J. Mol. Biol. 147, 195-197] and their scoring schemes. The error rate of all algorithms is greatly reduced by using statistical scores to evaluate matches rather than percentage identity or raw scores. The E-value statistical scores of SSEARCH and FASTA are reliable: the number of false positives found in our tests agrees well with the scores reported. However, the P-values reported by BLAST and WU-BLAST2 exaggerate significance by orders of magnitude. SSEARCH, FASTA ktup = 1, and WU-BLAST2 perform best, and they are capable of detecting almost all relationships between proteins whose sequence identities are >30%. For more distantly related proteins, they do much less well; only one-half of the relationships between proteins with 20-30% identity are found. Because many homologs have low sequence similarity, most distant relationships cannot be detected by any pairwise comparison method; however, those which are identified may be used with confidence.

SCOP, Structural Classification of Proteins database: applications to evaluation of the effectiveness of sequence alignment methods and statistics of protein structural data.

The Structural Classification of Proteins (SCOP) database provides a detailed and comprehensive description of the relationships of all known protein structures. The classification is on hierarchical levels: the first two levels, family and superfamily, describe near and far evolutionary relationships; the third, fold, describes geometrical relationships. The distinction between evolutionary relationships and those that arise from the physics and chemistry of proteins is a feature that is unique to this database, so far. The database can be used as a source of data to calibrate sequence search algorithms and for the generation of population statistics on protein structures. The database and its associated files are freely accessible from a number of WWW sites mirrored from URL http://scop. mrc-lmb.cam.ac.uk/scop/.

Population statistics of protein structures: lessons from structural classifications.

Structural classifications aid the interpretation of proteins by describing degrees of structural and evolutionary relatedness. They have also recently revealed strikingly skewed distributions at all levels; for example, a small number of folds are far more common than others, and just a few superfamilies are known to have diverged widely. The classifications also provide an indication of the total number of superfamilies in nature.

SCOP: a structural classification of proteins database.

The Structural Classification of Proteins (SCOP) database provides a detailed and comprehensive description of the relationships of all known proteins structures. The classification is on hierarchical levels: the first two levels, family and superfamily, describe near and far evolutionary relationships; the third, fold, describes geometrical relationships. The distinction between evolutionary relationships and those that arise from the physics and chemistry of proteins is a feature that is unique to this database, so far. SCOP also provides for each structure links to atomic co-ordinates, images of the structures, interactive viewers, sequence data, data on any conformational changes related to function and literature references. The database is freely accessible on the World Wide Web (WWW) with an entry point at URL http://scop.mrc-lmb.cam.ac.uk/scop/

Classification of multi-helical DNA-binding domains and application to predict the DBD structures of sigma factor, LysR, OmpR/PhoB, CENP-B, Rapl, and Xy1S/Ada/AraC.

We have systematically compared structures of multi-helical DNA-binding domains (DBDs) which have been determined by crystallography or NMR spectroscopy. All the known multi-helical DBDs are very similar. The core of these structures consists of two alpha-helices in the helix-turn-helix combination, associated with one or two other helices. The structures can be classified according to either additional structural compositions or the configuration of the helices. Many DBDs, whose structures are currently unknown, have sequences which resemble those of known structures, permitting outlines of the new structures to be predicted.