MOTIVATION: A codon position could perform different or multiple roles in alternative transcripts of a gene. For instance, a non-synonymous position in one transcript could be a synonymous site in another. Alternatively, a position could remain as non-synonymous in multiple transcripts. Here we examined the impact of codon position plurality on the frequency of deleterious single-nucleotide variations (SNVs) using data from 6500 human exomes. RESULTS: Our results showed that the proportion of deleterious SNVs was more than 2-fold higher in positions that remain non-synonymous in multiple transcripts compared with that observed in positions that are non-synonymous in one or some transcript(s) and synonymous or intronic in other(s). Furthermore, we observed a positive relationship between the fraction of deleterious non-synonymous SNVs and the number of proteins (alternative splice variants) affected. These results demonstrate that the plurality of codon positions is an important attribute, which could be useful in identifying mutations associated with diseases. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Commercial relational database systems lack support for complex business modeling. ANSI SQL cannot treat relations as multidimensional arrays and define multiple, interrelated formulas over them, operations which are needed for business modeling. Relational OLAP (ROLAP) applications have to perform such tasks using joins, SQL Window Functions, complex CASE expressions, and the GROUP BY operator simulating the pivot operation. The designated place in SQL for calculations is the SELECT clause, which is extremely limiting and forces the user to generate queries with nested views, subqueries and complex joins. Furthermore, SQL query optimizers are preoccupied with determining efficient join orders and choosing optimal access methods and largely disregard optimization of multiple, interrelated formulas. Research into execution methods has thus far concentrated on efficient computation of data cubes and cube compression rather than on access structures for random, interrow calculations. This has created a gap that has been filled by spreadsheets and specialized MOLAP engines, which are good at specification of formulas for modeling but lack the formalism of the relational model, are difficult to coordinate across large user groups, exhibit scalability problems, and require replication of data between the tool and RDBMS. This article presents an SQL extension called SQL Spreadsheet , to provide array calculations over relations for complex modeling. We present optimizations, access structures, and execution models for processing them efficiently. Special attention is paid to compile time optimization for expensive operations like aggregation. Furthermore, ANSI SQL does not provide a good separation between data and computation and hence cannot support parameterization for SQL Spreadsheets models. We propose two parameterization methods for SQL. One parameterizes ANSI SQL view using subqueries and scalars, which allows passing data to SQL Spreadsheet. Another method presents parameterization of the SQL Spreadsheet formulas. This supports building stand-alone SQL Spreadsheet libraries. These models are then subject to the SQL Spreadsheet optimizations during model invocation time.

Data in a relational data warehouse is usually sparse. That is, if no value exists for a given combination of dimension values, no row exists in the fact table. Densities of 0.1-2% are very common. However, users may want to view the data in a dense form, with rows for all combination of dimension values displayed even when no fact data exists for them. For example, if a product did not sell during a particular time period, users may still want to see the product for that time period with zero sales value next to it. Moreover, analytic window functions [1] and the SQL model clause [2] can more easily express time series calculations if data is dense along the time dimension because dense data will fill a consistent number of rows for each period.Data densification is the process of converting spare data into dense form. The current SQL technique for densification (using the combination of DISTINCT, CROSS JOIN and OUTER JOIN operations) is extremely unintuitive, difficult to express and inefficient to compute. Hence, we propose an extension to the ANSI SQL join operator, referred to as "PARTITIONED OUTER JOIN", which allows for a succinct expression of densification along the dimensions of interest. We also present various algorithms to evaluate the new join operator efficiently and compare it with existing methods of doing the equivalent operation. We also define a new window function "LAST_VALUE (IGNORE NULLS)" which is very useful with partitioned outer join.

One of the critical deficiencies of SQL is lack of support for n-dimensional array-based computations which are frequent in OLAP environments. Relational OLAP (ROLAP) applications have to emulate them using joins, recently introduced SQL Window Functions [18] and complex and inefficient CASE expressions. The designated place in SQL for specifying calculations is the SELECT clause, which is extremely limiting and forces the user to generate queries using nested views, subqueries and complex joins. Furthermore, SQL-query optimizer is pre-occupied with determining efficient join orders and choosing optimal access methods and largely disregards optimization of complex numerical formulas. Execution methods concentrated on efficient computation of a cube [11], [16] rather than on random access structures for inter-row calculations. This has created a gap that has been filled by spreadsheets and specialized MOLAP engines, which are good at formulas for mathematical modeling but lack the formalism of the relational model, are difficult to manage, and exhibit scalability problems. This paper presents SQL extensions involving array based calculations for complex modeling. In addition, we present optimizations, access structures and execution models for processing them efficiently. 1

BACKGROUND: Comparative analysis of RNA sequences is the basis for the detailed and accurate predictions of RNA structure and the determination of phylogenetic relationships for organisms that span the entire phylogenetic tree. Underlying these accomplishments are very large, well-organized, and processed collections of RNA sequences. This data, starting with the sequences organized into a database management system and aligned to reveal their higher-order structure, and patterns of conservation and variation for organisms that span the phylogenetic tree, has been collected and analyzed. This type of information can be fundamental for and have an influence on the study of phylogenetic relationships, RNA structure, and the melding of these two fields. RESULTS: We have prepared a large web site that disseminates our comparative sequence and structure models and data. The four major types of comparative information and systems available for the three ribosomal RNAs (5S, 16S, and 23S rRNA), transfer RNA (tRNA), and two of the catalytic intron RNAs (group I and group II) are: (1) Current Comparative Structure Models; (2) Nucleotide Frequency and Conservation Information; (3) Sequence and Structure Data; and (4) Data Access Systems. CONCLUSIONS: This online RNA sequence and structure information, the result of extensive analysis, interpretation, data collection, and computer program and web development, is accessible at our Comparative RNA Web (CRW) Site http://www.rna.icmb.utexas.edu. In the future, more data and information will be added to these existing categories, new categories will be developed, and additional RNAs will be studied and presented at the CRW Site.