BACKGROUND: High-throughput molecular approaches for gene expression profiling, such as Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS) or Sequencing-by-Synthesis (SBS) represent powerful techniques that provide global transcription profiles of different cell types through sequencing of short fragments of transcripts, denominated sequence tags. These techniques have improved our understanding about the relationships between these expression profiles and cellular phenotypes. Despite this, more reliable datasets are still necessary. In this work, we present a web-based tool named S3T: Score System for Sequence Tags, to index sequenced tags in accordance with their reliability. This is made through a series of evaluations based on a defined rule set. S3T allows the identification/selection of tags, considered more reliable for further gene expression analysis. RESULTS: This methodology was applied to a public SAGE dataset. In order to compare data before and after filtering, a hierarchical clustering analysis was performed in samples from the same type of tissue, in distinct biological conditions, using these two datasets. Our results provide evidences suggesting that it is possible to find more congruous clusters after using S3T scoring system. CONCLUSION: These results substantiate the proposed application to generate more reliable data. This is a significant contribution for determination of global gene expression profiles. The library analysis with S3T is freely available at http://gdm.fmrp.usp.br/s3t/. S3T source code and datasets can also be downloaded from the aforementioned website.

UNLABELLED: A critical step in any SAGE, MPSS and SBS data analysis is tag-to-gene assignment. Current available tools are limited by a tag-by-tag annotation process and/or do not provide the dataset that is used to produce a complete tag-to-gene mapping. We developed ACTG, a web-based application that allows a large-scale tag-to-gene mapping using several reference datasets. ACTG can annotate SAGE (14 or 21 bp), MPSS (17 or 20 bp) and SBS (16 bp) data for both human and mouse organisms. AVAILABILITY: http://retina.med.harvard.edu/ACTG/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Intron sliding is the relocation of intron-exon boundaries over short distances and is often also referred to as intron slippage or intron migration or intron drift. We have generated a database containing discordant intron positions in homologous genes (MIDB--Mismatched Intron DataBase). Discordant intron positions are those that are either closely located in homologous genes (within a window of 10 nucleotides) or an intron position that is present in one gene but not in any of its homologs. The MIDB database aims at systematically collecting information about mismatched introns in the genes from GenBank and organizing it into a form useful for understanding the genomics and dynamics of introns thereby helping understand the evolution of genes. RESULTS: Intron displacement or sliding is critically important for explaining the present distribution of introns among orthologous and paralogous genes. MIDB allows examining of intron movements and allows mapping of intron positions from homologous proteins onto a single sequence. The database is of potential use for molecular biologists in general and for researchers who are interested in gene evolution and eukaryotic gene structure. Partial analysis of this database allowed us to identify a few putative cases of intron sliding. AVAILABILITY: http://intron.bic.nus.edu.sg/midb/midb.html

Abstract Summary: IE-Kb (Intron Exon-Knowledge base) illustrates the intron–exon dynamics in eukaryotic genes. We have developed three different knowledge sets, namely ‘Non-redundant ExInt’, ‘Non-redundant Pfam-ExInt complement’ and ‘Non-redundant GenBank eukaryotic subdivisional sets’ to understand this phenomenon. Statistical analysis is performed on each knowledge set and the results are made available online. The entries in knowledge sets are ranked based on their intron length, exon length and protein length with relational hyper-links to the corresponding intron phase, intron position, intron sequence, gene definition and parent GenBank entry. Availability: http://intron.bic.nus.edu.sg/iekb/iekb.html Contact: [email protected] * To whom correspondence should be addressed.