MOTIVATION: Gene retrocopies arise from the reverse transcription and genomic insertion of processed mRNA transcripts. These elements have significantly contributed to genetic diversity and novelties throughout the evolution of many species. However, the study of retrocopies has been challenging, owing to the absence of comprehensive, complete, and user-friendly databases for diverse species. RESULTS: Here, we introduce an improved version of RCPedia, an integrative database meticulously designed for the study of retrocopies. RCPedia offers an extensive catalog of retrocopies identified across 44 species, which includes 13 primates, 4 rodents, 6 chiropterans, 12 other mammals, 4 birds, turtles, lizards, frogs, zebrafish, and Drosophila. The database offers the most complete compilation of retrocopies per species, accompanied by detailed genomic annotations, expression data, and links to other data portals. Furthermore, RCPedia features a streamlined representation of data and an efficient querying system, establishing it as an invaluable tool for researchers in the fields of genomics, evolutionary biology, and transposable elements (TEs). In summary, RCPedia aims to enhance the investigation of retrocopies and their pivotal roles in shaping the genomic landscapes of diverse species. AVAILABILITY AND IMPLEMENTATION: RCPedia is available at https://www.rcpediadb.org.

MOTIVATION: Retrocopies or processed pseudogenes are gene copies resulting from mRNA retrotransposition. These gene duplicates can be fixed, somatically inserted or polymorphic in the genome. However, knowledge regarding unfixed retrocopies (retroCNVs) is still limited, and the development of computational tools for effectively identifying and genotyping them is an urgent need. RESULTS: Here, we present sideRETRO, a pipeline dedicated not only to detecting retroCNVs in whole-genome or whole-exome sequencing data but also to revealing their insertion sites, zygosity and genomic context and classifying them as somatic or polymorphic events. We show that sideRETRO can identify novel retroCNVs and genotype them, in addition to finding polymorphic retroCNVs in whole-genome and whole-exome data. Therefore, sideRETRO fills a gap in the literature and presents an efficient and straightforward algorithm to accelerate the study of bona fide retroCNVs. AVAILABILITY AND IMPLEMENTATION: sideRETRO is available at https://github.com/galantelab/sideRETRO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Retrocopies are copies of mature RNAs that are usually devoid of regulatory sequences and introns. They have routinely been classified as processed pseudo-genes with little or no biological relevance. However, recent findings have revealed functional roles for retrocopies, as well as their high frequency in some organisms, such as primates. Despite their increasing importance, there is no user-friendly and publicly available resource for the study of retrocopies. RESULTS: Here, we present RCPedia, an integrative and user-friendly database designed for the study of retrocopied genes. RCPedia contains a complete catalogue of the retrocopies that are known to be present in human and five other primate genomes, their genomic context, inter-species conservation and gene expression data. RCPedia also offers a streamlined data representation and an efficient query system. AVAILABILITY AND IMPLEMENTATION: RCPedia is available at http://www.bioinfo.mochsl.org.br/rcpedia.

BACKGROUND: This study describes a large-scale manual re-annotation of data samples in the Gene Expression Omnibus (GEO), using variables and values derived from the National Cancer Institute thesaurus. A framework is described for creating an annotation scheme for various diseases that is flexible, comprehensive, and scalable. The annotation structure is evaluated by measuring coverage and agreement between annotators. RESULTS: There were 12,500 samples annotated with approximately 30 variables, in each of six disease categories - breast cancer, colon cancer, inflammatory bowel disease (IBD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and Type 1 diabetes mellitus (DM). The annotators provided excellent variable coverage, with known values for over 98% of three critical variables: disease state, tissue, and sample type. There was 89% strict inter-annotator agreement and 92% agreement when using semantic and partial similarity measures. CONCLUSION: We show that it is possible to perform manual re-annotation of a large repository in a reliable manner.

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.

BACKGROUND: Large repositories of biomedical research data are most useful to translational researchers if their data can be aggregated for efficient queries and analyses. However, inconsistent or non-existent annotations describing important sample details such as name of tissue or cell line, histopathological type, and subject characteristics like demographics, treatment, and survival are seldom present in data repositories, making it difficult to aggregate data. RESULTS: We created a flexible software tool that allows efficient annotation of samples using a controlled vocabulary, and report on its use for the annotation of over 12,500 samples. CONCLUSION: While the amount of data is very large and seemingly poorly annotated, a lot of information is still within reach. Consistent tool-based re-annotation enables many new possibilities for large scale interpretation and analyses that would otherwise be impossible.

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.