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      LeishDB: a database of coding gene annotation and non-coding RNAs in Leishmania braziliensis

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          Abstract

          Leishmania braziliensis is the etiological agent of cutaneous leishmaniasis, a disease with high public health importance, affecting 12 million people worldwide. Although its genome sequence was originally published in 2007, the two reference public annotations still presents at least 80% of the genes simply classified as hypothetical or putative proteins. Furthermore, it is notable the absence of non-coding RNA (ncRNA) sequences from Leishmania species in public databases. These poorly annotated coding genes and ncRNAs could be important players for the understanding of this protozoan biology, the mechanisms behind host-parasite interactions and disease control. Herein, we performed a new prediction and annotation of L. braziliensis protein-coding genes and non-coding RNAs, using recently developed predictive algorithms and updated databases. In summary, we identified 11 491 ORFs, with 5263 (45.80%) of them associated with proteins available in public databases. Moreover, we identified for the first time the repertoire of 11 243 ncRNAs belonging to different classes distributed along the genome. The accuracy of our predictions was verified by transcriptional evidence using RNA-seq, confirming that they are actually generating real transcripts. These data were organized in a public repository named LeishDB ( www.leishdb.com), which represents an improvement on the publicly available data related to genomic annotation for L. braziliensis. This updated information can be useful for future genomics, transcriptomics and metabolomics studies; being an additional tool for genome annotation pipelines and novel studies associated with the understanding of this protozoan genome complexity, organization, biology, and development of innovative methodologies for disease control and diagnostics.

          Database URL: www.leishdb.com

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          Most cited references25

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          Genomewide characterization of non-polyadenylated RNAs

          Background RNAs can be physically classified into poly(A)+ or poly(A)- transcripts according to the presence or absence of a poly(A) tail at their 3' ends. Current deep sequencing approaches largely depend on the enrichment of transcripts with a poly(A) tail, and therefore offer little insight into the nature and expression of transcripts that lack poly(A) tails. Results We have used deep sequencing to explore the repertoire of both poly(A)+ and poly(A)- RNAs from HeLa cells and H9 human embryonic stem cells (hESCs). Using stringent criteria, we found that while the majority of transcripts are poly(A)+, a significant portion of transcripts are either poly(A)- or bimorphic, being found in both the poly(A)+ and poly(A)- populations. Further analyses revealed that many mRNAs may not contain classical long poly(A) tails and such messages are overrepresented in specific functional categories. In addition, we surprisingly found that a few excised introns accumulate in cells and thus constitute a new class of non-polyadenylated long non-coding RNAs. Finally, we have identified a specific subset of poly(A)- histone mRNAs, including two histone H1 variants, that are expressed in undifferentiated hESCs and are rapidly diminished upon differentiation; further, these same histone genes are induced upon reprogramming of fibroblasts to induced pluripotent stem cells. Conclusions We offer a rich source of data that allows a deeper exploration of the poly(A)- landscape of the eukaryotic transcriptome. The approach we present here also applies to the analysis of the poly(A)- transcriptomes of other organisms.
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            RATT: Rapid Annotation Transfer Tool

            Second-generation sequencing technologies have made large-scale sequencing projects commonplace. However, making use of these datasets often requires gene function to be ascribed genome wide. Although tool development has kept pace with the changes in sequence production, for tasks such as mapping, de novo assembly or visualization, genome annotation remains a challenge. We have developed a method to rapidly provide accurate annotation for new genomes using previously annotated genomes as a reference. The method, implemented in a tool called RATT (Rapid Annotation Transfer Tool), transfers annotations from a high-quality reference to a new genome on the basis of conserved synteny. We demonstrate that a Mycobacterium tuberculosis genome or a single 2.5 Mb chromosome from a malaria parasite can be annotated in less than five minutes with only modest computational resources. RATT is available at http://ratt.sourceforge.net.
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              CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains

              CopraRNA (Comparative prediction algorithm for small RNA targets) is the most recent asset to the Freiburg RNA Tools webserver. It incorporates and extends the functionality of the existing tool IntaRNA (Interacting RNAs) in order to predict targets, interaction domains and consequently the regulatory networks of bacterial small RNA molecules. The CopraRNA prediction results are accompanied by extensive postprocessing methods such as functional enrichment analysis and visualization of interacting regions. Here, we introduce the functionality of the CopraRNA and IntaRNA webservers and give detailed explanations on their postprocessing functionalities. Both tools are freely accessible at http://rna.informatik.uni-freiburg.de.
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                Author and article information

                Journal
                Database (Oxford)
                Database (Oxford)
                databa
                Database: The Journal of Biological Databases and Curation
                Oxford University Press
                1758-0463
                2017
                13 June 2017
                13 June 2017
                : 2017
                : bax047
                Affiliations
                [1 ]Centro de Pesquisas Gonçalo Moniz (CPqGM), Fundação Oswaldo Cruz (FIOCRUZ), Salvador, Brazil
                [2 ]Programa de Pós-Graduação em Computação Aplicada (PGCA), Universidade Estadual de Feira de Santana, Feira de Santana, Brazil
                [3 ]Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
                [4 ]Universidade Federal da Bahia, Salvador, Brazil
                [5 ]Instituto Nacional de Ciência e Tecnologia de Investigação em Imunologia (iii-INCT), São Paulo, Brazil
                [6 ]Beagle Bioinformatics, Santiago, Chile
                [7 ]Instituto Vandique, João Pessoa, Brazil
                Author notes
                [* ]Corresponding author: Tel.: +56 9 51097362; Email: viniciusmaracaja@ 123456integrativebioinformatics.me

                Citation details: Torres,F., Arias-Carrasco,R., Caris-Maldonado,J.C. et al. LeishDB: a database of coding gene annotation and non-coding RNAs in Leishmania braziliensis. Database (2017) Vol. 2017: article ID bax047; doi:10.1093/database/bax047

                Article
                bax047
                10.1093/database/bax047
                5502370
                9ea6ac90-bfc2-4d76-b8a4-1475c654f8db
                © The Author(s) 2017. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 July 2016
                : 15 May 2017
                : 16 May 2017
                Page count
                Pages: 11
                Categories
                Database Tool

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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