Identification of minimal eukaryotic introns through GeneBase, a user-friendly tool for parsing the NCBI Gene databank

Piovesan, Allison; Caracausi, Maria; Ricci, Marco; Strippoli, Pierluigi; Vitale, Lorenza; Pelleri, Maria Chiara

doi:10.1093/dnares/dsv028

Cited by 47 publications

(60 citation statements)

References 56 publications

(69 reference statements)

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“…Although it will not be automatically kept up-to-date as other on-line gene browsers, we provide an empty template which may be used at any time to load ab initio the latest version or any desired subset of NCBI Gene data for any organism and by any user, following parsing by our scripts. In contrast with the first presentation of GeneBase (6), we decided to initially include in our study also gene models, in order to obtain a complete picture of human nuclear genes in the NCBI database. All the currently (‘alive’/‘live’ qualification) available human gene entries were downloaded from NCBI Gene on 19 January 2016, using the following text query: ‘Homo sapiens’[Organism] AND ‘source_genomic’[properties] AND alive[property].…”

Section: Methodsmentioning

confidence: 99%

“…This selection has already been proven successful in excluding from calculations model RefSeq records generated by automated pipelines (6). As mentioned above, GeneBase has been improved here and the fields useful for this gene selection are available in each software table.…”

Section: Methodsmentioning

confidence: 99%

“…To address these issues we improved GeneBase (1.0), a user-friendly local tool with a graphical interface incorporating data available in the NCBI Gene database and allowing users to perform original searches, calculations and analyses of the main gene-associated meta-information (6). The original GeneBase (1.0) has been revised adding in particular calculation and related fields leading to the release GeneBase 1.1 presented here which now allows summarization in terms of median, mean, SD and total for many quantitative parameters associated with genes, gene transcripts and gene features, dynamically calculated for any desired subset thus providing unique functionalities not offered by the NCBI Gene itself.…”

Section: Introductionmentioning

confidence: 99%

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GeneBase 1.1: a tool to summarize data from NCBI gene datasets and its application to an update of human gene statistics

et al. 2016

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We release GeneBase 1.1, a local tool with a graphical interface useful for parsing, structuring and indexing data from the National Center for Biotechnology Information (NCBI) Gene data bank. Compared to its predecessor GeneBase (1.0), GeneBase 1.1 now allows dynamic calculation and summarization in terms of median, mean, standard deviation and total for many quantitative parameters associated with genes, gene transcripts and gene features (exons, introns, coding sequences, untranslated regions). GeneBase 1.1 thus offers the opportunity to perform analyses of the main gene structure parameters also following the search for any set of genes with the desired characteristics, allowing unique functionalities not provided by the NCBI Gene itself. In order to show the potential of our tool for local parsing, structuring and dynamic summarizing of publicly available databases for data retrieval, analysis and testing of biological hypotheses, we provide as a sample application a revised set of statistics for human nuclear genes, gene transcripts and gene features. In contrast with previous estimations strongly underestimating the length of human genes, a ‘mean’ human protein-coding gene is 67 kbp long, has eleven 309 bp long exons and ten 6355 bp long introns. Median, mean and extreme values are provided for many other features offering an updated reference source for human genome studies, data useful to set parameters for bioinformatic tools and interesting clues to the biomedical meaning of the gene features themselves.Database URL: http://apollo11.isto.unibo.it/software/

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GeneBase 1.1: a tool to summarize data from NCBI gene datasets and its application to an update of human gene statistics

et al. 2016

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“…Currently available annotation of human genome maps does not give any clue that HR-DSCR contains sequences associated to known protein families, and its very small 34 kb size is lower than the 67 kb mean size of a human protein-coding gene (as determined by searching the recent GeneBase database (47): mean of gene size from 17 958 ‘reviewed’ or ‘validated’ entries in the National Center for Biotechnology Information—NCBI—Gene April 2015 annotation release). An unknown microRNA (miRNA) gene might represent an excellent candidate for being a single major contributor to DS because of its pleiotropy of action.…”

Section: Discussionmentioning

confidence: 99%

Systematic reanalysis of partial trisomy 21 cases with or without Down syndrome suggests a small region on 21q22.13 as critical to the phenotype

et al. 2016

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A ‘Down Syndrome critical region’ (DSCR) sufficient to induce the most constant phenotypes of Down syndrome (DS) had been identified by studying partial (segmental) trisomy 21 (PT21) as an interval of 0.6–8.3 Mb within human chromosome 21 (Hsa21), although its existence was later questioned. We propose an innovative, systematic reanalysis of all described PT21 cases (from 1973 to 2015). In particular, we built an integrated, comparative map from 125 cases with or without DS fulfilling stringent cytogenetic and clinical criteria. The map allowed to define or exclude as candidates for DS fine Hsa21 sequence intervals, also integrating duplication copy number variants (CNVs) data. A highly restricted DSCR (HR-DSCR) of only 34 kb on distal 21q22.13 has been identified as the minimal region whose duplication is shared by all DS subjects and is absent in all non-DS subjects. Also being spared by any duplication CNV in healthy subjects, HR-DSCR is proposed as a candidate for the typical DS features, the intellectual disability and some facial phenotypes. HR-DSCR contains no known gene and has relevant homology only to the chimpanzee genome. Searching for HR-DSCR functional loci might become a priority for understanding the fundamental genotype-phenotype relationships in DS.

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“…Based on these splice junctions, we observed that an intron length varies from 1 to as much as 12,40,200 nucleotides (nt). A recent study suggests that the shortest known eukaryotic intron length is 30 base pairs (bp) belonging to the human MST1L gene [24]. Introns shorter than 30bp are usually accounted to sequencing errors in genomes.…”

Section: Datamentioning

confidence: 99%

SpliceVec: distributed feature representations for splice junction prediction

Dutta

Dubey

Singh

et al. 2017

Preprint

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Background: Identification of intron boundaries, called splice junctions, is an important part of delineating gene structure and functions. This also provides valuable insights into the role of alternative splicing (AS) in increasing functional diversity of genes. Identification of splice junctions through RNA-seq is by mapping short reads to the reference genome which is prone to errors due to random sequence matches. This encourages identification of splicing junctions through computational methods based on machine learning. Existing models are dependent on feature extraction and selection for capturing splicing signals lying in vicinity of splice junctions. But such manually extracted features are not exhaustive. Methods: We introduce distributed feature representation, SpliceVec, to avoid explicit and biased feature extraction generally adopted for such tasks. SpliceVec is based on two widely used distributed representation models in natural language processing. Learned feature representation in form of SpliceVec is fed to multilayer perceptron for splice junction classification task. Results: An intrinsic evaluation of SpliceVec indicates that it is able to group true and false sites distinctly. Our study on optimal context to be considered for feature extraction indicates inclusion of entire intronic sequence is better than flanking upstream and downstream region around splice junctions. Further, SpliceVec is invariant to canonical and non-canonical splice junction detection. The proposed model is consistent in its performance even with reduced dataset and class-imbalanced dataset. Conclusions: SpliceVec is computationally efficient and can be trained with user defined data as well.

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Identification of minimal eukaryotic introns through GeneBase, a user-friendly tool for parsing the NCBI Gene databank

Cited by 47 publications

References 56 publications

GeneBase 1.1: a tool to summarize data from NCBI gene datasets and its application to an update of human gene statistics

GeneBase 1.1: a tool to summarize data from NCBI gene datasets and its application to an update of human gene statistics

Systematic reanalysis of partial trisomy 21 cases with or without Down syndrome suggests a small region on 21q22.13 as critical to the phenotype

SpliceVec: distributed feature representations for splice junction prediction

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