Basic parameters commonly used to describe genomes including length, weight and relative guanine-cytosine (GC) content are widely cited in absence of a primary source. By using updated data and original software we determined these values to the best of our knowledge as standard reference for the whole human nuclear genome, for each chromosome and for mitochondrial DNA. We also devised a method to calculate the relative GC content in the whole messenger RNA sequence set and in transcriptomes by multiplying the GC content of each gene by its mean expression level. The male nuclear diploid genome extends for 6.27 Gigabase pairs (Gbp), is 205.00 cm (cm) long and weighs 6.41 picograms (pg). Female values are 6.37 Gbp, 208.23 cm, 6.51 pg. The individual variability and the implication for the DNA informational density in terms of bits/volume were discussed. The genomic GC content is 40.9%. Following analysis in different transcriptomes and species, we showed that the greatest deviation was observed in the pathological condition analysed (trisomy 21 leukaemic cells) and in Caenorhabditis elegans. Our results may represent a solid basis for further investigation on human structural and functional genomics while also providing a framework for other genome comparative analysis.

A well-known limit of genome browsers is that the large amount of genome and gene data is not organized in the form of a searchable database, hampering full management of numerical data and free calculations. Due to the continuous increase of data deposited in genomic repositories, their content revision and analysis is recommended. Using GeneBase, a software with a graphical interface able to import and elaborate National Center for Biotechnology Information (NCBI) Gene database entries, we provide tabulated spreadsheets updated to 2019 about human nuclear protein-coding gene data set ready to be used for any type of analysis about genes, transcripts and gene organization. Comparison with previous reports reveals substantial change in the number of known nuclear protein-coding genes (now 19,116), the protein-coding non-redundant transcriptome space [now 59,281,518 base pair (bp), 10.1% increase], the number of exons (now 562,164, 36.2% increase) due to a relevant increase of the RNA isoforms recorded. Other parameters such as gene, exon or intron mean and extreme length appear to have reached a stability that is unlikely to be substantially modified by human genome data updates, at least regarding protein-coding genes. Finally, we confirm that there are no human introns shorter than 30 bp.

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/

Down Syndrome (DS) is the most common genetic alteration responsible for intellectual disability, which refers to deficits in both intellectual and adaptive functioning. According to this, individuals with Down Syndrome (DS) reach developmental milestones (e.g., sitting, walking, and babbling) in the same order as their typically developing peers, but later in life. Since developmental milestones are the first blocks on which development builds, the aims of the current study are to: (i) expand the knowledge of developmental milestone acquisition; and (ii) explore the relationship between developmental milestone acquisition and later development. For this purpose 105 children/adolescents with DS were involved in this study, divided in two groups, Preschoolers (n = 39) and School-age participants (n = 66). Information on the age of acquisition of Sitting, Walking, Babbling, and Sphincter Control was collected, together with cognitive, motor, and adaptive functioning. Sitting predicted later motor development, but, with age, it became less important in predicting motor development in everyday life. Babbling predicted later language development in older children. Finally, Sphincter Control emerged as the strongest predictor of motor, cognitive, language, and adaptive skills, with its role being more evident with increasing age. Our data suggest that the age of reaching the milestones considered in the study has an influence on successive development, a role that can be due to common neural substrates, the environment, and the developmental cascade effect.

Background: Several tools have been developed to perform global gene expression profile data analysis, to search for specific chromosomal regions whose features meet defined criteria as well as to study neighbouring gene expression. However, most of these tools are tailored for a specific use in a particular context (e.g. they are species-specific, or limited to a particular data format) and they typically accept only gene lists as input. Results: TRAM (Transcriptome Mapper) is a new general tool that allows the simple generation and analysis of quantitative transcriptome maps, starting from any source listing gene expression values for a given gene set (e.g. expression microarrays), implemented as a relational database. It includes a parser able to assign univocal and updated gene symbols to gene identifiers from different data sources. Moreover, TRAM is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes. When in 'Map' mode, the software generates a quantitative representation of the transcriptome of a sample (or of a pool of samples) and identifies if segments of defined lengths are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches for a set of over/under-expressed consecutive genes. Statistical significance for all results is calculated with respect to genes localized on the same chromosome or to all genome genes. Transcriptome maps, showing differential expression between two sample groups, relative to two different biological conditions, may be easily generated. We present the results of a biological model test, based on a meta-analysis comparison between a sample pool of human CD34+ hematopoietic progenitor cells and a sample pool of megakaryocytic cells. Biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte were identified. Conclusions: TRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. The release includes FileMaker Pro database management runtime application and it is freely available at http://apollo11.isto.unibo.it/software/, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes.

The incidence of Acute Megakaryoblastic Leukemia (AMKL) is 500-fold higher in children with Down Syndrome (DS) compared with non-DS children, but the relevance of trisomy 21 as a specific background of AMKL in DS is still an open issue. Several Authors have determined gene expression profiles by microarray analysis in DS and/or non-DS AMKL. Due to the rarity of AMKL, these studies were typically limited to a small group of samples. We generated integrated quantitative transcriptome maps by systematic meta-analysis from any available gene expression profile dataset related to AMKL in pediatric age. This task has been accomplished using a tool recently described by us for the generation and the analysis of quantitative transcriptome maps, TRAM (Transcriptome Mapper), which allows effective integration of data obtained from different experimenters, experimental platforms and data sources. This allowed us to explore gene expression changes involved in transition from normal megakaryocytes (MK, n=19) to DS (n=43) or non-DS (n=45) AMKL blasts, including the analysis of Transient Myeloproliferative Disorder (TMD, n=20), a pre-leukemia condition. We propose a biological model of the transcriptome depicting progressive changes from MK to TMD and then to DS AMKL. The data indicate the repression of genes involved in MK differentiation, in particular the cluster on chromosome 4 including PF4 (platelet factor 4) and PPBP (pro-platelet basic protein); the gene for the mitogen-activated protein kinase MAP3K10 and the thrombopoietin receptor gene MPL. Moreover, comparing both DS and non-DS AMKL with MK, we identified three potential clinical markers of progression to AMKL: TMEM241 (transmembrane protein 241) was the most over-expressed single gene, while APOC2 (apolipoprotein C-II) and ZNF587B (zinc finger protein 587B) appear to be the most discriminant markers of progression, specifically to DS AMKL. Finally, the chromosome 21 (chr21) genes resulted to be the most over-expressed in DS and non-DS AMKL, as well as in TMD, pointing out a key role of chr21 genes in differentiating AMKL from MK. Our study presents an integrated original model of the DS AMLK transcriptome, providing the identification of genes relevant for its pathophysiology which can potentially be new clinical markers.