- Research note
- Open Access
On the length, weight and GC content of the human genome
BMC Research Notes volume 12, Article number: 106 (2019)
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.
The genome is the complex of the genetic information of a cell and in eukaryota (and thus in humans) is stored in the nucleus and mitochondria . While mitochondrial DNA (mtDNA) sequence has been known since 1981 , the draft sequence of the nuclear human genome was first published in February 2001 [3, 4]. The last human reference genome GRCh38/hg38 was released in December 2013 by the Genome Reference Consortium (GRC) and is the most comprehensive and highest quality mosaic haploid representation compared to previous reference assembly versions, addressing issues about gaps, variants and component and tiling path errors; in addition, for the first time, it contains sequence-based representations for centromeres and telomeres [5, 6].
The fact that very long molecules of human DNA can be contained, following accurate and multiple rounds of folding, within the very limited space of the nucleus, has always attracted attention. In 1990 when sequencing of human genome was just at its beginning, geneticist Jérôme Lejeune affirmed that “we have got 2 meters of so to speak magnetic tape in which everything is coded” (Louisiana Legislature, House Committee on the Administration of Criminal Justice, June 7, 1990). Traditionally, it has actually roughly been estimated over the last decades that the total length of human diploid DNA is around 2 m (Table 1) [7,8,9,10,11,12,13]. The base composition is usually specified quoting the percentage of guanine (G) and cytosine (C) of a DNA molecule, or GC content  and was first estimated through the buoyant density centrifugation . The GC content has been well studied across organisms [15,16,17,18,19], showing its relationships with various genomic characteristics [20,21,22,23,24] and with gene structures such as exons and introns [25,26,27], for example showing that G-rich repeats are a consistent feature of human ultra-short introns [28, 29].
The availability of a high-quality reference sequence for the human genome currently offers the possibility to provide an accurate evaluation of these parameters. In this work we propose revised estimations for the length, weight and GC content of the reference human genome and of individual chromosomes, including mtDNA, in a standard human diploid cell and in a reference human being. Moreover, in this paper we discuss the meaning of the obtained results and we formulated a method to calculate the relative GC content in the whole messenger RNA set of sequences and in transcriptomes, comparing different tissues and organisms.
Human genome length and weight calculations
Lengths in centimeters (cm) and weight in picograms (pg) of all 24 human chromosome and mtDNA sequences were calculated as detailed in Additional file 1: Additional Methods.
GC content analysis
The genomic GC content was calculated among the certain bases for the 24 chromosomes and for mtDNA as detailed in Additional file 1: Additional Methods.
The “Transcriptomic GC Analysis” (TGCA) software was developed here to study the possible variation of GC content in the expression of whole transcriptomes.
Human quantitative transcriptome maps were previously obtained from publicly available microarray datasets analysed through TRAM (Transcriptome Mapper) software  as described [31,32,33]. Since quantitative gene expression values may anticipate mutational effects that will most likely affect a given human tissue , we compared a pathologic cell type with its normal counterpart and a whole organ with one of its subregions (Additional file 1: Additional Methods). For each analysis, only genes for which an expression value is available in both biological conditions were used. For each gene, the longest human mRNA sequence was obtained from the latest version of human 5′_ORF_Extender software  (Additional file 1: Additional Methods).
Since TRAM and 5′_ORF_Extender were implemented for other organisms [36, 37], TGCA software itself was implemented with the purpose to be easily used with any sequence and expression dataset of any organism. Thus, we performed GC calculations on other representative species genomes: Danio rerio, Caenorhabditis elegans, Saccharomyces cerevisiae and Escherichia coli (Additional file 1: Additional Methods).
Human nuclear genome length and weight
Individual chromosome lengths in bp and cm are given in Table 2. Certain base counts and uncertain base composition estimations given in Additional file 2: Table S1 were used to calculate each chromosome weight, obtaining the results shown in Table 2. The length and weight sums of the 24 chromosomes (22 autosomes and X and Y chromosomes) were used in order to proportionately estimate the length and weight of the unplaced bases, improving whole genome calculation accuracy (Table 2). Data for the previous assembly (GRCh37.p13) are also given in Additional file 3: Table S2 and Additional file 4: Table S3. The chromosomes varying to a greater extent between the two assembly versions are chromosomes 9 and Y (GRCh38 has 2.8 Mb and 2.1 Mb less than GRCh37, respectively) and chromosomes 17 and 18 (GRCh38 has 2.1 Mb and 2.3 Mb more than GRCh37, respectively).
Considering a mean length in a diploid cell of 206.62 cm and the latest estimation of a mean of 3 × 1012 nucleated cells for a reference human being [38, 39], the total extension in length of all nuclear DNA molecules present in a single human individual is of about 6.20 billion km (6.20 × 1012 m) and is sufficient to cover the Earth-Sun distance (https://cneos.jpl.nasa.gov/glossary/au.html) more than 41 times. Considering a mean weight in a diploid cell of 6.46 pg, the genome weight summed across nucleated human cells would be about 19.39 g, almost the weight of 100 carats (https://sizes.com/units/carat.htm).
The mitochondrial genome
Applying all the calculations previously performed for the nuclear genome, the human mtDNA length, weight and GC content were estimated (Table 2).
On average, a human cell could contain from a minimum of 2,845,394 ± 204,296 bp, 0.09 ± 0.0067 cm and 0.0029 ± 0.00021 pg to a maximum of 110,742,060 ± 4,568,736.06 bp, 3.62 ± 0.15 cm and 0.11 ± 0.0047 pg of mtDNA in total, depending on the uncertainty of the number DNA molecules per cell  (Additional file 1: Additional Methods). Therefore, the mtDNA, despite its size being greatly reduced in comparison to those of nuclear DNA (1/195,663 compared to haploid nuclear genome), constitutes a significant share of total DNA of a human cell: about 0.90–1.21% (diploid cell), being able to represent at least 52.03% of the DNA in the case of a mature oocyte.
GC content analysis
The human GC contents calculated among the certain bases (A, T, W, G, C, and S) counted in the 24 human chromosomes excluding the 150,630,700 uncertain bases are shown in Table 2 (Additional file 4: Table S3 for GRCh37.p13). Among the other investigated species, the calculated chromosome numbers, total genome bp lengths and genomic GC contents (Table 3) are in accordance with previous reports (Additional file 5: Table S4).
Human Down Syndrome (DS) Acute Megakaryoblastic Leukemia (AMKL) blasts and euploid megakaryoblasts (MK) transcriptome maps have an expression value in both conditions together with mRNA sequences available for 16,547 genes. This value for whole human hippocampus and whole brain transcriptome maps is of 17,579 genes. Among the other investigated species, this value is of 6642 genes for D. rerio brain, 19,281 for C. elegans, 4673 for S. cerevisiae and 2426 for E. coli. The mRNA GC contents calculated in these subsets using TGCA software are given in Table 3. For each biological condition, each mRNA GC absolute count was then multiplied by the corresponding expression value. The sum of these values related to each transcriptome map gives the transcriptomic GC content (Table 3). mRNA and transcriptomic GC contents for each chromosome in DS-AMKL and MK conditions are given in Additional file 6: Table S5. DS-AMKL condition has 7 chromosomes (9, 11, 20, 17, 16, 22, 19) with a transcriptomic GC content higher than 48.80 which is the total mRNA GC % (the maximum is 56.26% of chr19), while MK condition has 9 chromosomes (7, 15, 9, 11, 20, 17, 22, 16, 19) with a transcriptomic GC content higher than that value (the maximum is 59.02% of chr19, which is a very high value).
In this work we have determined, to the best of our knowledge, basic parameters describing the normal human reference genome: the length, expressed in terms of both bp and unit of length (cm, m), weight (in unit of mass, pg) and relative GC content expressed in percentages, for the whole human nuclear genome, for each chromosome and for mtDNA.
We have based our calculations on the GRCh38 assembly, which is longer and more contiguous than previous reference assembly versions and provides a sequence-based representation for genomic features such as centromeres and telomeres for the first time , which, although variable among cell types and ages, would affect our estimates to a small extent. However, the human genetic diversity ranges from the single-nucleotide variation to large chromosomal events [41, 42]. Following the sequencing of 1000 human genomes , a recent analysis estimated ~ 20 million bases of sequence variation in a typical diploid genome . Applying this order of magnitude of variation to our estimates, a proportional variability among individuals of ± 0.65 cm and 0.02 pg for the length and weight of a human mean diploid genome can be assumed.
Our results are not far from previous rough estimates (Table 1), however the more accurate determination of the human genome length and weight might offer interesting possibilities. A recent analysis of 70 genomes from prokaryotes to primates showed that five informational laws about genome structure complexity may have been found , suggested by indexes based on the value k = lg2(n), where k is the length of a string occurring in the genome and n is the genome length . Applying our analysis to other genomes would be useful to update these indexes. Another interesting possibility offered by the knowledge of human nuclear genome length is the derivation of the total human DNA volume, in order to estimate the efficiency of DNA in data storage, resulted to be in the order of 104 fold superior in comparison to the most currently advanced hard disks (Additional file 7: Discussion). The genome weight is a parameter useful for the correlation with the DNA extraction yields through different methods .
Regarding GC content analysis at genomic level, our results are in agreement with a recent study . Through the implementation of TGCA software we have also determined the GC content at mRNA and transcriptomic levels for the first time, a novel concept we propose here, which is the GC percentage calculated in the mRNA amount actually expressed in a tissue. The human genomic GC content results to be much lower than mRNA GC content. mRNA GC content is in turn similar to the transcriptomic GC content. This has been confirmed also in D. rerio and C. elegans and to a lesser extent in S. cerevisiae and in E. coli. Overall, it seems that the GC composition of highly and poorly expressed genes in specific tissues affects the mRNA GC content to a small extent and a global compensation between them may exist.
Comparing different biological conditions, the greatest deviation from the mRNA GC content was found in a condition of aneuploidy and leukaemia (DS-AMKL). Interestingly, DS-AMKL transcriptomic GC content skews in a greater extent from the transcriptomic GC content of the healthy euploid counterpart of MK cells. Recent works conducted on DS subjects showed typical alterations of the metabolome and whole transcriptome [46, 47]. Chromosome 21 GC content is one of the closest to the mean genomic GC content, thus the presence of a third copy of chromosome 21 would not cause a great change in GC composition at genomic level. Since the duplication of at least a restricted region of human chromosome 21 is associated to DS , further studies are necessary to determine whether the duplication of this chromosome 21 region and/or the leukaemia condition is responsible for this deviation pattern. For example, a recent work showed a high expression of high-GC-content mRNAs in psoriasis lesion transcriptome, while resolving lesions had a low expression of these mRNAs . More in-depth analysis will be needed to validate the use of these indexes as indicators in the comparison of disease versus normal conditions.
Genomic, mRNA and transcriptomic GC content determination can be useful in DNA and RNA sequencing analyses where GC content bias for the Illumina sequencing technology has been documented as likely introduced at the library preparation step, resulting in confounding DNA copy number studies and expression fold-change estimates .
In conclusion, we provide an update on fundamental human genome parameters and a first characterisation of the mRNA and transcriptome GC contents. Our results may represent a solid basis for further investigations on human structural and functional genomics [29, 51] while also providing a framework for the comparative analysis of other genomes.
Determination of the length, weight and relative GC content of genome is subjected to the accuracy of the genome assembly and to the variability existing among individuals . Regarding mtDNA, although its sequence has been exactly determined, the mtDNA molecule copy number per cell is of difficult estimation . Regarding GC content at mRNA and transcriptomic levels, the analysis is limited to genes for which an expression value together with the corresponding longest mRNA nucleotide sequence is publicly available. Finally, mRNA 5´ region is known to have a composition bias  and a better delimitation of this sequence may be necessary .
International Human Genome Sequencing Consortium
Genome Reference Consortium
A or T
G or C
Transcriptomic GC Analysis
acute megakaryoblastic leukaemia
copy number variants
Strachan T, Read A. Human Molecular Genetics. Garland science. 4th ed. New York: Taylor & Francis Group; 2010.
Anderson S, Bankier AT, Barrell BG, de Bruijn MH, Coulson AR, Drouin J, Eperon IC, Nierlich DP, Roe BA, Sanger F, et al. Sequence and organization of the human mitochondrial genome. Nature. 1981;290:457–65.
Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921.
Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, et al. The sequence of the human genome. Science. 2001;291:1304–51.
Schneider VA, Graves-Lindsay T, Howe K, Bouk N, Chen HC, Kitts PA, Murphy TD, Pruitt KD, Thibaud-Nissen F, Albracht D, et al. Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly. Genome Res. 2017;27:849–64.
Guo Y, Dai Y, Yu H, Zhao S, Samuels DC, Shyr Y. Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis. Genomics. 2017;109:83–90.
Alberts B, Johnson A, Lewis J, et al. Molecular biology of the cell. 4th ed. New York: Garland Science; 2002.
Ross DW. The human genome: information content and structure. Hosp Pract (1995). 1999;34:49–65.
Peterson CL, Laniel MA. Histones and histone modifications. Curr Biol. 2004;14:R546–51.
Venter C, Cohen D. The century of biology. New Perspect Q. 2004;21:73–7.
Ram M. Fundamentals of cytogenetics and genetics. New Delhi: PHI Learning Private Limited; 2010.
Clayton J, Dennis C. 50 years of DNA. UK: Palgrave Macmillan; 2016.
Divan A, Royds J. Molecular biology: a very short introduction. Oxford: Oxford University Press; 2016.
Schildkraut CL, Marmur J, Doty P. Determination of the base composition of deoxyribonucleic acid from its buoyant density in CsCl. J Mol Biol. 1962;4:430–43.
Saccone C, De Giorgi C, Gissi C, Pesole G, Reyes A. Evolutionary genomics in Metazoa: the mitochondrial DNA as a model system. Gene. 1999;238:195–209.
Satapathy SS, Dutta M, Ray SK. Variable correlation of genome GC% with transfer RNA number as well as with transfer RNA diversity among bacterial groups: alpha-proteobacteria and tenericutes exhibit strong positive correlation. Microbiol Res. 2010;165:232–42.
Vinogradov AE, Anatskaya OV. Genome size and metabolic intensity in tetrapods: a tale of two lines. Proc Biol Sci. 2006;273:27–32.
Kasai F, O’Brien PC, Ferguson-Smith MA. The bat genome: GC-biased small chromosomes associated with reduction in genome size. Chromosoma. 2013;122:535–40.
Pozzoli U, Menozzi G, Fumagalli M, Cereda M, Comi GP, Cagliani R, Bresolin N, Sironi M. Both selective and neutral processes drive GC content evolution in the human genome. BMC Evol Biol. 2008;8:99.
Romero H, Zavala A, Musto H. Compositional pressure and translational selection determine codon usage in the extremely GC-poor unicellular eukaryote Entamoeba histolytica. Gene. 2000;242:307–11.
Knight RD, Freeland SJ, Landweber LF. A simple model based on mutation and selection explains trends in codon and amino-acid usage and GC composition within and across genomes. Genome Biol. 2001;2:Research0010.
Hartono SR, Korf IF, Chedin F. GC skew is a conserved property of unmethylated CpG island promoters across vertebrates. Nucleic Acids Res. 2015;43:9729–41.
Kel-Margoulis OV, Tchekmenev D, Kel AE, Goessling E, Hornischer K, Lewicki-Potapov B, Wingender E. Composition-sensitive analysis of the human genome for regulatory signals. Silico Biol. 2003;3:145–71.
Vinogradov AE. DNA helix: the importance of being GC-rich. Nucleic Acids Res. 2003;31:1838–44.
Amit M, Donyo M, Hollander D, Goren A, Kim E, Gelfman S, Lev-Maor G, Burstein D, Schwartz S, Postolsky B, et al. Differential GC content between exons and introns establishes distinct strategies of splice-site recognition. Cell reports. 2012;1:543–56.
Duret L, Hurst LD. The elevated GC content at exonic third sites is not evidence against neutralist models of isochore evolution. Mol Biol Evol. 2001;18:757–62.
Kalari KR, Casavant M, Bair TB, Keen HL, Comeron JM, Casavant TL, Scheetz TE. First exons and introns—a survey of GC content and gene structure in the human genome. Silico Biol. 2006;6:237–42.
Shimada MK, Sasaki-Haraguchi N, Mayeda A. Identification and validation of evolutionarily conserved unusually short Pre-mRNA introns in the human genome. Int J Mol Sci. 2015;16:10376–88.
Piovesan A, Caracausi M, Ricci M, Strippoli P, Vitale L, Pelleri MC. Identification of minimal eukaryotic introns through GeneBase, a user-friendly tool for parsing the NCBI Gene databank. DNA Res. 2015;22:495–503.
Lenzi L, Facchin F, Piva F, Giulietti M, Pelleri MC, Frabetti F, Vitale L, Casadei R, Canaider S, Bortoluzzi S, et al. TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources. BMC Genomics. 2011;12:121.
Caracausi M, Vitale L, Pelleri MC, Piovesan A, Bruno S, Strippoli P. A quantitative transcriptome reference map of the normal human brain. Neurogenetics. 2014;15:267–87.
Pelleri MC, Piovesan A, Caracausi M, Berardi AC, Vitale L, Strippoli P. Integrated differential transcriptome maps of acute megakaryoblastic leukemia (AMKL) in children with or without Down Syndrome (DS). BMC Med Genomics. 2014;7:63.
Caracausi M, Rigon V, Piovesan A, Strippoli P, Vitale L, Pelleri MC. A quantitative transcriptome reference map of the normal human hippocampus. Hippocampus. 2016;26:13–26.
Caracausi M, Piovesan A, Vitale L, Pelleri MC. Integrated transcriptome map highlights structural and functional aspects of the normal human Heart. J Cell Physiol. 2017;232:759–70.
Casadei R, Piovesan A, Vitale L, Facchin F, Pelleri MC, Canaider S, Bianconi E, Frabetti F, Strippoli P. Genome-scale analysis of human mRNA 5′ coding sequences based on expressed sequence tag (EST) database. Genomics. 2012;100:125–30.
Piovesan A, Vitale L, Pelleri MC, Strippoli P. Universal tight correlation of codon bias and pool of RNA codons (codonome): The genome is optimized to allow any distribution of gene expression values in the transcriptome from bacteria to humans. Genomics. 2013;101:282–9.
Piovesan A, Caracausi M, Pelleri MC, Vitale L, Martini S, Bassani C, Gurioli A, Casadei R, Solda G, Strippoli P. Improving mRNA 5′ coding sequence determination in the mouse genome. Mamm Genome. 2014;25:149–59.
Bianconi E, Piovesan A, Facchin F, Beraudi A, Casadei R, Frabetti F, Vitale L, Pelleri MC, Tassani S, Piva F, et al. An estimation of the number of cells in the human body. Ann Hum Biol. 2013;40:463–71.
Sender R, Fuchs S, Milo R. Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. 2016;14:e1002533.
D’Erchia AM, Atlante A, Gadaleta G, Pavesi G, Chiara M, De Virgilio C, Manzari C, Mastropasqua F, Prazzoli GM, Picardi E, et al. Tissue-specific mtDNA abundance from exome data and its correlation with mitochondrial transcription, mass and respiratory activity. Mitochondrion. 2015;20:13–21.
Alkan C, Coe BP, Eichler EE. Genome structural variation discovery and genotyping. Nat Rev Genet. 2011;12:363–76.
Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Hsi-Yang Fritz M, et al. An integrated map of structural variation in 2504 human genomes. Nature. 2015;526:75–81.
Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR. A global reference for human genetic variation. Nature. 2015;526:68–74.
Bonnici V, Manca V. Informational laws of genome structures. Sci Rep. 2016;6:28840.
Preis M, Schneiderman J, Koren B, Ben-Yosef Y, Levin-Ashkenazi D, Shapiro S, Cohen T, Blich M, Israeli-Amit M, Sarnatzki Y, et al. Co-expression of fibulin-5 and VEGF165 increases long-term patency of synthetic vascular grafts seeded with autologous endothelial cells. Gene Ther. 2016;23:237–46.
Caracausi M, Ghini V, Locatelli C, Mericio M, Piovesan A, Antonaros F, Pelleri MC, Vitale L, Vacca RA, Bedetti F, et al. Plasma and urinary metabolomic profiles of Down Syndrome correlate with alteration of mitochondrial metabolism. Sci Rep. 2018;8:2977.
Pelleri MC, Cattani C, Vitale L, Antonaros F, Strippoli P, Locatelli C, Cocchi G, Piovesan A, Caracausi M. Integrated quantitative transcriptome maps of human trisomy 21 tissues and cells. Front Genet. 2018;9:125.
Pelleri MC, Cicchini E, Locatelli C, Vitale L, Caracausi M, Piovesan A, Rocca A, Poletti G, Seri M, Strippoli P, et al. Systematic reanalysis of partial trisomy 21 cases with or without Down Syndrome suggests a small region on 21q22.13 as critical to the phenotype. Hum Mol Genet. 2016;25:2525–38.
Swindell WR, Xing X, Voorhees JJ, Elder JT, Johnston A, Gudjonsson JE. Integrative RNA-seq and microarray data analysis reveals GC content and gene length biases in the psoriasis transcriptome. Physiol Genomics. 2014;46:533–46.
Risso D, Schwartz K, Sherlock G, Dudoit S. GC-content normalization for RNA-Seq data. BMC Bioinformatics. 2011;12:480.
Piovesan A, Caracausi M, Antonaros F, Pelleri MC, Vitale L. GeneBase 1.1: a tool to summarize data from NCBI Gene datasets and its application to an update of human gene statistics. Database Oxford. 2016;2016:baw153.
Bogenhagen DF. Mitochondrial DNA nucleoid structure. Biochim Biophys Acta. 2012;1819:914–20.
Pesole G, Liuni S, Grillo G, Saccone C. Structural and compositional features of untranslated regions of eukaryotic mRNAs. Gene. 1997;205:95–102.
Vitale L, Caracausi M, Casadei R, Pelleri MC, Piovesan A. Difficulty in obtaining the complete mRNA coding sequence at 5′ region (5′ end mRNA artifact): causes, consequences in biology and medicine and possible solutions for obtaining the actual amino acid sequence of proteins (Review). Int J Mol Med. 2017;39:1063–71.
Wang AH, Nathans J, van der Marel G, van Boom JH, Rich A. Molecular structure of a double helical DNA fragment intercalator complex between deoxy CpG and a terpyridine platinum compound. Nature. 1978;276:471–4.
AP developed the software, collected the data, performed the analysis, and wrote the manuscript draft. MCP and FA collected the data and critically revised the results of the analysis. PS designed the work, tested the software and wrote the manuscript draft. MC and LV supervised the project and critically revised the manuscript. All authors contributed to the interpretation of data. All authors agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. All authors read and approved the final manuscript.
We wish to sincerely thank the Fondazione Umano Progresso, Milano, Italy for their fundamental support to our research on trisomy 21 and to this study.
We thank all the other people that very kindly contributed by individual donations to support part of the fellowships as well as hardware and software. In particular, we are profoundly grateful to Matteo and Elisa Mele, to the Costa family, “Gruppo Arzdore”, Parrocchia di Dozza and Associazione Turistica Pro Loco di Dozza (Dozza, Bologna, Italy) and to Rina Bini.
We are grateful to Kirsten Welter for her kind and expert revision of the manuscript.
The authors declare that they have no competing interests.
Availability of data and materials
The datasets generated and analysed during the current study are publicly available online at http://apollo11.isto.unibo.it/software/. Some of them are also are available within the article and its additional information files.
The Python executable script used to count any type of nucleotide letter, the FileMaker stand-alone database with which the counts were analysed and the tutorial are publicly available at http://apollo11.isto.unibo.it/software/.
TGCA software was developed within the FileMaker Pro Advanced environment (FileMaker, Santa Clara, CA, version 12). Mac OS X and Windows stand-alone software (TGCA versions pre-loaded with transcriptome and sequence data and empty) are freely available to all for basic use at http://apollo11.isto.unibo.it/software/, including the FileMaker runtime and a tutorial.
Project name: TGCA_Software.
Project home page: http://apollo11.isto.unibo.it/software/.
Programming language: Python and FileMaker.
Operating systems: Mac OS X and Windows.
Minimum software requirements: Mac OS X 10.6, OS X Lion 10.7, OS X Mountain Lion 10.8; Windows XP Professional, Home Edition (Service Pack 3); Windows Vista Ultimate, Business, Home Premium (Service Pack 2); Windows 7 Ultimate, Professional, Home Premium; Windows 8 Standard and Pro edition.
Minimum system requirements: Mac OS X 10.6, Intel-based Mac CPU (Central Processing Unit), 1 GigaByte (GB) of RAM (Random Access Memory), 1024 × 768 or higher resolution video adapter and display. Windows XP Professional, Home Edition (Service Pack 3), 700 MegaHertz (MHz) CPU or faster, 256 MegaBytes (MB) of RAM, 1024 × 768 or higher resolution video adapter and display.
A connection to the Internet is required to display the software tutorial and to download data for set up, but not to run the tool.
Any restrictions to use by non-academics: freely available to all for basic use.
Consent to publish
Ethics approval and consent to participate
This work was supported by donations from Fondazione Umano Progresso and from other donors acknowledged below which supported the purchase of the hardware and software that were necessary to conduct the research.
Fellowships for AP and MC have been mainly funded by the Fondazione Umano Progresso, Milano, Italy. The fellowship for FA has been mainly funded by donations from ‘Gruppo Arzdore’, Dozza, (BO), Italy and by the Natali family, Petriolo (MC), Italy, in memory of Leonardo Natali.
The funding sources had no role in the design of this study and collection, analysis, and interpretation of data and in writing the manuscript.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Piovesan, A., Pelleri, M.C., Antonaros, F. et al. On the length, weight and GC content of the human genome. BMC Res Notes 12, 106 (2019) doi:10.1186/s13104-019-4137-z
- Human genome
- Genome length
- Genome weight
- GC content
- Mitochondrial DNA