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When is gene density an important parameter in experiments?

When is gene density an important parameter in experiments?


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As per wikipedia definition, gene density is defined as:

"In genetics, the gene density of an organism's genome is the ratio of the number of genes per number of base pairs, usually written in terms of a million base pairs, or megabase (Mb). The human genome has a gene density of 12-15 genes/Mb, while the genome of the C. elegans roundworm is estimated to have 200."

But I would like to know in what circumstances one would consider gene density as an important parameter? To be specific in what kind of experiments the model organism's gene density plays key role?


"Note that interaction effects among genes also depend on their physical distance." I think that's more of a pragmatic assumption, because we tend to associate regulatory features with their closest downstream TSS, though it's certainly not a hard and fast rule. e.g., in certain organisms like budding yeast, its known that the most transcription factor binding sites are downstream of gene bodies. even in humans, the association is not entirely clear as there are both cis and trans effects especially notable with things like super enhancers and things of that nature. Or consider the following, http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004461


This is an open-ended question and will be impossible to correctly answer. I am voting "to close as too broad". Note also that the question in the title is not the same as the question in the post.

But I still wanted to give you some information that may help you. Here are just a few examples for which knowing gene density matters.


Background selection is the process by which purifying selection reduces heterozygosity at nearby loci. Knowing the density (and positions) of genes (and other sequences potentially under selection such as regulatory sequences) is essential to understand the variation of background selection throughout the genome. Being able to design maps of variation of background selection throughout the genome is essential to improve our ability to detect local adaptation and positive selection as background selection leaves similar genetic signature.

The evolution of the genome size is in itself of interest to evolutionary biologists. Species having large genomes ave large genomes mainly due to repetitive neutral sequences. As such the question of gene density is essential to understand the evolution of genome size.

The gene density varies throughout the genome (with a variance greater than the mean, that is it does not follow a Poisson distribution). Understanding the evolution of gene density at different regions is of interest to many. Note that interaction effects among genes also depend on their physical distance.


Chromosome territory relocation paradigm during DNA damage response: Some insights from molecular biology to physics

Among the many facets of DNA damage response (DDR), relocation of chromosome territories (CTs) is most intriguing. We have previously reported that cisplatin induced DDR in human dermal fibroblasts led to relocation of CTs 12, 15 from the nuclear periphery to its interior while CTs 19, 17 repositioned from the interior to its periphery. Studies of CT relocation remain nascent as we begin unraveling the role of key players in DDR to demonstrate its mechanistic basis. Consolidating our recent reports, we argue that γH2AX-signaling leads to enhanced recruitment of nuclear myosin 1 (NM1) to chromatin, which via its motor function, results in CT repositioning. Next, we invoke a novel systems-level theory that subsumed CTs as pairs, not solo entities, to present the physical basis for plasticity in interphase CT arrangement. Subsequently, we posited that our systems-level theory describes a unified physical basis for non-random positioning of CTs in interphase nuclei across disparate eukaryotes.


Introduction

Human nuclei have a radial organization. Chromosomes with the highest gene density are preferentially disposed toward the nuclear interior, and gene-poor chromosomes locate towards the nuclear periphery (Croft et al., 1999 Boyle et al., 2001 Cremer et al., 2001). This organization is conserved in other vertebrates (Habermann et al., 2001 Tanabe et al., 2002), suggesting that the nuclear interior may facilitate, or create a permissive environment for, transcription. However, many human chromosomes are a patchwork of domains with varying gene density and so some very gene-rich regions of the human genome are contained on chromosomes located close to the nuclear periphery.

We have previously shown that individual human genes can be transcribed from within the interior of chromosome territories that are not located in the nuclear center (Mahy et al., 2002). This showed that genes do not need to be either at the visible surface of interphase chromosome territories, or at the centre of the nucleus, in order to be transcribed. These genes were located in regions of moderate gene-density (the R-band 11p13). In contrast, the gene-dense major histocompatibilty complex (MHC) * locus is frequently observed on loops of chromatin that extend away from the human chromosome 6 territory that is detected by FISH with a chromosome paint, particularly when transcription of genes from this region is induced (Volpi et al., 2000). Similarly, the epidermal differentiation complex (EDC) at 1q21 is frequently located outside of the chromosome 1 territory in keratinocytes, cells in which the genes of the EDC are highly expressed (Williams et al., 2002). It was not clear whether localization outside of chromosome territories was a particular feature of regions of the genome that contain genes with related functions, and that are coordinately expressed, or whether it might represent a more general facet of genome organization wherever genes are particularly clustered together, or where the overall levels of transcription from a large number of genes across a region is high.

To address this, we have used FISH to examine territory organization of regions of the human genome with high gene densities and generally high levels of transcription. The T-band 11p15.5 contains at least 47 known genes within the most distal 4.5 megabase (Mb) of DNA. We found that many megabases of this chromatin is frequently found outside of the visible confines of the 11p territory. By extending this observation to other gene-dense parts of the human genome including 11q13 and 16p13.3 and gene-dense regions of chromosomes 21 and 22, we suggest that there is a correlation between domains of high gene density and localization outside of chromosome territories. We show that the frequency of extraterritory localization decreases, but is not eliminated, when transcription is inhibited. This level of higher-order genome organization is conserved in the mouse, indicating that it likely has functional significance. We suggest that the propagation of a decondensed chromatin fibre outside of the confines of chromosome territories creates an environment that is permissive to transcription increasing the overall transcriptional potential of the domain (Tumbar et al., 1999), and that the structure of chromosome territories is, in part, driven by transcription.


Radial chromatin positioning is shaped by local gene density, not by gene expression

G- and R-bands of metaphase chromosomes are characterized by profound differences in gene density, CG content, replication timing, and chromatin compaction. The preferential localization of gene-dense, transcriptionally active, and early replicating chromatin in the nuclear interior and of gene-poor, later replicating chromatin at the nuclear envelope has been demonstrated to be evolutionary-conserved in various cell types. Yet, the impact of different local chromatin features on the radial nuclear arrangement of chromatin is still not well understood. In particular, it is not known whether radial chromatin positioning is preferentially shaped by local gene density per se or by other related parameters such as replication timing or transcriptional activity. The interdependence of these distinct chromatin features on the linear deoxyribonucleic acid (DNA) sequence precludes a simple dissection of these parameters with respect to their importance for the reorganization of the linear DNA organization into the distinct radial chromatin arrangements observed in the nuclear space. To analyze this problem, we generated probe sets of pooled bacterial artificial chromosome (BAC) clones from HSA 11, 12, 18, and 19 representing R/G-band-assigned chromatin, segments with different gene density and gene loci with different expression levels. Using multicolor 3D flourescent in situ hybridization (FISH) and 3D image analysis, we determined their localization in the nucleus and their positions within or outside the corresponding chromosome territory (CT). For each BAC data on local gene density within 2- and 10-Mb windows, as well as GC (guanine and cytosine) content, replication timing and expression levels were determined. A correlation analysis of these parameters with nuclear positioning revealed regional gene density as the decisive parameter determining the radial positioning of chromatin in the nucleus in contrast to band assignment, replication timing, and transcriptional activity. We demonstrate a polarized distribution of gene-dense vs gene-poor chromatin within CTs with respect to the nuclear border. Whereas we confirm previous reports that a particular gene-dense and transcriptionally highly active region of about 2 Mb on 11p15.5 often loops out from the territory surface, gene-dense and highly expressed sequences were not generally found preferentially at the CT surface as previously suggested.

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Potential role of the X circular code in the regulation of gene expression

The X circular code is a set of 20 trinucleotides (codons) that has been identified in the protein-coding genes of most organisms (bacteria, archaea, eukaryotes, plasmids, viruses). It has been shown previously that the X circular code has the important mathematical property of being an error-correcting code. Thus, motifs of the X circular code, i.e. a series of codons belonging to X and called X motifs, allow identification and maintenance of the reading frame in genes. X motifs are significantly enriched in protein-coding genes, but have also been identified in many transfer RNA (tRNA) genes and in important functional regions of the ribosomal RNA (rRNA), notably in the peptidyl transferase center and the decoding center. Here, we investigate the potential role of X motifs as functional elements of protein-coding genes. First, we identify the codons of the X circular code which are frequent or rare in each domain of life (archaea, bacteria, eukaryota) and show that, for the amino acids with the highest codon bias, the preferred codon is often an X codon. We also observe a correlation between the 20 X codons and the optimal codons/dicodons that have been shown to influence translation efficiency. Then, we examined recently published experimental results concerning gene expression levels in diverse organisms. The approach used is the analysis of X motifs according to their density ds(X), i.e. the number of X motifs per kilobase in a gene sequence s. Surprisingly, this simple parameter identifies several unexpected relations between the X circular code and gene expression. For example, the X motifs are significantly enriched in the minimal gene set belonging to the three domains of life, and in codon-optimized genes. Furthermore, the density of X motifs generally correlates with experimental measures of translation efficiency and mRNA stability. Taken together, these results lead us to propose that the X motifs may represent a genetic signal contributing to the maintenance of the correct reading frame and the optimization and regulation of gene expression.

Keywords: Circular code Codon optimization Codon usage Gene expression Genetic code.


Genome annotation

Genes' parameters were taken from Genomon gene models from the NCBI ftp://ftp.ncbi.nih.gov/genomes/) chicken map viewer Web site (Build 2.1, released November, 2006) that are displayed as Genes OnSequence.

Only nuclear genes with complete information on protein-coding sequence, and no evidence of multiple-splicing forms were included. The products of genes can be found from http://www.ncbi.nlm.nih.gov/UniGene/. Some genes with a partial protein-coding sequence (CDS) were not included. Many genes have multiple mRNA (multiple splicing) also ruled out. Some genes' CDS length is not consistent with the total length of all exons of gene obviously. These genes were defined annotation errors and were not included in this study, too. Finally, total 10, 289 genes' parameters, including gene size, CDS length, first intron length, average intron length, and total intron length were estimated. Gene compactness was defined as genes with shorter size, shorter intron length, or shorter CDS length.

Expression datasets

Two gene expression datasets were included in this study. Gene expression dataset 1, EST databases, were taken from the NCBI FTP site. 633,321 EST sequences were available. We used the number of EST sequences in this databases that align unequivocally to a given gene, and compared the set of chicken mRNA/cDNA sequences with the ESTs using the program BLASTN. We accepted EST hits of > 400 nt and with > 96% identity to a mRNA/cDNA sequence as matches. If they showed > 98% identity, we accepted hits of 100 - 400 nt, and we discarded hits of < 100 nt [1]. After excluding genes with multiple-splicing forms and genes with obvious annotation errors, the data on 10, 289 genes for 18 tissues were taken into account: blood, brain, cecum, connective tissue, embryonic tissue, epiphyseal growth plate, gonad, head, heart, limb, liver, muscle, ovary, pancreas, spleen, testis, and thymus. Tags per million were then calculated for each tissue of each gene. Two measures of expression level were defined: total expression level, which is the sum of the total 18 tissues' EST, and expression breadth, the numbers of tissues in which EST was found. EST-based method was used to identify genes' expression breadth [14]. Genes were defined as ubiquitously or narrowly expressed if they are expressed in > 14 tissues, or < 3 tissues, respectively (when "> 15 or < 2", "> 16 or < 2" defined, we get the similar result). For somatic cells, narrowly expressed genes were defined as those expressed in less than 20% of total normal samples excluding germline cells, reproductive organs, or early developmental stage. The tissue specificity index (τ) measured both qualitative variations (i.e. presence/absence) and quantitative variations of expression level among tissues, was defined as:

Where N is the number of tissue samples examined, x i is the expression level of the gene in sample i, and x max is the highest expression level of the gene across the N samples examined [16]. The protein characters were estimated using the SwissPfam version 20 http://pfam.janelia.org/.

Gene expression dataset 2 derived from a high-density oligonucleotide chip arrays, GSE12974 (GEO, http://www.ncbi.nlm.nih.gov/geo) [39]. As recommended [40], a gene was assumed to be expressed in a tissue significantly if its intensity exceeded the 99th percentile of intensities from the negative controls (Using 90% and 95% as the thresholds gave similar results, date not show). After excluding genes with multiple-splicing forms and genes with obvious annotation errors, only 4, 086 genes for 20 tissues were taken into account: Bursa of fabricius, cerebellum, cerebral cortex, eye, femur with bone marrow, gallbladder, gizzard, heart, intestine, kidney, liver, lung, muscle, ovary, oviduct, skin, spleen, stomach, testis and thymus. As the two expression datasets given the similar result, we only displayed the result of dataset 1 in this report (The result of dataset 2 can be seen from Additional files 2, 3).

Recombination rate estimate

The recombination rates for 4 Mb windows were estimated. The versions of the genome assemblies (NCBI build 2.1, released November, 2006) and genetic linkage map WUR (NCBI Mapview build 2.1) were used. Locations of individual markers were determined based on alignments of the full sequence of the marker using BLAST. Markers placement information is available for download from the UCSC Genome Browser (Kent et al. 2002, http://www.genome.ucsc.edu). The linear function was fit to the points representing genetic and physical map position in 4 Mb windows. The slope of this line was taken as the estimate of recombination rate [41]. When only two markers were anchored to the sequence, a straight line was calculated [42]. Total windows included is 210, covering approximately 80% of the chicken genome. Eleven windows contain only two markers. The average expression level for each window was estimated based on genes' expression level located in this window.


1.E: Overview, DNA, and Genes (Exercises)

  • Contributed by Todd Nickle and Isabelle Barrette-Ng
  • Professors (Biology) at Mount Royal University & University of Calgary

These are homework exercises to accompany Nickle and Barrette-Ng's "Online Open Genetics" TextMap. Genetics is the scientific study of heredity and the variation of inherited characteristics. It includes the study of genes, themselves, how they function, interact, and produce the visible and measurable characteristics we see in individuals and populations of species as they change from one generation to the next, over time, and in different environments.

1.1 How would the results of the cross in Figure 1.11 have been different if heredity worked through blending inheritance rather than particulate inheritance?

1.2 Imagine that astronauts provide you with living samples of multicellular organisms discovered on another planet. These organisms reproduce with a short generation time, but nothing else is known about their genetics.

a) How could you define laws of heredity for these organisms?

b) How could you determine what molecules within these organisms contained genetic information?

c) Would the mechanisms of genetic inheritance likely be similar for all organisms from this planet?

d) Would the mechanisms of genetic inheritance likely be similar to organisms from earth?

1.3 It is relatively easy to extract DNA and protein from cells biochemists had been doing this since at least the 1800&rsquos. Why then did Hershey and Chase need to use radioactivity to label DNA and proteins in their experiments?

1.4 Compare Watson and Crick&rsquos discovery with Avery, MacLeod and McCarty&rsquos discovery.

a) What did each discover, and what was the impact of these discoveries on biology?

b) How did Watson and Crick&rsquos approach generally differ from Avery, MacLeod and McCarty&rsquos?

c) Briefly research Rosalind Franklin on the internet. Why is her contribution to the structure of DNA controversial?

1.5 Starting with mice and R and S strains of S. pneumoniae, what experiments in additional to those shown in Figure 1.3 to demonstrate that DNA is the genetic material?

1.6 List the information that Watson and Crick used to deduce the structure of DNA.

1.7 Refer to Watson and Crick&rsquo

a) List the defining characteristics of the structure of a DNA molecule.

b) Which of these characteristics are most important to replication?

c) Which characteristics are most important to the Central Dogma?

1.8 Compare Figure 1.13 and Table 1.1. Which of the mutants (#1, #2, #3) shown in Figure 1.13 matches each of the phenotypes expected for mutations in genes A, B,C?

1.9 Refer to Table 1.2

a) What is the relationship between DNA content of a genome, number of genes, gene density, and chromosome number?

b) What feature of genomes explains the c-value paradox?

c) Do any of the numbers in Table 1.2 show a correlation with organismal complexity?

1.10 a) List the characteristics of an ideal model organism.

b) Which model organism can be used most efficiently to identify genes related to:

1.11 Refer to Figure 1.8

a) Identify the part of the DNA molecule that would be radioactively labeled in the manner used by Hershey & Chase

b) DNA helices that are rich in G-C base pairs are harder to separate (e.g. by heating) than A-T rich helices. Why?


Discussion

We present a computational framework called DEPICT, which enables gene prioritization, gene set enrichment analysis and tissue/cell type enrichment analysis to generate specific testable hypotheses that are critical to inform experimental follow-up of GWAS. DEPICT implements these three distinct functionalities into a single, publicly available tool. Apart from providing useful insights into pathways and biological annotations of relevance to a phenotype, a key application of the gene set enrichment functionality is to use it for selecting in vitro phenotypes that may serve as readouts in cellular assays used to validate prioritized genes for a complex trait. A key advantage of DEPICT over existing tools is the gene set reconstitution, which enables prioritization of previously poorly annotated genes, as well as more specific and powerful gene set enrichment analysis. By using data sets and methods that are not specific to any particular disease or trait, DEPICT does not depend on phenotype-specific hypotheses (for example, particular neuronal gene sets being important for schizophrenia).

On the basis of our current experience, we recommend employing DEPICT on genome-wide significant loci as well as all loci with association P values <10 −5 (see Supplementary Fig. 10 for results based on LDL loci using the relaxed threshold and for an example on visualizing DEPICT results). We also recommend a locus definition of r 2 >0.5 from lead SNPs. It is important to note that reconstituted gene sets should be interpreted in light of the genes that are mapped to them, rather than strictly by their identifiers (which are carried over from the predefined gene sets).

Despite DEPICT’s ability to identify relevant gene sets for a large number of traits and diseases, the method may be less sensitive to phenotypes caused by genes that have specialized functions that cannot be well predicted based on integrating gene expression data with the currently existing predefined gene sets. Indeed, there are multiple ways in which the DEPICT framework could be improved further. Additional future work includes iteratively conditioning on significant genes, gene sets and tissue/cell types to enhance prioritization of genes with weaker, yet significant, relationships, and quantification of the relative importance of significant predictions. Additional expression data would enhance the data sources available for DEPICT, especially for prioritization of tissues/cell types. Other data types, such as epigenetic data, have yet to be integrated into the DEPICT framework, and DEPICT does not yet use information that could further prioritize genes within loci, such as LD with eQTLs or missense variation, or being the nearest gene to the lead SNP. Finally, DEPICT is currently optimized for GWAS results, but could be adapted to other types of data sets (custom arrays, exome chip or sequencing).

In conclusion, there is a need for approaches that are not driven by phenotype-specific hypotheses and that consider multiple lines of complementary evidence to accomplish gene prioritization, pathway analysis and tissue/cell type enrichment analysis. We have developed a computational and publicly available tool—DEPICT—that can address this need by performing integrative analysis, thereby generating novel, testable hypotheses from genetic association studies across a wide spectrum of traits and diseases.


Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.4915581.

Published by the Royal Society. All rights reserved.

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Author information

Affiliations

Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania, USA

Cheemeng Tan, Russell Schwartz & Philip LeDuc

Department of Chemistry, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania, USA

Saumya Saurabh & Marcel P. Bruchez

Molecular Biosensor and Imaging Center, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania, USA

Saumya Saurabh & Marcel P. Bruchez

Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania, USA

Marcel P. Bruchez, Russell Schwartz & Philip LeDuc

Department of Mechanical Engineering and Biomedical Engineering, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania, USA


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