The genomic features that affect the lengths of 5’ untranslated regions in multicellular eukaryotes

<p>Abstract</p> <p>Background</p> <p>The lengths of 5’UTRs of multicellular eukaryotes have been suggested to be subject to stochastic changes, with upstream start codons (uAUGs) as the major constraint to suppress 5’UTR elongation. However, this stochastic model cannot fully explain the variations in 5’UTR length. We hypothesize that the selection pressure on a combination of genomic features is also important for 5’UTR evolution. The ignorance of these features may have limited the explanatory power of the stochastic model. Furthermore, different selective constraints between vertebrates and invertebrates may lead to differences in the determinants of 5’UTR length, which have not been systematically analyzed.</p> <p>Methods</p> <p>Here we use a multiple linear regression model to delineate the correlation between 5’UTR length and the combination of a series of genomic features (G+C content, observed-to-expected (OE) ratios of uAUGs, upstream stop codons (uSTOPs), methylation-related CG/UG dinucleotides, and mRNA-destabilizing UU/UA dinucleotides) in six vertebrates (human, mouse, rat, chicken, African clawed frog, and zebrafish) and four invertebrates (fruit fly, mosquito, sea squirt, and nematode). The relative contributions of each feature to the variation of 5’UTR length were also evaluated.</p> <p>Results</p> <p>We found that 14%~33% of the 5’UTR length variations can be explained by a linear combination of the analyzed genomic features. The most important genomic features are the OE ratios of uSTOPs and G+C content. The surprisingly large weightings of uSTOPs highlight the importance of selection on upstream open reading frames (which include both uAUGs and uSTOPs), rather than on uAUGs <it>per se</it>. Furthermore, G+C content is the most important determinants for most invertebrates, but for vertebrates its effect is second to uSTOPs. We also found that shorter 5’UTRs are affected more by the stochastic process, whereas longer 5’UTRs are affected more by selection pressure on genomic features.</p> <p>Conclusions</p> <p>Our results suggest that upstream open reading frames may be the real target of selection, rather than uAUGs. We also show that the selective constraints on genomic features of 5’UTRs differ between vertebrates and invertebrates, and between longer and shorter 5’UTRs. A more comprehensive model that takes these findings into consideration is needed to better explain 5’UTR length evolution.</p

Alu distribution and mutation types of cancer genes

Background: Alu elements are the most abundant retrotransposable elements comprising ~11% of the human genome. Many studies have highlighted the role that Alu elements have in genetic instability and how their contribution to the assortment of mutagenic events can lead to cancer. As of yet, little has been done to quantitatively assess the association between Alu distribution and genes that are causally implicated in oncogenesis.Results: We have investigated the effect of various Alu densities on the mutation type based classifications of cancer genes. In order to establish the direct relationship between Alus and the cancer genes of interest, genome wide Alu-related densities were measured using genes rather than the sliding windows of fixed length as the units. Several novel genomic features, such as the density of the adjacent Alu pairs and the number of Alu-Exon-Alu triplets, were developed in order to extend the investigation via the multivariate statistical analysis toward more advanced biological insight. In addition, we characterized the genome-wide intron Alu distribution with a mixture model that distinguished genes containing Alu elements from those with no Alus, and evaluated the gene-level effect of the 5\u27-TTAAAA motif associated with Alu insertion sites using a two-step regression analysis method.Conclusions: The study resulted in several novel findings worthy of further investigation. They include: (1) Recessive cancer genes (tumor suppressor genes) are enriched with Alu elements (p \u3c 0.01) compared to dominant cancer genes (oncogenes) and the entire set of genes in the human genome; (2) Alu-related genomic features can be used to cluster cancer genes into biological meaningful groups; (3) The retention of exon Alus has been restricted in the human genome development, and an upper limit to the chromosome-level exon Alu densities is suggested by the distribution profile; (4) For the genes with at least one intron Alu repeat in individual chromosomes, the intron Alu densities can be well fitted by a Gamma distribution; (5) The effect of the 5\u27-TTAAAA motif on Alu densities varies across different chromosomes

Recombination Drives Vertebrate Genome Contraction

Selective and/or neutral processes may govern variation in DNA content and, ultimately, genome size. The observation in several organisms of a negative correlation between recombination rate and intron size could be compatible with a neutral model in which recombination is mutagenic for length changes. We used whole-genome data on small insertions and deletions within transposable elements from chicken and zebra finch to demonstrate clear links between recombination rate and a number of attributes of reduced DNA content. Recombination rate was negatively correlated with the length of introns, transposable elements, and intergenic spacer and with the rate of short insertions. Importantly, it was positively correlated with gene density, the rate of short deletions, the deletion bias, and the net change in sequence length. All these observations point at a pattern of more condensed genome structure in regions of high recombination. Based on the observed rates of small insertions and deletions and assuming that these rates are representative for the whole genome, we estimate that the genome of the most recent common ancestor of birds and lizards has lost nearly 20% of its DNA content up until the present. Expansion of transposable elements can counteract the effect of deletions in an equilibrium mutation model; however, since the activity of transposable elements has been low in the avian lineage, the deletion bias is likely to have had a significant effect on genome size evolution in dinosaurs and birds, contributing to the maintenance of a small genome. We also demonstrate that most of the observed correlations between recombination rate and genome contraction parameters are seen in the human genome, including for segregating indel polymorphisms. Our data are compatible with a neutral model in which recombination drives vertebrate genome size evolution and gives no direct support for a role of natural selection in this process

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases

BACKGROUND: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25–30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome. METHODS: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants. RESULTS: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving. CONCLUSIONS: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases.

BACKGROUND: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome. METHODS: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants. RESULTS: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving. CONCLUSIONS: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing

A high throughput screen for active human transposable elements.

Transposable elements (TEs) are mobile genetic sequences that randomly propagate within their host's genome. This mobility has the potential to affect gene transcription and cause disease. However, TEs are technically challenging to identify, which complicates efforts to assess the impact of TE insertions on disease. Here we present a targeted sequencing protocol and computational pipeline to identify polymorphic and novel TE insertions using next-generation sequencing: TE-NGS. The method simultaneously targets the three subfamilies that are responsible for the majority of recent TE activity (L1HS, AluYa5/8, and AluYb8/9) thereby obviating the need for multiple experiments and reducing the amount of input material required.Here we describe the laboratory protocol and detection algorithm, and a benchmark experiment for the reference genome NA12878. We demonstrate a substantial enrichment for on-target fragments, and high sensitivity and precision to both reference and NA12878-specific insertions. We report 17 previously unreported loci for this individual which are supported by orthogonal long-read evidence, and we identify 1470 polymorphic and novel TEs in 12 additional samples that were previously undocumented in databases of insertion polymorphisms.We anticipate that future applications of TE-NGS alongside exome sequencing of patients with sporadic disease will reduce the number of unresolved cases, and improve estimates of the contribution of TEs to human genetic disease

The complete costs of genome sequencing: a microcosting study in cancer and rare diseases from a single center in the United Kingdom

Purpose: The translation of genome sequencing into routine health care has been slow, partly because of concerns about affordability. The aspirational cost of sequencing a genome is $1000, but there is little evidence to support this estimate. We estimate the cost of using genome sequencing in routine clinical care in patients with cancer or rare diseases. Methods: We performed a microcosting study of Illumina-based genome sequencing in a UK National Health Service laboratory processing 399 samples/year. Cost data were collected for all steps in the sequencing pathway, including bioinformatics analysis and reporting of results. Sensitivity analysis identified key cost drivers. Results: Genome sequencing costs £6841 per cancer case (comprising matched tumor and germline samples) and £7050 per rare disease case (three samples). The consumables used during sequencing are the most expensive component of testing (68–72 Conclusion: The cost of genome sequencing is underestimated if only sequencing costs are considered, and likely surpasses$1000/genome in a single laboratory. This aspirational sequencing cost will likely only be achieved if consumable costs are considerably reduced and sequencing is performed at scale.</br