New genomes are being sequenced at an increasingly rapid rate, far outpacing the rate at which manual gene annotation can be performed. Automated genome annotation is thus necessitated by this growth in genome projects; however, full-fledged annotation systems are usually home-grown and customized to a particular genome. There is thus a renewed need for accurate ab initio gene prediction methods. However, it is apparent that fully ab initio methods fall short of the required level of sensitivity and specificity for a quality annotation. Evidence in the form of expressed sequences gives the single biggest improvement in accuracy when used to inform gene predictions. Here, we present a lightweight pipeline for first-pass gene prediction on newly sequenced genomes. The two main components are ASPic, a program that derives highly accurate, albeit not necessarily complete, EST-based transcript annotations from EST alignments, and GeneID, a standard gene prediction program, which we have modified to take as evidence intron annotations. The introns output by ASPic CDS predictions is given to GeneID to constrain the exon-chaining process and produce predictions consistent with the underlying EST alignments. The pipeline was successfully tested on the entire C. elegans genome and the 44 ENCODE human pilot regions.
Eukaryotic topoisomerase II (topo II) is the essential decatenase of newly replicated chromosomes and the main relaxase of nucleosomal DNA. Apart from these general tasks, topo II participates in more specialized functions. In mammals, topo IIα interacts with specific RNA polymerases and chromatin-remodeling complexes, whereas topo IIβ regulates developmental genes in conjunction with chromatin remodeling and heterochromatin transitions. Here we show that in budding yeast, topo II regulates the expression of specific gene subsets. To uncover this, we carried out a genomic transcription run-on shortly after the thermal inactivation of topo II. We identified a modest number of genes not involved in the general stress response but strictly dependent on topo II. These genes present distinctive functional and structural traits in comparison with the genome average. Yeast topo II is a positive regulator of genes with well-defined promoter architecture that associates to chromatin remodeling complexes; it is a negative regulator of genes extremely hypo-acetylated with complex promoters and undefined nucleosome positioning, many of which are involved in polyamine transport. These findings indicate that yeast topo II operates on singular chromatin architectures to activate or repress DNA transcription and that this activity produces functional responses to ensure chromatin stability.
The study of transcription using genomic tiling arrays has lead to the identification of numerous additional exons. One example is the MECP2 gene on the X chromosome; using 5’RACE and RT-PCR in human tissues and cell lines, we have found more than 70 novel exons (RACEfrags) connecting to at least one annotated exon.. We sequenced all MECP2-connected exons and flanking sequences in 3 groups: 46 patients with the Rett syndrome and without mutations in the currently annotated exons of the MECP2 and CDKL5 genes; 32 patients with the Rett syndrome and identified mutations in the MECP2 gene; 100 control individuals from the same geoethnic group. Approximately 13kb were sequenced per sample, (2.4Mb of DNA resequencing). A total of 75 individuals had novel rare variants (mostly private variants) but no statistically significant difference was found among the 3 groups. These results suggest that variants in the newly discovered exons may not contribute to Rett syndrome. Interestingly however, there are about twice more variants in the novel exons than in the flanking sequences (44 vs. 21 for approximately 1.3 Mb sequenced for each class of sequences, p = 0.0025). Thus the evolutionary forces that shape these novel exons may be different than those of neighboring sequences.
MECP2; Rett syndrome; RACEfrags; SNP; rare variants; positive selection
Although protein recognition of DNA motifs in promoter regions has been traditionally considered as a critical regulatory element in transcription, the location of promoters, and in particular transcription start sites (TSSs), still remains a challenge. Here we perform a comprehensive analysis of putative core promoter sequences relative to non-annotated predicted TSSs along the human genome, which were defined by distinct DNA physical properties implemented in our ProStar computational algorithm. A representative sampling of predicted regions was subjected to extensive experimental validation and analyses. Interestingly, the vast majority proved to be transcriptionally active despite the lack of specific sequence motifs, indicating that physical signaling is indeed able to detect promoter activity beyond conventional TSS prediction methods. Furthermore, highly active regions displayed typical chromatin features associated to promoters of housekeeping genes. Our results enable to redefine the promoter signatures and analyze the diversity, evolutionary conservation and dynamic regulation of human core promoters at large-scale. Moreover, the present study strongly supports the hypothesis of an ancient regulatory mechanism encoded by the intrinsic physical properties of the DNA that may contribute to the complexity of transcription regulation in the human genome.
Motivation: The avalanche of data arriving since the development of NGS technologies have prompted the need for developing fast, accurate and easily automated bioinformatic tools capable of dealing with massive datasets. Among the most productive applications of NGS technologies is the sequencing of cellular RNA, known as RNA-Seq. Although RNA-Seq provides similar or superior dynamic range than microarrays at similar or lower cost, the lack of standard and user-friendly pipelines is a bottleneck preventing RNA-Seq from becoming the standard for transcriptome analysis.
Results: In this work we present a pipeline for processing and analyzing RNA-Seq data, that we have named Grape (Grape RNA-Seq Analysis Pipeline Environment). Grape supports raw sequencing reads produced by a variety of technologies, either in FASTA or FASTQ format, or as prealigned reads in SAM/BAM format. A minimal Grape configuration consists of the file location of the raw sequencing reads, the genome of the species and the corresponding gene and transcript annotation.
Grape first runs a set of quality control steps, and then aligns the reads to the genome, a step that is omitted for prealigned read formats. Grape next estimates gene and transcript expression levels, calculates exon inclusion levels and identifies novel transcripts.
Grape can be run on a single computer or in parallel on a computer cluster. It is distributed with specific mapping and quantification tools, but given its modular design, any tool supporting popular data interchange formats can be integrated.
Availability: Grape can be obtained from the Bioinformatics and Genomics website at: http://big.crg.cat/services/grape.
firstname.lastname@example.org or email@example.com
Motivation: Novel technologies brought in unprecedented amounts of
high-throughput sequencing data along with great challenges in their analysis and
interpretation. The percent-spliced-in (PSI, )
metric estimates the incidence of single-exon–skipping events and can be computed
directly by counting reads that align to known or predicted splice junctions. However, the
majority of human splicing events are more complex than single-exon skipping.
Results: In this short report, we present a framework that generalizes the
metric to arbitrary classes of splicing
events. We change the view from exon centric to intron centric and split the value of
into two indices,
measuring the rate of splicing at the 5′ and 3′ end of the intron,
respectively. The advantage of having two separate indices is that they deconvolute two
distinct elementary acts of the splicing reaction. The completeness of splicing index is
decomposed in a similar way. This framework is implemented as
bam2ssj, a BAM-file–processing pipeline for strand-specific
counting of reads that align to splice junctions or overlap with splice sites. It can be
used as a consistent protocol for quantifying splice junctions from RNA-seq data because
no such standard procedure currently exists.
Availability: The C
code of bam2ssj is open source and is available at https://github.com/pervouchine/bam2ssj
High-throughput sequencing of cDNA libraries constructed from cellular RNA complements (RNA-Seq) naturally provides a digital quantitative measurement for every expressed RNA molecule. Nature, impact and mutual interference of biases in different experimental setups are, however, still poorly understood—mostly due to the lack of data from intermediate protocol steps. We analysed multiple RNA-Seq experiments, involving different sample preparation protocols and sequencing platforms: we broke them down into their common—and currently indispensable—technical components (reverse transcription, fragmentation, adapter ligation, PCR amplification, gel segregation and sequencing), investigating how such different steps influence abundance and distribution of the sequenced reads. For each of those steps, we developed universally applicable models, which can be parameterised by empirical attributes of any experimental protocol. Our models are implemented in a computer simulation pipeline called the Flux Simulator, and we show that read distributions generated by different combinations of these models reproduce well corresponding evidence obtained from the corresponding experimental setups. We further demonstrate that our in silico RNA-Seq provides insights about hidden precursors that determine the final configuration of reads along gene bodies; enhancing or compensatory effects that explain apparently controversial observations can be observed. Moreover, our simulations identify hitherto unreported sources of systematic bias from RNA hydrolysis, a fragmentation technique currently employed by most RNA-Seq protocols.
Previous work has demonstrated that chromatin feature levels correlate with gene expression. The ENCODE project enables us to further explore this relationship using an unprecedented volume of data. Expression levels from more than 100,000 promoters were measured using a variety of high-throughput techniques applied to RNA extracted by different protocols from different cellular compartments of several human cell lines. ENCODE also generated the genome-wide mapping of eleven histone marks, one histone variant, and DNase I hypersensitivity sites in seven cell lines.
We built a novel quantitative model to study the relationship between chromatin features and expression levels. Our study not only confirms that the general relationships found in previous studies hold across various cell lines, but also makes new suggestions about the relationship between chromatin features and gene expression levels. We found that expression status and expression levels can be predicted by different groups of chromatin features, both with high accuracy. We also found that expression levels measured by CAGE are better predicted than by RNA-PET or RNA-Seq, and different categories of chromatin features are the most predictive of expression for different RNA measurement methods. Additionally, PolyA+ RNA is overall more predictable than PolyA- RNA among different cell compartments, and PolyA+ cytosolic RNA measured with RNA-Seq is more predictable than PolyA+ nuclear RNA, while the opposite is true for PolyA- RNA.
Our study provides new insights into transcriptional regulation by analyzing chromatin features in different cellular contexts.
We present a fast mapping-based algorithm to compute the mappability of each region of a reference genome up to a specified number of mismatches. Knowing the mappability of a genome is crucial for the interpretation of massively parallel sequencing experiments. We investigate the properties of the mappability of eukaryotic DNA/RNA both as a whole and at the level of the gene family, providing for various organisms tracks which allow the mappability information to be visually explored. In addition, we show that mappability varies greatly between species and gene classes. Finally, we suggest several practical applications where mappability can be used to refine the analysis of high-throughput sequencing data (SNP calling, gene expression quantification and paired-end experiments). This work highlights mappability as an important concept which deserves to be taken into full account, in particular when massively parallel sequencing technologies are employed. The GEM mappability program belongs to the GEM (GEnome Multitool) suite of programs, which can be freely downloaded for any use from its website (http://gemlibrary.sourceforge.net).
The transcriptome of a cell is represented by a myriad of different RNA molecules with and without protein-coding capacities. In recent years, advances in sequencing technologies have allowed researchers to more fully appreciate the complexity of whole transcriptomes, showing that the vast majority of the genome is transcribed, producing a diverse population of non-protein coding RNAs (ncRNAs). Thus, the biological significance of non-coding RNAs (ncRNAs) have been largely underestimated. Amongst these multiple classes of ncRNAs, the long non-coding RNAs (lncRNAs) are apparently the most numerous and functionally diverse. A small but growing number of lncRNAs have been experimentally studied, and a view is emerging that these are key regulators of epigenetic gene regulation in mammalian cells. LncRNAs have already been implicated in human diseases such as cancer and neurodegeneration, highlighting the importance of this emergent field. In this article, we review the catalogs of annotated lncRNAs and the latest advances in our understanding of lncRNAs.
non-coding RNAs; regulation; long non-coding RNA; epigenetics
The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5′ and 3′ transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network.
Many genomic alterations associated to human diseases localize in non-coding regulatory elements located far from the promoters they regulate, making the association of non-coding mutations or risk associated variants to target genes challenging. The range of action of a given set of enhancers is thought to be defined by insulator elements bound by CTCF. Here, we analyzed the genomic distribution of CTCF in various human, mouse and chicken cell types, demonstrating the existence of evolutionarily conserved CTCF-bound sites beyond mammals. These sites preferentially flank transcription factor-encoding genes, often associated to human diseases, and function as enhancer blockers in vivo, suggesting that they act as evolutionary invariant gene boundaries. We then applied this concept to predict and functionally demonstrate that the polymorphic variants associated to multiple sclerosis located within the EVI5 gene are actually impinging on the adjacent gene GFI1.
Alternative splicing (AS) has the potential to greatly expand the functional repertoire of mammalian transcriptomes. However, few variant transcripts have been characterized functionally, making it difficult to assess the contribution of AS to the generation of phenotypic complexity and to study the evolution of splicing patterns. We have compared the AS of 309 protein-coding genes in the human ENCODE pilot regions against their mouse orthologs in unprecedented detail, utilizing traditional transcriptomic and RNAseq data. The conservation status of every transcript has been investigated, and each functionally categorized as coding (separated into coding sequence [CDS] or nonsense-mediated decay [NMD] linked) or noncoding. In total, 36.7% of human and 19.3% of mouse coding transcripts are species specific, and we observe a 3.6 times excess of human NMD transcripts compared with mouse; in contrast to previous studies, the majority of species-specific AS is unlinked to transposable elements. We observe one conserved CDS variant and one conserved NMD variant per 2.3 and 11.4 genes, respectively. Subsequently, we identify and characterize equivalent AS patterns for 22.9% of these CDS or NMD-linked events in nonmammalian vertebrate genomes, and our data indicate that functional NMD-linked AS is more widespread and ancient than previously thought. Furthermore, although we observe an association between conserved AS and elevated sequence conservation, as previously reported, we emphasize that 30% of conserved AS exons display sequence conservation below the average score for constitutive exons. In conclusion, we demonstrate the value of detailed comparative annotation in generating a comprehensive set of AS transcripts, increasing our understanding of AS evolution in vertebrates. Our data supports a model whereby the acquisition of functional AS has occurred throughout vertebrate evolution and is considered alongside amino acid change as a key mechanism in gene evolution.
alternative splicing; nonsense-mediated decay; vertebrate evolution; RBM39
Recent advances in the field of high-throughput genomics have rendered possible the performance of genome-scale studies to define the nucleosomal landscapes of eukaryote genomes. Such analyses are aimed towards providing a better understanding of the process of nucleosome positioning, for which several models have been suggested. Nevertheless, questions regarding the sequence constraints of nucleosomal DNA and how they may have been shaped through evolution remain open. In this paper, we analyze in detail different experimental nucleosome datasets with the aim of providing a hypothesis for the emergence of nucleosome-forming sequences.
We compared the complete sets of nucleosome positions for the budding yeast (Saccharomyces cerevisiae) as defined in the output of two independent experiments with the use of two different experimental techniques. We found that < 10% of the experimentally defined nucleosome positions were consistently positioned in both datasets. This subset of well-positioned nucleosomes, when compared with the bulk, was shown to have particular properties at both sequence and structural levels. Consistently positioned nucleosomes were also shown to occur preferentially in pairs of dinucleosomes, and to be surprisingly less conserved compared with their adjacent nucleosome-free linkers.
Our findings may be combined into a hypothesis for the emergence of a weak nucleosome-positioning code. According to this hypothesis, consistent nucleosomes may be partly guided by nearby nucleosome-free regions through statistical positioning. Once established, a set of well-positioned consistent nucleosomes may impose secondary constraints that further shape the structure of the underlying DNA. We were able to capture these constraints through the application of a recently introduced structural property that is related to the symmetry of DNA curvature. Furthermore, we found that both consistently positioned nucleosomes and their adjacent nucleosome-free regions show an increased tendency towards conservation of this structural feature.
Selenocysteine (Sec), the 21st amino acid, is incorporated into proteins through the recoding of a termination codon, an inefficient translational process mediated by a complex molecular machinery. Sec is a rare amino acid in extant proteins, chemically similar to cysteine (Cys), found in homologous position to Cys of nonselenoprotein families. Selenoproteins account for the dependence of vertebrates on environmental selenium (Se) and have an important role in several Se-deficiency diseases. Selenoproteins are poorly characterized enzymes and reports on the functional exchangeability of Sec with Cys are limited and controversial. Whether the unique role of Sec in some selenoenzymes illustrates the broader contribution of Se to protein function is unknown (Gromer S, Johansson L, Bauer H, Arscott LD, Rauch S, Ballou DP, Williams CH Jr, Schirmer RH, Arnér ES. 2003. Active sites of thioredoxin reductases: why selenoproteins? Proc Natl Acad Sci USA. 100:12618–12623). Here, we address this question from an evolutionary perspective by the simultaneous identification of the patterns of divergence in almost half a billion years of vertebrate evolution and diversity within the human lineage for the full complement of enzymatic Sec residues in these proteomes. We complete this analysis with data for the homologous Cys residues in the same genomes. Our results indicate concerted purifying selection across Sec and Cys sites in all selenoproteomes, consistent with a unique role of Sec in protein function, low exchangeability, and an unknown degree of functional divergence with Cys homologs. The distinct biochemical properties of Sec, rather than the geographical distribution of Se, global O2 levels or Sec metabolic cost, appear to play a major role in driving adaptive changes in vertebrate selenoproteomes. A better understanding of the selenoproteomes and neutral evolutionary patterns in other taxa will be necessary to fully assess the generality of this conclusion.
selenium; selenocysteine; cysteine; selenoproteins; vertebrates; exchangeability
A review of the main computational pipelines used to generate the human reference protein-coding gene sets.
The vast majority of the biology of a newly sequenced genome is inferred from the set of encoded proteins. Predicting this set is therefore invariably the first step after the completion of the genome DNA sequence. Here we review the main computational pipelines used to generate the human reference protein-coding gene sets.
Heterozygous HNF1A mutations cause pancreatic-islet β-cell dysfunction and monogenic diabetes (MODY3). Hnf1α is known to regulate numerous hepatic genes, yet knowledge of its function in pancreatic islets is more limited. We now show that Hnf1a deficiency in mice leads to highly tissue-specific changes in the expression of genes involved in key functions of both islets and liver. To gain insights into the mechanisms of tissue-specific Hnf1α regulation, we integrated expression studies of Hnf1a-deficient mice with identification of direct Hnf1α targets. We demonstrate that Hnf1α can bind in a tissue-selective manner to genes that are expressed only in liver or islets. We also show that Hnf1α is essential only for the transcription of a minor fraction of its direct-target genes. Even among genes that were expressed in both liver and islets, the subset of targets showing functional dependence on Hnf1α was highly tissue specific. This was partly explained by the compensatory occupancy by the paralog Hnf1β at selected genes in Hnf1a-deficient liver. In keeping with these findings, the biological consequences of Hnf1a deficiency were markedly different in islets and liver. Notably, Hnf1a deficiency led to impaired large-T-antigen-induced growth and oncogenesis in β cells yet enhanced proliferation in hepatocytes. Collectively, these findings show that Hnf1α governs broad, highly tissue-specific genetic programs in pancreatic islets and liver and reveal key consequences of Hnf1a deficiency relevant to the pathophysiology of monogenic diabetes.
RACE (Rapid Amplification of cDNA Ends) is a widely used approach for transcript identification. Random clone selection from the RACE mixture, however, is an ineffective sampling strategy if the dynamic range of transcript abundances is large. Here, we describe a strategy that uses array hybridization to improve sampling efficiency of human transcripts. The products of the RACE reaction are hybridized onto tiling arrays, and the exons detected are used to delineate a series of RT-PCR reactions, through which the original RACE mixture is segregated into simpler RT-PCR reactions. These are independently cloned, and randomly selected clones are sequenced. This approach is superior to direct cloning and sequencing of RACE products: it specifically targets novel transcripts, and often results in overall normalization of transcript abundances. We show theoretically and experimentally that this strategy leads indeed to efficient sampling of novel transcripts, and we investigate multiplexing it by pooling RACE reactions from multiple interrogated loci prior to hybridization.
Despite decades of research, the question of how the mRNA splicing machinery precisely identifies short exonic islands within the vast intronic oceans remains to a large extent obscure. In this study, we analyzed Alu exonization events, aiming to understand the requirements for correct selection of exons. Comparison of exonizing Alus to their non-exonizing counterparts is informative because Alus in these two groups have retained high sequence similarity but are perceived differently by the splicing machinery. We identified and characterized numerous features used by the splicing machinery to discriminate between Alu exons and their non-exonizing counterparts. Of these, the most novel is secondary structure: Alu exons in general and their 5′ splice sites (5′ss) in particular are characterized by decreased stability of local secondary structures with respect to their non-exonizing counterparts. We detected numerous further differences between Alu exons and their non-exonizing counterparts, among others in terms of exon–intron architecture and strength of splicing signals, enhancers, and silencers. Support vector machine analysis revealed that these features allow a high level of discrimination (AUC = 0.91) between exonizing and non-exonizing Alus. Moreover, the computationally derived probabilities of exonization significantly correlated with the biological inclusion level of the Alu exons, and the model could also be extended to general datasets of constitutive and alternative exons. This indicates that the features detected and explored in this study provide the basis not only for precise exon selection but also for the fine-tuned regulation thereof, manifested in cases of alternative splicing.
A typical human gene consists of 9 exons around 150 nucleotides in length, separated by introns that are ∼3,000 nucleotides long. The challenge of the splicing machinery is to precisely identify and ligate the exons, while removing the introns. We aimed to understand how the splicing machinery meets this momentous challenge, based on Alu exonization events. Alus are transposable elements, of which approximately one million copies exist in the human genome, a large portion of which within introns. Throughout evolution, some intronic Alus accumulated mutations and became recognized by the splicing machinery as exons, a process termed exonization. Such Alus remain highly similar to their non-exonizing counterparts but are perceived as different by the splicing machinery. By comparing exonizing Alus to their non-exonizing counterparts, we were able to identify numerous features in which they differ and which presumably lead to the recognition only of the former by the splicing machinery. Our findings reveal insights regarding the role of local RNA secondary structures, exon–intron architecture constraints, and splicing regulatory signals. We integrated these features in a computational model, which was able to successfully mimic the function of the splicing machinery and discriminate between true Alu exons and their intronic counterparts, highlighting the functional importance of these features.
Summary: Selenoproteins contain the 21st amino acid selenocysteine which is encoded by an inframe UGA codon, usually read as a stop. In eukaryotes, its co-translational recoding requires the presence of an RNA stem–loop structure, the SECIS element in the 3 untranslated region of (UTR) selenoprotein mRNAs. Despite little sequence conservation, SECIS elements share the same overall secondary structure. Until recently, the lack of a significantly high number of selenoprotein mRNA sequences hampered the identification of other potential sequence conservation. In this work, the web-based tool SECISaln provides for the first time an extensive structure-based sequence alignment of SECIS elements resulting from the well-defined secondary structure of the SECIS RNA and the increased size of the eukaryotic selenoproteome. We have used SECISaln to improve our knowledge of SECIS secondary structure and to discover novel, conserved nucleotide positions and we believe it will be a useful tool for the selenoprotein and RNA scientific communities.
Availability: SECISaln is freely available as a web-based tool at http://genome.crg.es/software/secisaln/.
Supplementary information: Supplementary data are available at Bioinformatics online.
Transcriptional analysis of chromatin regulator mutants in Drosophila melanogaster identified clusters of functionally related genes conserved in other insect species.
The trithorax group (trxG) and Polycomb group (PcG) proteins are responsible for the maintenance of stable transcriptional patterns of many developmental regulators. They bind to specific regions of DNA and direct the post-translational modifications of histones, playing a role in the dynamics of chromatin structure.
We have performed genome-wide expression studies of trx and ash2 mutants in Drosophila melanogaster. Using computational analysis of our microarray data, we have identified 25 clusters of genes potentially regulated by TRX. Most of these clusters consist of genes that encode structural proteins involved in cuticle formation. This organization appears to be a distinctive feature of the regulatory networks of TRX and other chromatin regulators, since we have observed the same arrangement in clusters after experiments performed with ASH2, as well as in experiments performed by others with NURF, dMyc, and ASH1. We have also found many of these clusters to be significantly conserved in D. simulans, D. yakuba, D. pseudoobscura and partially in Anopheles gambiae.
The analysis of genes governed by chromatin regulators has led to the identification of clusters of functionally related genes conserved in other insect species, suggesting this chromosomal organization is biologically important. Moreover, our results indicate that TRX and other chromatin regulators may act globally on chromatin domains that contain transcriptionally co-regulated genes.
Selenoproteins are a diverse family of proteins notable for the presence of the 21st amino acid, selenocysteine. Until very recently, all metazoan genomes investigated encoded selenoproteins, and these proteins had therefore been believed to be essential for animal life. Challenging this assumption, recent comparative analyses of insect genomes have revealed that some insect genomes appear to have lost selenoprotein genes.
In this paper we investigate in detail the fate of selenoproteins, and that of selenoprotein factors, in all available arthropod genomes. We use a variety of in silico comparative genomics approaches to look for known selenoprotein genes and factors involved in selenoprotein biosynthesis. We have found that five insect species have completely lost the ability to encode selenoproteins and that selenoprotein loss in these species, although so far confined to the Endopterygota infraclass, cannot be attributed to a single evolutionary event, but rather to multiple, independent events. Loss of selenoproteins and selenoprotein factors is usually coupled to the deletion of the entire no-longer functional genomic region, rather than to sequence degradation and consequent pseudogenisation. Such dynamics of gene extinction are consistent with the high rate of genome rearrangements observed in Drosophila. We have also found that, while many selenoprotein factors are concomitantly lost with the selenoproteins, others are present and conserved in all investigated genomes, irrespective of whether they code for selenoproteins or not, suggesting that they are involved in additional, non-selenoprotein related functions.
Selenoproteins have been independently lost in several insect species, possibly as a consequence of the relaxation in insects of the selective constraints acting across metazoans to maintain selenoproteins. The dispensability of selenoproteins in insects may be related to the fundamental differences in antioxidant defense between these animals and other metazoans.
Understanding the molecular mechanisms responsible for the regulation of the transcriptome present in eukaryotic cells is one of the most challenging tasks in the postgenomic era. In this regard, alternative splicing (AS) is a key phenomenon contributing to the production of different mature transcripts from the same primary RNA sequence. As a plethora of different transcript forms is available in databases, a first step to uncover the biology that drives AS is to identify the different types of reflected splicing variation. In this work, we present a general definition of the AS event along with a notation system that involves the relative positions of the splice sites. This nomenclature univocally and dynamically assigns a specific “AS code” to every possible pattern of splicing variation. On the basis of this definition and the corresponding codes, we have developed a computational tool (AStalavista) that automatically characterizes the complete landscape of AS events in a given transcript annotation of a genome, thus providing a platform to investigate the transcriptome diversity across genes, chromosomes, and species. Our analysis reveals that a substantial part—in human more than a quarter—of the observed splicing variations are ignored in common classification pipelines. We have used AStalavista to investigate and to compare the AS landscape of different reference annotation sets in human and in other metazoan species and found that proportions of AS events change substantially depending on the annotation protocol, species-specific attributes, and coding constraints acting on the transcripts. The AStalavista system therefore provides a general framework to conduct specific studies investigating the occurrence, impact, and regulation of AS.
The genome sequence is said to be an organism's blueprint, a set of instructions driving the organism's biology. The unfolding of these instructions—the so-called genes—is initiated by the transcription of DNA into RNA molecules, which subsequently are processed before they can take their functional role. During this processing step, initially identical RNA molecules may result in different products through a process known as alternative splicing (AS). AS therefore allows for widening the diversity from the limited repertoire of genes, and it is often postulated as an explanation for the apparent paradox that complex and simple organisms resemble in their number of genes; it characterizes species, individuals, and developmental and cellular conditions. Comparing the differences of AS products between cells may help to reveal the broad molecular basis underlying phenotypic differences—for instance, between a cancer and a normal cell. An obstacle for such comparisons has been that, so far, no paradigm existed to delineate each single quantum of AS, so-called AS events. Here, we describe a possibility of exhaustively decomposing AS complements into qualitatively different groups of events and a nomenclature to unequivocally denote them. This typological catalogue of AS events along with their observed frequencies represent the AS landscape, and we propose a procedure to automatically identify such landscapes. We use it to describe the human AS landscape and to investigate how it has changed throughout evolution.
Comparative genomics of multiple related species is a powerful methodology for the discovery of functional genomic elements, and its power should increase with the number of species compared. Here, we use 12 Drosophila genomes to study the power of comparative genomics metrics to distinguish between protein-coding and non-coding regions. First, we study the relative power of different comparative metrics and their relationship to single-species metrics. We find that even relatively simple multi-species metrics robustly outperform advanced single-species metrics, especially for shorter exons (≤240 nt), which are common in animal genomes. Moreover, the two capture largely independent features of protein-coding genes, with different sensitivity/specificity trade-offs, such that their combinations lead to even greater discriminatory power. In addition, we study how discovery power scales with the number and phylogenetic distance of the genomes compared. We find that species at a broad range of distances are comparably effective informants for pairwise comparative gene identification, but that these are surpassed by multi-species comparisons at similar evolutionary divergence. In particular, while pairwise discovery power plateaued at larger distances and never outperformed the most advanced single-species metrics, multi-species comparisons continued to benefit even from the most distant species with no apparent saturation. Last, we find that genes in functional categories typically considered fast-evolving can nonetheless be recovered at very high rates using comparative methods. Our results have implications for comparative genomics analyses in any species, including the human.
Comparing the genomes of related species is a powerful approach to the discovery of functional elements such as protein-coding genes. Theoretically, using more species should lead to more discovery power. Many questions remain, however, surrounding the optimal choice of species to compare and how to best use multi-species alignments. It is even possible that practical limitations in the sequencing, assembly, and alignment of genomes could effectively negate the benefit of using more species. Here, we used 12 complete fly genomes to study a variety of metrics used to identify protein-coding genes, including methods that analyze only the genome of interest and comparative methods that examine evolutionary signatures in genome alignments. We found that species over a surprisingly broad range of phylogenetic distances were effective in comparative analyses, and that discovery power continued to scale with each additional species without apparent saturation. We also examined whether comparative methods systematically miss genes considered fast-evolving, and studied how performance is influenced by genome alignment strategies. Our results can help guide species selection for future comparative studies and provide methodological guidance for a variety of gene identification tasks, including the design of future de novo gene predictors and the search for unusual gene structures.