Improved techniques for the identification of pseudogenes

Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science

Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel's concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.

Profile hidden Markov model sequence analysis can help remove putative pseudogenes from DNA barcoding and metabarcoding datasets

BACKGROUND

Pseudogenes are non-functional copies of protein coding genes that typically follow a different molecular evolutionary path as compared to functional genes. The inclusion of pseudogene sequences in DNA barcoding and metabarcoding analysis can lead to misleading results. None of the most widely used bioinformatic pipelines used to process marker gene (metabarcode) high throughput sequencing data specifically accounts for the presence of pseudogenes in protein-coding marker genes. The purpose of this study is to develop a method to screen for nuclear mitochondrial DNA segments (nuMTs) in large COI datasets. We do this by: (1) describing gene and nuMT characteristics from an artificial COI barcode dataset, (2) show the impact of two different pseudogene removal methods on perturbed community datasets with simulated nuMTs, and (3) incorporate a pseudogene filtering step in a bioinformatic pipeline that can be used to process Illumina paired-end COI metabarcode sequences. Open reading frame length and sequence bit scores from hidden Markov model (HMM) profile analysis were used to detect pseudogenes.

RESULTS

Our simulations showed that it was more difficult to identify nuMTs from shorter amplicon sequences such as those typically used in metabarcoding compared with full length DNA barcodes that are used in the construction of barcode libraries. It was also more difficult to identify nuMTs in datasets where there is a high percentage of nuMTs. Existing bioinformatic pipelines used to process metabarcode sequences already remove some nuMTs, especially in the rare sequence removal step, but the addition of a pseudogene filtering step can remove up to 5% of sequences even when other filtering steps are in place.

CONCLUSIONS

Open reading frame length filtering alone or combined with hidden Markov model profile analysis can be used to effectively screen out apparent pseudogenes from large datasets. There is more to learn from COI nuMTs such as their frequency in DNA barcoding and metabarcoding studies, their taxonomic distribution, and evolution. Thus, we encourage the submission of verified COI nuMTs to public databases to facilitate future studies.

Revisiting the classification of adhesion GPCRs

G protein–coupled receptors (GPCRs) are encoded by over 800 genes in the human genome. Motivated by different scientific rationales, the two classification systems that are mainly in use, the ABC and GRAFS systems, organize GPCRs according to their pharmacological features and phylogenetic relations, respectively. Within those systems, adhesion GPCRs (aGPCRs) constitute a group of over 30 mammalian homologs, most of which are still orphans with undefined activating signals and signal transduction properties. Previous efforts have further subdivided mammalian aGPCRs into nine subfamilies to indicate phylogenetic relationships. However, this subclassification scheme has shortcomings and inconsistencies that require attention. Here, we have reassessed the phylogenetic relationships of aGPCRs from vertebrate and invertebrate species. Our findings confirm that secretin receptor–like GPCRs most probably emerged from ancestral aGPCRs. We show that reassignment of several aGPCRs to families essentially requires input from functional data. Our analyses establish the need for introducing novel aGPCR subfamilies due to aGPCR sequences from invertebrate species that are not readily assignable to any existing subfamily. We conclude that the current classification systems ought to be updated to consider an unambiguous taxonomy of a hierarchically organized classification and pharmacological properties, and to accommodate phylogenetic affiliations between aGPCR genes within mammals and across the animal kingdom.

Comparative genomic analysis of SET domain family reveals the origin, expansion, and putative function of the arthropod-specific SmydA genes as histone modifiers in insects

The SET domain is an evolutionarily conserved motif present in histone lysine methyltransferases, which are important in the regulation of chromatin and gene expression in animals. In this study, we searched for SET domain–containing genes (SET genes) in all of the 147 arthropod genomes sequenced at the time of carrying out this experiment to understand the evolutionary history by which SET domains have evolved in insects. Phylogenetic and ancestral state reconstruction analysis revealed an arthropod-specific SET gene family, named SmydA, that is ancestral to arthropod animals and specifically diversified during insect evolution. Considering that pseudogenization is the most probable fate of the new emerging gene copies, we provided experimental and evolutionary evidence to demonstrate their essential functions. Fluorescence in situ hybridization analysis and in vitro methyltransferase activity assays showed that the SmydA-2 gene was transcriptionally active and retained the original histone methylation activity. Expression knockdown by RNA interference significantly increased mortality, implying that the SmydA genes may be essential for insect survival. We further showed predominantly strong purifying selection on the SmydA gene family and a potential association between the regulation of gene expression and insect phenotypic plasticity by transcriptome analysis. Overall, these data suggest that the SmydA gene family retains essential functions that may possibly define novel regulatory pathways in insects. This work provides insights into the roles of lineage-specific domain duplication in insect evolution.

The Effect of Botulinum Toxin Type A on Expression Profiling of Long Noncoding RNAs in Human Dermal Fibroblasts

Objective. This study was aimed at analyzing the expressions of long noncoding RNAs (lncRNAs) in Botulinum Toxin Type A (BoNTA) treated human dermal fibroblasts (HDFs) in vitro. Methods. We used RNA sequencing to characterize the lncRNAs and mRNAs transcriptome in the control and BoNTA treated group, in conjunction with application of GO (gene ontology) analysis and KEGG (kyoto encyclopedia of genes and genomes) analysis to delineate the alterations in gene expression. We also obtained quantitative real time polymerase chain reaction (qRT-PCR) to confirm some differentially expressed genes. Results. Numerous differentially expressed genes were observed by microarrays between the two groups. qRT-PCR confirmed the changes of six lncRNAs (RP11-517C16.2-001, FR271872, LOC283352, RP11-401E9.3, FGFR3P, and XXbac-BPG16N22.5) and nine mRNAs (NOS2, C13orf15, FOS, FCN2, SPINT1, PLAC8, BIRC5, NOS2, and COL19A1). Farther studies indicated that the downregulating effect of BoNTA on the expression of FGFR3P was time-related and the dosage of BoNTA at a range from 2.5 U/10⁶ cells to 7.5 U/10⁶ cells increased the expression of FGFR3P and COL19A1 in HDFs as well. Conclusion. The expression profiling of lncRNAs was visibly changed in BoNTA treated HDFs. Further studies should focus on several lncRNAs to investigate their functions in BoNTA treated HDFs and the underlying mechanisms.

Comparative genomics and phylogenomics of hemotrophic mycoplasmas

Hemotrophic mycoplasmas (hemoplasmas) are a group of animal pathogens of the Mollicutes class. Recently, the genomes of 8 hemoplasmas have been completely sequenced. The aim of this study was to gain a better understanding of their genomic features and relationship to other Mycoplasma species. The genome structure and dynamics of hemoplasmas were analyzed by evaluating gene synteny, adaptive evolution of paralogous gene families (PGF) and horizontal gene transfer (HGT). The Mollicutes class was then phylogenetically analyzed by constructing a distance matrix of the 16S rRNA genes and a phylogenetic tree with 32 conserved, concatenated proteins. Our results suggest that the hemoplasmas have dynamic genomes. The genome size variation (from 547 to 1,545 genes) indicates substantial gene gain/loss throughout evolution. Poorly conserved gene syntenies among hemoplasmas, positional shuffling of paralogous genes between strains, HGT, and codons under positive selection in PGFs were also observed. When compared to other Mollicutes species, the hemoplasmas experienced further metabolic reduction, and the 16S rRNA gene distance matrix of the available mollicutes suggests that these organisms presently constitute the most divergent clade within its class. Our phylogenetic tree of concatenated proteins showed some differences when compared to the 16S rRNA gene tree, but non-mycoplasma organisms, such as Ureaplasma spp. and Mesoplasma spp., continue to branch within Mycoplasma clades. In conclusion, while the hemoplasmas experienced further metabolic shrinkage through gene loss, PGFs with positively selected codons are likely beneficial to these species. Phylogeny of the mollicutes based on 16S rRNA genes or concatenated proteins do not obey the current taxonomy. The metabolism and genetic diversity of the mollicutes, the presence of HGT, and lack of standard for genus circumscription are likely to hinder attempts to classify these organisms based on phylogenetic analyses.

Computational methods of identification of pseudogenes based on functionality: entropy and GC content

Spectral entropy and GC content analyses reveal comprehensive structural features of DNA sequences. To illustrate the significance of these features, we analyze the β-esterase gene cluster, including the Est-6 gene and the ψEst-6 putative pseudogene, in seven species of the Drosophila melanogaster subgroup. The spectral entropies show distinctly lower structural ordering for ψEst-6 than for Est-6 in all species studied. However, entropy accumulation is not a completely random process for either gene and it shows to be nucleotide dependent. Furthermore, GC content in synonymous positions is uniformly higher in Est-6 than in ψEst-6, in agreement with the reduced GC content generally observed in pseudogenes and nonfunctional sequences. The observed differences in entropy and GC content reflect an evolutionary shift associated with the process of pseudogenization and subsequent functional divergence of ψEst-6 and Est-6 after the duplication event. The data obtained show the relevance and significance of entropy and GC content analyses for pseudogene identification and for the comparative study of gene-pseudogene evolution.

Frequency matrix approach demonstrates high sequence quality in avian BARCODEs and highlights cryptic pseudogenes

The accuracy of DNA barcode databases is critical for research and practical applications. Here we apply a frequency matrix to assess sequencing errors in a very large set of avian BARCODEs. Using 11,000 sequences from 2,700 bird species, we show most avian cytochrome c oxidase I (COI) nucleotide and amino acid sequences vary within a narrow range. Except for third codon positions, nearly all (96%) sites were highly conserved or limited to two nucleotides or two amino acids. A large number of positions had very low frequency variants present in single individuals of a species; these were strongly concentrated at the ends of the barcode segment, consistent with sequencing error. In addition, a small fraction (0.1%) of BARCODEs had multiple very low frequency variants shared among individuals of a species; these were found to represent overlooked cryptic pseudogenes lacking stop codons. The calculated upper limit of sequencing error was 8 × 10(-5) errors/nucleotide, which was relatively high for direct Sanger sequencing of amplified DNA, but unlikely to compromise species identification. Our results confirm the high quality of the avian BARCODE database and demonstrate significant quality improvement in avian COI records deposited in GenBank over the past decade. This approach has potential application for genetic database quality control, discovery of cryptic pseudogenes, and studies of low-level genetic variation.

A cryptic, intergeneric cytochrome c oxidase I pseudogene in tyrant flycatchers (family: Tyrannidae)

Nuclear mitochondrial pseudogenes, or "numts", are nonfunctional copies of mitochondrial genes that have been translocated to the nuclear genome. Numts have been used to study differences in mutation rates between the nuclear and mitochondrial genomes, but have also been implicated as troublesome for phylogenetic studies and DNA-based species identification (i.e., DNA barcoding). In this study, a suspected numt discovered during a study of mitochondrial cytochrome c oxidase I (COI) diversity in North American birds was targeted and sequenced from tyrant flycatchers (family: Tyrannidae). In total, the numt was found in five taxa representing two genera. Substitution rates were compared between COI and numt sequences. None of the numt sequences harboured stop codons nor frameshift mutations, but phylogenetic analysis revealed they had accumulated more amino acid substitutions than the mitochondrial COI sequences. Mitochondrial COI appeared to be preferentially amplified in most cases, but methods for numt detection are discussed for cases like this where sequences lack obvious features for identification. Because of its persistence across a broad taxonomic lineage, this numt could form a valuable model system for studying evolution in numts. The full size of the numt and its location within the nuclear genome are yet to be determined.

Probabilistic phylogenetic inference with insertions and deletions

A fundamental task in sequence analysis is to calculate the probability of a multiple alignment given a phylogenetic tree relating the sequences and an evolutionary model describing how sequences change over time. However, the most widely used phylogenetic models only account for residue substitution events. We describe a probabilistic model of a multiple sequence alignment that accounts for insertion and deletion events in addition to substitutions, given a phylogenetic tree, using a rate matrix augmented by the gap character. Starting from a continuous Markov process, we construct a non-reversible generative (birth-death) evolutionary model for insertions and deletions. The model assumes that insertion and deletion events occur one residue at a time. We apply this model to phylogenetic tree inference by extending the program dnaml in phylip. Using standard benchmarking methods on simulated data and a new "concordance test" benchmark on real ribosomal RNA alignments, we show that the extended program dnamlepsilon improves accuracy relative to the usual approach of ignoring gaps, while retaining the computational efficiency of the Felsenstein peeling algorithm.

Pseudogenes in the ENCODE regions: consensus annotation, analysis of transcription, and evolution

Arising from either retrotransposition or genomic duplication of functional genes, pseudogenes are "genomic fossils" valuable for exploring the dynamics and evolution of genes and genomes. Pseudogene identification is an important problem in computational genomics, and is also critical for obtaining an accurate picture of a genome's structure and function. However, no consensus computational scheme for defining and detecting pseudogenes has been developed thus far. As part of the ENCyclopedia Of DNA Elements (ENCODE) project, we have compared several distinct pseudogene annotation strategies and found that different approaches and parameters often resulted in rather distinct sets of pseudogenes. We subsequently developed a consensus approach for annotating pseudogenes (derived from protein coding genes) in the ENCODE regions, resulting in 201 pseudogenes, two-thirds of which originated from retrotransposition. A survey of orthologs for these pseudogenes in 28 vertebrate genomes showed that a significant fraction ( approximately 80%) of the processed pseudogenes are primate-specific sequences, highlighting the increasing retrotransposition activity in primates. Analysis of sequence conservation and variation also demonstrated that most pseudogenes evolve neutrally, and processed pseudogenes appear to have lost their coding potential immediately or soon after their emergence. In order to explore the functional implication of pseudogene prevalence, we have extensively examined the transcriptional activity of the ENCODE pseudogenes. We performed systematic series of pseudogene-specific RACE analyses. These, together with complementary evidence derived from tiling microarrays and high throughput sequencing, demonstrated that at least a fifth of the 201 pseudogenes are transcribed in one or more cell lines or tissues.

FGF: a web tool for Fishing Gene Family in a whole genome database

Gene duplication is an important process in evolution. The availability of genome sequences of a number of organisms has made it possible to conduct comprehensive searches for duplicated genes enabling informative studies of their evolution. We have established the FGF (Fishing Gene Family) program to efficiently search for and identify gene families. The FGF output displays the results as visual phylogenetic trees including information on gene structure, chromosome position, duplication fate and selective pressure. It is particularly useful to identify pseudogenes and detect changes in gene structure. FGF is freely available on a web server at http://fgf.genomics.org.cn/

A probabilistic classifier for olfactory receptor pseudogenes

BACKGROUND

Olfactory receptors (ORs), the largest mammalian gene superfamily (900-1400 genes), has >50% pseudogenes in humans. While most of these inactive genes are identified via coding frame (nonsense) disruptions, seemingly intact genes may also be inactive due to other deleterious (missense) mutations. An ultimate assessment of the actual size of the functional human OR repertoire thus requires an accurate distinction between genes and pseudogenes.

RESULTS

To characterize inactive ORs with intact open reading frame, we have developed a probabilistic Classifier for Olfactory Receptor Pseudogenes (CORP). This algorithm is based on deviations from a functionally crucial consensus, constituting sixty highly conserved positions identified by a comparison of two evolutionarily-constrained OR repertoires (mouse and dog) with a small pseudogene fraction. We used a logistic regression analysis to assign appropriate coefficients to the conserved position and thus achieving maximal separation between active and inactive ORs. Consequently, the algorithms identified only 5% of the mouse functional ORs as pseudogenes, setting an upper limit of 0.05 to the false positive detection. Finally we used this algorithm to classify the 384 purportedly intact human OR genes. Of these, 135 were predicted as likely encoding non-functional proteins, and 38 were segregating between active and inactive forms due to missense polymorphisms.

CONCLUSION

We demonstrated that the CORP algorithm is capable to distinguish between functional and non-functional OR genes with high precision even when the encoded protein would differ by a single amino acid. Using the CORP algorithm, we predict that approximately 70% of human OR genes are likely non-functional pseudogenes, a much higher number than hitherto suspected. The method we present may be employed for better annotation of inactive members in other gene families as well. CORP algorithm is available at: http://bioportal.weizmann.ac.il/HORDE/CORP/

Statistical alignment of retropseudogenes and their functional paralogs

We describe a model for the sequence evolution of a processed pseudogene and its paralog from a common protein-coding ancestor. The model accounts for substitutions, insertions, and deletions and combines nucleotide- and codon-level mutation models. We give a dynamic programming method for calculating the likelihood of homology between two sequences in the model and describe the accompanying alignment algorithm. We also describe how ancestral codons can be computed when the same gene produced multiple pseudogene homologs. We apply our methods to the evolution of human cytochrome c.

Recognizing the pseudogenes in bacterial genomes

Pseudogenes are now known to be a regular feature of bacterial genomes and are found in particularly high numbers within the genomes of recently emerged bacterial pathogens. As most pseudogenes are recognized by sequence alignments, we use newly available genomic sequences to identify the pseudogenes in 11 genomes from 4 bacterial genera, each of which contains at least 1 human pathogen. The numbers of pseudogenes range from 27 in Staphylococcus aureus MW2 to 337 in Yersinia pestis CO92 (e.g. 1–8% of the annotated genes in the genome). Most pseudogenes are formed by small frameshifting indels, but because stop codons are A + T-rich, the two low-G + C Gram-positive taxa (Streptococcus and Staphylococcus) have relatively high fractions of pseudogenes generated by nonsense mutations when compared with more G + C-rich genomes. Over half of the pseudogenes are produced from genes whose original functions were annotated as ‘hypothetical’ or ‘unknown’; however, several broadly distributed genes involved in nucleotide processing, repair or replication have become pseudogenes in one of the sequenced Vibrio vulnificus genomes. Although many of our comparisons involved closely related strains with broadly overlapping gene inventories, each genome contains a largely unique set of pseudogenes, suggesting that pseudogenes are formed and eliminated relatively rapidly from most bacterial genomes.

Evolutionary models for insertions and deletions in a probabilistic modeling framework

BACKGROUND

Probabilistic models for sequence comparison (such as hidden Markov models and pair hidden Markov models for proteins and mRNAs, or their context-free grammar counterparts for structural RNAs) often assume a fixed degree of divergence. Ideally we would like these models to be conditional on evolutionary divergence time. Probabilistic models of substitution events are well established, but there has not been a completely satisfactory theoretical framework for modeling insertion and deletion events.

RESULTS

I have developed a method for extending standard Markov substitution models to include gap characters, and another method for the evolution of state transition probabilities in a probabilistic model. These methods use instantaneous rate matrices in a way that is more general than those used for substitution processes, and are sufficient to provide time-dependent models for standard linear and affine gap penalties, respectively. Given a probabilistic model, we can make all of its emission probabilities (including gap characters) and all its transition probabilities conditional on a chosen divergence time. To do this, we only need to know the parameters of the model at one particular divergence time instance, as well as the parameters of the model at the two extremes of zero and infinite divergence. I have implemented these methods in a new generation of the RNA genefinder QRNA (eQRNA).

CONCLUSION

These methods can be applied to incorporate evolutionary models of insertions and deletions into any hidden Markov model or stochastic context-free grammar, in a pair or profile form, for sequence modeling.