Nonfunctional Sequences Research Articles

Comparison and benchmark of deep learning methods for non-coding RNA classification.

The involvement of non-coding RNAs in biological processes and diseases has made the exploration of their functions crucial. Most non-coding RNAs have yet to be studied, creating the need for methods that can rapidly classify large sets of non-coding RNAs into functional groups, or classes. In recent years, the success of deep learning in various domains led to its application to non-coding RNA classification. Multiple novel architectures have been developed, but these advancements are not covered by current literature reviews. We present an exhaustive comparison of the different methods proposed in the state-of-the-art and describe their associated datasets. Moreover, the literature lacks objective benchmarks. We perform experiments to fairly evaluate the performance of various tools for non-coding RNA classification on popular datasets. The robustness of methods to non-functional sequences and sequence boundary noise is explored. We also measure computation time and CO2 emissions. With regard to these results, we assess the relevance of the different architectural choices and provide recommendations to consider in future methods.

Predicting transcriptional activation domain function using Graph Neural Networks.

Analysis of factors that lead to the functionality of transcriptional activation domains remains a crucial and yet challenging task owing to the significant diversity in their sequences and their intrinsically disordered nature. Almost all existing methods that have aimed to predict activation domains have involved traditional machine learning approaches, such as logistic regression, that are unable to capture complex patterns in data or plain convolutional neural networks and have been limited in exploration of structural features. However, there is a tremendous potential in the inspection of the structural properties of activation domains, and an opportunity to investigate complex relationships between features of residues in the sequence. To address these, we have utilized the power of graph neural networks which can represent structural data in the form of nodes and edges, allowing nodes to exchange information among themselves. We have experimented with two kinds of graph formulations, one involving residues as nodes and the other assigning atoms to be the nodes. A logistic regression model was also developed to analyze feature importance. For all the models, several feature combinations were experimented with. The residue-level GNN model with amino acid type, residue position, acidic/basic/aromatic property and secondary structure feature combination gave the best performing model with accuracy, F1 score and AUROC of 97.9%, 71% and 97.1% respectively which outperformed other existing methods in the literature when applied on the dataset we used. Among the other structure-based features that were analyzed, the amphipathic property of helices also proved to be an important feature for classification. Logistic regression results showed that the most dominant feature that makes a sequence functional is the frequency of different types of amino acids in the sequence. Our results consistent have shown that functional sequences have more acidic and aromatic residues whereas basic residues are seen more in non-functional sequences.

The Tonnetz at First Sight: Cognitive Issues of Human–Computer Interaction with Pitch Spaces

Pitch spaces allow pitch relations to be expressed through geometrical representations for many different purposes. The Tonnetz is a well-known pitch space in the field of music theory; equivalent representations have been described in the field of cognitive science, especially Krumhansl's model of perceived triadic distance. Despite her empirical approach, we know very little about the way people interact, cognitively speaking, with Tonnetz-based computational platforms involving multimodal stimuli. Our study has approached this issue by means of empirical experimentation for the first time. A total of 88 participants, with varying backgrounds in music and mathematics, were asked to interact with a Tonnetz interface; they did not have prior knowledge of this pitch space. Results of our experiment confirmed our main hypotheses. On the one hand, strong skills in music theory are needed to partially grasp the overall structure of the Tonnetz at first sight; this aspect is mainly related to the quality recognition of triads and the detection of shared pitch classes in harmonic motions. On the other hand, the particular geometry of the Tonnetz may bias this understanding when non-functional harmonic sequences are displayed on it.

Regulating PCCA gene expression by modulation of pseudoexon splicing patterns to rescue enzyme activity in propionic acidemia

Pseudoexons are nonfunctional intronic sequences that can be activated by deep-intronic sequence variation. Activation increases pseudoexon inclusion in mRNA and interferes with normal gene expression. The PCCA c.1285-1416A>G variation activates a pseudoexon and causes the severe metabolic disorder propionic acidemia by deficiency of the propionyl-CoA carboxylase enzyme encoded by PCCA and PCCB. We characterized this pathogenic pseudoexon activation event in detail and identified hnRNP A1 to be important for normal repression. The PCCA c.1285-1416A>G variation disrupts an hnRNP A1-binding splicing silencer and simultaneously creates a splicing enhancer. We demonstrate that blocking this region of regulation with splice-switching antisense oligonucleotides restores normal splicing and rescues enzyme activity in patient fibroblasts and in a cellular model created by CRISPR gene editing. Interestingly, the PCCA pseudoexon offers an unexploited potential to upregulate gene expression because healthy tissues show relatively high inclusion levels. By blocking inclusion of the nonactivated wild-type pseudoexon, we can increase both PCCA and PCCB protein levels, which increases the activity of the heterododecameric enzyme. Surprisingly, we can increase enzyme activity from residual levels in not only patient fibroblasts harboring PCCA missense variants but also those harboring PCCB missense variants. This is a potential treatment strategy for propionic acidemia.

Prediction of Tumor Microenvironment Characteristics and Treatment Response in Lung Squamous Cell Carcinoma by Pseudogene OR7E47P-related Immune Genes.

Pseudogenes are initially regarded as nonfunctional genomic sequences, but some pseudogenes regulate tumor initiation and progression by interacting with other genes to modulate their transcriptional activities. Olfactory receptor family 7 subfamily E member 47 pseudogene (OR7E47P) is expressed broadly in lung tissues and has been identified as a positive regulator in the tumor microenvironment (TME) of lung adenocarcinoma (LUAD). This study aimed to elucidate the correlation between OR7E47P and tumor immunity in lung squamous cell carcinoma (LUSC). Clinical and molecular information from The Cancer Genome Atlas (TCGA) LUSC cohort was used to identify OR7E47P-related immune genes (ORIGs) by weighted gene correlation network analysis (WGCNA). Based on the ORIGs, 2 OR7E47P clusters were identified using non-negative matrix factorization (NMF) clustering, and the stability of the clustering was tested by an extreme gradient boosting classifier (XGBoost). LASSO-Cox and stepwise regressions were applied to further select prognostic ORIGs and to construct a predictive model (ORPScore) for immunotherapy. The Botling cohorts and 8 immunotherapy cohorts (the Samstein, Braun, Jung, Gide, IMvigor210, Lauss, Van Allen, and Cho cohorts) were included as independent validation cohorts. OR7E47P expression was positively correlated with immune cell infiltration and enrichment of immune-related pathways in LUSC. A total of 57 ORIGs were identified to classify the patients into 2 OR7E47P clusters (Cluster 1 and Cluster 2) with distinct immune, mutation, and stromal programs. Compared to Cluster 1, Cluster 2 had more infiltration by immune and stromal cells, lower mutation rates of driver genes, and higher expression of immune-related proteins. The clustering performed well in the internal and 5 external validation cohorts. Based on the 7 ORIGs (HOPX, STX2, WFS, DUSP22, SLFN13, GGCT, and CCSER2), the ORPScore was constructed to predict the prognosis and the treatment response. In addition, the ORPScore was a better prognostic factor and correlated positively with the immunotherapeutic response in cancer patients. The area under the curve values ranged from 0.584 to 0.805 in the 6 independent immunotherapy cohorts. Our study suggests a significant correlation between OR7E47P and TME modulation in LUSC. ORIGs can be applied to molecularly stratify patients, and the ORPScore may serve as a biomarker for clinical decision-making regarding individualized prognostication and immunotherapy.

The Ga1 locus of the genus Zea is associated with novel genome structures derived from multiple, independent non-homologous recombination events.

The Ga1 locus controls cross incompatibility between field corn and popcorn. The Ga1-S haplotype contains two types of pectin methylesterase (PME) genes, ZmPme3 and several copies of ZmGa1P, that are expressed in silk and pollen, respectively. The ga1 haplotype contains non-functional tandem repeat sequences related to ZmPme3 and ZmGa1P. This haplotype can cross pollinate freely and is widely present in field corn. The primary objective of this study is to characterize the repeat sequences from a diverse collection of maize and teosinte lines and use this information to understand the evolution of the Ga1 locus. First, we characterized the complexity of the Ga1 genome region in high quality maize genome assemblies which led to their categorization into five groups based on the number and type of PME-like sequences found at this region. Second, we studied duplication events that led to the ga1 and Ga1-S repeats using maximum-likelihood phylogenetic reconstruction. Divergence estimates of the ga1 haplotype suggest that the duplication events occurred more than 600 kya whereas those in Ga1-S occurred at three time points i.e., > 600 kya, ∼ 260 kya and ∼ 100 kya. These estimates suggest that the ga1 and Ga1-S tandem duplication events occurred independently. Finally, analysis of ZmPme3 and ZmGa1P homologs in Zea and Tripsacum genomes suggests that ga1 and Ga1-S repeats originated from an ancestral pair of PME genes that duplicated and diverged through two evolutionary branches prior to the domestication of maize.

Rescue of Escherichia coli auxotrophy by de novo small proteins.

Increasing numbers of small proteins with diverse physiological roles are being identified and characterized in both prokaryotic and eukaryotic systems, but the origins and evolution of these proteins remain unclear. Recent genomic sequence analyses in several organisms suggest that new functions encoded by small open reading frames (sORFs) may emerge de novo from noncoding sequences. However, experimental data demonstrating if and how randomly generated sORFs can confer beneficial effects to cells are limited. Here, we show that by upregulating hisB expression, de novo small proteins (≤50 amino acids in length) selected from random sequence libraries can rescue Escherichia coli cells that lack the conditionally essential SerB enzyme. The recovered small proteins are hydrophobic and confer their rescue effect by binding to the 5' end regulatory region of the his operon mRNA, suggesting that protein binding promotes structural rearrangements of the RNA that allow increased hisB expression. This study adds RNA regulatory elements as another interacting partner for de novo proteins isolated from random sequence libraries and provides further experimental evidence that small proteins with selective benefits can originate from the expression of nonfunctional sequences.

The Cup Whisperer

The Cup Whisperer

Characterization of the complete mitochondrial genome of Dendroctonus valens and elimination of nuclear mitochondrial pseudogene interference

AbstractNuclear mitochondrial pseudogenes (numts) are short, nonfunctional DNA sequences resulting from mitochondrial gene transfer to the nuclear genome; these sequences seriously affect the accuracy of mitochondrial gene for population genetic and phylogenetic analyses. The nuclear genome of Dendroctonus valens (Coleoptera: Curculionidae: Scolytidae), a major forest quarantine pest that seriously threatens the ecological security of pine forests, contains a large number of numts, and these interfere with analyses of genetic variation based on the mitochondrial cox1 gene. To eliminate interference by numts, we sequenced, assembled and analysed the complete mitochondrial genome of D. valens. The mitogenome is a circular molecule of 16,547 bp and shows typical gene content and arrangement. We identified its codon preference, tRNA secondary structure and nucleotide evolution rate. Furthermore, we designed primers for long fragment amplification to eliminate numts in the mitochondrial cox1 gene. Finally, amplified products were sequenced with universal primers, and cox1 gene sequences (464 bp) without numts were successfully obtained. The Heilihe Nature Reserve (harbouring Pinus tabulaeformis) in Inner Mongolia, is in the early stage of D. valens northward invasion, with a high population genetic diversity. We hypothesized that the high genetic diversity was related to human activities around the reserve and the reproductive characteristics in the early stage of D. valens invasion. The newly developed method resolves the issue of numt interference and lays a foundation for the analyses of genetic diversity in D. valens populations in the region as well as its invasion routes.

Pseudoexon activation in disease by non‐splice site deep intronic sequence variation — wild type pseudoexons constitute high‐risk sites in the human genome

Accuracy of pre-messenger RNA (pre-mRNA) splicing is crucial for normal gene expression. Complex regulation supports the spliceosomal distinction between authentic exons and the many seemingly functional splice sites delimiting pseudoexons. Pseudoexons are nonfunctional intronic sequences that can be activated for aberrant inclusion in mRNA, which may cause disease. Pseudoexon activation is very challenging to predict, in particular when activation occurs by sequence variants that alter the splicing regulatory environment without directly affecting splice sites. Aspseudoexon inclusion often evades detection due to activation of nonsense-mediated mRNA decay, and because conventional diagnostic procedures miss deep intronic sequence variation, pseudoexon activation is a heavily underreported disease mechanism. Pseudoexon characteristics have mainly been studied based on in silico predicted sequences. Moreover, because recognition of sequence variants that create or strengthen splice sites is possible by comparison with well-established consensus sequences, this type of pseudoexon activation is by far the most frequently reported. Here we review all known human disease-associated pseudoexons that carry functional splice sites and are activated by deep intronic sequence variants located outside splice site sequences. We delineate common characteristics that make this type of wild type pseudoexons distinct high-risk sites in the human genome.

Improving sequence-based modeling of protein families using secondary-structure quality assessment.

MotivationModeling of protein family sequence distribution from homologous sequence data recently received considerable attention, in particular for structure and function predictions, as well as for protein design. In particular, direct coupling analysis, a method to infer effective pairwise interactions between residues, was shown to capture important structural constraints and to successfully generate functional protein sequences. Building on this and other graphical models, we introduce a new framework to assess the quality of the secondary structures of the generated sequences with respect to reference structures for the family.ResultsWe introduce two scoring functions characterizing the likeliness of the secondary structure of a protein sequence to match a reference structure, called Dot Product and Pattern Matching. We test these scores on published experimental protein mutagenesis and design dataset, and show improvement in the detection of nonfunctional sequences. We also show that use of these scores help rejecting nonfunctional sequences generated by graphical models (Restricted Boltzmann Machines) learned from homologous sequence alignments.Availability and implementationData and code available at https://github.com/CyrilMa/ssqaSupplementary information Supplementary data are available at Bioinformatics online.

Low-N protein engineering with data-efficient deep learning.

Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high throughput to find rare, enhanced variants. Here we introduce a machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution. As demonstrated in two dissimilar proteins, GFP from Aequorea victoria (avGFP) and E. coli strain TEM-1 β-lactamase, top candidates from a single round are diverse and as active as engineered mutants obtained from previous high-throughput efforts. By distilling information from natural protein sequence landscapes, our model learns a latent representation of 'unnaturalness', which helps to guide search away from nonfunctional sequence neighborhoods. Subsequent low-N supervision then identifies improvements to the activity of interest. In sum, our approach enables efficient use of resource-intensive high-fidelity assays without sacrificing throughput, and helps to accelerate engineered proteins into the fermenter, field and clinic.

Selection influences naive CD8+ TCR-β repertoire sharing.

Within each individual, the adaptive immune system generates a repertoire of cells expressing receptors capable of recognizing diverse potential pathogens. The theoretical diversity of the T-cell receptor (TCR) repertoire exceeds the actual size of the T-cell population in an individual by several orders of magnitude - making the observation of identical TCRs in different individuals extremely improbable if all receptors were equally likely. Despite this disparity between the theoretical and the realized diversity of the repertoire, these 'public' receptor sequences have been identified in autoimmune, cancer and pathogen interaction contexts. Biased generation processes explain the presence of public TCRs in the naive repertoire, but do not adequately explain the different abundances of these public TCRs. We investigate and characterize the distribution of genomic TCR-β sequences of naive CD8+ T cells from three genetically identical mice, comparing non-productive (non-functional sequences) and productive sequences. We find public TCR-β sequences at higher abundances compared with unshared sequences in the productive, but not in the non-productive, repertoire. We show that neutral processes such as recombination biases, codon degeneracy and generation probability do not fully account for these differences, and conclude that thymic or peripheral selection plays an important role in increasing the abundances of public TCR-β sequences.

Aligning Protein-Coding Nucleotide Sequences with MACSE.

Most genomic and evolutionary comparative analyses rely on accurate multiple sequence alignments. With their underlying codon structure, protein-coding nucleotide sequences pose a specific challenge for multiple sequence alignment. Multiple Alignment of Coding Sequences (MACSE) is a multiple sequence alignment program that provided the first automatic solution for aligning protein-coding gene datasets containing both functional and nonfunctional sequences (pseudogenes). Through its unique features, reliable codon alignments can be built in the presence of frameshifts and stop codons suitable for subsequent analysis of selection based on the ratio of nonsynonymous to synonymous substitutions. Here we offer a practical overview and guidelines on the use of MACSE v2. This major update of the initial algorithm now comes with a graphical interface providing user-friendly access to different subprograms to handle multiple alignments of protein-coding sequences. We also present new pipelines based on MACSE v2 subprograms to handle large datasets and distributed as Singularity containers. MACSE and associated pipelines are available at: https://bioweb.supagro.inra.fr/macse/ .

Characterization of an eutherian gene cluster generated after transposon domestication identifies Bex3 as relevant for advanced neurological functions

BackgroundOne of the most unusual sources of phylogenetically restricted genes is the molecular domestication of transposable elements into a host genome as functional genes. Although these kinds of events are sometimes at the core of key macroevolutionary changes, their origin and organismal function are generally poorly understood.ResultsHere, we identify several previously unreported transposable element domestication events in the human and mouse genomes. Among them, we find a remarkable molecular domestication that gave rise to a multigenic family in placental mammals, the Bex/Tceal gene cluster. These genes, which act as hub proteins within diverse signaling pathways, have been associated with neurological features of human patients carrying genomic microdeletions in chromosome X. The Bex/Tceal genes display neural-enriched patterns and are differentially expressed in human neurological disorders, such as autism and schizophrenia. Two different murine alleles of the cluster member Bex3 display morphological and physiopathological brain modifications, such as reduced interneuron number and hippocampal electrophysiological imbalance, alterations that translate into distinct behavioral phenotypes.ConclusionsWe provide an in-depth understanding of the emergence of a gene cluster that originated by transposon domestication and gene duplication at the origin of placental mammals, an evolutionary process that transformed a non-functional transposon sequence into novel components of the eutherian genome. These genes were integrated into existing signaling pathways involved in the development, maintenance, and function of the CNS in eutherians. At least one of its members, Bex3, is relevant for higher brain functions in placental mammals and may be involved in human neurological disorders.

Prediction of piRNAs and their function based on discriminative intelligent model using hybrid features into Chou’s PseKNC

Piwi interacting RNA (piRNA) is a recognized group of small non-coding RNA molecules. The piRNA molecules are associated with multiple tumors type diagnosis and drug development. It is also linked to regulate gene expression, suppressing transposon and maintains genome integrity. Due to a vital role of piRNAs in biology, the identification of piRNAs and their function has become an important area of research in computational biology. This paper proposes a robust two-layer predictor called “piRNA (2L)-PseKNC” to improve prediction of piRNAs and their function. The proposed predictor employing hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, unsupervised principal component analysis (PCA) algorithm for discriminant feature selection and deep neural network (DNN) as a classifier. The proposed predictor was designed based on two layers approach. The first layer predicts either the encoded sequence belongs to piRNA or non-piRNA sequence and the second layer predict the selected piRNA sequence is functional piRNA or non-functional piRNA sequence. The overall accuracies of the proposed model using 5-fold cross validation test were 94.73% at first layer and 85.21% at second layer, surpassing the existing predictors with accuracies improvement 7.59% and 2.81% at the first layer and at the second layer respectively.

Distribution and Nucleotide Diversity of Yr15 in Wild Emmer Populations and Chinese Wheat Germplasm.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a devastating fungal disease of wheat. The wild emmer gene, Yr15 (Wtk1), which confers a strong broad-spectrum resistance to Pst isolates, is composed of kinase and pseudokinase domains. The analysis of 361 wild emmer accessions from a wide range of natural habitats confirms that functional Wtk1 is distributed mainly along a narrow axis from Mt. Carmel to Mt. Hermon regions, in the northern part of Israel, where environmental conditions are favorable to the onset of stripe rust. An analysis of full-length Wtk1 DNA sequences from 49 wild emmer accessions identified three haplotypes and extremely low nucleotide diversity (π = 0.00002). The sequence diversity of Wtk1 is 9.5 times lower than that of broad-spectrum partial resistance gene Yr36 (π = 0.00019), and both are in sharp contrast to the high level of nucleotide diversity previously reported for race-specific resistance genes (e.g., Lr10 and Pm3). However, the nonfunctional wtk1 sequences possess high level of nucleotide diversity (π = 0.07). These results may reflect the different resistance mechanisms and the different evolutionary processes that shaped these resistance genes. Yr15 was absent in 189 Chinese wheat landraces and was present in only 1.02% of the 583 tested modern Chinese wheat cultivars. These results corroborate our previous results showing that Yr15 was absent in 94% of a worldwide collection of 513 wheat cultivars, therefore indicating the importance of Yr15 in wheat stripe rust resistance breeding programs in China and elsewhere around the globe.

MYC-regulated pseudogene HMGA1P6 promotes ovarian cancer malignancy via augmenting the oncogenic HMGA1/2

Pseudogenes have long been considered as nonfunctional genomic sequences. Recent studies have shown that they can potentially regulate the expression of protein-coding genes and are dysregulated in diseases including cancer. However, the potential roles of pseudogenes in ovarian cancer have not been well studied. Here we characterized the pseudogene expression profile in HGSOC (high-grade serous ovarian carcinoma) by microarray. We identified 577 dysregulated pseudogenes and most of them were up-regulated (538 of 577). HMGA1P6 (High mobility group AT-hook 1 pseudogene 6) was one of the overexpressed pseudogenes and its expression was inversely correlated with patient survival. Mechanistically, HMGA1P6 promoted ovarian cancer cell malignancy by acting as a ceRNA (competitive endogenous RNA) that led to enhanced HMGA1 and HMGA2 expression. Importantly, HMGA1P6 was transcriptionally activated by oncogene MYC in ovarian cancer. Our findings reveal that MYC may contribute to oncogenesis through transcriptional regulation of pseudogene HMGA1P6 in ovarian cancer.

PCADD: SNV prioritisation in Sus scrofa

BackgroundIn animal breeding, identification of causative genetic variants is of major importance and high economical value. Usually, the number of candidate variants exceeds the number of variants that can be validated. One way of prioritizing probable candidates is by evaluating their potential to have a deleterious effect, e.g. by predicting their consequence. Due to experimental difficulties to evaluate variants that do not cause an amino-acid substitution, other prioritization methods are needed. For human genomes, the prediction of deleterious genomic variants has taken a step forward with the introduction of the combined annotation dependent depletion (CADD) method. In theory, this approach can be applied to any species. Here, we present pCADD (p for pig), a model to score single nucleotide variants (SNVs) in pig genomes.ResultsTo evaluate whether pCADD captures sites with biological meaning, we used transcripts from miRNAs and introns, sequences from genes that are specific for a particular tissue, and the different sites of codons, to test how well pCADD scores differentiate between functional and non-functional elements. Furthermore, we conducted an assessment of examples of non-coding and coding SNVs, which are causal for changes in phenotypes. Our results show that pCADD scores discriminate between functional and non-functional sequences and prioritize functional SNVs, and that pCADD is able to score the different positions in a codon relative to their redundancy. Taken together, these results indicate that based on pCADD scores, regions with biological relevance can be identified and distinguished according to their rate of adaptation.ConclusionsWe present the ability of pCADD to prioritize SNVs in the pig genome with respect to their putative deleteriousness, in accordance to the biological significance of the region in which they are located. We created scores for all possible SNVs, coding and non-coding, for all autosomes and the X chromosome of the pig reference sequence Sscrofa11.1, proposing a toolbox to prioritize variants and evaluate sequences to highlight new sites of interest to explain biological functions that are relevant to animal breeding.

De Novo Emergence of Peptides That Confer Antibiotic Resistance.

The origin of novel genes and beneficial functions is of fundamental interest in evolutionary biology. New genes can originate from different mechanisms, including horizontal gene transfer, duplication-divergence, and de novo from noncoding DNA sequences. Comparative genomics has generated strong evidence for de novo emergence of genes in various organisms, but experimental demonstration of this process has been limited to localized randomization in preexisting structural scaffolds. This bypasses the basic requirement of de novo gene emergence, i.e., lack of an ancestral gene. We constructed highly diverse plasmid libraries encoding randomly generated open reading frames and expressed them in Escherichia coli to identify short peptides that could confer a beneficial and selectable phenotype in vivo (in a living cell). Selections on antibiotic-containing agar plates resulted in the identification of three peptides that increased aminoglycoside resistance up to 48-fold. Combining genetic and functional analyses, we show that the peptides are highly hydrophobic, and by inserting into the membrane, they reduce membrane potential, decrease aminoglycoside uptake, and thereby confer high-level resistance. This study demonstrates that randomized DNA sequences can encode peptides that confer selective benefits and illustrates how expression of random sequences could spark the origination of new genes. In addition, our results also show that this question can be addressed experimentally by expression of highly diverse sequence libraries and subsequent selection for specific functions, such as resistance to toxic compounds, the ability to rescue auxotrophic/temperature-sensitive mutants, and growth on normally nonused carbon sources, allowing the exploration of many different phenotypes.IMPORTANCEDe novo gene origination from nonfunctional DNA sequences was long assumed to be implausible. However, recent studies have shown that large fractions of genomic noncoding DNA are transcribed and translated, potentially generating new genes. Experimental validation of this process so far has been limited to comparative genomics, in vitro selections, or partial randomizations. Here, we describe selection of novel peptides in vivo using fully random synthetic expression libraries. The peptides confer aminoglycoside resistance by inserting into the bacterial membrane and thereby partly reducing membrane potential and decreasing drug uptake. Our results show that beneficial peptides can be selected from random sequence pools in vivo and support the idea that expression of noncoding sequences could spark the origination of new genes.