sORF finder: a program package to identify small open reading frames with high coding potential

The role of polypeptides encoded by ncRNAs in cancer

It was previously thought that ncRNA could not encode polypeptides, but recent reports have challenged this notion. As research into ncRNA progresses, it is increasingly clear that it serves roles beyond traditional mechanisms, playing significant regulatory roles in various diseases, notably cancer, which is responsible for 70% of human deaths. Numerous studies have highlighted the diverse regulatory mechanisms of ncRNA that are pivotal in cancer initiation and progression. The role of ncRNA-encoded polypeptides in cancer regulation has gained prominence. This article explores the newly identified regulatory functions of these polypeptides in three types of ncRNA-lncRNA, pri-miRNA, and circRNA. These polypeptides can interact with proteins, influence signaling pathways, enhance miRNA stability, and regulate cancer progression, malignancy, resistance, and other clinical challenges. Furthermore, we discuss the evolutionary significance of these polypeptides in the transition from RNA to protein, examining their emergence and conservation throughout evolution.

D-sORF: Accurate Ab Initio Classification of Experimentally Detected Small Open Reading Frames (sORFs) Associated with Translational Machinery

Small open reading frames (sORFs; <300 nucleotides or <100 amino acids) are widespread across all genomes, and an increasing variety of them appear to be translating from non-genic regions. Over the past few decades, peptides produced from sORFs have been identified as functional in various organisms, from bacteria to humans. Despite recent advances in next-generation sequencing and proteomics, accurate annotation and classification of sORFs remain a rate-limiting step toward reliable and high-throughput detection of small proteins from non-genic regions. Additionally, the cost of computational methods utilizing machine learning is lower than that of biological experiments, and they can be employed to detect sORFs, laying the groundwork for biological experiments. We present D-sORF, a machine-learning framework that integrates the statistical nucleotide context and motif information around the start codon to predict coding sORFs. D-sORF scores directly for coding identity and requires only the underlying genomic sequence, without incorporating parameters such as the conservation, which, in the case of sORFs, may increase the dispersion of scores within the significantly less conserved non-genic regions. D-sORF achieves 94.74% precision and 92.37% accuracy for small ORFs (using the 99 nt medium length window). When D-sORF is applied to sORFs associated with ribosomes, the identification of transcripts producing peptides (annotated by the Ensembl IDs) is similar to or superior to experimental methodologies based on ribosome-sequencing (Ribo-Seq) profiling. In parallel, the recognition of putative negative data, such as the intron-containing transcripts that associate with ribosomes, remains remarkably low, indicating that D-sORF could be efficiently applied to filter out false-positive sORFs from Ribo-Seq data because of the non-productive ribosomal binding or noise inherent in these protocols.

Current understanding of functional peptides encoded by lncRNA in cancer

Dysregulated gene expression and imbalance of transcriptional regulation are typical features of cancer. RNA always plays a key role in these processes. Human transcripts contain many RNAs without long open reading frames (ORF, > 100 aa) and that are more than 200 bp in length. They are usually regarded as long non-coding RNA (lncRNA) which play an important role in cancer regulation, including chromatin remodeling, transcriptional regulation, translational regulation and as miRNA sponges. With the advancement of ribosome profiling and sequencing technologies, increasing research evidence revealed that some ORFs in lncRNA can also encode peptides and participate in the regulation of multiple organ tumors, which undoubtedly opens a new chapter in the field of lncRNA and oncology research. In this review, we discuss the biological function of lncRNA in tumors, the current methods to evaluate their coding potential and the role of functional small peptides encoded by lncRNA in cancers. Investigating the small peptides encoded by lncRNA and understanding the regulatory mechanisms of these functional peptides may contribute to a deeper understanding of cancer and the development of new targeted anticancer therapies.

Beyond traditional translation: ncRNA derived peptides as modulators of tumor behaviors

Within the intricate tapestry of molecular research, noncoding RNAs (ncRNAs) were historically overshadowed by a pervasive presumption of their inability to encode proteins or peptides. However, groundbreaking revelations have challenged this notion, unveiling select ncRNAs that surprisingly encode peptides specifically those nearing a succinct 100 amino acids. At the forefront of this epiphany stand lncRNAs and circRNAs, distinctively characterized by their embedded small open reading frames (sORFs). Increasing evidence has revealed different functions and mechanisms of peptides/proteins encoded by ncRNAs in cancer, including promotion or inhibition of cancer cell proliferation, cellular metabolism (glucose metabolism and lipid metabolism), and promotion or concerted metastasis of cancer cells. The discoveries not only accentuate the depth of ncRNA functionality but also open novel avenues for oncological research and therapeutic innovations. The main difficulties in the study of these ncRNA-derived peptides hinge crucially on precise peptide detection and sORFs identification. Here, we illuminate cutting-edge methodologies, essential instrumentation, and dedicated databases tailored for unearthing sORFs and peptides. In addition, we also conclude the potential of clinical applications in cancer therapy.

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

A survey of experimental and computational identification of small proteins

Small proteins (SPs) are typically characterized as eukaryotic proteins shorter than 100 amino acids and prokaryotic proteins shorter than 50 amino acids. Historically, they were disregarded because of the arbitrary size thresholds to define proteins. However, recent research has revealed the existence of many SPs and their crucial roles. Despite this, the identification of SPs and the elucidation of their functions are still in their infancy. To pave the way for future SP studies, we briefly introduce the limitations and advancements in experimental techniques for SP identification. We then provide an overview of available computational tools for SP identification, their constraints, and their evaluation. Additionally, we highlight existing resources for SP research. This survey aims to initiate further exploration into SPs and encourage the development of more sophisticated computational tools for SP identification in prokaryotes and microbiomes.

Novel insights into small open reading frame-encoded micropeptides in hepatocellular carcinoma: A potential breakthrough

Traditionally, non-coding RNAs (ncRNAs) are regarded as a class of RNA transcripts that lack encoding capability; however, advancements in technology have revealed that some ncRNAs contain small open reading frames (sORFs) that are capable of encoding micropeptides of approximately 150 amino acids in length. sORF-encoded micropeptides (SEPs) have emerged as intriguing entities in hepatocellular carcinoma (HCC) research, shedding light on this previously unexplored realm. Recent studies have highlighted the regulatory functions of SEPs in the occurrence and progression of HCC. Some SEPs exhibit inhibitory effects on HCC, but others facilitate its development. This discovery has revolutionized the landscape of HCC research and clinical management. Here, we introduce the concept and characteristics of SEPs, summarize their associations with HCC, and elucidate their carcinogenic mechanisms in HCC metabolism, signaling pathways, cell proliferation, and metastasis. In addition, we propose a step-by-step workflow for the investigation of HCC-associated SEPs. Lastly, we discuss the challenges and prospects of applying SEPs in the diagnosis and treatment of HCC. This review aims to facilitate the discovery, optimization, and clinical application of HCC-related SEPs, inspiring the development of early diagnostic, individualized, and precision therapeutic strategies for HCC.

sOCP: a framework predicting smORF coding potential based on TIS and in-frame features and effectively applied in the human genome

Small open reading frames (smORFs) have been acknowledged to play various roles on essential biological pathways and affect human beings from diabetes to tumorigenesis. Predicting smORFs in silico is quite a prerequisite for processing the omics data. Here, we proposed the smORF-coding-potential-predicting framework, sOCP, which provides functions to construct a model for predicting novel smORFs in some species. The sOCP model constructed in human was based on in-frame features and the nucleotide bias around the start codon, and the small feature subset was proved to be competent enough and avoid overfitting problems for complicated models. It showed more advanced prediction metrics than previous methods and could correlate closely with experimental evidence in a heterogeneous dataset. The model was applied to Rattus norvegicus and exhibited satisfactory performance. We then scanned smORFs with ATG and non-ATG start codons from the human genome and generated a database containing about a million novel smORFs with coding potential. Around 72 000 smORFs are located on the lncRNA regions of the genome. The smORF-encoded peptides may be involved in biological pathways rare for canonical proteins, including glucocorticoid catabolic process and the prokaryotic defense system. Our work provides a model and database for human smORF investigation and a convenient tool for further smORF prediction in other species.

Improved super-resolution ribosome profiling reveals prevalent translation of upstream ORFs and small ORFs in Arabidopsis

A crucial step in functional genomics is identifying actively translated ORFs and linking them to biological functions. The challenge lies in identifying short ORFs, as their identification is greatly influenced by data quality and depth. Here, we improved the coverage of super-resolution Ribo-seq in Arabidopsis (Arabidopsis thaliana), revealing uncharacterized translation events for nuclear, chloroplastic, and mitochondrial genes. Assisted by a transcriptome assembly, we identified 7,751 unconventional translation events, comprising 6,996 upstream ORFs (uORFs) and 209 downstream ORFs on annotated protein-coding genes, as well as 546 ORFs in presumed noncoding RNAs. Proteomic data confirmed the production of stable proteins from some of these unannotated translation events. We present evidence of active translation from primary transcripts of trans-acting small interfering RNAs (TAS1-4) and microRNAs (pri-MIR163 and pri-MIR169) and periodic ribosome stalling supporting cotranslational decay. Additionally, we developed a method for identifying extremely short uORFs, including 370 minimum uORFs (AUG-stop), and 2,921 tiny uORFs (2 to 10 amino acids) and 681 uORFs that overlap with each other. Remarkably, these short uORFs exhibit strong translational repression as do longer uORFs. We also systematically discovered 594 uORFs regulated by alternative splicing, suggesting widespread isoform-specific translational control. Finally, these prevalent uORFs are associated with numerous important pathways. In summary, our improved Arabidopsis translational landscape provides valuable resources to study gene expression regulation.

No country for old methods: New tools for studying microproteins

Microproteins encoded by small open reading frames (sORFs) have emerged as a fascinating frontier in genomics. Traditionally overlooked due to their small size, recent technological advancements such as ribosome profiling, mass spectrometry-based strategies and advanced computational approaches have led to the annotation of more than 7000 sORFs in the human genome. Despite the vast progress, only a tiny portion of these microproteins have been characterized and an important challenge in the field lies in identifying functionally relevant microproteins and understanding their role in different cellular contexts. In this review, we explore the recent advancements in sORF research, focusing on the new methodologies and computational approaches that have facilitated their identification and functional characterization. Leveraging these new tools hold great promise for dissecting the diverse cellular roles of microproteins and will ultimately pave the way for understanding their role in the pathogenesis of diseases and identifying new therapeutic targets.

Identified Small Open Reading Frame-Encoded Peptides in Human Serum with Nanoparticle Protein Coronas

The low-molecular-weight proteins (LMWP) in serum and plasma are related to various human diseases and can be valuable biomarkers. A small open reading frame-encoded peptide (SEP) is one kind of LMWP, which has been found to function in many bioprocesses and has also been found in human blood, making it a potential biomarker. The detection of LMWP by a mass spectrometry (MS)-based proteomic assay is often inhibited by the wide dynamic range of serum/plasma protein abundance. Nanoparticle protein coronas are a newly emerging protein enrichment method. To analyze SEPs in human serum, we have developed a protocol integrated with nanoparticle protein coronas and liquid chromatography (LC)/MS/MS. With three nanoparticles, TiO2, Fe3O4@SiO2, and Fe3O4@SiO2@TiO2, we identified 164 new SEPs in the human serum sample. Fe3O4@SiO2 and a nanoparticle mixture obtained the maximum number and the largest proportion of identified SEPs, respectively. Compared with acetonitrile-based extraction, nanoparticle protein coronas can cover more small proteins and SEPs. The magnetic nanoparticle is also fit for high-throughput parallel protein separation before LC/MS. This method is fast, efficient, reproducible, and easy to operate in 96-well plates and centrifuge tubes, which will benefit the research on SEPs and biomarkers.

Subcellular localization and relevant mechanisms of human cancer-related micropeptides

Rapid advances in high-quality sequencing and bioinformatics have invalidated the argument that noncoding RNAs (ncRNAs) are junk transcripts that do not encode proteins. Increasing evidence suggests that small open reading frames (sORFs) in ncRNAs can encode micropeptides and polypeptides within 100 amino acids in length. Several micropeptides have been characterized and proven to have various functions in human physiology and pathology, particularly in cancer. The present review mainly highlights the latest studies on ncRNA-encoded micropeptides in different cancers and categorizes them based on their subcellular localization, thereby providing a theoretical basis for micropeptide applications in the early diagnosis and prognosis of cancer and as therapeutic targets. However, considering the inherent characteristics of micropeptides and the limitations of the assay technology methods, more detailed information is warranted.

Small proteins in bacteria - Big challenges in prediction and identification

Proteins with up to 100 amino acids have been largely overlooked due to the challenges associated with predicting and identifying them using traditional methods. Recent advances in bioinformatics and machine learning, DNA sequencing, RNA and Ribo-seq technologies, and mass spectrometry (MS) have greatly facilitated the detection and characterisation of these elusive proteins in recent years. This has revealed their crucial role in various cellular processes including regulation, signalling and transport, as toxins and as folding helpers for protein complexes. Consequently, the systematic identification and characterisation of these proteins in bacteria have emerged as a prominent field of interest within the microbial research community. This review provides an overview of different strategies for predicting and identifying these proteins on a large scale, leveraging the power of these advanced technologies. Furthermore, the review offers insights into the future developments that may be expected in this field.

Small proteins in Gram-positive bacteria

Small proteins comprising less than 100 amino acids have been often ignored in bacterial genome annotations. About 10 years ago, focused efforts started to investigate whole peptidomes, which resulted in the discovery of a multitude of small proteins, but only a number of them have been characterized in detail. Generally, small proteins can be either membrane or cytosolic proteins. The latter interact with larger proteins, RNA or even metal ions. Here, we summarize our current knowledge on small proteins from Gram-positive bacteria with a special emphasis on the model organism Bacillus subtilis. Our examples include membrane-bound toxins of type I toxin-antitoxin systems, proteins that block the assembly of higher order structures, regulate sporulation or modulate the RNA degradosome. We do not consider antimicrobial peptides. Furthermore, we present methods for the identification and investigation of small proteins.

LncRNA-encoded peptides: unveiling their significance in cardiovascular physiology and pathology-current research insights

Long non-coding RNAs (lncRNAs), which are RNA transcripts exceeding 200 nucleotides were believed to lack any protein-coding capacity. But advancements in -omics technology have revealed that some lncRNAs have small open reading frames (sORFs) that can be translated by ribosomes to encode peptides, some of which have important biological functions. These encoded peptides subserve important biological functions by interacting with their targets to modulate transcriptional or signalling axes, thereby enhancing or suppressing cardiovascular disease (CVD) occurrence and progression. In this review, we summarize what is known about the research strategy of lncRNA-encoded peptides, mainly comprising predictive websites/tools and experimental methods that have been widely used for prediction, identification, and validation. More importantly, we have compiled a list of lncRNA- encoded peptides, with a focus on those that play significant roles in cardiovascular physiology and pathology, including ENSRNOT (RNO)-sORF6/RNO-sORF7/RNO-sORF8, dwarf open reading frame (DOWRF), myoregulin (NLN), etc. Additionally, we have outlined the functions and mechanisms of these peptides in cardiovascular physiology and pathology, such as cardiomyocyte hypertrophy, myocardial contraction, myocardial infarction, and vascular remodelling. Finally, an overview of the existing challenges and potential future developments in the realm of lncRNA-encoded peptides was provided, with consideration given to prospective avenues for further research. Given that many lncRNA-encoded peptides have not been functionally annotated yet, their application in CVD diagnosis and treatment still requires further research.

CoraL: interpretable contrastive meta-learning for the prediction of cancer-associated ncRNA-encoded small peptides

NcRNA-encoded small peptides (ncPEPs) have recently emerged as promising targets and biomarkers for cancer immunotherapy. Therefore, identifying cancer-associated ncPEPs is crucial for cancer research. In this work, we propose CoraL, a novel supervised contrastive meta-learning framework for predicting cancer-associated ncPEPs. Specifically, the proposed meta-learning strategy enables our model to learn meta-knowledge from different types of peptides and train a promising predictive model even with few labeled samples. The results show that our model is capable of making high-confidence predictions on unseen cancer biomarkers with only five samples, potentially accelerating the discovery of novel cancer biomarkers for immunotherapy. Moreover, our approach remarkably outperforms existing deep learning models on 15 cancer-associated ncPEPs datasets, demonstrating its effectiveness and robustness. Interestingly, our model exhibits outstanding performance when extended for the identification of short open reading frames derived from ncPEPs, demonstrating the strong prediction ability of CoraL at the transcriptome level. Importantly, our feature interpretation analysis discovers unique sequential patterns as the fingerprint for each cancer-associated ncPEPs, revealing the relationship among certain cancer biomarkers that are validated by relevant literature and motif comparison. Overall, we expect CoraL to be a useful tool to decipher the pathogenesis of cancer and provide valuable information for cancer research. The dataset and source code of our proposed method can be found at https://github.com/Johnsunnn/CoraL.

Small secreted peptides (SSPs) in tomato and their potential roles in drought stress response

Tomato (Solanum lycopersicum) is one of the most important vegetable crops in the world and abiotic stresses often cause serious problems in tomato production. It is thus important to identify new regulators in stress response and to devise new approaches to promote stress tolerance in tomato. Previous studies have shown that small secreted peptides (SSPs) are important signal molecules regulating plant growth and stress response by mediating intercellular communication. However, little is known about tomato SSPs, especially their roles in responding to abiotic stresses. Here we report the identification of 1,050 putative SSPs in the tomato genome, 557 of which were classified into 38 known SSP families based on their conserved domains. GO and transcriptome analyses revealed that a large proportion of SlSSPs might be involved in abiotic stress response. Further analysis indicated that stress response related cis-elements were present on the SlCEP promotors and a number of SlCEPs were significantly upregulated by drought treatments. Among the drought-inducible SlCEPs, SlCEP10 and SlCEP11b were selected for further analysis via exogenous application of synthetic peptides. The results showed that treatments with both SlCEP10 and SlCEP11b peptides enhanced tomato drought stress tolerance, indicating the potential roles of SlSSPs in abiotic stress response.

Small Open Reading Frame-Encoded Micro-Peptides: An Emerging Protein World

Small open reading frames (sORFs) are often overlooked features in genomes. In the past, they were labeled as noncoding or "transcriptional noise". However, accumulating evidence from recent years suggests that sORFs may be transcribed and translated to produce sORF-encoded polypeptides (SEPs) with less than 100 amino acids. The vigorous development of computational algorithms, ribosome profiling, and peptidome has facilitated the prediction and identification of many new SEPs. These SEPs were revealed to be involved in a wide range of basic biological processes, such as gene expression regulation, embryonic development, cellular metabolism, inflammation, and even carcinogenesis. To effectively understand the potential biological functions of SEPs, we discuss the history and development of the newly emerging research on sORFs and SEPs. In particular, we review a range of recently discovered bioinformatics tools for identifying, predicting, and validating SEPs as well as a variety of biochemical experiments for characterizing SEP functions. Lastly, this review underlines the challenges and future directions in identifying and validating sORFs and their encoded micropeptides, providing a significant reference for upcoming research on sORF-encoded peptides.

Unveiling the secrets of non-coding RNA-encoded peptides in plants: A comprehensive review of mining methods and research progress

Non-coding RNAs (ncRNAs) are not conventionally involved in protein encoding. However, recent findings indicate that ncRNAs possess the capacity to code for proteins or peptides. These ncRNA-encoded peptides (ncPEPs) are vital for diverse plant life processes and exhibit significant potential value. Despite their importance, research on plant ncPEPs is limited, with only a few studies conducted and less information on the underlying mechanisms, and the field remains in its nascent stage. This manuscript provides a comprehensive overview of ncPEPs mining methods in plants, focusing on prediction, identification, and functional analysis. We discuss the strengths and weaknesses of various techniques, identify future research directions in the ncPEPs domain, and elucidate the biological functions and agricultural application prospects of plant ncPEPs. By highlighting the immense potential and research value of ncPEPs, we aim to lay a solid foundation for more in-depth studies in plant science.

Identification of small open reading frames in plant lncRNA using class-imbalance learning

Recently, small open reading frames (sORFs) in long noncoding RNA (lncRNA) have been demonstrated to encode small peptides that can help study the mechanisms of growth and development in organisms. Since machine learning-based computational methods are less costly compared with biological experiments, they can be used to identify sORFs and provide a basis for biological experiments. However, few computational methods and data resources have been exploited for identifying sORFs in plant lncRNA. Besides, machine learning models produce underperforming classifiers when faced with a class-imbalance problem. In this study, an alternative method called SMOTE based on weighted cosine distance (WCDSMOTE) which enables interaction with feature selection is put forward to synthesize minority class samples and weighted edited nearest neighbor (WENN) is applied to clean up majority class samples, thus, hybrid sampling WCDSMOTE-ENN is proposed to deal with imbalanced datasets with the multi-angle feature. A heterogeneous classifier ensemble is introduced to complete the classification task. Therefore, a novel computational method that is based on class-imbalance learning to identify the sORFs with coding potential in plant lncRNA (sORFplnc) is presented. Experimental results manifest that sORFplnc outperforms existing computational methods in identifying sORFs with coding potential. We anticipate that the proposed work can be a reference for relevant research and contribute to agriculture and biomedicine.

BmNPV circular RNA-encoded peptide VSP39 promotes viral replication

CircRNAs are covalently closed single-stranded circular RNA molecules, which are not easily degraded by endonucleases and play vital roles in many biological processes. Currently, most studies on circRNAs focus on endogenous circRNAs in cells, and there are few studies on virus-encoded circRNAs. In this study, a viral circRNA (circRNA-000010) derived from the region (-/bp: 114514-115,319) of the complementary strand of Bombyx mori Nucleopolyhedrovirus (BmNPV) genome was identified with the circRNA-sequencing. The authenticity of viral circRNA-000010 was further confirmed by reverse transcription PCR, reverse transcription-rolling circle amplification (TCA), in situ hybridization, immunofluorescent staining, and Northern blotting. The results of overexpression and knockdown experiments showed that circRNA-000010 promoted viral replication. Furthermore, a viral small peptide VSP39 with 39 amino acid residues translated by circRNA-000010 but not its linear molecule was confirmed. Finally, VSP39 was found to promote viral replication. Our findings indicated that a viral circRNA encoded by BmNPV promoted viral replication. These findings will provide new clues for further understanding coding information of the BmNPV genome and open a new insight for investigating host-virus interactions.

sORFPred: A Method Based on Comprehensive Features and Ensemble Learning to Predict the sORFs in Plant LncRNAs

Long non-coding RNAs (lncRNAs) are important regulators of biological processes. It has recently been shown that some lncRNAs include small open reading frames (sORFs) that can encode small peptides of no more than 100 amino acids. However, existing methods are commonly applied to human and animal datasets and still suffer from low feature representation capability. Thus, accurate and credible prediction of sORFs with coding ability in plant lncRNAs is imperative. This paper proposes a new method termed sORFPred, in which we design a model named MCSEN by combining multi-scale convolution and Squeeze-and-Excitation Networks to fully mine distinct information embedded in sORFs, integrate and optimize multiple sequence-based and physicochemical feature descriptors, and built a two-layer prediction classifier based on Bayesian optimization algorithm and Extra Trees. sORFPred has been evaluated on sORFs datasets of three species and experimentally validated sORFs dataset. Results indicate that sORFPred outperforms existing methods and achieves 97.28% accuracy, 97.06% precision, 97.52% recall, and 97.29% F1-score on Arabidopsis thaliana, which shows a significant improvement in prediction performance compared to various conventional shallow machine learning and deep learning models.

Comprehensive analysis of peptide-coding genes and initial characterization of an LRR-only microprotein in Marchantia polymorpha

In the past two decades, many plant peptides have been found to play crucial roles in various biological events by mediating cell-to-cell communications. However, a large number of small open reading frames (sORFs) or short genes capable of encoding peptides remain uncharacterized. In this study, we examined several candidate genes for peptides conserved between two model plants: Arabidopsis thaliana and Marchantia polymorpha. We examined their expression pattern in M. polymorpha and subcellular localization using a transient assay with Nicotiana benthamiana. We found that one candidate, MpSGF10B, was expressed in meristems, gemma cups, and male reproductive organs called antheridiophores. MpSGF10B has an N-terminal signal peptide followed by two leucine-rich repeat (LRR) domains and was secreted to the extracellular region in N. benthamiana and M. polymorpha. Compared with the wild type, two independent Mpsgf10b mutants had a slightly increased number of antheridiophores. It was revealed in gene ontology enrichment analysis that MpSGF10B was significantly co-expressed with genes related to cell cycle and development. These results suggest that MpSGF10B may be involved in the reproductive development of M. polymorpha. Our research should shed light on the unknown role of LRR-only proteins in land plants.

Peptidomics Methods Applied to the Study of Flower Development

Understanding the global and dynamic nature of plant developmental processes requires not only the study of the transcriptome, but also of the proteome, including its largely uncharacterized peptidome fraction. Recent advances in proteomics and high-throughput analyses of translating RNAs (ribosome profiling) have begun to address this issue, evidencing the existence of novel, uncharacterized, and possibly functional peptides. To validate the accumulation in tissues of sORF-encoded polypeptides (SEPs), the basic setup of proteomic analyses (i.e., LC-MS/MS) can be followed. However, the detection of peptides that are small (up to ~100 aa, 6-7 kDa) and novel (i.e., not annotated in reference databases) presents specific challenges that need to be addressed both experimentally and with computational biology resources. Several methods have been developed in recent years to isolate and identify peptides from plant tissues. In this chapter, we outline two different peptide extraction protocols and the subsequent peptide identification by mass spectrometry using the database search or the de novo identification methods.

Advances in peptides encoded by non-coding RNAs: A cargo in exosome

The metastasis of malignant tumors determines patient prognosis. This is the main reason for the poor prognosis of patients with cancer and the most challenging aspect of treating malignant tumors. Therefore, it is important to identify early tumor markers and molecules that can predict patient prognosis. However, there are currently no molecular markers with good clinical accuracy and specificity. Many non-coding RNA (ncRNAs)have been identified, which can regulate the process of tumor development at multiple levels. Interestingly, some ncRNAs are translated to produce functional peptides. Exosomes act as signal carriers, are encapsulated in nucleic acids and proteins, and play a messenger role in cell-to-cell communication. Recent studies have identified exosome peptides with potential diagnostic roles. This review aims to provide a theoretical basis for ncRNA-encoded peptides or proteins transported by exosomes and ultimately to provide ideas for further development of new diagnostic and prognostic cancer markers.

csORF-finder: an effective ensemble learning framework for accurate identification of multi-species coding short open reading frames

Short open reading frames (sORFs) refer to the small nucleic fragments no longer than 303 nt in length that probably encode small peptides. To date, translatable sORFs have been found in both untranslated regions of messenger ribonucleic acids (RNAs; mRNAs) and long non-coding RNAs (lncRNAs), playing vital roles in a myriad of biological processes. As not all sORFs are translated or essentially translatable, it is important to develop a highly accurate computational tool for characterizing the coding potential of sORFs, thereby facilitating discovery of novel functional peptides. In light of this, we designed a series of ensemble models by integrating Efficient-CapsNet and LightGBM, collectively termed csORF-finder, to differentiate the coding sORFs (csORFs) from non-coding sORFs in Homo sapiens, Mus musculus and Drosophila melanogaster, respectively. To improve the performance of csORF-finder, we introduced a novel feature encoding scheme named trinucleotide deviation from expected mean (TDE) and computed all types of in-frame sequence-based features, such as i-framed-3mer, i-framed-CKSNAP and i-framed-TDE. Benchmarking results showed that these features could significantly boost the performance compared to the original 3-mer, CKSNAP and TDE features. Our performance comparisons showed that csORF-finder achieved a superior performance than the state-of-the-art methods for csORF prediction on multi-species and non-ATG initiation independent test datasets. Furthermore, we applied csORF-finder to screen the lncRNA datasets for identifying potential csORFs. The resulting data serve as an important computational repository for further experimental validation. We hope that csORF-finder can be exploited as a powerful platform for high-throughput identification of csORFs and functional characterization of these csORFs encoded peptides.

Pervasive translation of small open reading frames in plant long non-coding RNAs

Long non-coding RNAs (lncRNAs) are primarily recognized as non-coding transcripts longer than 200 nucleotides with low coding potential and are present in both eukaryotes and prokaryotes. Recent findings reveal that lncRNAs can code for micropeptides in various species. Micropeptides are generated from small open reading frames (smORFs) and have been discovered frequently in short mRNAs and non-coding RNAs, such as lncRNAs, circular RNAs, and pri-miRNAs. The most accepted definition of a smORF is an ORF containing fewer than 100 codons, and ribosome profiling and mass spectrometry are the most prevalent experimental techniques used to identify them. Although the majority of micropeptides perform critical roles throughout plant developmental processes and stress conditions, only a handful of their functions have been verified to date. Even though more research is being directed toward identifying micropeptides, there is still a dearth of information regarding these peptides in plants. This review outlines the lncRNA-encoded peptides, the evolutionary roles of such peptides in plants, and the techniques used to identify them. It also describes the functions of the pri-miRNA and circRNA-encoded peptides that have been identified in plants.

Probing the sORF-Encoded Peptides of Deinococcus radiodurans in Response to Extreme Stress

Organisms have developed different mechanisms to respond to stresses. However, the roles of small ORF-encoded peptides (SEPs) in these regulatory systems remain elusive, which is partially because of the lack of comprehensive knowledge regarding these biomolecules. We chose the extremophile Deinococcus radiodurans R1 as a model species and conducted large-scale profiling of the SEPs related to the stress response. The integrated workflow consisting of multiple omics approaches for SEP identification was streamlined, and an SEPome of D. radiodurans containing 109 novel and high-confidence SEPs was drafted. Forty-four percent of these SEPs were predicted to function as antimicrobial peptides. Quantitative peptidomics analysis indicated that the expression of SEP068184 was upregulated upon oxidative treatment and gamma irradiation of the bacteria. SEP068184 was conserved in Deinococcus and exhibited negative regulation of oxidative stress resistance in a comparative phenotypic assay of its mutants. Further quantitative and interactive proteomics analyses suggested that SEP068184 might function through metabolic pathways and interact with cytoplasmic proteins. Collectively, our findings demonstrate that SEPs are involved in the regulation of oxidative resistance, and the SEPome dataset provides a rich resource for research on the molecular mechanisms of the response to extreme stress in organisms.

Identifying LncRNA-Encoded Short Peptides Using Optimized Hybrid Features and Ensemble Learning

Long non-coding RNA (lncRNA) contains short open reading frames (sORFs), and sORFs-encoded short peptides (SEPs) have become the focus of scientific studies due to their crucial role in life activities. The identification of SEPs is vital to further understanding their regulatory function. Bioinformatics methods can quickly identify SEPs to provide credible candidate sequences for verifying SEPs by biological experimenrts. However, there is a lack of methods for identifying SEPs directly. In this study, a machine learning method to identify SEPs of plant lncRNA (ISPL) is proposed. Hybrid features including sequence features and physicochemical features are extracted manually or adaptively to construct different modal features. In order to keep the stability of feature selection, the non-linear correction applied in Max-Relevance-Max-Distance (nocRD) feature selection method is proposed, which integrates multiple feature ranking results and uses the iterative random forest for different modal features dimensionality reduction. Classification models with different modal features are constructed, and their outputs are combined for ensemble classification. The experimental results show that the accuracy of ISPL is 89.86% percent on the independent test set, which will have important implications for further studies of functional genomic.

Identification and analysis of smORFs in Chlamydomonas reinhardtii

Small open reading frames (smORFs) have been acknowledged as an important partner in organism functions ranging from bacteria to higher eukaryotes. However, lack of investigation of smORFs in green algae, despite their importance in ecology and evolution. We applied bioinformatic analysis, ribosome profiling, and small peptide proteomics to provide a genome-wide and high-confident smORF database in the model green alga Chlamydomonas reinhardtii. The whole genome was screened first to mine potential coding smORFs. Then conservative analysis, ribosome profiling, and proteomics data were processed to identify conserved smORFs and generate translation evidence. The combination of procedures resulted in 2014 smORFs that might exist in the C. reinhardtii genome. The expression of smORFs in Cd treatment suggested that two smORFs might participate in redox reaction, three in inorganic phosphate transport, and one in DNA repair under stress. Our study built a genome-widely database in C. reinhardtii, providing target smORFs for further research.

Functional Micropeptides Encoded by Long Non-Coding RNAs: A Comprehensive Review

Long non-coding RNAs (lncRNAs) were originally defined as non-coding RNAs (ncRNAs) which lack protein-coding ability. However, with the emergence of technologies such as ribosome profiling sequencing and ribosome-nascent chain complex sequencing, it has been demonstrated that most lncRNAs have short open reading frames hence the potential to encode functional micropeptides. Such micropeptides have been described to be widely involved in life-sustaining activities in several organisms, such as homeostasis regulation, disease, and tumor occurrence, and development, and morphological development of animals, and plants. In this review, we focus on the latest developments in the field of lncRNA-encoded micropeptides, and describe the relevant computational tools and techniques for micropeptide prediction and identification. This review aims to serve as a reference for future research studies on lncRNA-encoded micropeptides.

Micropeptide vsp21 translated by Reovirus circular RNA 000048 attenuates viral replication

To date, some DNA viruses and single-stranded RNA viruses have been found to generate circRNAs. However, the reports on circRNAs produced by double-stranded RNA viruses are very limited. In this study, Bombyx mori cypovirus (BmCPV), a typical double-stranded RNA virus belonging to the Reoviridae, was demonstrated to generate viral circRNAs (vcircRNAs) and a vcircRNA_000048 whose sequence corresponds with the region 164-1245 nt on the BmCPV genomic dsRNA S5 segment (GQ294468.1) was validated by PCR, Sanger sequencing, reverse transcription-rolling circle amplification, and Northern blotting. Furthermore, we verified that vcircRNA_000048 translates a micropeptide vsp21 with 21 amino acid residues in an IRES-dependent manner, and vsp21 attenuates the viral replication. These findings provided a novel clue to understanding the regulation of viral multiplication and interaction of reovirus with the host.

Proteomic-driven identification of short open reading frame-encoded peptides

Accumulating evidence has shown that a large number of short open reading frames (sORFs) also have the ability to encode proteins. The discovery of sORFs opens up a new research area, leading to the identification and functional study of sORF encoded peptides (SEPs) at the omics level. Besides bioinformatics prediction and ribosomal profiling, mass spectrometry (MS) has become a significant tool as it directly detects the sequence of SEPs. Though MS-based proteomics methods have proved to be effective for qualitative and quantitative analysis of SEPs, the detection of SEPs is still a great challenge due to their low abundance and short sequence. To illustrate the progress in method development, we described and discussed the main steps of large-scale proteomics identification of SEPs, including SEP extraction and enrichment, MS detection, data processing and quality control, quantification, and function prediction and validation methods. This article is protected by copyright. All rights reserved.

Short open reading frames (sORFs) and microproteins: an update on their identification and validation measures

A short open reading frame (sORFs) constitutes ≤ 300 bases, encoding a microprotein or sORF-encoded protein (SEP) which comprises ≤ 100 amino acids. Traditionally dismissed by genome annotation pipelines as meaningless noise, sORFs were found to possess coding potential with ribosome profiling (RIBO-Seq), which unveiled sORF-based transcripts at various genome locations. Nonetheless, the existence of corresponding microproteins that are stable and functional was little substantiated by experimental evidence initially. With recent advancements in multi-omics, the identification, validation, and functional characterisation of sORFs and microproteins have become feasible. In this review, we discuss the history and development of an emerging research field of sORFs and microproteins. In particular, we focus on an array of bioinformatics and OMICS approaches used for predicting, sequencing, validating, and characterizing these recently discovered entities. These strategies include RIBO-Seq which detects sORF transcripts via ribosome footprints, and mass spectrometry (MS)-based proteomics for sequencing the resultant microproteins. Subsequently, our discussion extends to the functional characterisation of microproteins by incorporating CRISPR/Cas9 screen and protein–protein interaction (PPI) studies. Our review discusses not only detection methodologies, but we also highlight on the challenges and potential solutions in identifying and validating sORFs and their microproteins. The novelty of this review lies within its validation for the functional role of microproteins, which could contribute towards the future landscape of microproteomics.

Small open reading frames in plant research: from prediction to functional characterization

Gene prediction is a laborious and time-consuming task. The advancement of sequencing technologies and bioinformatics tools, coupled with accelerated rate of ribosome profiling and mass spectrometry development, have made identification of small open reading frames (sORFs) (< 100 codons) in various plant genomes possible. The past 50 years have seen sORFs being isolated from many organisms. However, to date, a comprehensive sORF annotation pipeline is as yet unavailable, hence, addressed in our review. Here, we also provide current information on classification and functions of plant sORFs and their potential applications in crop improvement programs.

Diversity and conservation of plant small secreted proteins associated with arbuscular mycorrhizal symbiosis

Arbuscular mycorrhizal symbiosis (AMS) is widespread mutualistic association between plants and fungi, which plays an essential role in nutrient exchange, enhancement in plant stress resistance, development of host, and ecosystem sustainability. Previous studies have shown that plant small secreted proteins (SSPs) are involved in beneficial symbiotic interactions. However, the role of SSPs in the evolution of AMS has not been well studied yet. In this study, we performed computational analysis of SSPs in 60 plant species and identified three AMS-specific ortholog groups containing SSPs only from at least 30% of the AMS species in this study and three AMS-preferential ortholog groups containing SSPs from both AMS and non-AMS species, with AMS species containing significantly more SSPs than non-AMS species. We found that independent lineages of monocot and eudicot plants contained genes in the AMS-specific ortholog groups and had significant expansion in the AMS-preferential ortholog groups. Also, two AMS-preferential ortholog groups showed convergent changes, between monocot and eudicot species, in gene expression in response to arbuscular mycorrhizal fungus Rhizophagus irregularis. Furthermore, conserved cis-elements were identified in the promoter regions of the genes showing convergent gene expression. We found that the SSPs, and their closely related homologs, in each of three AMS-preferential ortholog groups, had some local variations in the protein structural alignment. We also identified genes co-expressed with the Populus trichocarpa SSP genes in the AMS-preferential ortholog groups. This first plant kingdom-wide analysis on SSP provides insights on plant-AMS convergent evolution with specific SSP gene expression and local diversification of protein structures.

Small Open Reading Frames, How to Find Them and Determine Their Function

Advances in genomics and molecular biology have revealed an abundance of small open reading frames (sORFs) across all types of transcripts. While these sORFs are often assumed to be non-functional, many have been implicated in physiological functions and a significant number of sORFs have been described in human diseases. Thus, sORFs may represent a hidden repository of functional elements that could serve as therapeutic targets. Unlike protein-coding genes, it is not necessarily the encoded peptide of an sORF that enacts its function, sometimes simply the act of translating an sORF might have a regulatory role. Indeed, the most studied sORFs are located in the 5′UTRs of coding transcripts and can have a regulatory impact on the translation of the downstream protein-coding sequence. However, sORFs have also been abundantly identified in non-coding RNAs including lncRNAs, circular RNAs and ribosomal RNAs suggesting that sORFs may be diverse in function. Of the many different experimental methods used to discover sORFs, the most commonly used are ribosome profiling and mass spectrometry. These can confirm interactions between transcripts and ribosomes and the production of a peptide, respectively. Extensions to ribosome profiling, which also capture scanning ribosomes, have further made it possible to see how sORFs impact the translation initiation of mRNAs. While high-throughput techniques have made the identification of sORFs less difficult, defining their function, if any, is typically more challenging. Together, the abundance and potential function of many of these sORFs argues for the necessity of including sORFs in gene annotations and systematically characterizing these to understand their potential functional roles. In this review, we will focus on the high-throughput methods used in the detection and characterization of sORFs and discuss techniques for validation and functional characterization.

Inter-tissue and inter-organ signaling in drought stress response and phenotyping of drought tolerance

Plant response to drought stress includes systems for intracellular regulation of gene expression and signaling, as well as inter-tissue and inter-organ signaling, which helps entire plants acquire stress resistance. Plants sense water-deficit conditions both via the stomata of leaves and roots, and transfer water-deficit signals from roots to shoots via inter-organ signaling. Abscisic acid is an important phytohormone involved in the drought stress response and adaptation, and is synthesized mainly in vascular tissues and guard cells of leaves. In leaves, stress-induced abscisic acid is distributed to various tissues by transporters, which activates stomatal closure and expression of stress-related genes to acquire drought stress resistance. Moreover, the stepwise stress response at the whole-plant level is important for proper understanding of the physiological response to drought conditions. Drought stress is sensed by multiple types of sensors as molecular patterns of abiotic stress signals, which are transmitted via separate parallel signaling networks to induce downstream responses, including stomatal closure and synthesis of stress-related proteins and metabolites. Peptide molecules play important roles in the inter-organ signaling of dehydration from roots to shoots, as well as signaling of osmotic changes and reactive oxygen species/Ca²⁺ . In this review, we have summarized recent advances in research on complex plant drought stress responses, focusing on inter-tissue signaling in leaves and inter-organ signaling from roots to shoots. We have discussed the mechanisms via which drought stress adaptations and resistance are acquired at the whole-plant level, and have proposed the importance of quantitative phenotyping for measuring plant growth under drought conditions.

The Small Open Reading Frame-Encoded Peptides: Advances in Methodologies and Functional Studies

Small open reading frames (sORFs) are an important class of genes with less than 100 codons. They were historically annotated as noncoding or even junk sequences. In recent years, accumulating evidence suggests that sORFs could encode a considerable number of polypeptides, many of which play important roles in both physiology and disease pathology. However, it has been technically challenging to directly detect sORF-encoded peptides (SEPs). Here, we discuss the latest advances in methodologies for identifying SEPs with mass spectrometry, as well as the progress on functional studies of SEPs.

LncRNA-Encoded Short Peptides Identification Using Feature Subset Recombination and Ensemble Learning

Long non-coding RNA (lncRNA), which is a type of non-coding RNA, was reported to contain short open reading frames (sORFs). SORFs-encoded short peptides (SEPs) have been demonstrated to play a crucial role in regulating the biological processes such as growth, development, and resistance response. The identification of SEPs is vital to further understanding their function. However, there is still a lack of methods for identifying SEPs effectively and rapidly. In this study, a novel method for lncRNA-encoded short peptides identification based on feature subset recombination and ensemble learning, lncPepid, is developed. lncPepid transforms the data of Zea mays and Arabidopsis thaliana into hybrid features from two aspects including sequence composition and physicochemical properties separately. It optimizes hybrid features by proposing a novel weighted iteration-based feature selection method to recombine a stable subset that characterizes SEPs effectively. Different classification models with different optimized features are constructed and tested separately. The outputs of the optimal models are integrated for ensemble classification to improve efficiency. Experimental results manifest that the geometric mean of sensitivity and specificity of lncPepid is about 70% on the identification of functional SEPs derived from multiple species. It is an effective and rapid method for the identification of lncRNA-encoded short peptides. This study can be extended to the research on SEPs from other species and have crucial implications for further findings and studies of functional genomics.

Advances and perspectives in discovery and functional analysis of small secreted proteins in plants

Small secreted proteins (SSPs) are less than 250 amino acids in length and are actively transported out of cells through conventional protein secretion pathways or unconventional protein secretion pathways. In plants, SSPs have been found to play important roles in various processes, including plant growth and development, plant response to abiotic and biotic stresses, and beneficial plant-microbe interactions. Over the past 10 years, substantial progress has been made in the identification and functional characterization of SSPs in several plant species relevant to agriculture, bioenergy, and horticulture. Yet, there are potentially a lot of SSPs that have not been discovered in plant genomes, which is largely due to limitations of existing computational algorithms. Recent advances in genomics, transcriptomics, and proteomics research, as well as the development of new computational algorithms based on machine learning, provide unprecedented capabilities for genome-wide discovery of novel SSPs in plants. In this review, we summarize known SSPs and their functions in various plant species. Then we provide an update on the computational and experimental approaches that can be used to discover new SSPs. Finally, we discuss strategies for elucidating the biological functions of SSPs in plants.

The role of micropeptides in biology

Micropeptides are small polypeptides coded by small open-reading frames. Progress in computational biology and the analyses of large-scale transcriptomes and proteomes have revealed that mammalian genomes produce a large number of transcripts encoding micropeptides. Many of these have been previously annotated as long noncoding RNAs. The role of micropeptides in cellular homeostasis maintenance has been demonstrated. This review discusses different types of micropeptides as well as methods to identify them, such as computational approaches, ribosome profiling, and mass spectrometry.

Revisiting sORFs: overcoming challenges to identify and characterize functional microproteins

Short ORFs (sORFs), that is, occurrences of a start and stop codon within 100 codons or less, can be found in organisms of all domains of life, outnumbering annotated protein-coding ORFs by orders of magnitude. Even though functional proteins smaller than 100 amino acids are known, the coding potential of sORFs has often been overlooked, as it is not trivial to predict and test for functionality within the large number of sORFs. Recent advances in ribosome profiling and mass spectrometry approaches, together with refined bioinformatic predictions, have enabled a huge leap forward in this field and identified thousands of likely coding sORFs. A relatively low number of small proteins or microproteins produced from these sORFs have been characterized so far on the molecular, structural, and/or mechanistic level. These however display versatile and, in some cases, essential cellular functions, allowing for the exciting possibility that many more, previously unknown small proteins might be encoded in the genome, waiting to be discovered. This review will give an overview of the steadily growing microprotein field, focusing on eukaryotic small proteins. We will discuss emerging themes in the molecular action of microproteins, as well as advances and challenges in microprotein identification and characterization.

A Mini-review of Computational Approaches to Predict Functions and Findings of Novel Micro Peptides

:

New techniques in bioinformatics and the study of the transcriptome at a wide-scale have uncovered the fact that a large part of the genome is being translated than recently perceived thoughts and research, bringing about the creation of a various quantity of RNA with proteincoding and noncoding potential. A lot of RNA particles have been considered as noncoding due to many reasons, according to developing proofs. Like many sORFs that encode many functional micro peptides have neglected due to their tiny sizes.

:

Advanced studies reveal many major biological functions of these sORFs and their encoded micro peptides in a different and wide range of species. All the achievement in the identification of these sORFs and micro peptides is due to the progressive bioinformatics and high-throughput sequencing methods. This field has pulled in more consideration due to the detection of a large number of more sORFs and micro peptides. Nowadays, COVID-19 grabs all the attention of science as it is a sudden outbreak. sORFs of COVID-19 should be revealed for new ways to understand this virus. This review discusses ongoing progress in the systems for the identification and distinguishing proof of sORFs and micro peptides.

Long-distance stress and developmental signals associated with abscisic acid signaling in environmental responses

Flowering plants consist of highly differentiated organs, including roots, leaves, shoots and flowers, which have specific roles: root system for water and nutrient uptake, leaves for photosynthesis and gas exchange and reproductive organs for seed production. The communication between organs through the vascular system, by which water, nutrient and signaling molecules are transported, is essential for coordinated growth and development of the whole plant, particularly under adverse conditions. Here, we highlight recent progress in understanding how signaling pathways of plant hormones are associated with long-distance stress and developmental signals, with particular focus on environmental stress responses. In addition to the root-to-shoot peptide signal that induces abscisic acid accumulation in leaves under drought stress conditions, we summarize the diverse stress-responsive peptide signals reported to date to play a role in environmental responses.

Using AnABlast for intergenic sORF prediction in the Caenorhabditis elegans genome

Motivation

Short bioactive peptides encoded by small open reading frames (sORFs) play important roles in eukaryotes. Bioinformatics prediction of ORFs is an early step in a genome sequence analysis, but sORFs encoding short peptides, often using non-AUG initiation codons, are not easily discriminated from false ORFs occurring by chance.

Results

AnABlast is a computational tool designed to highlight putative protein-coding regions in genomic DNA sequences. This protein-coding finder is independent of ORF length and reading frame shifts, thus making of AnABlast a potentially useful tool to predict sORFs. Using this algorithm, here, we report the identification of 82 putative new intergenic sORFs in the Caenorhabditis elegans genome. Sequence similarity, motif presence, expression data and RNA interference experiments support that the underlined sORFs likely encode functional peptides, encouraging the use of AnABlast as a new approach for the accurate prediction of intergenic sORFs in annotated eukaryotic genomes.

Availability and implementation

AnABlast is freely available at http://www.bioinfocabd.upo.es/ab/. The C.elegans genome browser with AnABlast results, annotated genes and all data used in this study is available at http://www.bioinfocabd.upo.es/celegans.

Supplementary information

Supplementary data are available at Bioinformatics online.

sORF-Encoded MicroPeptides: New players in inflammation, metabolism, and precision medicine

Significant technological advances have enabled the discovery and identification of a new class of molecules, micropeptides or small ORF encoded peptides (SEPs) within non-coding RNAs (ncRNAs). As ncRNAs are well known to be transcriptionally silent, the discovery of SEPs implies that many ncRNAs are misannotated or play both coding and non-coding functions. SEPs have reportedly diverse regulatory roles in embryogenesis, myogenesis, inflammation, diseases, and cancer. SEPs appearing in different subcellular compartments show distinct functions. In this review, we summarized the functions of SEPs that have been characterized thus far. As SEPs are amenable to therapeutic development as biologics, understanding their underlying functions will provide novel targets for the treatment of inflammatory or metabolic disorders.

Circular RNAs: Emerging Role in Cancer Diagnostics and Therapeutics

Circular RNAs (circRNAs) are rapidly coming to the fore as major regulators of gene expression and cellular functions. They elicit their influence via a plethora of diverse molecular mechanisms. It is not surprising that aberrant circRNA expression is common in cancers and they have been implicated in multiple aspects of cancer pathophysiology such as apoptosis, invasion, migration, and proliferation. We summarize the emerging role of circRNAs as biomarkers and therapeutic targets in cancer.