MiPepid: MicroPeptide identification tool using machine learning

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.

LncRNA-encoded peptides in cancer

Long non-coding RNAs (lncRNAs), once considered transcriptional noise, have emerged as critical regulators of gene expression and key players in cancer biology. Recent breakthroughs have revealed that certain lncRNAs can encode small open reading frame (sORF)-derived peptides, which are now understood to contribute to the pathogenesis of various cancers. This review synthesizes current knowledge on the detection, functional roles, and clinical implications of lncRNA-encoded peptides in cancer. We discuss technological advancements in the detection and validation of sORFs, including ribosome profiling and mass spectrometry, which have facilitated the discovery of these peptides. The functional roles of lncRNA-encoded peptides in cancer processes such as gene transcription, translation regulation, signal transduction, and metabolic reprogramming are explored in various types of cancer. The clinical potential of these peptides is highlighted, with a focus on their utility as diagnostic biomarkers, prognostic indicators, and therapeutic targets. The challenges and future directions in translating these findings into clinical practice are also discussed, including the need for large-scale validation, development of sensitive detection methods, and optimization of peptide stability and delivery.

Exploring the Dark Matter of Human Proteome: The Emerging Role of Non-Canonical Open Reading Frame (ncORF) in Cancer Diagnosis, Biology, and Therapy

Cancer develops from abnormal cell growth in the body, causing significant mortalities every year. To date, potent therapeutic approaches have been developed to eradicate tumor cells, but intolerable toxicity and drug resistance can occur in treated patients, limiting the efficiency of existing treatment strategies. Therefore, searching for novel genes critical for cancer progression and therapeutic response is urgently needed for successful cancer therapy. Recent advances in bioinformatics and proteomic techniques have allowed the identification of a novel category of peptides encoded by non-canonical open reading frames (ncORFs) from historically non-coding genomic regions. Surprisingly, many ncORFs express functional microproteins that play a vital role in human cancers. In this review, we provide a comprehensive description of different ncORF types with coding capacity and technological methods in discovering ncORFs among human genomes. We also summarize the carcinogenic role of ncORFs such as pTINCR and HOXB-AS3 in regulating hallmarks of cancer, as well as the roles of ncORFs such as HOXB-AS3 and CIP2A-BP in cancer diagnosis and prognosis. We also discuss how ncORFs such as AKT-174aa and DDUP are involved in anti-cancer drug response and the underestimated potential of ncORFs as therapeutic targets.

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.

PSPI: A deep learning approach for prokaryotic small protein identification

Small Proteins (SPs) are pivotal in various cellular functions such as immunity, defense, and communication. Despite their significance, identifying them is still in its infancy. Existing computational tools are tailored to specific eukaryotic species, leaving only a few options for SP identification in prokaryotes. In addition, these existing tools still have suboptimal performance in SP identification. To fill this gap, we introduce PSPI, a deep learning-based approach designed specifically for predicting prokaryotic SPs. We showed that PSPI had a high accuracy in predicting generalized sets of prokaryotic SPs and sets specific to the human metagenome. Compared with three existing tools, PSPI was faster and showed greater precision, sensitivity, and specificity not only for prokaryotic SPs but also for eukaryotic ones. We also observed that the incorporation of (n, k)-mers greatly enhances the performance of PSPI, suggesting that many SPs may contain short linear motifs. The PSPI tool, which is freely available at https://www.cs.ucf.edu/∼xiaoman/tools/PSPI/, will be useful for studying SPs as a tool for identifying prokaryotic SPs and it can be trained to identify other types of SPs as well.

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.

Microscale marvels: unveiling the macroscopic significance of micropeptides in human health

Non-coding RNA encodes micropeptides from small open reading frames located within the RNA. Interestingly, these micropeptides are involved in a variety of functions within the body. They are emerging as the resolving piece of the puzzle for complex biomolecular signaling pathways within the body. Recent studies highlight the pivotal role of small peptides in regulating important biological processes like DNA repair, gene expression, muscle regeneration, immune responses, etc. On the contrary, altered expression of micropeptides also plays a pivotal role in the progression of various diseases like cardiovascular diseases, neurological disorders and several types of cancer, including colorectal cancer, hepatocellular cancer, lung cancer, etc. This review delves into the dual impact of micropeptides on health and pathology, exploring their pivotal role in preserving normal physiological homeostasis and probing their involvement in the triggering and progression of diseases.

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.

AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria

Introduction

Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics for combating plant pathogenic bacteria in agriculture and the environment. However, identifying potent AMPs through laborious experimental assays is resource-intensive and time-consuming. To address these limitations, this study presents a bioinformatics approach utilizing machine learning models for predicting and selecting AMPs active against plant pathogenic bacteria.

Methods

N-gram representations of peptide sequences with 3-letter and 9-letter reduced amino acid alphabets were used to capture the sequence patterns and motifs that contribute to the antimicrobial activity of AMPs. A 5-fold cross-validation technique was used to train the machine learning models and to evaluate their predictive accuracy and robustness.

Results

The models were applied to predict putative AMPs encoded by intergenic regions and small open reading frames (ORFs) of the citrus genome. Approximately 7% of the 10,000-peptide dataset from the intergenic region and 7% of the 685,924-peptide dataset from the whole genome were predicted as probable AMPs. The prediction accuracy of the reported models range from 0.72 to 0.91. A subset of the predicted AMPs was selected for experimental test against Spiroplasma citri, the causative agent of citrus stubborn disease. The experimental results confirm the antimicrobial activity of the selected AMPs against the target bacterium, demonstrating the predictive capability of the machine learning models.

Discussion

Hydrophobic amino acid residues and positively charged amino acid residues are among the key features in predicting AMPs by the Random Forest Algorithm. Aggregation propensity appears to be correlated with the effectiveness of the AMPs. The described models would contribute to the development of effective AMP-based strategies for plant disease management in agricultural and environmental settings. To facilitate broader accessibility, our model is publicly available on the AGRAMP (Agricultural Ngrams Antimicrobial Peptides) server.

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.

Intracellular and Extracellular Peptidomes of the Model Plant, Physcomitrium patens

Here, we report our approach to peptidomic analysis of the plant model Physcomitrium patens. Intracellular and extracellular peptides were extracted under conditions preventing proteolytic digestion by endogenous proteases. The extracts were fractionated on size exclusion columns to isolate intracellular peptides and on reversed-phase cartridges to isolate extracellular peptides, with the isolated peptides subjected to LC-MS/MS analysis. Mass spectrometry data were analyzed for the presence of peptides derived from the known proteins or microproteins encoded by small open reading frames (<100 aa, smORFs) predicted in the moss genome. Experimental details are provided for each step.

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.

Shining in the dark: the big world of small peptides in plants

Small peptides represent a subset of dark matter in plant proteomes. Through differential expression patterns and modes of action, small peptides act as important regulators of plant growth and development. Over the past 20 years, many small peptides have been identified due to technical advances in genome sequencing, bioinformatics, and chemical biology. In this article, we summarize the classification of plant small peptides and experimental strategies used to identify them as well as their potential use in agronomic breeding. We review the biological functions and molecular mechanisms of small peptides in plants, discuss current problems in small peptide research and highlight future research directions in this field. Our review provides crucial insight into small peptides in plants and will contribute to a better understanding of their potential roles in biotechnology and agriculture.

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.

Microproteins: Overlooked regulators of physiology and disease

Ongoing efforts to generate a complete and accurate annotation of the genome have revealed a significant blind spot for small proteins (<100 amino acids) originating from short open reading frames (sORFs). The recent discovery of numerous sORF-encoded proteins, termed microproteins, that play diverse roles in critical cellular processes has ignited the field of microprotein biology. Large-scale efforts are currently underway to identify sORF-encoded microproteins in diverse cell-types and tissues and specialized methods and tools have been developed to aid in their discovery, validation, and functional characterization. Microproteins that have been identified thus far play important roles in fundamental processes including ion transport, oxidative phosphorylation, and stress signaling. In this review, we discuss the optimized tools available for microprotein discovery and validation, summarize the biological functions of numerous microproteins, outline the promise for developing microproteins as therapeutic targets, and look forward to the future of the field of microprotein biology.

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.

Peptidomics

Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.

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.

Prediction of protein-coding small ORFs in multi-species using integrated sequence-derived features and the random forest model

Proteins encoded by small open reading frames (sORFs) can serve as functional elements playing important roles in vivo. Such sORFs also constitute the potential pool for facilitating the de novo gene birth, driving evolutionary innovation and species diversity. Therefore, their theoretical and experimental identification has become a critical issue. Herein, we proposed a protein-coding sORFs prediction method merely based on integrative sequence-derived features. Our prediction performance is better or comparable compared with other nine prevalent methods, which shows that our method can provide a relatively reliable research tool for the prediction of protein-coding sORFs. Our method allows users to estimate the potential expression of a queried sORF, which has been demonstrated by the correlation analysis between our possibility estimation and codon adaption index (CAI). Based on the features that we used, we demonstrated that the sequence features of the protein-coding sORFs in the two domains have significant differences implying that it might be a relatively hard task in terms of cross-domain prediction, hence domain-specific models were developed, which allowed users to predict protein-coding sORFs both in eukaryotes and prokaryotes. Finally, a web-server was developed and provided to boost and facilitate the study of the related field, which is freely available at http://guolab.whu.edu.cn/codingCapacity/index.html.

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.

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.

A systematic review on the state-of-the-art strategies for protein representation

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.

Discovering misannotated lncRNAs using deep learning training dynamics

MOTIVATION

Recent experimental evidence has shown that some long non-coding RNAs (lncRNAs) contain small open reading frames (sORFs) that are translated into functional micropeptides, suggesting that these lncRNAs are misannotated as non-coding. Current methods to detect misannotated lncRNAs rely on ribosome-profiling (Ribo-Seq) and mass-spectrometry experiments, which are cell-type dependent and expensive.

RESULTS

Here, we propose a computational method to identify possible misannotated lncRNAs from sequence information alone. Our approach first builds deep learning models to discriminate coding and non-coding transcripts and leverages these models' training dynamics to identify misannotated lncRNAs-i.e. lncRNAs with coding potential. The set of misannotated lncRNAs we identified significantly overlap with experimentally validated ones and closely resemble coding protein sequences as evidenced by significant BLAST hits. Our analysis on a subset of misannotated lncRNA candidates also shows that some ORFs they contain yield high confidence folded structures as predicted by AlphaFold2. This methodology offers promising potential for assisting experimental efforts in characterizing the hidden proteome encoded by misannotated lncRNAs and for curating better datasets for building coding potential predictors.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/nabiafshan/DetectingMisannotatedLncRNAs.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

A Neural Networks Approach for the Analysis of Reproducible Ribo–Seq Profiles

In recent years, the Ribosome profiling technique (Ribo–seq) has emerged as a powerful method for globally monitoring the translation process in vivo at single nucleotide resolution. Based on deep sequencing of mRNA fragments, Ribo–seq allows to obtain profiles that reflect the time spent by ribosomes in translating each part of an open reading frame. Unfortunately, the profiles produced by this method can vary significantly in different experimental setups, being characterized by a poor reproducibility. To address this problem, we have employed a statistical method for the identification of highly reproducible Ribo–seq profiles, which was tested on a set of E. coli genes. State-of-the-art artificial neural network models have been used to validate the quality of the produced sequences. Moreover, new insights into the dynamics of ribosome translation have been provided through a statistical analysis on the obtained sequences.

A tool for feature extraction from biological sequences

With the advances in sequencing technologies, a huge amount of biological data is extracted nowadays. Analyzing this amount of data is beyond the ability of human beings, creating a splendid opportunity for machine learning methods to grow. The methods, however, are practical only when the sequences are converted into feature vectors. Many tools target this task including iLearnPlus, a Python-based tool which supports a rich set of features. In this paper, we propose a holistic tool that extracts features from biological sequences (i.e. DNA, RNA and Protein). These features are the inputs to machine learning models that predict properties, structures or functions of the input sequences. Our tool not only supports all features in iLearnPlus but also 30 additional features which exist in the literature. Moreover, our tool is based on R language which makes an alternative for bioinformaticians to transform sequences into feature vectors. We have compared the conversion time of our tool with that of iLearnPlus: we transform the sequences much faster. We convert small nucleotides by a median of 2.8X faster, while we outperform iLearnPlus by a median of 6.3X for large sequences. Finally, in amino acids, our tool achieves a median speedup of 23.9X.

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.

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.

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.

A vast pool of lineage-specific microproteins encoded by long non-coding RNAs in plants

Pervasive transcription of eukaryotic genomes results in expression of long non-coding RNAs (lncRNAs) most of which are poorly conserved in evolution and appear to be non-functional. However, some lncRNAs have been shown to perform specific functions, in particular, transcription regulation. Thousands of small open reading frames (smORFs, <100 codons) located on lncRNAs potentially might be translated into peptides or microproteins. We report a comprehensive analysis of the conservation and evolutionary trajectories of lncRNAs-smORFs from the moss Physcomitrium patens across transcriptomes of 479 plant species. Although thousands of smORFs are subject to substantial purifying selection, the majority of the smORFs appear to be evolutionary young and could represent a major pool for functional innovation. Using nanopore RNA sequencing, we show that, on average, the transcriptional level of conserved smORFs is higher than that of non-conserved smORFs. Proteomic analysis confirmed translation of 82 novel species-specific smORFs. Numerous conserved smORFs containing low complexity regions (LCRs) or transmembrane domains were identified, the biological functions of a selected LCR-smORF were demonstrated experimentally. Thus, microproteins encoded by smORFs are a major, functionally diverse component of the plant proteome.

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.

Computational Analysis Predicts Hundreds of Coding lncRNAs in Zebrafish

Simple Summary

Noncoding RNAs (ncRNAs) regulate a variety of fundamental life processes such as development, physiology, metabolism and circadian rhythmicity. RNA-sequencing (RNA-seq) technology has facilitated the sequencing of the whole transcriptome, thereby capturing and quantifying the dynamism of transcriptome-wide RNA expression profiles. However, much remains unrevealed in the huge noncoding RNA datasets that require further bioinformatic analysis. In this study, we applied six bioinformatic tools to investigate coding potentials of approximately 21,000 lncRNAs. A total of 313 lncRNAs are predicted to be coded by all the six tools. Our findings provide insights into the regulatory roles of lncRNAs and set the stage for the functional investigation of these lncRNAs and their encoded micropeptides.

Abstract

Recent studies have demonstrated that numerous long noncoding RNAs (ncRNAs having more than 200 nucleotide base pairs (lncRNAs)) actually encode functional micropeptides, which likely represents the next regulatory biology frontier. Thus, identification of coding lncRNAs from ever-increasing lncRNA databases would be a bioinformatic challenge. Here we employed the Coding Potential Alignment Tool (CPAT), Coding Potential Calculator 2 (CPC2), LGC web server, Coding-Non-Coding Identifying Tool (CNIT), RNAsamba, and MicroPeptide identification tool (MiPepid) to analyze approximately 21,000 zebrafish lncRNAs and computationally to identify 2730–6676 zebrafish lncRNAs with high coding potentials, including 313 coding lncRNAs predicted by all the six bioinformatic tools. We also compared the sensitivity and specificity of these six bioinformatic tools for identifying lncRNAs with coding potentials and summarized their strengths and weaknesses. These predicted zebrafish coding lncRNAs set the stage for further experimental studies.

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

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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.

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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.

sORF-Encoded Polypeptide SEP1 Is a Novel Virulence Factor of Phytophthora Pathogens

Diseases caused by the notorious Phytophthora spp. result in enormous economic losses to crops and forests. Increasing evidence suggests that small open reading frame-encoded polypeptides (SEPs) participate in environmental responses of animals, plants, and fungi. However, it remains largely unknown whether Phytophthora pathogens produce SEPs. Here, we systematically predicted and identified 96 SEP candidates in P. capsici. Among them, three may induce stable cell death in Nicotiana benthamiana. Phytophthora-specific and conserved SEP1 facilitated P. capsici infection. PcSEP1-induced cell death is BAK1 and SOBIR1 independent and is correlated with its virulence function. Finally, PcSEP1 may be targeted to the apoplast for carrying out its functions, for which the C terminus is indispensable. Together, our results demonstrated that SEP1 is a new virulence factor, and previously unknown SEPs may act as effector proteins in Phytophthora pathogens.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Automated Prediction and Annotation of Small Open Reading Frames in Microbial Genomes

Small open reading frames (smORFs) and their encoded microproteins play central roles in microbes. However, there is a vast unexplored space of smORFs within human-associated microbes. A recent bioinformatic analysis used evolutionary conservation signals to enhance prediction of small protein families. To facilitate the annotation of specific smORFs, we introduce SmORFinder. This tool combines profile hidden Markov models of each smORF family and deep learning models that better generalize to smORF families not seen in the training set, resulting in predictions enriched for Ribo-seq translation signals. Feature importance analysis reveals that the deep learning models learn to identify Shine-Dalgarno sequences, deprioritize the wobble position in each codon, and group codon synonyms found in the codon table. A core-genome analysis of 26 bacterial species identifies several core smORFs of unknown function. We pre-compute smORF annotations for thousands of RefSeq isolate genomes and Human Microbiome Project metagenomes and provide these data through a public web portal.

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.

The peptide encoded by a novel putative lncRNA HBVPTPAP inducing the apoptosis of hepatocellular carcinoma cells by modulating JAK/STAT signaling pathways

When the hepatitis B virus (HBV) enters target cells, there are complex trans-regulatory mechanisms involved in the interactions between the virus and the target cells. In the present study, a new gene screened from the hepatoblastoma cell line HepG2 using suppression subtractive hybridization, referred to as lncRNA HBVPTPAP, was used to study the trans-regulation of HBV DNA polymerase. According to the structural characteristics of the full-length sequences, it was classified as long non-coding RNA. However, a unique and complete open reading frame (ORF) was still present. Therefore, to further identify the lncRNA HBVPTPAP gene's encoding potential, this study used several online tools to analyze and verify its encoding polypeptide authenticity. On that basis, the effects of the lncRNA HBVPTPAP gene on the biological behaviors of HepG2 cells and its molecular regulatory mechanism were investigated. It was found that the lncRNA HBVPTPAP subcellular was mainly located in the cytoplasm, and possibly activated the downstream JAK/STAT signaling pathway through the interaction between the encoding polypeptide and PILRA intracellular domain. Then, the mitochondrial apoptosis pathway may have been initiated to induce apoptosis. These results provided a basis for further study of the biological functions of the lncRNA HBVPTPAP gene.

Long Noncoding RNAs in Atherosclerosis and Vascular Injury: Pathobiology, Biomarkers, and Targets for Therapy

Despite major advances in the primary and secondary prevention of atherosclerosis and its risk factors, atherosclerotic cardiovascular disease remains a major clinical and financial burden on individuals and health systems worldwide. In addition, neointima formation and proliferation due to mechanical trauma to the vessel wall during percutaneous coronary interventions can lead to vascular restenosis and limit the longevity and effectiveness of coronary revascularization. Long noncoding RNAs (lncRNAs) have emerged as a novel class of epigenetic regulators with critical roles in the pathogenesis of atherosclerosis and restenosis following vascular injury. Here, we provide an in-depth review of lncRNAs that regulate the development of atherosclerosis or contribute to the pathogenesis of restenosis following mechanical vascular injury. We describe the diverse array of intracellular mechanisms by which lncRNAs exert their regulatory effects. We highlight the utility and challenges of lncRNAs as biomarkers. Finally, we discuss the immense translational potential of lncRNAs and strategies for targeting them therapeutically using oligonucleotide-based therapeutics and novel gene therapy platforms.

The hunt for sORFs: A multidisciplinary strategy

Growing evidence illustrates the shortcomings on the current understanding of the full complexity of the proteome. Previously overlooked small open reading frames (sORFs) and their encoded microproteins have filled important gaps, exerting their function as biologically relevant regulators. The characterization of the full small proteome has potential applications in many fields. Continuous development of techniques and tools led to an improved sORF discovery, where these can originate from bioinformatics analyses, from sequencing routines or proteomics approaches. In this mini review, we discuss the ongoing trends in the three fields and suggest some strategies for further characterization of high potential candidates.