Temporal Dynamic Analysis of Alternative Splicing During Embryonic Development in Zebrafish.

Alternative splicing is pervasive in mammalian genomes and involved in embryo development, whereas research on crosstalk of alternative splicing and embryo development was largely restricted to mouse and human and the alternative splicing regulation during embryogenesis in zebrafish remained unclear. We constructed the alternative splicing atlas at 18 time-course stages covering maternal-to-zygotic transition, gastrulation, somitogenesis, pharyngula stages, and post-fertilization in zebrafish. The differential alternative splicing events between different developmental stages were detected. The results indicated that abundance alternative splicing and differential alternative splicing events are dynamically changed and remarkably abundant during the maternal-to-zygotic transition process. Based on gene expression profiles, we found splicing factors are expressed with specificity of developmental stage and largely expressed during the maternal-to-zygotic transition process. The better performance of cluster analysis was achieved based on the inclusion level of alternative splicing. The biological function analysis uncovered the important roles of alternative splicing during embryogenesis. The identification of isoform switches of alternative splicing provided a new insight into mining the regulated mechanism of transcript isoforms, which always is hidden by gene expression. In conclusion, we inferred that alternative splicing activation is synchronized with zygotic genome activation and discovered that alternative splicing is coupled with transcription during embryo development in zebrafish. We also unveiled that the temporal expression dynamics of splicing factors during embryo development, especially co-orthologous splicing factors. Furthermore, we proposed that the inclusion level of alternative splicing events can be employed for cluster analysis as a novel parameter. This work will provide a deeper insight into the regulation of alternative splicing during embryogenesis in zebrafish.

Nuclear Transfer Arrest Embryos Show Massive Dysregulation of Genes Involved in Transcription Pathways.

Somatic cell nuclear transfer (SCNT) technology can reprogram terminally differentiated cell nuclei into a totipotent state. However, the underlying molecular barriers of SCNT embryo development remain incompletely elucidated. Here, we observed that transcription-related pathways were incompletely activated in nuclear transfer arrest (NTA) embryos compared to normal SCNT embryos and in vivo fertilized (WT) embryos, which hinders the development of SCNT embryos. We further revealed the transcription pathway associated gene regulatory networks (GRNs) and found the aberrant transcription pathways can lead to the massive dysregulation of genes in NTA embryos. The predicted target genes of transcription pathways contain a series of crucial factors in WT embryos, which play an important role in catabolic process, pluripotency regulation, epigenetic modification and signal transduction. In NTA embryos, however, these genes were varying degrees of inhibition and show a defect in synergy. Overall, our research found that the incomplete activation of transcription pathways is another potential molecular barrier for SCNT embryos besides the incomplete reprogramming of epigenetic modifications, broadening the understanding of molecular mechanism of SCNT embryonic development.

HelPredictor models single-cell transcriptome to predict human embryo lineage allocation.

The in-depth understanding of cellular fate decision of human preimplantation embryos has prompted investigations on how changes in lineage allocation, which is far from trivial and remains a time-consuming task by experimental methods. It is desirable to develop a novel effective bioinformatics strategy to consider transitions of coordinated embryo lineage allocation and stage-specific patterns. There are rapidly growing applications of machine learning models to interpret complex datasets for identifying candidate development-related factors and lineage-determining molecular events. Here we developed the first machine learning platform, HelPredictor, that integrates three feature selection methods, namely, principal components analysis, F-score algorithm and squared coefficient of variation, and four classical machine learning classifiers that different combinations of methods and classifiers have independent outputs by increment feature selection method. With application to single-cell sequencing data of human embryo, HelPredictor not only achieved 94.9% and 90.9% respectively with cross-validation and independent test, but also fast classified different embryonic lineages and their development trajectories using less HelPredictor-predicted factors. The above-mentioned candidate lineage-specific genes were discussed in detail and were clustered for exploring transitions of embryonic heterogeneity. Our tool can fast and efficiently reveal potential lineage-specific and stage-specific biomarkers and provide insights into how advanced computational tools contribute to development research. The source code is available at https://github.com/liameihao/HelPredictor.

RaacLogo: a new sequence logo generator by using reduced amino acid clusters.

Sequence logos give a fast and concise display in visualizing consensus sequence. Protein exhibits greater complexity and diversity than DNA, which usually affects the graphical representation of the logo. Reduced amino acids perform powerful ability for simplifying complexity of sequence alignment, which motivated us to establish RaacLogo. As a new sequence logo generator by using reduced amino acid alphabets, RaacLogo can easily generate many different simplified logos tailored to users by selecting various reduced amino acid alphabets that consisted of more than 40 clustering algorithms. This current web server provides 74 types of reduced amino acid alphabet, which were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with protein alignment. A two-dimensional selector was proposed for easily selecting desired RAACs with underlying biology knowledge. It is anticipated that the RaacLogo web server will play more high-potential roles for protein sequence alignment, topological estimation and protein design experiments. RaacLogo is freely available at http://bioinfor.imu.edu.cn/raaclogo.

ICTC-RAAC: An improved web predictor for identifying the types of ion channel-targeted conotoxins by using reduced amino acid cluster descriptors.

Conotoxins are small peptide toxins which are rich in disulfide and have the unique diversity of sequences. It is significant to correctly identify the types of ion channel-targeted conotoxins because that they are considered as the optimal pharmacological candidate medicine in drug design owing to their ability specifically binding to ion channels and interfering with neural transmission. Comparing with other feature extracting methods, the reduced amino acid cluster (RAAC) better resolved in simplifying protein complexity and identifying functional conserved regions. Thus, in our study, 673 RAACs generated from 74 types of reduced amino acid alphabet were comprehensively assessed to establish a state-of-the-art predictor for predicting ion channel-targeted conotoxins. The results showed Type 20, Cluster 9 (T = 20, C = 9) in the tripeptide composition (N = 3) achieved the best accuracy, 89.3%, which was based on the algorithm of amino acids reduction of variance maximization. Further, the ANOVA with incremental feature selection (IFS) was used for feature selection to improve prediction performance. Finally, the cross-validation results showed that the best overall accuracy we calculated was 96.4% and 1.8% higher than the best accuracy of previous studies. Based on the predictor we proposed, a user-friendly webserver was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ictcraac.

Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer.

Breast cancer is the most common cancer and the leading cause of cancer death among women in the world. Tumour-infiltrating lymphocytes were defined as the white blood cells left in the vasculature and localized in tumours. Recently, tumour-infiltrating lymphocytes were found to be associated with good prognosis and response to immunotherapy in tumours. In this study, to examine the influence of FLI1 in immune system in breast cancer, we interrogated the relationship between the FLI1 expression levels with infiltration levels of 28 immune cell types. By splitting the breast cancer samples into high and low expression FLI1 subtypes, we found that the high expression FLI1 subtype was enriched in many immune cell types, and the up-regulated differentially expressed genes between them were enriched in immune system processes, immune-related KEGG pathways and biological processes. In addition, many important immune-related features were found to be positively correlated with the FLI1 expression level. Furthermore, we found that the FLI1 was correlated with the immune-related genes. Our findings may provide useful help for recognizing the relationship between tumour immune microenvironment and FLI1, and may unravel clinical outcomes and immunotherapy utility for FLI1 in breast cancer.

iSP-RAAC: Identify Secretory Proteins of Malaria Parasite Using Reduced Amino Acid Composition.

BACKGROUND: As the pathogen of malaria, malaria parasite secretes a variety of proteins for its growth and reproduction. OBJECTIVE: The identification of the secretory proteins of malaria parasite has crucial reference significance for the development of anti-malaria vaccines as well as medicine. METHODS: In this study, a computational classification method was developed to identify the secreted proteins of Plasmodium. Amino acid composition, dipeptide composition, and tripeptide composition as well as reduced amino acids alphabets were proposed to illuminate protein sequences. We further used SVM to train and predict respectively and optimized the features. RESULTS: 74 types of reduced amino acids alphabets were employed to predict secretory proteins. The results showed that the accuracy improved to 91.67% with 0.84 Mathew's correlation coefficient (MCC) by dipeptide composition, and the highest prediction accuracy reached 92.26% after feature selection, which demonstrated that our method is prominent and reliable in the field of malaria parasite secreted proteins prediction. CONCLUSION: A intuitive web server iSP-RAAC (http://bioinfor.imu.edu.cn/isppseraac) was established for the convenience of most experimental scientists.

Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis.

Human preimplantation development is a complex process involving dramatic changes in transcriptional architecture. For a better understanding of their time-spatial development, it is indispensable to identify key genes. Although the single-cell RNA sequencing (RNA-seq) techniques could provide detailed clustering signatures, the identification of decisive factors remains difficult. Additionally, it requires high experimental cost and a long experimental period. Thus, it is highly desired to develop computational methods for identifying effective genes of development signature. In this study, we first developed a predictor called EmPredictor to identify developmental stages of human preimplantation embryogenesis. First, we compared the F-score of feature selection algorithms with differential gene expression (DGE) analysis to find specific signatures of the development stage. In addition, by training the support vector machine (SVM), four types of signature subsets were comprehensively discussed. The prediction results showed that a feature subset with 1,881 genes from the F-score algorithm obtained the best predictive performance, which achieved the highest accuracy of 93.3% on the cross-validation set. Further function enrichment demonstrated that the gene set selected by the feature selection method was involved in more development-related pathways and cell fate determination biomarkers. This indicates that the F-score algorithm should be preferentially proposed for detecting key genes of multi-period data in mammalian early development.

Dynamic Alternative Splicing During Mouse Preimplantation Embryo Development.

The mechanism of alternative pre-mRNA splicing (AS) during preimplantation development is largely unknown. In order to capture the dynamic changes of AS occurring during embryogenesis, we carried out bioinformatics analysis based on scRNA-seq data over the time-course preimplantation development in mouse. We detected numerous previously-unreported differentially expressed genes at specific developmental stages and investigated the nature of AS at both minor and major zygotic genome activation (ZGA). The AS and differential AS atlas over preimplantation development were established. The differentially alternatively spliced genes (DASGs) are likely to be key splicing factors (SFs) during preimplantation development. We also demonstrated that there is a regulatory cascade of AS events in which some key SFs are regulated by differentially AS of their own gene transcripts. Moreover, 212 isoform switches (ISs) during preimplantation development were detected, which may be critical for decoding the mechanism of early embryogenesis. Importantly, we uncovered that zygotic AS activation (ZASA) is in conformity with ZGA and revealed that AS is coupled with transcription during preimplantation development. Our results may provide a deeper insight into the regulation of early embryogenesis.

A Comparative Analysis of Single-Cell Transcriptome Identifies Reprogramming Driver Factors for Efficiency Improvement.

Terminally differentiated somatic cells can be reprogrammed into a totipotent state through somatic cell nuclear transfer (SCNT). The incomplete reprogramming is the major reason for developmental arrest of SCNT embryos at early stages. In our studies, we found that pathways for autophagy, endocytosis, and apoptosis were incompletely activated in nuclear transfer (NT) 2-cell arrest embryos, whereas extensively inhibited pathways for stem cell pluripotency maintenance, DNA repair, cell cycle, and autophagy may result in NT 4-cell embryos arrest. As for NT normal embryos, a significant shift in expression of developmental transcription factors (TFs) Id1, Pou6f1, Cited1, and Zscan4c was observed. Compared with pluripotent gene Ascl2 being activated only in NT 2-cell, Nanog, Dppa2, and Sall4 had major expression waves in normal development of both NT 2-cell and 4-cell embryos. Additionally, Kdm4b/4d and Kdm5b had been confirmed as key markers in NT 2-cell and 4-cell embryos, respectively. Histone acetylases Kat8, Elp6, and Eid1 were co-activated in NT 2-cell and 4-cell embryos to facilitate normal development. Gadd45a as a key driver functions with Tet1 and Tet2 to improve the efficiency of NT reprogramming. Taken together, our findings provided an important theoretical basis for elucidating the potential molecular mechanisms and identified reprogramming driver factor to improve the efficiency of SCNT reprogramming.

A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae.

Meiotic recombination is one of the most important driving forces of biological evolution, which is initiated by double-strand DNA breaks. Recombination has important roles in genome diversity and evolution. This review firstly provides a comprehensive survey of the 15 computational methods developed for identifying recombination hotspots in Saccharomyces cerevisiae. These computational methods were discussed and compared in terms of underlying algorithms, extracted features, predictive capability and practical utility. Subsequently, a more objective benchmark data set was constructed to develop a new predictor iRSpot-Pse6NC2.0 (http://lin-group.cn/server/iRSpot-Pse6NC2.0). To further demonstrate the generalization ability of these methods, we compared iRSpot-Pse6NC2.0 with existing methods on the chromosome XVI of S. cerevisiae. The results of the independent data set test demonstrated that the new predictor is superior to existing tools in the identification of recombination hotspots. The iRSpot-Pse6NC2.0 will become an important tool for identifying recombination hotspot.

Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods.

Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance-especially the multidrug-resistant (MDR) and extensively drug-resistance (XDR)-poses a huge threat to the treatment. However, traditional biochemical experimental method to tackle TB is time-consuming and costly. Benefited by the appearance of the enormous genomic and proteomic sequence data, TB can be treated via sequence-based biological computational approach-bioinformatics. Studies on predicting subcellular localization of mycobacterial protein (MBP) with high precision and efficiency may help figure out the biological function of these proteins and then provide useful insights for protein function annotation as well as drug design. In this review, we reported the progress that has been made in computational prediction of subcellular localization of MBP including the following aspects: 1) Construction of benchmark datasets. 2) Methods of feature extraction. 3) Techniques of feature selection. 4) Application of several published prediction algorithms. 5) The published results. 6) The further study on prediction of subcellular localization of MBP.

The Development of Machine Learning Methods in Cell-Penetrating Peptides Identification: A Brief Review.

BACKGROUND: Cell-penetrating Peptides (CPPs) are important short peptides that facilitate cellular intake or uptake of various molecules. CPPs can transport drug molecules through the plasma membrane and send these molecules to different cellular organelles. Thus, CPP identification and related mechanisms have been extensively explored. In order to reveal the penetration mechanisms of a large number of CPPs, it is necessary to develop convenient and fast methods for CPPs identification. METHODS: Biochemical experiments can provide precise details for accurately identifying CPP, but these methods are expensive and laborious. To overcome these disadvantages, several computational methods have been developed to identify CPPs. We have performed review on the development of machine learning methods in CPP identification. This review provides an insight into CPP identification. RESULTS: We summarized the machine learning-based CPP identification methods and compared the construction strategies of 11 different computational methods. Furthermore, we pointed out the limitations and difficulties in predicting CPPs. CONCLUSION: In this review, the last studies on CPP identification using machine learning method were reported. We also discussed the future development direction of CPP recognition with computational methods.

IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types.

Ion channels (IC) are ion-permeable protein pores located in the lipid membranes of all cells. Different ion channels have unique functions in different biological processes. Due to the rapid development of high-throughput mass spectrometry, proteomic data are rapidly accumulating and provide us an opportunity to systematically investigate and predict ion channels and their types. In this paper, we constructed a support vector machine (SVM)-based model to quickly predict ion channels and their types. By considering the residue sequence information and their physicochemical properties, a novel feature-extracted method which combined dipeptide composition with the physicochemical correlation between two residues was employed. A feature selection strategy was used to improve the performance of the model. Comparison results of in jackknife cross-validation demonstrated that our method was superior to other methods for predicting ion channels and their types. Based on the model, we built a web server called IonchanPred which can be freely accessed from http://lin.uestc.edu.cn/server/IonchanPredv2.0.

Recent Advances in Conotoxin Classification by Using Machine Learning Methods.

Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

One parameter to describe the mechanism of splice sites competition.

The choice of a splice site is not only related to its own intrinsic strength, but also is influenced by its flanking competitors. Splice site competition is an important mechanism for splice site prediction, especially, it is a new insight for alternative splice site prediction. In this paper, the position weight matrix scoring function is used to represent splice site strength, and the mechanism of splice site competition is described by only one parameter: scoring function subtraction. While applying on the alternative splice site prediction, based on the only one parameter, 68.22% of donor sites and 70.86% of acceptor sites are correctly classified into alternative and constitutive. The prediction abilities are approximately equal to the recent method which is based on the mechanism of splice site competition. The results reveal that the scoring function subtraction is the best parameter to describe the mechanism of splice sites competition.