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In the present study, we performed precise annotation of Drosophila melanogaster, D. simulans, D. grimshawi, Bactrocera oleae mitochondrial (mt) genomes using pan RNA-seq analysis. Using PacBio cDNA-seq data from D. simulans, we precisely annotated the Transcription Initiation Sites (TISs) of the mt Heavy and Light strands in Drosophila mt genomes and reported that the polyA(+) and polyA(-) motifs in the CRs are associated with TISs. The discovery of the conserved polyA(+) and polyA(-) motifs provides insights into many polyA and polyT sequences in CRs of insect mt genomes, leading to reveal the mt transcription and its regulation in invertebrates. Notably, we propose that: (1) polyA/polyT motifs in CRs function as signals to initiate mtDNA transcription; (2) the duplication, recombination or mutation of these polyA/polyT sequences formed the AT-rich regions during evolution; and (3) since CRs of many invertebrate species still contain many polyA/polyT sequences, there is a high probability that several TISs and TTSs exist in invertebrate mt genomes.

Background Identification of transposons or retrotransposons is still a difficult task and can not be accomplished without specialist knowledge. Tc1/mariner is a superfamily of DNA transposons that have been discovered in animals, plants, and fungi. Tc1/mariner transposons are probably the most widespread DNA transposons in nature. However, Tc1 transposons have not been identified and characterized in yeast and their nucleotide sequences are not available in public databases. Results In the present study, we reported the discovery of two intact Tc1 transposons in yeast and. filamentous fungi, respectively. The first one, named Tc1-OP1, can be used as a signature of Ogataea parapolymorpha. The second one, named Tc1-MP1, represents a subfamlily of Tc1 transposons in Rhizopus, Mucor, Circinella, etc. Notablely, Tc1-OP1 may encode a protease and a transposase, challenging the classical model of DNA transposons. Conclusions Tc1-OP1 is the first reported Tc1 transposon in yeast. Tc1-OP1 and Tc1-MP1 can be used for the identification and characterization of Tc1 transposons in future studies. The discovery of Tc1-OP1 revealed a nonclassical model of DNA transposons, which could challenge and enrich fundamental concepts of DNA transposons.

In the present study, we performed precise annotation of Drosophila melanogaster, D. simulans, D. grimshawi, Bactrocera oleae mitochondrial (mt) genomes by pan RNA-seq analysis. Our new annotations corrected or modified some of the previous annotations and two important findings were reported for the first time, including the discovery of the conserved polyA(+) and polyA(−) motifs in the control regions (CRs) of insect mt genomes and the adding of CCAs to the 3’ ends of two antisense tRNAs in D. melanogaster mt genome. Using PacBio cDNA-seq data from D. simulans, we precisely annotated the Transcription Initiation Sites (TISs) of the mt Heavy and Light strands in Drosophila mt genomes and reported that the polyA(+) and polyA(−) motifs in the CRs are associated with TISs. The discovery of the conserved polyA(+) and polyA(−) motifs provides insights into many polyA and polyT sequences in CRs of insect mt genomes, leading to reveal the mt transcription and its regulation in invertebrates. In addition, we provided a high-quality, well-curated and precisely annotated D. simulans mt genome (GenBank: MN611461), which should be included into the NCBI RefSeq database to replace the current reference genome NC_005781.

Many computational methods have been proposed to predict essential proteins from protein-protein interaction (PPI) networks. However, it is still challenging to improve the prediction accuracy. In this study, we propose a new method, esPOS (essential proteins Predictor using Order Statistics) to predict essential proteins from PPI networks. Firstly, we refine the networks by using gene expression information and subcellular localization information. Secondly, we design some new features, which combine the protein predicted secondary structure with PPI network. We show that these new features are useful to predict essential proteins. Thirdly, we optimize these features by using a greedy method, and combine the optimized features by order statistic method. Our method achieves the prediction accuracy of 0.76-0.79 on two network datasets. The proposed method is available at https://sourceforge.net/projects/espos/.

Background: Currently, methylotrophic yeasts (e.g., Pichia pastoris, Hansenula polymorpha, and Candida boindii) are subjects of intense genomics studies in basic research and industrial applications. In the genus Ogataea, most research is focused on three basic O. polymorpha strains—CBS4732, NCYC495, and DL-1. However, these three strains are of independent origin and unclear relationship. As a high-yield engineered O. polymorpha strain, HU-11 can be regarded as identical to CBS4732, because the only difference between them is a 5-bp insertion. Results: In the present study, we have assembled the full-length genome of O. polymorpha HU-11 using high-depth PacBio and Illumina data. Long terminal repeat (LTR) retrotransposons, rDNA, 5' and 3' telomeric, subtelomeric, low complexity and other repeat regions were curated to improve the genome quality. We took advantage of the full-length HU-11 genome sequence for the genome annotation and comparison. Particularly, we determined the exact location of the rDNA genes and LTR retrotransposons in seven chromosomes and detected large duplicated segments in the subtelomeric regions. Three novel findings are: (1) O. polymorpha NCYC495 is so phylogenetically close to CBS4732/HU-11 that the syntenic regions covers nearly 100% of their genomes with a nucleotide identity of 99.5%, while NCYC495 is significantly distinct from DL-1; (2) large segment duplication in subtelomeric regions is the main reason for genome expansion in yeasts; and (3) the duplicated segments in subtelomeric regions may be integrated at telomeric tandem repeats (TRs) through a molecular mechanism, which can be used to develop a simple and highly efficient genome editing system to integrate or cleave large segments into yeast genomes. Conclusions: Our findings provide new opportunities for in-depth understanding of genome evolution in methylotrophic yeasts and lay the foundations for the industrial applications of O. polymorpha HU-11 and CBS4732. The full-length genome of the O. polymorpha strain HU-11 should be included into the NCBI RefSeq database for future studies of O. polymorpha CBS4732, NCYC495, and their derivative strains.

BackgroundCurrently, methylotrophic yeasts (e.g., Pichia pastoris, Ogataea polymorpha, and Candida boindii) are subjects of intense genomics studies in basic research and industrial applications. In the genus Ogataea, most research is focused on three basic O. polymorpha strains—CBS4732, NCYC495, and DL-1. However, the relationship between CBS4732, NCYC495, and DL-1 remains unclear, as the genomic differences between them have not be exactly determined without their high-quality complete genomes. As a nutritionally deficient mutant derived from CBS4732, the O. polymorpha strain CBS4732 ura3Δ (named HU-11) is being used for high-yield production of several important proteins or peptides. HU-11 has the same reference genome as CBS4732 (noted as HU-11/CBS4732), because the only genomic difference between them is a 5-bp insertion.ResultsIn the present study, we have assembled the full-length genome of O. polymorpha HU-11/CBS4732 using high-depth PacBio and Illumina data. Long terminal repeat (LTR) retrotransposons, rDNA, 5’ and 3’ telomeric, subtelomeric, low complexity and other repeat regions were curated to improve the genome quality. Particularly, we detected large duplicated segments (LDSs) in the subtelomeric regions and exactly determined all the structural variations (SVs) between CBS4732 and NCYC495.New findings mainly include(1) the genomic differences between HU-11/CBS4732 and NCYC495 include single nucleotide polymorphisms, small insertions and deletions, and only three SVs; (2) six genes were incorporated into CBS4732 from Cyberlindnera jadinii by horizontal gene transfer and may bring HU-11/CBS4732 new biological functions or physiological properties; (3) many recombination events may have occurred on chromosome 4 and 5 of CBS4732 and NCYC495’ ancestors and two large segments were acquired by CBS4732 and NCYC495 from chromosome 6 and C. jadinii during recombination, respectively; and (4) the genome expansion in methylotrophic yeasts is mainly driven by large segment duplication in subtelomeric regions.ConclusionsThe present study preliminarily revealed the complex relationship between CBS4732, NCYC495, and DL-1. The new findings provide new opportunities for in-depth understanding of genome evolution in methylotrophic yeasts and lay the foundations for the industrial applications of O. polymorpha CBS4732, NCYC495, DL-1, and their derivative strains. The full-length genome of the O. polymorpha strain HU-11/CBS4732 should be included into the NCBI RefSeq database for future studies of Ogataea spp..

In December 2019, the world awoke to a new betacoronavirus strain named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Betacoronavirus consists of A, B, C and D subgroups. Both SARS-CoV and SARS-CoV-2 belong to betacoronavirus subgroup B. In the present study, we divided betacoronavirus subgroup B into the SARS1 and SARS2 classes by six key insertions and deletions (InDels) in betacoronavirus genomes, and identified a recently detected betacoronavirus strains RmYN02 as a recombinant strain across the SARS1 and SARS2 classes, which has potential to generate a new strain with similar risk as SARS-CoV and SARS-CoV-2. By analyzing genomic features of betacoronavirus, we concluded: (1) the jumping transcription and recombination of CoVs share the same molecular mechanism, which inevitably causes CoV outbreaks; (2) recombination, receptor binding abilities, junction furin cleavage sites (FCSs), first hairpins and ORF8s are main factors contributing to extraordinary transmission, virulence and host adaptability of betacoronavirus; and (3) the strong recombination ability of CoVs integrated other main factors to generate multiple recombinant strains, two of which evolved into SARS-CoV and SARS-CoV-2, resulting in the SARS and COVID-19 pandemics. As the most important genomic features of SARS-CoV and SARS-CoV-2, an enhanced ORF8 and a novel junction FCS, respectively, are indispensable clues for future studies of their origin and evolution. The WIV1 strain without the enhanced ORF8 and the RaTG13 strain without the junction FCS “RRAR” may contribute to, but are not the immediate ancestors of SARS-CoV and SARS-CoV-2, respectively.

BackgroundCoronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although unprecedented efforts are underway to develop therapeutic strategies against this disease, scientists have acquired only a little knowledge regarding the structures and functions of the CoV replication and transcription complex (RTC) and 16 non-structural proteins, named NSP1-16.ResultsIn the present study, we proposed a two-route model to answer how the RTC functions in the jumping transcription of CoVs. The key step leading to this model was that the motif AAACH for METTL3 recognition flanking the transcription regulatory sequence (TRS) motif was discovered to determine the m6A methylation of SARS-CoV-2 RNAs, by reanalyzing public Nanopore RNA-seq data. As the most important finding, TRS hairpins were reported for the first time to interpret NSP15 cleavage, RNA methylation of CoVs and their association at the molecular level. In addition, we reported canonical TRS motifs of all CoVs to prove the importance of our findings.ConclusionsThe main conclusions are: (1) TRS hairpins can be used to identify recombination regions in CoV genomes; (2) RNA methylation of CoVs participates in the determination of the RNA secondary structures by affecting the formation of base pairing; and (3) The eventual determination of the CoV RTC global structure needs to consider METTL3 in the experimental design. Our findings enrich fundamental knowledge in the field of gene expression and its regulation, providing a crucial basis for future studies.

BackgroundCoronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although a preliminary understanding of the replication and transcription of SARS-CoV-2 has recently emerged, their regulation remains unknown.ResultsBy comprehensive analysis of genome sequence and protein structure data, we propose a negative feedback model to explain the regulation of CoV replication and transcription, providing a molecular basis of the “leader-to-body fusion” model. The key step leading to the proposal of our model was that the transcription regulatory sequence (TRS) motifs were identified as the cleavage sites of nsp15, a nidoviral RNA uridylate-specific endoribonuclease (NendoU). According to this model, nsp15 regulates the synthesis of subgenomic RNAs (sgRNAs), and genomic RNAs (gRNAs) by cleaving TRSs. The expression level of nsp15 controls the relative proportions of sgRNAs and gRNAs, which in turn change the expression level of nsp15 to reach equilibrium between the CoV replication and transcription.ConclusionThe replication and transcription of CoVs are regulated by a negative feedback mechanism that influences the persistence of CoVs in hosts. Our findings enrich fundamental knowledge in the field of gene expression and its regulation, and provide new clues for future studies. One important clue is that nsp15 may be an important and ideal target for the development of drugs (e.g., uridine derivatives) against CoVs.

Background: In December 2019, the world awoke to a new zoonotic strain of coronavirus named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).Results: In the present study, we classified betacoronavirus subgroup B into the SARS-CoV-2, SARS-CoV and SARS-like CoV clusters, and the ORF8 genes of these three clusters into types 1, 2 and 3, respectively. One important result of our study is that we reported—for the first time—a recombination event of ORF8 at the whole-gene level in a bat, which had been co-infected by two betacoronavirus strains. This result provides substantial proof for long-existing hypotheses regarding the recombination and biological functions of ORF8. Based on the analysis of recombination events in the Spike gene, we propose that the Spike protein of SARS-CoV-2 may have more than one specific receptor for its function as gp120 of HIV has CD4 and CCR5. In the present study, we also found that the ancestor of betacoronavirus had a strong first Internal Ribosome Entry Site (IRES) and at least one furin cleavage site (FCS) in the junction region between S1 and S2 subunits.Conclusions: We concluded that the junction FCS in SARS-CoV-2 may increase the efficiency of its entry into cells, while the type 2 ORF8 acquired by SARS-CoV may increase its replication efficiency. These two most critical events provide the most likely explanation for SARS and COVID-19 pandemics.

In December 2019, the world awoke to a new zoonotic strain of coronavirus named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). In the present study, we identified key recombination regions and mutation sites cross the SARS-CoV-2, SARS-CoV and SARS-like CoV clusters of betacoronavirus subgroup B. Based on the analysis of these recombination events, we proposed that the Spike protein of SARS-CoV-2 may have more than one specific receptor for its function. In addition, we reported—for the first time—a recombination event of ORF8 at the whole-gene level in a bat and ultimately determined that ORF8 enhances the viral replication. In conjunction with our previous discoveries, we found that receptor binding abilities, junction furin cleavage sites (FCSs), strong first ribosome binding sites (RBSs) and enhanced ORF8s are main factors contributing to transmission, virulence and host adaptability of CoVs. Junction FCSs and enhanced ORF8s increase the efficiencies in viral entry into cells and replication, respectively while strong first RBSs enhance the translational initiation. The strong recombination ability of CoVs integrated these factors to generate multiple recombinant strains, two of which evolved into SARS-CoV and SARS-CoV-2 by nature selection, resulting in the SARS and COVID-19 pandemics.

To assess the efficacy and safety in patients with chronic heart failure (CHF) of Western medication plus Traditional Chinese Medicine (TCM) preparations.This prospective, single-blind, randomized, controlled, and multicenter clinical trial began on September 17, 2008, and was completed on June 25, 2011. A total of 340 inpatients, aged 40-79 years, with exacerbating CHF from 10 hospitals were enrolled and randomly allocated within 24 h of admission. The trial included three intervention periods. During hospitalization, the control group received western medication for CHF and the treatment group received Danhong injection with Shenfu injection or Shenmai injection. After discharge, all patients were treated with Qiliqiangxin capsules and Buyiqiangxin tablets or a placebo for 6 months. After the 6-month intervention, both groups received only continuous western medication. The primary endpoint was all-cause mortality. The efficacy assessments were as follows: B-type natriuretic peptide (BNP), Lee's HF score, the 6-minute walking test (6MWT), left ventricular ejection fraction (LVEF), and the Minnesota Living with Heart Failure Questionnaire (MLHFQ). The safety assessments were as follows: blood and urine routine examination, hepatic and renal function, electrolytes in blood and adverse events.Compared with the control group, the treatment group showed a 30.99% reduction in all-cause mortality and an improved survival rate. The treatment group showed greater improvement in 6MWT (P = 0.02) than the control group on discharge, after 12-month follow-up, there was a time-group interaction for MLHFQ (P = 0.03). Incidence rate of adverse events and other relevant safety indexes were not statistically significant between the two groups.Western medication plus TCM treatment can increase 6-minute walking distance (improve exercise tolerance) and quality of life with heart failure patients.

U1 small nuclear RNA (U1 snRNA), as one of the most abundant ncRNAs in human cells, plays an important role in splicing of pre-mRNAs. Compared to previous studies which have focused on the primary function of U1 snRNA and the neurodegenerative diseases caused by abnormalities of U1 snRNA, this study is to investigate how U1 snRNA over-expression affects the expression of mammal genes on a genome-wide scale. By comparing the gene expression profiles of U1 snRNA over-expressed cells with those of their controls using microarray experiments, 916 genes or loci were identified significantly Differentially Expressed (DE). These 595 up-regulated DE genes and 321 down-regulated DE genes were analyzed using annotations from GO categories and pathways from the KEGG database. As a result, three of 12 enriched pathways were well-known cancer pathways, while the other nine pathways were associated to cancers in previous studies. The further analysis of 73 genes involved in 12 pathways suggested that U1 snRNA could regulate cancer gene expression. The microarray data under the GEO Series accession number GSE84304 is available in the NCBI GEO database.

Accurate prediction of atomic-level protein structure is important for annotating the biological functions of protein molecules and for designing new compounds to regulate the functions. Template-based modeling (TBM), which aims to construct structural models by copying and refining the structural frameworks of other known proteins, remains the most accurate method for protein structure prediction. Due to the difficulty in recognizing distant-homology templates, however, the accuracy of TBM decreases rapidly when the evolutionary relationship between the query and template vanishes. In this study, we propose a new method, CEthreader, which first predicts residue-residue contacts by coupling evolutionary precision matrices with deep residual convolutional neural-networks. The predicted contact maps are then integrated with sequence profile alignments to recognize structural templates from the PDB. The method was tested on two independent benchmark sets consisting collectively of 1,153 non-homologous protein targets, where CEthreader detected 176% or 36% more correct templates with a TM-score >0.5 than the best state-of-the-art profile- or contact-based threading methods, respectively, for the Hard targets that lacked homologous templates. Moreover, CEthreader was able to identify 114% or 20% more correct templates with the same Fold as the query, after excluding structures from the same SCOPe Superfamily, than the best profile- or contact-based threading methods. Detailed analyses show that the major advantage of CEthreader lies in the efficient coupling of contact maps with profile alignments, which helps recognize global fold of protein structures when the homologous relationship between the query and template is weak. These results demonstrate an efficient new strategy to combine ab initio contact map prediction with profile alignments to significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins., Author summary Despite decades of effort in computational method development, template-based modeling (TBM) still remains the most reliable approach to high-resolution protein structure prediction. Previous studies have shown that the PDB library is complete for single-domain proteins and TBM is in principle sufficient to solve the structure prediction problem if the most similar structure in the PDB could be reliably identified and used as template for model reconstruction. But in reality, the success of TBM depends on the availability of closely-homologous templates, where its accuracy and reliability decrease sharply when the evolutionary relationship between query and template becomes more distant. We developed a new threading approach, CEthreader, which allows for dynamic programing alignments of predicted contact-maps through eigen-decomposition. The large-scale benchmark tests show that the coupling of contact map with profile and secondary structure alignments through the proposed protocol can significantly improve the accuracy of template recognition for distantly-homologous protein targets.

In this study, we used pan RNA-seq analysis to reveal the ubiquitous existence of 5’ end and 3’ end small RNAs. 5’ and 3’ sRNAs alone can be used to annotate mitochondrial with 1-bp resolution and nuclear non-coding genes and identify new steady-state RNAs, which are usually from functional genes. Using 5’, 3’ and intronic sRNAs, we revealed that the enzymatic dsRNA cleavage and RNAi could involve in the RNA degradation and gene expression regulation of U1 snRNA in human. The further study of 5’, 3’ and intronic sRNAs help rediscover double-stranded RNA (dsRNA) cleavage, RNA interference (RNAi) and the regulation of gene expression, which challenges the classical theories. In this study, we provided a simple and cost effective way for the annotation of mitochondrial and nuclear non-coding genes and the identification of new steady-state RNAs, particularly long non-coding RNAs (lncRNAs). We also provided a different point of view for cancer and virus, based on the new discoveries of dsRNA cleavage, RNAi and the regulation of gene expression.

In this study, we report for the first time the existence of complemented palindromic small RNAs (cpsRNAs) and propose that cpsRNAs and palindromic small RNAs (psRNAs) constitute a novel class of small RNAs. The first discovered 19-nt cpsRNA UUAACAAGCUUGUUAAAGA, named SARS-CoV-cpsR-19, was detected from a 22-bp DNA complemented palindrome TCTTTAACAAGCTTGTTAAAGA in the severe acute respiratory syndrome coronavirus (SARS-CoV) genome. The phylogenetic analysis supported that this DNA complemented palindrome originated from bat betacoronavirus. The results of RNA interference (RNAi) experiments showed that one 19-nt segment corresponding to SARS-CoV-cpsR-19 significantly induced cell apoptosis. Using this joint analysis of the molecular function and phylogeny, our results suggested that SARS-CoV-cpsR-19 could play a role in SARS-CoV infection or pathogenesis. The discovery of cpsRNAs has paved a way to find novel markers for pathogen detection and to reveal the mechanisms underlying infection or pathogenesis from a different point of view. Researchers can use cpsRNAs to study the infection or pathogenesis of pathogenic viruses when these viruses are not available. The discovery of psRNAs and cpsRNAs, as a novel class of small RNAs, also inspire researchers to investigate DNA palindromes and DNA complemented palindromes with lengths of psRNAs and cpsRNAs in viral genomes.

Disease gene prediction is a challenging task that has a variety of applications such as early diagnosis and drug development. The existing machine learning methods suffer from the imbalanced sample issue because the number of known disease genes (positive samples) is much less than that of unknown genes which are typically considered to be negative samples. In addition, most methods have not utilized clinical data from patients with a specific disease to predict disease genes. In this study, we propose a disease gene prediction algorithm (called dgSeq) by combining protein-protein interaction (PPI) network, clinical RNA-Seq data, and Online Mendelian Inheritance in Man (OMIN) data. Our dgSeq constructs differential networks based on rewiring information calculated from clinical RNA-Seq data. To select balanced sets of non-disease genes (negative samples), a disease-gene network is also constructed from OMIM data. After features are extracted from the PPI networks and differential networks, the logistic regression classifiers are trained. Our dgSeq obtains AUC values of 0.88, 0.83, and 0.80 for identifying breast cancer genes, thyroid cancer genes, and Alzheimer's disease genes, respectively, which indicates its superiority to other three competing methods. Both gene set enrichment analysis and predicted results demonstrate that dgSeq can effectively predict new disease genes.

We address the problem of subspace clustering.A reweighted scheme is introduced to the existing SSC algorithm.The proposed RSSC algorithm achieves lower clustering error than LSR, SMR, LRR and SSC algorithms. Motion segmentation and human face clustering are two fundamental problems in computer vision. The state-of-the-art algorithms employ the subspace clustering scheme when processing the two problems. Among these algorithms, sparse subspace clustering (SSC) achieves the state-of-the-art clustering performance via solving a ?1 minimization problem and employing the spectral clustering technique for clustering data points into different subspaces. In this paper, we propose an iterative weighting (reweighted) ?1 minimization framework which largely improves the performance of the traditional ?1 minimization framework. The reweighted ?1 minimization framework makes a better approximation to the ?0 minimization than tradition ?1 minimization framework. Following the reweighted ?1 minimization framework, we propose a new subspace clustering algorithm, namely, reweighted sparse subspace clustering (RSSC). Through an extensive evaluation on three benchmark datasets, we demonstrate that the proposed RSSC algorithm significantly reduces the clustering errors over the SSC algorithm while the additional reweighted step has a moderate impact on the computational cost. The proposed RSSC also achieves lowest clustering errors among recently proposed algorithms. On the other hand, as majority of the algorithms were evaluated on the Hopkins155 dataset, which is insufficient of non-rigid motion sequences, the dataset can hardly reflect the ability of the existing algorithms on processing non-rigid motion segmentation. Therefore, we evaluate the performance of the proposed RSSC and state-of-the-art algorithms on the Freiburg-Berkeley Motion Segmentation Dataset, which mainly contains non-rigid motion sequences. The performance of these state-of-the-art algorithms, as well as RSSC, will drop dramatically on this dataset with mostly non-rigid motion sequences. Though the proposed RSSC achieves the better performance than other algorithms, the results suggest that novel algorithms that focus on segmentation of non-rigid motions are still in need.

In this study, we reported two featured series of rRNA-derived RNA fragments (rRFs) from the small RNA sequencing (sRNA-seq) data of Amblyomma testudinarium using the Illunima platform. Two series of rRFs (rRF5 and rRF3) were precisely aligned to the 5' and 3' ends of the 5.8S and 28S rRNA gene. The rRF5 and rRF3 series were significantly more highly expressed than the rRFs located in the body of the rRNA genes. These series contained perfectly aligned reads, the lengths of which varied progressively with 1-bp differences. The rRF5 and rRF3 series in the same expression pattern exist ubiquitously from ticks to human. The cellular experiments showed the RNAi knockdown of one 20-nt rRF3 induced the cell apoptosis and inhibited the cell proliferation. In addition, the RNAi knockdown resulted in a significant decrease of H1299 cells in the G2 phase of the cell cycle. These results indicated the rRF5 and rRF3 series were not random intermediates or products during rRNA degradation, but could constitute a new class of small RNAs that deserves further investigation.

Epitopes are immunogenic regions in antigen protein. Prediction of B-cell epitopes is critical for immunological applications. B-cell epitopes are categorized into linear and conformational. The majority of B-cell epitopes are conformational. Several machine learning methods have been proposed to identify conformational B-cell epitopes. However, the quality of these methods is not ideal. One question is whether or not the prediction of conformational B-cell epitopes can be improved by using ensemble methods. In this paper, we propose an ensemble method, which combined 12 support vector machine-based predictors, to predict the conformational B-cell epitopes, using an unbound dataset. AdaBoost and resampling methods are used to deal with an imbalanced labeled dataset. The proposed method achieves AUC of 0.642–0.672 on training dataset with 5-fold cross validation and AUC of 0.579–0.604 on test dataset. We also find some interesting results with the bound and unbound datasets. Epitopes are more accessible than non-epitopes, in bound and unbound datasets. Epitopes are also preferred in beta-turn, in bound and unbound datasets. The flexibility and polarity of epitopes are higher than non-epitopes. In a bound dataset, Asn (N), Glu (E), Gly (G), Lys (K), Ser (S), and Thr (T) are preferred in epitope regions, while Ala (A), Leu (L) and Val (V) are preferred in non-epitope regions. In the unbound dataset, Glu (E) and Lys (K) are preferred in epitope sites, while Leu (L) and Val (V) are preferred in non-epitiopes sites.

The identification of disease genes is an essential issue to decipher the mechanisms of complex diseases. Many existing methods combine machine learning algorithms and network information to predict disease genes and are based on the ‘guilt by association’ assumption, where disease genes are considered to be close to each other in a biomolecular network. Although these methods have gained many novel findings, most of them ignored the edge dynamic changes of biomolecular networks under different conditions when only utilizing the ‘guilt by association’ principle, which will limit their performance. To address this problem, we propose an algorithm that combines the ‘guilt by association’ and the ‘guilt by rewiring’ of biomolecular networks at the same time. The difference of gene co-expression between case and control samples are first processed to obtain the edge dynamic changes (rewiring) of biomolecular networks through weighting the edges of protein-protein interaction (PPI) networks. Then, features are extracted from the weighted PPI network. Finally, a logistic regression is adopted to identify the disease genes. The algorithm achieves AUC values of 0.95, 0.90 and 0.92 on the identification of breast-cancer-related, lung-cancer-related and schizophrenia-related genes, respectively. Two new schizophrenia-related genes are also found from the ranked unknown genes list.

In this study, we introduced a general framework to use PacBio full-length transcriptome sequencing for the investigation of the fundamental problems in mitochondrial biology, e.g. genome arrangement, heteroplasmy, RNA processing and the regulation of transcription or replication. As a result, we produced the first full-length human mitochondrial transcriptome from the MCF7 cell line based on the PacBio platform and characterized the human mitochondrial transcriptome with more comprehensive and accurate information. The most important finding is two novel genes hsa-MDL1 and hsa-MDL1AS, which are encoded by the mitochondrial D-loop regions. We propose hsa-MDL1 and hsa-MDL1AS, as the precursors of transcription initiation RNAs (tiRNAs), belong to a novel class of long non-coding RNAs (lnRNAs), which is named as long tiRNAs (ltiRNAs). Based on the mitochondrial RNA processing model, the primary tiRNAs, precursors and mature tiRNAs could be discovered to completely reveal tiRNAs from their origins to functions. The ltiRNA/tiRNA system and their regulation mechanisms could exist ubiquitously in eukaryotes and prokaryotes. These findings will enrich the basic concepts in the field of mitochondrial biology, lnRNA functions and regulation of gene expression.

Amidation plays an important role in a variety of pathological processes and serious diseases like neural dysfunction and hypertension. However, identification of protein amidation sites through traditional experimental methods is time consuming and expensive. In this paper, we proposed a novel predictor for Prediction of Amidation Sites (PrAS), which is the first software package for academic users. The method incorporated four representative feature types, which are position-based features, physicochemical and biochemical properties features, predicted structure-based features and evolutionary information features. A novel feature selection method, positive contribution feature selection was proposed to optimize features. PrAS achieved AUC of 0.96, accuracy of 92.1%, sensitivity of 81.2%, specificity of 94.9% and MCC of 0.76 on the independent test set. PrAS is freely available at https://sourceforge.net/p/praspkg.

In this study, we sequenced the first full-length insect transcriptome using the Erthesina fullo Thunberg based on the PacBio platform. We constructed the first quantitative transcription map of animal mitochondrial genomes and built a straightforward and concise methodology to investigate mitochondrial gene transcription, RNA processing, mRNA maturation and several other related topics. Most of the results were consistent with the previous studies, while to the best of our knowledge some findings were reported for the first time in this study. The new findings included the high levels of mitochondrial gene expression, the 3' polyadenylation and possible 5' m(7)G caps of rRNAs, the isoform diversity of 12S rRNA, the polycistronic transcripts and natural antisense transcripts of mitochondrial genes et al. These findings could challenge and enrich fundamental concepts of mitochondrial gene transcription and RNA processing, particularly of the rRNA primary (sequence) structure. The methodology constructed in this study can also be used to study gene expression or RNA processing of nuclear genomes.