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Fecal microbiota transplantation (FMT) of human fecal samples into germ-free (GF) mice is useful for establishing causal relationships between the gut microbiota and human phenotypes. However, due to the intrinsic differences between human and mouse intestines and the different diets of the two organisms, it may not be possible to replicate human phenotypes in mice through FMT; similarly, treatments that are effective in mouse models may not be effective in humans. In this study, we aimed to identify human gut microbes that undergo significant and consistent changes (i.e., in relative abundances) after transplantation into GF mice in multiple experimental settings. We collected 16S rDNA-seq data from four published studies and analyzed the gut microbiota profiles from 1713 human-mouse pairs. Strikingly, on average, we found that only 47% of the human gut microbes could be re-established in mice at the species level, among which more than 1/3 underwent significant changes (referred to as "variable taxa"). Most of the human gut microbes that underwent significant changes were consistent across multiple human-mouse pairs and experimental settings. Consequently, about 1/3 of human samples changed their enterotypes, i.e., significant changes in their leading species after FMT. Mice fed with a controlled diet showed a lower enterotype change rate (23.5%) than those fed with a noncontrolled diet (49.0%), suggesting a possible solution for rescue. Most of the variable taxa have been reported to be implicated in human diseases, with some recognized as the causative species. Our results highlight the challenges of using a mouse model to replicate human gut microbiota-associated phenotypes, provide useful information for researchers using mice in gut microbiota studies, and call for additional validations after FMT. An online database named FMT-DB is publicly available at http://fmt2mice.humangut.info/#/.

Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular 'omics' data, e.g., transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purposes are to increase the reusability and accessibility of human gut metagenomic data, and enable cross-project and phenotype comparisons. To achieve these goals, we performed manual curation on the meta-data and organized the datasets in a phenotype-centric manner. GMrepo v2 contains 353 projects and 71,642 runs/samples, which are significantly increased from the previous version. Among these runs/samples, 45,111 and 26,531 were obtained by 16S rRNA amplicon and whole-genome metagenomics sequencing, respectively. We also increased the number of phenotypes from 92 to 133. In addition, we introduced disease-marker identification and cross-project/phenotype comparison. We first identified disease markers between two phenotypes (e.g. health versus diseases) on a per-project basis for selected projects. We then compared the identified markers for each phenotype pair across datasets to facilitate the identification of consistent microbial markers across datasets. Finally, we provided a marker-centric view to allow users to check if a marker has different trends in different diseases. So far, GMrepo includes 592 marker taxa (350 species and 242 genera) for 47 phenotype pairs, identified from 83 selected projects. GMrepo v2 is freely available at: https://gmrepo.humangut.info.

Ubiquitination is one of the most important post-translational modifications which involves in many biological processes. Because mass spectrometry-based ubiquitination site identification methods are costly and time consuming, computational approaches provide alternative ways to the determination of ubiquitination sites. Although machine learning based methods can effectively predict ubiquitination sites, most of them rely on feature engineering, which may lead to bias or incomplete feature. Recently, deep learning has achieved great success in prediction of post-translational modification sites. However, deep learning method has not been explored in the prediction of species-specific ubiquitination sites. In this paper, we propose a novel transfer deep learning method, named DeepTL-Ubi, for predicting ubiquitination sites of multiple species. DeepTL-Ubi enhances the performance of species-specific ubiquitination site prediction by transferring common knowledge from the large amount of human data to other species, which effectively solves the problem of insufficient training data for other species. Besides, we train and test our model by collecting ubiquitination sites for multiple species from several sources. Experiment results show that our transfer learning technique can effectively improve the predictive performance of species with small sample size, and DeepTL-Ubi is superior to existing tools in many species. The source code and training data of DeepTL-Ubi are publicly deposited at https://github.com/USTC-HIlab/DeepTL-Ubi.

The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets covering different types of data (i.e., genomics, transcriptomics, imaging, etc.) has shed light on the mechanisms underlying brain disorders in greater detail across both the microscopic and macroscopic levels. In this review, we first briefly introduce the popular large datasets for the brain. Then, we discuss in detail how integration of multimodal human brain datasets can reveal the genetic predispositions and the abnormal molecular pathways of brain disorders. Finally, we present an outlook on how future data integration efforts may advance the diagnosis and treatment of brain disorders.

Attention-deficit/hyperactivity disorder (ADHD) has been reported to be associated with longer screen time utilization (STU) at the behavioral level. However, whether there are shared neural links between ADHD symptoms and prolonged STU is not clear and has not been explored in a single large-scale dataset. Leveraging the genetics, neuroimaging and behavioral data of 11,000+ children aged 9-11 from the Adolescent Brain Cognitive Development cohort, this study investigates the associations between the polygenic risk and trait for ADHD, STU, and white matter microstructure through cross-sectionally and longitudinal analyses. Children with higher polygenic risk scores for ADHD tend to have longer STU and more severe ADHD symptoms. Fractional anisotropy (FA) values in several white matter tracts are negatively correlated with both the ADHD polygenic risk score and STU, including the inferior frontal-striatal tract, inferior frontal-occipital fasciculus, superior longitudinal fasciculus and corpus callosum. Most of these tracts are linked to visual-related functions. Longitudinal analyses indicate a directional effect of white matter microstructure on the ADHD scale, and a bi-directional effect between the ADHD scale and STU. Furthermore, reduction of FA in several white matter tracts mediates the association between the ADHD polygenic risk score and STU. These findings shed new light on the shared neural overlaps between ADHD symptoms and prolonged STU, and provide evidence that the polygenic risk for ADHD is related, via white matter microstructure and the ADHD trait, to STU. This study was mainly supported by NSFC and National Key R&D Program of China. [Abstract copyright: Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.]

OGEE is an Online GEne Essentiality database. Gene essentiality is not a static and binary property, rather a context-dependent and evolvable property in all forms of life. In OGEE we collect not only experimentally tested essential and non-essential genes, but also associated gene properties that contributes to gene essentiality. We tagged conditionally essential genes that show variable essentiality statuses across datasets to highlight complex interplays between gene functions and environmental/experimental perturbations. OGEE v3 contains gene essentiality datasets for 91 species; almost doubled from 48 species in previous version. To accommodate recent advances on human cancer essential genes (as known as tumor dependency genes) that could serve as targets for cancer treatment and/or drug development, we expanded the collection of human essential genes from 16 cell lines in previous to 581. These human cancer cell lines were tested with high-throughput experiments such as CRISPR-Cas9 and RNAi; in total, 150 of which were tested by both techniques. We also included factors known to contribute to gene essentiality for these cell lines, such as genomic mutation, methylation and gene expression, along with extensive graphical visualizations for ease of understanding of these factors. OGEE v3 can be accessible freely at https://v3.ogee.info.

Extrachromosomal mobile genetic elements (eMGEs), including phages and plasmids, that can move across different microbes, play important roles in genome evolution and shaping the structure of microbial communities. However, we still know very little about eMGEs, especially their abundances, distributions and putative functions in microbiomes. Thus, a comprehensive description of eMGEs is of great utility. Here we present mMGE, a comprehensive catalog of 517 251 non-redundant eMGEs, including 92 492 plasmids and 424 759 phages, derived from diverse body sites of 66 425 human metagenomic samples. About half the eMGEs could be further grouped into 70 074 clusters using relaxed criteria (referred as to eMGE clusters below). We provide extensive annotations of the identified eMGEs including sequence characteristics, taxonomy affiliation, gene contents and their prokaryotic hosts. We also calculate the prevalence, both within and across samples for each eMGE and eMGE cluster, enabling users to see putative associations of eMGEs with human phenotypes or their distribution preferences. All eMGE records can be browsed or queried in multiple ways, such as eMGE clusters, metagenomic samples and associated hosts. The mMGE is equipped with a user-friendly interface and a BLAST server, facilitating easy access/queries to all its contents easily. mMGE is freely available for academic use at: https://mgedb.comp-sysbio.org.

The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.

Subjective or objective subtle cognitive decline (SCD) is considered the preclinical manifestation of Alzheimer's disease (AD), which is a potentially crucial window for preventing or delaying the progression of the disease.To explore the potential mechanism of disease progression and identify relevant biomarkers, we comprehensively assessed the peripheral blood transcriptomic alterations in SCD, covering lncRNA, mRNA, and miRNA.Dysregulated protein-coding mRNA at both gene and isoform levels implicated impairment in the type I interferon signaling pathway in SCD. Specifically, this pathway was regulated by the transcription factor STAT1 and ncRNAs NRIR and has-miR-146a-5p. The miRNA-mRNA-lncRNA co-expression network revealed hub genes for the interferon module. Individuals with lower interferon signaling activity and lower expression of a hub gene STAT1 exhibited a higher conversion rate to mild cognitive impairment (MCI).Our findings illustrated the down-regulation of interferon signaling activity would potentially increase the risk of disease progression and thus serve as a pre-disease biomarker.This work was partly supported by National Key RD Program of China (2020YFA0712403), National Natural Science Foundation of China (61932008), Shanghai Municipal Science and Technology Major Project (2018SHZDZX01), the 111 Project (No. B18015) of China, Greater Bay Area Institute of Precision Medicine (Guangzhou) (Grand No. IPM21C008), Natural Science Foundation of Shanghai (21ZR1403200), and Shanghai Center for Brain Science and Brain-Inspired Technology.

Alzheimer's disease (AD), a progressive neurodegenerative disease, is the most common cause of dementia worldwide. Accumulating data support the contributions of the peripheral immune system in AD pathogenesis. However, there is a lack of comprehensive understanding about the molecular characteristics of peripheral immune cells in AD.To explore the alterations of cellular composition and the alterations of intrinsic expression of individual cell types in peripheral blood, we performed cellular deconvolution in a large-scale bulk blood expression cohort and identified cell-intrinsic differentially expressed genes in individual cell types with adjusting for cellular proportion.We detected a significant increase and decrease in the proportion of neutrophils and B lymphocytes in AD blood, respectively, which had a robust replicability across other three AD cohorts, as well as using alternative algorithms. The differentially expressed genes in AD neutrophils were enriched for some AD-associated pathways, such as ATP metabolic process and mitochondrion organization. We also found a significant enrichment of protein-protein interaction network modules of leukocyte cell-cell activation, mitochondrion organization, and cytokine-mediated signaling pathway in neutrophils for AD risk genes including CD33 and IL1B. Both changes in cellular composition and expression levels of specific genes were significantly associated with the clinical and pathological alterations. A similar pattern of perturbations on the cellular proportion and gene expression levels of neutrophils could be also observed in mild cognitive impairment (MCI). Moreover, we noticed an elevation of neutrophil abundance in the AD brains.We revealed the landscape of molecular perturbations at the cellular level for AD. These alterations highlight the putative roles of neutrophils in AD pathobiology.

Alzheimer’s disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer’s brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer’s Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer’s brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme (grant 686070: DD-DeCaF to P.B.) and Marie Skłodowska-Curie Actions (grant 713673 to A.R.d.R.), the European Research Council (ERC) MicrobioS (ERC-AdG-669830 to P.B.), JTC project jumpAR (01KI1706 to P.B.), a BMBF Grant (grant 031L0181A: LAMarCK to P.B.), the European Molecular Biology Laboratory (P.B.), the ETH and Helmut Horten Foundation (S.S.), the National Key R&D Program of China (grant 2020YFA0712403 to X.-M.Z.), (grant 61932008 to X.-M.Z.; grant 61772368 to X.-M.Z.; grant 31950410544 to L.P.C.), the Shanghai Municipal Science and Technology Major Project (grant 2018SHZDZX01 to X.-M.Z. and L.P.C.) and Zhangjiang Lab (X.-M.Z. and L.P.C.), the International Development Research Centre (grant 109304, EMBARK under the JPI AMR framework; to L.P.C.), la Caixa Foundation (grant 100010434, fellowship code LCF/BQ/DI18/11660009 to A.R.d.R.), the Severo Ochoa Program for Centres of Excellence in R&D from the Agencia Estatal de Investigación of Spain (grant SEV-2016-0672 (2017–2021) to C.P.C.), the Ministerio de Ciencia, Innovación y Universidades (grant PGC2018-098073-A-I00 MCIU/AEI/FEDER to J.H.-C. and J.G.-L.), the Innovation Fund Denmark (grant 4203-00005B, PNM), the Biotechnology and Biological Sciences research Council (BBSrC) Gut MicroInstitute Strategic Programmebes and Health BB/r012490/1 and its constituent project BBS/e/F/000Pr10355 (F.H.). R.A. is a member of the Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences.

Neurological and psychiatric disorders have overlapped phenotypic profiles, but the underlying tissue-specific functional processes remain largely unknown. In this study, we explore the shared tissue-specificity among 14 neuropsychiatric disorders through the disrupted long-range gene regulations by GWAS-identified regulatory SNPs. Through Hi-C interactions, averagely 38.0% and 17.2% of the intergenic regulatory SNPs can be linked to target protein-coding genes in brain and non-brain tissues, respectively. Interestingly, while the regulatory target genes in the brain tend to enrich in nervous system development related processes, those in the non-brain tissues are inclined to interfere with synapse and neuroinflammation related processes. Compared to psychiatric disorders, neurological disorders present more prominently the neuroinflammatory processes in both brain and non-brain tissues, indicating an intrinsic difference in mechanisms. Through tissue-specific gene regulatory networks, we then constructed disorder similarity networks in two brain and three non-brain tissues, highlighting both known disorder clusters (e.g. the neurodevelopmental disorders) and unexpected disorder clusters (e.g. Parkinson's disease is consistently grouped with psychiatric disorders). We showcase the potential pharmaceutical applications of the small bowel and its disorder clusters, illustrated by the known drug targets NR1I3 and NFACT1, and their small bowel-specific regulatory modules. In conclusion, disrupted long-range gene regulations in both brain and non-brain tissues contribute to the similarity among distinct clusters of neuropsychiatric disorders, and the tissue-specifically shared functions and regulators for disease clusters may provide insights for future therapeutic investigations.

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purpose is to facilitate the reusability and accessibility of the rapidly growing human metagenomic data. This is achieved by consistently annotating the microbial contents of collected samples using state-of-art toolsets and by manual curation of the meta-data of the corresponding human hosts. GMrepo organizes the collected samples according to their associated phenotypes and includes all possible related meta-data such as age, sex, country, body-mass-index (BMI) and recent antibiotics usage. To make relevant information easier to access, GMrepo is equipped with a graphical query builder, enabling users to make customized, complex and biologically relevant queries. For example, to find (1) samples from healthy individuals of 18 to 25 years old with BMIs between 18.5 and 24.9, or (2) projects that are related to colorectal neoplasms, with each containing >100 samples and both patients and healthy controls. Precomputed species/genus relative abundances, prevalence within and across phenotypes, and pairwise co-occurrence information are all available at the website and accessible through programmable interfaces. So far, GMrepo contains 58 903 human gut samples/runs (including 17 618 metagenomes and 41 285 amplicons) from 253 projects concerning 92 phenotypes. GMrepo is freely available at: https://gmrepo.humangut.info.

Many of the gut-microbiome-derived signatures for liver cirrhosis, especially the important ones, were likely under the influence of proton pump inhibitors (PPIs). Wu et al. suggest that drug usage is a confounding factor in metagenomics analysis that should be controlled for.

The human cerebral cortex undergoes profound structural and functional dynamic variations across the lifespan, whereas the underlying molecular mechanisms remain unclear. Here, with a novel method transcriptome-connectome correlation analysis (TCA), which integrates the brain functional magnetic resonance images and region-specific transcriptomes, we identify age-specific cortex (ASC) gene signatures for adolescence, early adulthood and late adulthood. The ASC gene signatures are significantly correlated with the cortical thickness (P-value Availability: All scripts are written in R. Scripts for the TCA method and related statistics result can be freely accessed at https://github.com/Soulnature/TCA. Additional data related to this paper may be requested from the authors.

Motivation: Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multimarker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. In biology, SNPs may also affect genes that are far away in the genome, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue specificity of signals have not been taken into account. Results: We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks. When applied to schizophrenia (SCZ), nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are involved in SCZ compared with MAGMA and H-MAGMA, and more of nMAGMA results can be validated with known SCZ risk genes. Some disease-related functions (e.g. the ATPase pathway in Cortex) are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins.

Base editing tools with diversified editing scopes and minimized RNA off-target activities are required for broad applications. Nevertheless, current Streptococcus pyogenes Cas9 (SpCas9)-based adenine base editors (ABEs) with minimized RNA off-target activities display constrained editing scopes with efficient editing activities at positions 4-8. Here, functional ABE variants with diversified editing scopes and reduced RNA off-target activities are identified using domain insertion profiling inside SpCas9 and with different combinations of TadA variants. Engineered ABE variants in this study display narrowed, expanded or shifted editing scopes with efficient editing activities across protospacer positions 2-16. And when combined with deaminase engineering, the RNA off-target activities of engineered ABE variants are further minimized. Thus, domain insertion profiling provides a framework to improve and expand ABE toolkits, and its combination with other strategies for ABE engineering deserves comprehensive explorations in the future., Current SpCas9 adenine base editors that minimise RNA off-target activities have constrained editing windows. Here the authors use domain insertion of TadA into Cas9 to narrow, expand or shift the editing window with RNA off-target minimization simultaneously

Background Compromised intestinal barrier (CIB) has been associated with many enteropathies, including colorectal cancer (CRC) and inflammatory bowel disease (IBD). We hypothesized that CIB could lead to increased host-derived contents including epithelial cells into the gut, change its physio-metabolic properties, and globally alter microbial community and metabolic capacities. Results Consistently, we found host DNA contents (HDCs), calculated as the percentage of metagenomic sequencing reads mapped to the host genome, were significantly elevated in patients of CRC and Crohn’s disease (CD). Consistent with our hypothesis, we found that HDC correlated with microbial- and metabolic-biomarkers of these diseases, contributed significantly to machine-learning models for patient stratification and was consequently ranked as a top contributor. CD patients with treatment could partially reverse the changes of many CD-signature species over time, with reduced HDC and fecal calprotectin (FCP) levels. Strikingly, HDC showed stronger correlations with the reversing changes of the CD-related species than FCP, and contributed greatly in classifying treatment responses, suggesting that it was also a biomarker for effective treatment. Conclusions Together, we revealed that association between HDCs and gut dysbiosis, and identified HDC as a novel biomarker from fecal metagenomics for diagnosis and effective treatment of intestinal diseases; our results also suggested that host-derived contents may have greater impact on gut microbiota than previously anticipated.

Signal transduction plays important roles in biological systems. Unfortunately, our knowledge about signaling pathways is far from complete. Specifically, the direction of signaling flows is less known even though the signaling molecules of some signaling pathways have been determined. In this paper, we propose a novel hybrid intelligent method, namely HISP (Hybrid Intelligent approach for identifying directed Signaling Pathways), to determine both the topologies of signaling pathways and the direction of signaling flows within a pathway based on integer linear programming and genetic algorithm. By integrating the protein-protein interaction, gene expression, and gene knockout data, our HISP approach is able to determine the optimal topologies of signaling pathways in an accurate way. Benchmark results on yeast MAPK signaling pathways demonstrate the efficiency of our proposed approach. When applied to the EGFR/ErbB signaling pathway in human hepatocytes, HISP unveils a high-resolution signaling pathway, where many signaling interactions were missing by existing computational approaches.

Phages invade microbes, accomplish host lysis and are of vital importance in shaping the community structure of environmental microbiota. More importantly, most phages have very specific hosts; they are thus ideal tools to manipulate environmental microbiota at species-resolution. The main purpose of MVP (Microbe Versus Phage) is to provide a comprehensive catalog of phage–microbe interactions and assist users to select phage(s) that can target (and potentially to manipulate) specific microbes of interest. We first collected 50 782 viral sequences from various sources and clustered them into 33 097 unique viral clusters based on sequence similarity. We then identified 26 572 interactions between 18 608 viral clusters and 9245 prokaryotes (i.e. bacteria and archaea); we established these interactions based on 30 321 evidence entries that we collected from published datasets, public databases and re-analysis of genomic and metagenomic sequences. Based on these interactions, we calculated the host range for each of the phage clusters and accordingly grouped them into subgroups such as ‘species-’, ‘genus-’ and ‘family-’ specific phage clusters. MVP is equipped with a modern, responsive and intuitive interface, and is freely available at: http://mvp.medgenius.info.

Disease comorbidity is the presence of one or more diseases along with a primary disorder, which causes additional pain to patients and leads to the failure of standard treatments compared with single diseases. Therefore, the identification of potential comorbidity can help prevent those comorbid diseases when treating a primary disease. Unfortunately, most of current known disease comorbidities are discovered occasionally in clinic, and our knowledge about comorbidity is far from complete. Despite the fact that many efforts have been made to predict disease comorbidity, the prediction accuracy of existing computational approaches needs to be improved. By investigating the factors underlying disease comorbidity, e.g., mutated genes and rewired protein-protein interactions (PPIs), we here present a novel algorithm to predict disease comorbidity by integrating multi-scale data ranging from genes to phenotypes. Benchmark results on real data show that our approach outperforms existing algorithms, and some of our novel predictions are validated with those reported in literature, indicating the effectiveness and predictive power of our approach. In addition, we identify some pathway and PPI patterns that underlie the co-occurrence between a primary disease and certain disease classes, which can help explain how the comorbidity is initiated from molecular perspectives.

The prediction of drug-disease associations holds great potential for precision medicine in the era of big data and is important for the identification of new indications for existing drugs. The associations between drugs and diseases can be regarded as a complex heterogeneous network with multiple types of nodes and links. In this paper, we propose a method, namely HED (Heterogeneous network Embedding for Drug-disease association), to predict potential associations between drugs and diseases based on a drug-disease heterogeneous network. Specifically, with the heterogeneous network constructed from known drug-disease associations, HED employs network embedding to characterize drug-disease associations and then trains a classifier to predict novel potential drug-disease associations. The results on two real datasets show that HED outperforms existing popular approaches. Furthermore, some of our predictions have been verified by evidence from literature. For instance, carvedilol, a drug that was originally used for heart failure, left ventricular dysfunction, and hypertension, is predicted to be useful for atrial fibrillation by HED, which is supported by clinical trials.

MicroRNAs (miRNAs) are a class of small endogenous non-coding genes, acting as regulators in the post-transcriptional processes. Recently, the miRNAs are found to be widely involved in different types of diseases. Therefore, the identification of disease associated miRNAs can help understand the mechanisms that underlie the disease and identify new biomarkers. However, it is not easy to identify the miRNAs related to diseases due to its extensive involvements in various biological processes. In this work, we present a new approach to identify disease associated miRNAs based on domains, the functional and structural blocks of proteins. The results on real datasets demonstrate that our method can effectively identify disease related miRNAs with high precision.

Drug repositioning, i.e., identifying new indications for known drugs, has attracted a lot of attentions recently and is becoming an effective strategy in drug development. In literature, several computational approaches have been proposed to identify potential indications of old drugs based on various types of data sources. In this paper, by formulating the drug-disease associations as a low-rank matrix, we propose a novel method, namely DrPOCS, to identify candidate indications of old drugs based on projection onto convex sets (POCS). With the integration of drug structure and disease phenotype information, DrPOCS predicts potential associations between drugs and diseases with matrix completion. Benchmarking results demonstrate that our proposed approach outperforms popular existing approaches with high accuracy. In addition, a number of novel predicted indications are validated with various types of evidences, indicating the predictive power of our proposed approach.

Identifying drug targets plays essential roles in designing new drugs and combating diseases. Unfortunately, our current knowledge about drug targets is far from comprehensive. Screening drug targets in the lab is an expensive and time-consuming procedure. In the past decade, the accumulation of various types of omics data makes it possible to develop computational approaches to predict drug targets. In this paper, we make a survey on the recent progress being made on computational methodologies that have been developed to predict drug targets based on different kinds of omics data and drug property data.

The microRNAs (miRNAs), regulators of post-transcriptional processes, have been found to affect the efficacy of drugs by regulating the biological processes in which the target proteins of drugs may be involved. For example, some drugs develop resistance when certain miRNAs are overexpressed. Therefore, identifying miRNAs that affect drug effects can help understand the mechanisms of drug actions and design more efficient drugs. Although some computational approaches have been developed to predict miRNA-drug associations, such associations rarely provide explicit information about which miRNAs and how they affect drug efficacy. On the other hand, there are rich information about which miRNAs affect the efficacy of which drugs in the literature. In this paper, we present a novel text mining approach, named as EmDL (Extracting miRNA-Drug interactions from Literature), to extract the relationships of miRNAs affecting drug efficacy from literature. Benchmarking on the drug-miRNA interactions manually extracted from MEDLINE and PubMed Central, EmDL outperforms traditional text mining approaches as well as other popular methods for predicting drug-miRNA associations. Specifically, EmDL can effectively identify the sentences that describe the relationships of miRNAs affecting drug effects. The drug-miRNA interactome presented here can help understand how miRNAs affect drug effects and provide insights into the mechanisms of drug actions. In addition, with the information about drug-miRNA interactions, more effective drugs or combinatorial strategies can be designed in the future. The data used here can be accessed at http://mtd.comp-sysbio.org/.

Drug resistance is becoming a serious problem that leads to the failure of standard treatments, which is generally developed because of genetic mutations of certain molecules. Here, we present GEAR (A database of G enomic E lements A ssociated with drug R esistance) that aims to provide comprehensive information about genomic elements (including genes, single-nucleotide polymorphisms and microRNAs) that are responsible for drug resistance. Right now, GEAR contains 1631 associations between 201 human drugs and 758 genes, 106 associations between 29 human drugs and 66 miRNAs, and 44 associations between 17 human drugs and 22 SNPs. These relationships are firstly extracted from primary literature with text mining and then manually curated. The drug resistome deposited in GEAR provides insights into the genetic factors underlying drug resistance. In addition, new indications and potential drug combinations can be identified based on the resistome. The GEAR database can be freely accessed through http://gear.comp-sysbio.org.

Cell state transition is one of the fundamental events in the development of multicellular organisms, and the transition trajectory path has recently attracted much attention. With the accumulation of large amounts of “-omics” data, it is becoming possible to get insights into the molecule mechanisms underlying the transitions between cell states. Here, we present CSTEA (Cell State Transition Expression Atlas), a webserver that organizes, analyzes and visualizes the time-course gene expression data during cell differentiation, cellular reprogramming and trans-differentiation in human and mouse. In particular, CSTEA defines gene signatures for uncharacterized stages during cell state transitions, thereby enabling both experimental and computational biologists to better understand the mechanisms of cell fate determination in mammals. To our best knowledge, CSTEA is the first webserver dedicated to the analysis of time-series gene expression data during cell state transitions. CSTEA is freely available at http://comp-sysbio.org/cstea/.

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

It has been well recognized that the pace of the development of new drugs and therapeutic interventions lags far behind biological knowledge discovery. Network-based approaches have emerged as a promising alternative to accelerate the discovery of new safe and effective drugs. Based on the integration of several biological resources including two recently published datasets i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was firstly constructed, followed by topological analysis and functional characterization of the network. The results show that My-DDome has a very clear modular structure, where drugs interacting with the same domain(s) within each module tend to have similar therapeutic effects. Moreover it has been found that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome (p0.000001), indicating that by incorporating protein domain information into My-DTome, more detailed insights into the interplay between drugs, their known targets, and seemingly unrelated proteins can be revealed.

OGEE is an Online GEne Essentiality database. To enhance our understanding of the essentiality of genes, in OGEE we collected experimentally tested essential and non-essential genes, as well as associated gene properties known to contribute to gene essentiality. We focus on large-scale experiments, and complement our data with text-mining results. We organized tested genes into data sets according to their sources, and tagged those with variable essentiality statuses across data sets as conditionally essential genes, intending to highlight the complex interplay between gene functions and environments/experimental perturbations. Developments since the last public release include increased numbers of species and gene essentiality data sets, inclusion of non-coding essential sequences and genes with intermediate essentiality statuses. In addition, we included 16 essentiality data sets from cancer cell lines, corresponding to 9 human cancers; with OGEE, users can easily explore the shared and differentially essential genes within and between cancer types. These genes, especially those derived from cell lines that are similar to tumor samples, could reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer types, and can be further screened to identify targets for cancer therapy and/or new drug development. OGEE is freely available at http://ogee.medgenius.info.

Objective: A complex disease is generally caused by the mutation of multiple genes or by the dysfunction of multiple biological processes. Systematic identification of causal disease genes and module biomarkers can provide insights into the mechanisms underlying complex diseases, and help develop efficient therapies or effective drugs. Materials and Methods: In this paper, we present a novel approach to predict disease genes and identify dysfunctional networks or modules, based on the analysis of differential interactions between disease and control samples, in contrast to the analysis of differential gene or protein expressions widely adopted in existing methods. Results and Discussion: As an example, we applied our method to the study of three-stage microarray data for gastric cancer. We identified network modules or module biomarkers that include a set of genes related to gastric cancer, implying the predictive power of our method. The results on holdout validation data sets show that our identified module can serve as an effective module biomarker for accurately detecting or diagnosing gastric cancer, thereby validating the efficiency of our method. Conclusion: We proposed a new approach to detect module biomarkers for diseases, and the results on gastric cancer demonstrated that the differential interactions are useful to detect dysfunctional modules in the molecular interaction network, which in turn can be used as robust module biomarkers.

Adverse drug reactions (ADRs) are the leading factors of drug attrition in drug development and post-market drug withdrawal. The identification of potential ADRs can help prevent the failure of drug discovery and improve development efficiency. Furthermore, elucidating possible ADRs for known drugs can help better understand the mechanism of drug actions and even find new indications of old drugs. Unfortunately, only the ADRs of some well-studied drugs are available and our knowledge about ADRs of available drugs is far from complete. Recently, with more structural and omics data available, some computational approaches have been developed for predicting drug ADRs. In this review, we present a survey on the recent progresses on computational methodologies that have been developed for ADR prediction based on various kinds of data, and some ADR related resources are also introduced.

Post-translational modifications (PTMs) are essential for regulating conformational changes, activities and functions of proteins, and are involved in almost all cellular pathways and processes. Identification of protein PTMs is the basis for understanding cellular and molecular mechanisms. In contrast with labor-intensive and time-consuming experiments, the PTM prediction using various bioinformatics approaches can provide accurate, convenient, and efficient strategies and generate valuable information for further experimental consideration. In this review, we summarize the current progresses made by Chineses bioinformaticians in the field of PTM Bioinformatics, including the design and improvement of computational algorithms for predicting PTM substrates and sites, design and maintenance of online and offline tools, establishment of PTM-related databases and resources, and bioinformatics analysis of PTM proteomics data. Through comparing similar studies in China and other countries, we demonstrate both advantages and limitations of current PTM bioinformatics as well as perspectives for future studies in China.

This paper presents a novel approach to extracting features from motif content and protein composition for protein sequence classification. First, we formulate a protein sequence as a fixed-dimensional vector using the motif content and protein composition. Then, we further project the vectors into a low-dimensional space by the Principal Component Analysis (PCA) so that they can be represented by a combination of the eigenvectors of the covariance matrix of these vectors. Subsequently, the Genetic Algorithm (GA) is used to extract a subset of biological and functional sequence features from the eigen-space and to optimize the regularization parameter of the Support Vector Machine (SVM) simultaneously. Finally, we utilize the SVM classifiers to classify protein sequences into corresponding families based on the selected feature subsets. In comparison with the existing PSI-BLAST and SVM-pairwise methods, the experiments show the promising results of our approach.

Motivation: MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in post-transcriptional regulations as well as other important biological processes. Recently, accumulating evidences indicate that miRNAs are extensively involved in cancer. However, it is a big challenge to identify which miRNAs are related to which cancer considering the complex processes involved in tumors, where one miRNA may target hundreds or even thousands of genes and one gene may regulate multiple miRNAs. Despite integrative analysis of matched gene and miRNA expression data can help identify cancer-associated miRNAs, such kind of data is not commonly available. On the other hand, there are huge amount of gene expression data that are publicly accessible. It will significantly improve the efficiency of characterizing miRNA’s function in cancer if we can identify cancer miRNAs directly from gene expression data. Results: We present a novel computational framework to identify the cancer-related miRNAs based solely on gene expression profiles without requiring either miRNA expression data or the matched gene and miRNA expression data. The results on multiple cancer datasets show that our proposed method can effectively identify cancer-related miRNAs with higher precision compared with other popular approaches. Furthermore, some of our novel predictions are validated by both differentially expressed miRNAs and evidences from literature, implying the predictive power of our proposed method. In addition, we construct a cancer-miRNA-pathway network, which can help explain how miRNAs are involved in cancer. Availability and implementation: The R code and data files for the proposed method are available at http://comp-sysbio.org/miR_Path/ Contact: liukeq@gmail.com Supplementary information: supplementary data are available at Bioinformatics online.

Motivation: Tremendous amount of omics data being accumulated poses a pressing challenge of meta-analyzing the heterogeneous data for mining new biological knowledge. Most existing methods deal with each gene independently, thus often resulting in high false positive rates in detecting differentially expressed genes (DEG). To our knowledge, no or little effort has been devoted to methods that consider dependence structures underlying transcriptomics data for DEG identification in meta-analysis context. Results: This article proposes a new meta-analysis method for identification of DEGs based on joint non-negative matrix factorization (jNMFMA). We mathematically extend non-negative matrix factorization (NMF) to a joint version (jNMF), which is used to simultaneously decompose multiple transcriptomics data matrices into one common submatrix plus multiple individual submatrices. By the jNMF, the dependence structures underlying transcriptomics data can be interrogated and utilized, while the high-dimensional transcriptomics data are mapped into a low-dimensional space spanned by metagenes that represent hidden biological signals. jNMFMA finally identifies DEGs as genes that are associated with differentially expressed metagenes. The ability of extracting dependence structures makes jNMFMA more efficient and robust to identify DEGs in meta-analysis context. Furthermore, jNMFMA is also flexible to identify DEGs that are consistent among various types of omics data, e.g. gene expression and DNA methylation. Experimental results on both simulation data and real-world cancer data demonstrate the effectiveness of jNMFMA and its superior performance over other popular approaches. Availability and implementation: R code for jNMFMA is available for non-commercial use viahttp://micblab.iim.ac.cn/Download/. Contact: hqwang@ustc.edu Supplementary information: Supplementary Data are available at Bioinformatics online.

Mendelian disorders are often caused by mutations in genes that are not lethal but induce functional distortions leading to diseases. Here we study the extent of gene duplicates that might compensate genes causing monogenic diseases. We provide evidence for pervasive functional redundancy of human monogenic disease genes (MDs) by duplicates by manifesting 1) genes involved in human genetic disorders are enriched in duplicates and 2) duplicated disease genes tend to have higher functional similarities with their closest paralogs in contrast to duplicated non-disease genes of similar age. We propose that functional compensation by duplication of genes masks the phenotypic effects of deleterious mutations and reduces the probability of purging the defective genes from the human population; this functional compensation could be further enhanced by higher purification selection between disease genes and their duplicates as well as their orthologous counterpart compared to non-disease genes. However, due to the intrinsic expression stochasticity among individuals, the deleterious mutations could still be present as genetic diseases in some subpopulations where the duplicate copies are expressed at low abundances. Consequently the defective genes are linked to genetic disorders while they continue propagating within the population. Our results provide insight into the molecular basis underlying the spreading of duplicated disease genes., Author Summary Duplicated genes, as opposed to singletons, are genes that have additional copies in a genome due to evolutionary mechanisms such as whole genome duplication, homologous recombination or retrotransposition events. Duplicates can have similar functions and thus mask the phenotypic consequences when one copy is mutated. Conversely, the corresponding phenotypes would manifest themselves when mutations occur in singletons, since functional compensation is rare among non-duplicated genes. It would thus be expected that the primary source of monogenic diseases, diseases caused by mutations within a single gene, is singletons. However, the opposite was found to be true. Additionally, we found the functional similarity of duplicated disease genes to be greater than that of duplicated non-disease genes of an equivalent duplication age. So how could the stronger functional compensation among duplicates increase their likelihood to associate with diseases? We propose that due to functional compensation in duplicates, disease-causing mutations are less likely to be removed from a human population in large scale since the phenotypes are masked; however, the functional compensation could be lost in a subpopulation, perhaps due to intrinsic variation in gene expression, and therefore lead to diseases. As a result, the duplicated disease genes are linked to genetic diseases, yet they continue to spread within the human population.

The disease biomarkers can help make accurate diagnosis and therefore give appropriate interventions. In the past years, the accumulation of various kinds of 'omics' data, e.g. genomics and transcriptomics, makes it possible to identify disease biomarkers in a more efficient way. In particular, the molecular networks that describe the functional relationships among molecules enable the identification of disease biomarkers from a systematic perspective. In this paper, we surveyed the recent progress on the computational approaches that have been developed to identify disease biomarkers based on molecular networks. In addition, we introduced the popular resources about human interactomes and regulatomes as well as human diseasomes, whose availability makes it possible to predict the disease biomarkers with the utility of networks.

Translational bioinformatics is an emerging field that aims to exploit various kinds of biological data for useful knowledge to be translated into clinical practice. However, the flooding of the huge amount of omics data makes it a big challenge to analyze and to interpret these data. Therefore, it is highly demanded to develop new efficient computational methodologies, especially data mining approaches, for translational bioinformatics. Under these circumstances, this special issue aims to present the recent progress on data mining techniques that have been developed for handling the huge amount of biological data arising in translational bioinformatics field. In data mining, one of the most important problems is how to represent the data so that the computational approaches could handle these data appropriately. In this special issue, B. Gan et al. utilized the latent low-rank representation to extract useful signals from noisy gene expression data and then classified tumors with sparse representation classifier and obtained promising results on benchmark datasets. C. Zhao et al. proposed a new feature representation of facial complexion for diagnosis in traditional Chinese medicine and achieved high recognition accuracy. G. Zhang et al. formulated the skin biopsy image annotation as a multi-instance multilabel (MLML) problem and automatically annotated the skin biopsy images with a sparse Bayesian MLML algorithm based on region structures and texture features. Except for feature extraction, feature selection is also very important in data mining. Z. Ji et al. proposed a particle swarm optimization-based feature selection approach to predict syndromes for hepatocellular carcinoma and improved diagnosis accuracy. With the accumulation of various data in translational bioinformatics, it is becoming a challenging task for traditional intelligent approaches to handle and interpret these data; S. Li et al. presented a survey on the recent progress about the hybrid intelligences and their applications in bioinformatics, where the hybrid intelligence is more powerful and robust compared with traditional intelligent approaches. The rapid accumulation of various kinds of biological data requires more powerful statistical approaches to extract useful signals from the huge amount of noisy data. L. Sun et al. built a new pipeline to investigate the DNA methylation profiles in male and female nonagenarians/centenarians and identified some differentially methylated probes between male and female nonagenarians/centenarians, which provide insights into the mechanism of longevity gender gap of human beings. Z. Teng et al. developed a new algorithm to predict protein function based on weighted mapping of domains and GO terms, which outperforms other popular approaches on benchmark datasets. J.-L. Huang et al. presented an online cross-species comparative system to identify conserved and exclusive simple sequence repeats within model species, which can facilitate both evolutionary studies and understanding of gene functions. L. Guo et al. proposed a new approach to identify microRNAs (miRNAs) associated with breast cancer and found that miRNA gene clusters demonstrate consistent deregulation patterns despite their different expression levels, which may provide insights into the regulatory roles of miRNAs in tumors. Recently, network biology is becoming a promising research field by organizing different kinds of data into a network representation. T. Jacquemin et al. proposed a new approach to identify disease associated protein complexes based on a heterogeneous network that consists of a disease similarity network and a tissue-specific protein-protein interactions network and successfully found disease associated complexes. X. Li et al. proposed a new pipeline to detect symptom-gene associations by integrating multiple data sources and found some potential disease genes. It is known that DNA mutations will affect gene expression. However, it is difficult to know which mutations will affect the gene expression and how the genes are regulated within the biological system. D. Kim et al. developed a novel approach that can both identify the Quantitative Trait Loci and infer the gene regulation network and successfully identified the genes associated with psychiatric disorder. R. Zhang et al. presented a new approach to identify the pathways linking TGF β to ovarian carcinoma immunoreactive antigen-like protein 2 (OCIAD2) by exploring the pathway bridge, and the resultant pathway explained how TGF β affects the expression of OCIAD2 in cancer microenvironment. Xing-Ming Zhao Jean X. Gao Jose C. Nacher

Motivation: Caspases and granzyme B (GrB) are important proteases involved in fundamental cellular processes and play essential roles in programmed cell death, necrosis and inflammation. Although a number of substrates for both types have been experimentally identified, the complete repertoire of caspases and granzyme B substrates remained to be fully characterized. Accordingly, systematic bioinformatics studies of known cleavage sites may provide important insights into their substrate specificity and facilitate the discovery of novel substrates. Results: We develop a new bioinformatics tool, termed Cascleave 2.0, which builds on previous success of the Cascleave tool for predicting generic caspase cleavage sites. It can be efficiently used to predict potential caspase-specific cleavage sites for the human caspase-1, 3, 6, 7, 8 and GrB. In particular, we integrate heterogeneous sequence and protein functional information from various sources to improve the prediction accuracy of Cascleave 2.0. During classification, we use both maximum relevance minimum redundancy and forward feature selection techniques to quantify the relative contribution of each feature to prediction and thus remove redundant as well as irrelevant features. A systematic evaluation of Cascleave 2.0 using the benchmark data and comparison with other state-of-the-art tools using independent test data indicate that Cascleave 2.0 outperforms other tools on protease-specific cleavage site prediction of caspase-1, 3, 6, 7 and GrB. Cascleave 2.0 is anticipated to be used as a powerful tool for identifying novel substrates and cleavage sites of caspases and GrB and help understand the functional roles of these important proteases in human proteolytic cascades. Availability and implementation: http://www.structbioinfor.org/cascleave2/. Contact: Jiangning.Song@monash.edu or James.Whisstock@monash.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Background Type 1 diabetes (T1D) is a complex disease and harmful to human health, and most of the existing biomarkers are mainly to measure the disease phenotype after the disease onset (or drastic deterioration). Until now, there is no effective biomarker which can predict the upcoming disease (or pre-disease state) before disease onset or disease deterioration. Further, the detail molecular mechanism for such deterioration of the disease, e.g., driver genes or causal network of the disease, is still unclear. Methods In this study, we detected early-warning signals of T1D and its leading biomolecular networks based on serial gene expression profiles of NOD (non-obese diabetic) mice by identifying a new type of biomarker, i.e., dynamical network biomarker (DNB) which forms a specific module for marking the time period just before the drastic deterioration of T1D. Results Two dynamical network biomarkers were obtained to signal the emergence of two critical deteriorations for the disease, and could be used to predict the upcoming sudden changes during the disease progression. We found that the two critical transitions led to peri-insulitis and hyperglycemia in NOD mices, which are consistent with other independent experimental results from literature. Conclusions The identified dynamical network biomarkers can be used to detect the early-warning signals of T1D and predict upcoming disease onset before the drastic deterioration. In addition, we also demonstrated that the leading biomolecular networks are causally related to the initiation and progression of T1D, and provided the biological insight into the molecular mechanism of T1D. Experimental data from literature and functional analysis on DNBs validated the computational results.

The complex biological systems consist of distinct molecules that exert their functions by interacting with each other, which makes it a big challenge to understand how the cellular machinery works. Recently, the accumulation of a large amount of multiscale omics data, such as next-generation sequencing data and protein interaction data, provides opportunity to investigate the functions of molecules from a systematic perspective. On the other hand, the analysis of these huge datasets demands efficient and robust computational methods. In this special issue, we reported the recent progress made in developing new computational methodologies to analyze the genomics data, construct gene networks, and identify disease genes. Understanding the Functions of Molecules in the Postgenomic Age. In recent years, the advance of next-generation sequencing (NGS) technology makes it more easier for researchers to access and analyze genetics data and has influential effects on the biomedical research community. However, compared with sequencing, computational analysis of the flooding sequencing data with appropriate tools is becoming a more important task when interpreting the data. In their review paper, M. P. Dolled-Filhart et al. described the pipeline for bioinformatics analysis of the NGS data, starting from alignment to variant calling as well as filtering and annotation. In each step, they discussed the tools or software that should be used as well as their advantages and caveats. This survey of the bioinformatics analysis of NGS data can help researchers to choose appropriate tools when dealing with the sequencing data. Along with the sequencing technology, lines of evidence show that a lot of noncoding RNAs (ncRNAs) play important roles in various biological processes. Unlike the protein-coding genes that are well studied, the functions of most ncRNAs are not clear. Therefore, it is highly desirable to develop computational methods to predict the functions of the ncRNAs. H. Ma et al. conducted a survey about the computational approaches developed to predict and annotate the long noncoding RNAs (lncRNAs), which can help researchers to learn the progress in this filed and future directions in which bioinformaticists should work while annotating lncRNAs. While annotating the functions of molecules, standard and controlled vocabularies are required. Hence, the ontologies that are represented as abstract description systems of knowledge are becoming more and more popular recently. At the same time, it is becoming a difficult task to calculate the semantic similarity between ontology terms quantitatively. M. Gan et al. introduced popular methods in quantitating the semantic similarity between ontology terms and their software implementations. Furthermore, they classified these methods into distinct categories and discussed their advantages and shortcomings, which can help researchers to select appropriate tools and methods when working on ontologies. Gene expression profiles can describe the molecular mechanisms that underlie certain phenotypes. However, while analyzing the gene expression data, it is inappropriate to treat genes independently considering genes interact with each other within the cell. O. Frings et al. proposed a network-based approach to analyze the gene expression data and applied it to investigate the development of sex-specific chicken gonad and brain tissues. By combining the chicken network and the gene expression data, they identified some sex-biased characteristics, for example, same sex-biased genes tend to be tightly connected in the network, and provided new insights into the molecular underpinnings of sex-biased genes. Construction and Analysis of Gene Networks. Construction of gene regulatory networks (GRNs) is a crucial step in systems biology, where gene expression data is widely explored to infer the GRNs. However, the high dimensionality and notorious noise of the gene expression data makes it a nontrivial task to infer the GRNs. N. You et al. presented a new Laplace error penalty (LEP) model to calculate the partial correlation coefficients between genes and construct the GRNs. Compared with the popular least absolute shrinkage and selection operator (LASSO) and smoothly clipped absolute deviation (SCAD) approaches, the LEP method reached the highest precision. Except for gene expression data, integration of different data sources may improve the accuracy of inferred GRNs. H. Chen et al. surveyed the strategies to integrate distinct data sources and their effectiveness and recommended how to choose an appropriate strategy while integrating distinct data sources. N. Nakajima et al. proposed a novel network completion approach, DPLSQ, to infer gene networks. Benchmarking on artificial datasets, their proposed DPLSQ outperforms popular ARACNE and GeneNet with the highest accuracy. By investigating a 2-gene network, A. V. Spirov et al. found that gene cooption can affect the robustness of GRNs, and the findings provide new insights into the evolvability and robustness of GRNs. Network modules are found to be functional blocks of gene networks, the identification of which is becoming a hot research topic. By taking the hierarchical modular structure into account, S. Zhang presented a new stochastic block model to detect the hierarchical modules. Applied to the real yeast gene coexpression network, the proposed method can efficiently detect the hierarchical modular structures that are consistent with biological functions. Recently, it is found that a particular type of ncRNAs, microRNAs, plays important roles in gene regulation by working together with transcription factors. W. Mu et al. proposed a new local genetic algorithm to predict condition-specific regulatory modules that consist of microRNAs, transcription factors, and their commonly regulated genes, and these modules provide useful insights into the regulatory mechanisms underlying gene expression. Computational Approaches to Hunting Disease-Associated Genes. The identification of genetic variants that are responsible for human diseases is critical for understanding the development of diseases and designing new effective drugs. Thanks to the genome-wide association studies (GWASs), some genetic variants that drive diseases have been identified, among which single nucleotide polymorphisms (SNPs) and nonsynonymous single nucleotide polymorphisms (nsSNPs) are receiving more and more attention. In this issue, J. Wu and R. Jiang reviewed the databases that collect nsSNPs and summarized popular computational methods that identify deleterious nsSNPs. In addition, they introduced machine learning models that are useful in predicting deleterious nsSNPs. Beyond SNP-based association analysis, gene-based association analysis is receiving increasing attention. X. Guo et al. comprehensively compared these two approaches on the data from the study of addiction and found that these two approaches complement with each other and can get better results when used together. The differentially expressed genes identified from microarray data are generally regarded as candidate disease genes. However, the number of differentially expressed genes may reach hundreds or even thousands, thereby making it difficult to identify the potential disease genes. In this issue, L. Li et al. proposed a new hybrid approach to predict disease genes based on estimation of distribution algorithm and support vector machine. Benchmarking on B-cell lymphoma and colon cancer datasets, their method outperforms two other popular approaches and identify some new candidate genes for future validation.

Background Drug combination that consists of distinctive agents is an attractive strategy to combat complex diseases and has been widely used clinically with improved therapeutic effects. However, the identification of efficacious drug combinations remains a non-trivial and challenging task due to the huge number of possible combinations among the candidate drugs. As an important factor, the molecular context in which drugs exert their functions can provide crucial insights into the mechanism underlying drug combinations. Results In this work, we present a network biology approach to investigate drug combinations and their target proteins in the context of genetic interaction networks and the related human pathways, in order to better understand the underlying rules of effective drug combinations. Our results indicate that combinatorial drugs tend to have a smaller effect radius in the genetic interaction networks, which is an important parameter to describe the therapeutic effect of a drug combination from the network perspective. We also find that drug combinations are more likely to modulate functionally related pathways. Conclusions This study confirms that the molecular networks where drug combinations exert their functions can indeed provide important insights into the underlying rules of effective drug combinations. We hope that our findings can help shortcut the expedition of the future discovery of novel drug combinations.