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By implicitly recognizing a user based on his/her speech input, speaker identification enables many downstream applications, such as personalized system behavior and expedited shopping checkouts. Based on whether the speech content is constrained or not, both text-dependent (TD) and text-independent (TI) speaker recognition models may be used. We wish to combine the advantages of both types of models through an ensemble system to make more reliable predictions. However, any such combined approach has to be robust to incomplete inputs, i.e., when either TD or TI input is missing. As a solution we propose a fusion of embeddings network foenet architecture, combining joint learning with neural attention. We compare foenet with four competitive baseline methods on a dataset of voice assistant inputs, and show that it achieves higher accuracy than the baseline and score fusion methods, especially in the presence of incomplete inputs.

Information networks, such as social and citation networks, are ubiquitous in the real world so that network analysis plays an important role in data mining and knowledge discovery. To alleviate the sparsity problem of network analysis, it is common to capture the network semantics by projecting nodes onto a vector space as network embeddings. Moreover, random walks are usually exploited to efficiently learn node embeddings and preserve network proximity. In addition to proximity structure, heterogeneous networks have more knowledge about the types of nodes. However, to profit from heterogeneous knowledge, most of the existing approaches guide the random walks through predefined meta-paths or specific strategies, which can distort the understanding of network structures. Furthermore, traditional random walk-based approaches much favor the nodes with higher degrees while other nodes are equivalently important for the downstream applications. In this paper, we propose Meta-context Aware Random Walks (MARU) to overcome these challenges, thereby learning richer and more unbiased representations for heterogeneous networks. To reduce the bias in classical random walks, the algorithm of bidirectional extended random walks is introduced to improve the fairness of representation learning. Based on the enhanced random walks, the meta-context aware skip-gram model is then presented to learn robust network embeddings with dynamic meta-contexts. Therefore, MARU can not only fairly understand the overall network structures but also leverage the sophisticated heterogeneous knowledge in the networks. Extensive experiments have been conducted on three real-world large-scale publicly available datasets. The experimental results demonstrate that MARU significantly outperforms state-of-the-art heterogeneous network embedding methods across three general machine learning tasks, including multi-label node classification, node clustering, and link prediction.

The widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limited. Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species. In this paper, we propose the transferred multi-relational embedding model Bio-JOIE to capture the knowledge of gene ontology and PPI networks, which demonstrates superb capability in modeling the SARS-CoV-2-human protein interactions. Bio-JOIE jointly trains two model components. The knowledge model encodes the relational facts from the protein and GO domains into separated embedding spaces, using a hierarchy-aware encoding technique employed for the GO terms. On top of that, the transfer model learns a non-linear transformation to transfer the knowledge of PPIs and gene ontology annotations across their embedding spaces. By leveraging only structured knowledge, Bio-JOIE significantly outperforms existing state-of-the-art methods in PPI type prediction on multiple species. Furthermore, we also demonstrate the potential of leveraging the learned representations on clustering proteins with enzymatic function into enzyme commission families. Finally, we show that Bio-JOIE can accurately identify PPIs between the SARS-CoV-2 proteins and human proteins, providing valuable insights for advancing research on this new disease., ACM BCB 2020, Best Student Paper

RNA-Sequencing has been the leading technology to quantify expression of thousands of genes simultaneously. The data analysis of an RNA-Seq experiment starts from aligning short reads to the reference genome/transcriptome or reconstructed transcriptome. However, current aligners lack the sensitivity to distinguish reads that come from homologous regions of an genome. One group of these homologies is the paralog pseudogenes. Pseudogenes arise from duplication of a set of protein coding genes, and have been considered as degraded paralogs in the genome due to their lost of functionality. Recent studies have provided evidence to support their novel regulatory roles in biological processes. With the growing interests in quantifying the expression level of pseudogenes at different tissues or cell lines, it is critical to have a sensitive method that can correctly align ambiguous reads and accurately estimate the expression level among homologous genes. Previously in PseudoLasso, we proposed a linear regression approach to learn read alignment behaviors, and to leverage this knowledge for abundance estimation and alignment correction. In this paper, we extend the work of PseudoLasso by grouping the homologous genomic regions into different communities using a community detection algorithm, followed by building a linear regression model separately for each community. The results show that this approach is able to retain the same accuracy as PseudoLasso. By breaking the genome into smaller homologous communities, the running time is improved from quadratic growth to linear with respect to the number of genes.

Background Microbes are greatly associated with human health and disease, especially in densely populated cities. It is essential to understand the microbial ecosystem in an urban environment for cities to monitor the transmission of infectious diseases and detect potentially urgent threats. To achieve this goal, the DNA sample collection and analysis have been conducted at subway stations in major cities. However, city-scale sampling with the fine-grained geo-spatial resolution is expensive and laborious. In this paper, we introduce , a neural network based approach to infer microbial communities at unsampled locations given information reflecting different factors, including subway line networks, sampling material types, and microbial composition patterns. Results We evaluate the effectiveness of based on the public metagenomics dataset collected from multiple locations in the New York and Boston subway systems. The experimental results suggest that consistently performs better than other five conventional classifiers under different taxonomic ranks. At genus level, can achieve F1 scores of 0.63 and 0.72 on the New York and the Boston datasets, respectively. Conclusions By exploiting heterogeneous features, captures the hidden interactions between microbial compositions and the urban environment, which enables precise predictions of microbial communities at unmeasured locations.

Transposable elements (TE) comprise roughly half of the human genome. Though initially derided as junk DNA, they have been widely hypothesized to contribute to the evolution of gene regulation. However, the contribution of TE to the genetic architecture of diseases remains unknown. Here, we analyze data from 41 independent diseases and complex traits to draw three conclusions. First, TE are uniquely informative for disease heritability. Despite overall depletion for heritability (54% of SNPs, 39 ± 2% of heritability), TE explain substantially more heritability than expected based on their depletion for known functional annotations. This implies that TE acquire function in ways that differ from known functional annotations. Second, older TE contribute more to disease heritability, consistent with acquiring biological function. Third, Short Interspersed Nuclear Elements (SINE) are far more enriched for blood traits than for other traits. Our results can help elucidate the biological roles that TE play in the genetic architecture of diseases., Transposable elements (TE) make up a large component of the human genome and have been shown to contribute to human diseases. Here, Hormozdiari et al. estimate the contribution of TEs to the heritability of 41 complex traits and diseases and find enrichment of SINEs in blood traits.

Identifying and recommending potential new customers for local businesses are crucial to the survival and success of local businesses. A key component to identifying the right customers is to understand the decision-making process of choosing a business over the others. However, modeling this process is an extremely challenging task as a decision is influenced by multiple factors. These factors include but are not limited to an individual's taste or preference, the location accessibility of a business, and the reputation of a business from social media. Most of the recommender systems lack the power to integrate multiple factors together and are hardly extensible to accommodate new incoming factors. In this paper, we introduce a unified framework, CORALS, which considers the personal preferences of different customers, the geographical influence, and the reputation of local businesses in the customer recommendation task. To evaluate the proposed model, we conduct a series of experiments to extensively compare with 12 state-of-the-art methods using two real-world datasets. The results demonstrate that CORALS outperforms all these baselines by a significant margin in most scenarios. In addition to identifying potential new customers, we also break down the analysis for different types of businesses to evaluate the impact of various factors that may affect customers' decisions. This information, in return, provides a great resource for local businesses to adjust their advertising strategies and business services to attract more prospective customers.

Microbes play a critical role in human health and disease, especially in cities with high population densities. Understanding the microbial ecosystem in an urban environment is essential for monitoring the transmission of infectious diseases and identifying potentially urgent threats. To achieve this goal, researchers have started to collect and analyze metagenomic samples from subway stations in major cities. However, it is too costly and time-consuming to achieve city-wide sampling with fine-grained geo-spatial resolution. In this paper, we present MetaMLAnn, a neural network based approach to infer microbial communities at unmeasured locations, based upon information from various data sources in an urban environment, including subway line information, sampling material, and microbial compositions. MetaMLAnn exploits these heterogeneous features to capture the latent dependencies between microbial compositions and the urban environment, thereby precisely inferring microbial communities at unsampled locations. Moreover, we propose a regularization framework to incorporate the species relatedness as prior knowledge. We evaluate our approach using the public metagenomics dataset collected from multiple subway stations in New York and Boston. The experimental results show that MetaMLAnn consistently outperforms five conventional classifiers across several evaluation metrics. The code, features and labels are available at https://github.com/zgy921028/MetaMLAnn

k-mer profiling has been one of the trending approaches to analyze read data generated by high-throughput sequencing technologies. The tasks of k-mer profiling include, but are not limited to, counting the frequencies and determining the occurrences of short sequences in a dataset. The notion of k-mer has been extensively used to build de Bruijn graphs in genome or transcriptome assembly, which requires examining all possible k-mers presented in the dataset. Recently, an alternative way of profiling has been proposed, which constructs a set of representative k-mers as genomic markers and profiles their occurrences in the sequencing data. This technique has been applied in both transcript quantification through RNA-Seq and taxonomic classification of metagenomic reads. Most of these applications use a set of fixed-size k-mers since the majority of existing k-mer counters are inadequate to process genomic sequences with variable-length k-mers. However, choosing the appropriate k is challenging, as it varies for different applications. As a pioneer work to profile a set of variable-length k-mers, we propose TahcoRoll in order to enhance the Aho-Corasick algorithm. More specifically, we use one bit to represent each nucleotide, and integrate the rolling hash technique to construct an efficient in-memory data structure for this task. Using both synthetic and real datasets, results show that TahcoRoll outperforms existing approaches in either or both time and memory efficiency without using any disk space. In addition, compared to the most efficient state-of-the-art k-mer counters, such as KMC and MSBWT, TahcoRoll is the only approach that can process long read data from both PacBio and Oxford Nanopore on a commodity desktop computer. The source code of TahcoRoll is implemented in C++14, and available at https://github.com/chelseaju/TahcoRoll.git.

There is increasing evidence that many risk loci found using genome-wide association studies are molecular quantitative trait loci (QTLs). Here we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTLs, using data from the Genotype-Tissue Expression (GTEx) and BLUEPRINT consortia. We show that these annotations are more strongly enriched for heritability (5.84× for eQTLs; P = 1.19 × 10-31) across 41 diseases and complex traits than annotations containing all significant molecular QTLs (1.80× for expression (e)QTLs). eQTL annotations obtained by meta-analyzing all GTEx tissues generally performed best, whereas tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. eQTL annotations restricted to loss-of-function intolerant genes were even more enriched for heritability (17.06×; P = 1.20 × 10-35). All molecular QTLs except splicing QTLs remained significantly enriched in joint analysis, indicating that each of these annotations is uniquely informative for disease and complex trait architectures.

The advent of RNA-Seq has made it possible to quantify transcript expression on a large scale simultaneously. This technology generates small fragments of each transcript sequence, known as sequencing reads. As the first step of data analysis towards expression quantification, most of the existing methods align these reads to a reference genome or transcriptome to establish their origins. However, read alignment is computationally costly. Recently, a series of methods have been proposed to perform a lightweight quantification analysis in an alignment-free manner. These methods utilize the notion of k-mers, which are short consecutive sequences representing the signatures of each transcript, to estimate the relative abundance from RNA-Seq reads. Current k-mer based approaches make use of a set of fixed size k-mers; however, the true signatures of each transcript may not exist in a fixed size. In this paper, we demonstrate the importance of k-mers selection in transcript abundance estimation. We propose a novel method, Fleximer, to efficiently discover and select an optimal set of k-mers with flexible lengths. Using both simulated and real datasets, we show that, with fewer k-mers, Fleximer is able to cover the similar amount of reads as Sailfish and Kallisto. The selected k-mers own more distinguishing features, and thus substantially reduce the errors in transcript abundance estimation.

Recent successes in genome-wide association studies (GWASs) make it possible to address important questions about the genetic architecture of complex traits, such as allele frequency and effect size. One lesser-known aspect of complex traits is the extent of allelic heterogeneity (AH) arising from multiple causal variants at a locus. We developed a computational method to infer the probability of AH and applied it to three GWAS and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4152 loci with strong evidence of AH. The proportion of all loci with identified AH is 4-23% in eQTLs, 35% in GWAS of High-Density Lipoprotein (HDL), and 23% in schizophrenia. For eQTLs, we observed a strong correlation between sample size and the proportion of loci with AH (R2=0.85, P = 2.2e-16), indicating that statistical power prevents identification of AH in other loci. Understanding the extent of AH may guide the development of new methods for fine mapping and association mapping of complex traits.

While many studies have characterized changes to the transcriptome of plants attacked by shoot-eating insect pests, few have examined transcriptome-level effects of root pests. Maize (Zea mays) seedlings were subjected to infestation for approximately 2 weeks by the root herbivore southern corn rootworm (SCR) Diabrotica undecimpunctata howardi, and changes in transcript abundance within both roots and shoots were analyzed using a 57K element microarray. A total of 541 genes showed statistically significant changes in transcript abundance in infested roots, including genes encoding many pathogenesis-related proteins such as chitinases, proteinase inhibitors, peroxidases and β-1,3-glucanases. Several WRKY transcription factors – often associated with biotic responses – exhibited increased transcript abundance upon SCR feeding. Differentially expressed (DE) genes were also detected in shoots of infested vs control plants. Quantitative Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) was used to confirm patterns of transcript abundance for several significant DE genes using an independent experiment with a 2–6 day period of SCR infestation. Because of the well-documented roles that jasmonic acid (JA) or salicylic acid (SA) play in herbivory responses, the effect of exogenous JA or SA application on transcript abundance corresponding to the same subset of SCR-responsive genes was assessed. The response of these genes at the level of transcript abundance to SA and JA differed between roots and shoots and also differed among the genes that were examined. These data suggested that SA- and JA-dependent and independent signals contributed to the transcriptome-level changes in maize roots and shoots in response to SCR infestation.

Bud formation is an adaptive trait that temperate forest trees have acquired to facilitate seasonal synchronization. We have characterized transcriptome-level changes that occur during bud formation of white spruce [Picea glauca (Moench) Voss], a primarily determinate species in which preformed stem units contained within the apical bud constitute most of next season's growth. Microarray analysis identified 4460 differentially expressed sequences in shoot tips during short day-induced bud formation. Cluster analysis revealed distinct temporal patterns of expression, and functional classification of genes in these clusters implied molecular processes that coincide with anatomical changes occurring in the developing bud. Comparing expression profiles in developing buds under long day and short day conditions identified possible photoperiod-responsive genes that may not be essential for bud development. Several genes putatively associated with hormone signalling were identified, and hormone quantification revealed distinct profiles for abscisic acid (ABA), cytokinins, auxin and their metabolites that can be related to morphological changes to the bud. Comparison of gene expression profiles during bud formation in different tissues revealed 108 genes that are differentially expressed only in developing buds and show greater transcript abundance in developing buds than other tissues. These findings provide a temporal roadmap of bud formation in white spruce.

Colorado potato beetle (CPB) is a devastating herbivorous pest of solanaceous plants. Despite the economic impact, little is known about the molecular interaction of CPB with these plants. Using an 11 421 expressed sequence tag (EST) potato microarray, we identified 320 genes differentially expressed in potato leaves in response to CPB herbivory. Amongst these were genes putatively encoding proteinase inhibitors along with enzymes of terpenoid, alkaloid, and phenylpropanoid biosynthetic pathways, suggesting the defensive chemistries that constitute potato’s defense against CPB herbivory. Several genes, such as those encoding proteinase inhibitors, represent mechanisms implicated in other plant–herbivory interactions, and could correspond with general defensive chemistry strategies. In other cases, products of the differentially expressed genes may represent taxa-specific defensive chemistry. For example, the presumed alkaloid products of a putative tropinone reductase I are specific to a subset of the Solanaceae. Two herbivory-induced genes, not specific to potato, are implicated in the synthesis of volatiles known to attract CPB predators. Comparison of continuous herbivore attack versus recovery from CPB attack indicates that fewer genes involved in defensive chemistry are induced after continuous feeding than after feeding and recovery, suggesting the plant’s ability to mount a full defense response is enhanced under light versus heavy attack.

Pseudogenes have long been considered to be nonfunctional segments in the genome, but recent studies have provided evidence to support their novel regulatory roles in biological processes. With the growing interests in pseudogene research, scientists rely on RNA sequencing technology to estimate expression level of pseudogenes at different tissues or cell lines. The major challenge of RNASeq on pseudogene quantification falls in the high sequence similarity between pseudogenes and their homologous parents. Reads can be ambiguously aligned to multiple homologous regions. In this article, we present PseudoLasso, a genome-wide approach to accurately estimate the abundance of pseudogenes and their parents, and correctly align reads to their origins. Our approach focuses on learning read alignment behaviors, and leveraging this knowledge for abundance estimation and alignment correction. Compared to the read count estimates reported by TopHat2, PseudoLasso is able to provide estimates with a reduced error rate of 10-fold.

Metabolomics investigations hold promise for the characterization of small molecules, metabolites, which govern the ultimate manifestation of cardiac phenotypes. In this study, we employed a mass spectrometry-based metabolomics approach to identify metabolic marker(s), which dynamically reflect the cardiac performance of heart failure patients amid the implantation of mechanical circulatory support. Using the MRM-based and triple quadrupole technology platform, we have quantified 266 metabolites native to human plasma and collected from thirteen heart failure patients. The temporal profile of these metabolites was sampled from 1 day prior to the implantation of mechanical circulatory support, as well as 1-, 3-, 5-, and 7-day following their surgical interventions. We identified subgroups of these metabolites with coordinated behaviors that are interesting to their diseased phenotypes. In a pair-wise correlation analysis, 36.8% (98 out of 266) of metabolites were significantly correlated. Intriguingly, majority of which (65 out of 98) are representing the functional groups of phosphatidylcholines; several of them are known to have close associations with the pathogenesis of cardiovascular diseases. In addition, there are 33 metabolites contributing to multiple functional groups, including twelve of them belong to sphingomyelines, ten of them in the family of lysophosphatidylcholines, eight amino acids (Gln, Ser, Ala, His, Lys, Gly, Thr, and Arg), as well as three fatty acids (eicosapentaenoic acid, pentadecenoic acid, and heptadecenoic acid). The behaviors of these 266 metabolites have constituted individualized metabolic fingerprints. Delineation of the intrinsic relationships among alterations in distinct metabolite groups and their reflected cardiac function will enable us to identify new metabolic markers aiding stratification and/or prediction on the clinical outcome of each individual patient undergoing the treatment of mechanical circulatory support. This personalized metabolic fingerprint will offer unique prognostic utilities, supporting clinical decision-making process to deliver intervention that is most effective and beneficial to an individual.

In the autumn, stems of woody perennials such as forest trees undergo a transition from active growth to dormancy. We used microarray transcriptomic profiling in combination with a proteomics analysis to elucidate processes that occur during this growth-to-dormancy transition in a conifer, white spruce (Picea glauca[Moench] Voss). Several differentially expressed genes were likely associated with the developmental transition that occurs during growth cessation in the cambial zone and the concomitant completion of cell maturation in vascular tissues. Genes encoding for cell wall and membrane biosynthetic enzymes showed transcript abundance patterns consistent with completion of cell maturation, and also of cell wall and membrane modifications potentially enabling cells to withstand the harsh conditions of winter. Several differentially expressed genes were identified that encoded putative regulators of cambial activity, cell development and of the photoperiodic pathway. Reconfiguration of carbon allocation figured centrally in the tree's overwintering preparations. For example, genes associated with carbon-based defences such as terpenoids were down-regulated, while many genes associated with protein-based defences and other stress mitigation mechanisms were up-regulated. Several of these correspond to proteins that were accumulated during the growth-to-dormancy transition, emphasizing the importance of stress protection in the tree's adaptive response to overwintering.

Bud formation is an adaptive trait that temperate forest trees have acquired to facilitate seasonal synchronization. We have characterized transcriptome-level changes that occur during bud formation of white spruce [Picea glauca (Moench) Voss], a primarily determinate species in which preformed stem units contained within the apical bud constitute most of next season's growth. Microarray analysis identified 4460 differentially expressed sequences in shoot tips during short day-induced bud formation. Cluster analysis revealed distinct temporal patterns of expression, and functional classification of genes in these clusters implied molecular processes that coincide with anatomical changes occurring in the developing bud. Comparing expression profiles in developing buds under long day and short day conditions identified possible photoperiod-responsive genes that may not be essential for bud development. Several genes putatively associated with hormone signalling were identified, and hormone quantification revealed distinct profiles for abscisic acid (ABA), cytokinins, auxin and their metabolites that can be related to morphological changes to the bud. Comparison of gene expression profiles during bud formation in different tissues revealed 108 genes that are differentially expressed only in developing buds and show greater transcript abundance in developing buds than other tissues. These findings provide a temporal roadmap of bud formation in white spruce.

Colorado potato beetle (CPB) is a leading pest of solanaceous plants. Despite the economic importance of this pest, surprisingly few studies have been carried out to characterize its molecular interaction with the potato plant. In particular, little is known about the effect of CPB elicitors on gene expression associated with the plant's defense response. In order to discover putative CPB elicitor-responsive genes, the TIGR 11,421 EST Solanaceae microarray was used to identify genes that are differentially expressed in response to the addition of CPB regurgitant to wounded potato leaves. By applying a cutoff corresponding to an adjusted P-value of0.01 and a fold change of1.5 or0.67, we found that 73 of these genes are induced by regurgitant treatment of wounded leaves when compared to wounding alone, whereas 54 genes are repressed by this treatment. This gene set likely includes regurgitant-responsive genes as well as wounding-responsive genes whose expression patterns are further enhanced by the presence of regurgitant. Real-time polymerase chain reaction was used to validate differential expression by regurgitant treatment for five of these genes. In general, genes that encoded proteins involved in secondary metabolism and stress were induced by regurgitant; genes associated with photosynthesis were repressed. One induced gene that encodes aromatic amino acid decarboxylase is responsible for synthesis of the precursor of 2-phenylethanol. This is significant because 2-phenylethanol is recognized by the CPB predator Perillus bioculatis. In addition, three of the 16 type 1 and type 2 proteinase inhibitor clones present on the potato microarray were repressed by application of CPB regurgitant to wounded leaves. Given that proteinase inhibitors are known to interfere with digestion of proteins in the insect midgut, repression of these proteinase inhibitors by CPB may inhibit this component of the plant's defense arsenal. These data suggest that beyond the wound response, CPB elicitors play a role in mediating the plant/insect interaction.