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ObjectivesGenomic signatures like k-mers have become one of the most prominent approaches to describe genomic data. As a result, myriad real-world applications, such as the construction of de Bruijn graphs in genome assembly, have been benefited by recognizing genomic signatures. In other words, an efficient approach of genomic signature profiling is an essential need for tackling high-throughput sequencing reads. However, most of the existing approaches only recognize fixed-size k-mers while many research studies have shown the importance of considering variable-length k-mers.MethodsIn this paper, we present a novel genomic signature profiling approach, TahcoRoll, by extending the Aho-Corasick algorithm (AC) for the task of profiling variable-length k-mers. We first group nucleotides into two clusters and represent each cluster with a bit. The rolling hash technique is further utilized to encode signatures and read patterns for efficient matching.ResultsIn extensive experiments, TahcoRoll significantly outperforms the most state-of-the-art k-mer counters and has the capability of processing reads across different sequencing platforms on a budget desktop computer.ConclusionsThe single-thread version of TahcoRoll is as efficient as the eight-thread version of the state-of-the-art, JellyFish, while the eight-thread TahcoRoll outperforms the eight-thread JellyFish by at least four times.

MotivationCircular RNA (circRNA) is a novel class of long non-coding RNAs that have been broadly discovered in the eukaryotic transcriptome. The circular structure arises from a non-canonical splicing process, where the donor site backspliced to an upstream acceptor site. These circRNA sequences are conserved across species. More importantly, rising evidence suggests their vital roles in gene regulation and association with diseases. As the fundamental effort toward elucidating their functions and mechanisms, several computational methods have been proposed to predict the circular structure from the primary sequence. Recently, advanced computational methods leverage deep learning to capture the relevant patterns from RNA sequences and model their interactions to facilitate the prediction. However, these methods fail to fully explore positional information of splice junctions and their deep interaction.ResultsWe present a robust end-to-end framework, Junction Encoder with Deep Interaction (JEDI), for circRNA prediction using only nucleotide sequences. JEDI first leverages the attention mechanism to encode each junction site based on deep bidirectional recurrent neural networks and then presents the novel cross-attention layer to model deep interaction among these sites for backsplicing. Finally, JEDI can not only predict circRNAs but also interpret relationships among splice sites to discover backsplicing hotspots within a gene region. Experiments demonstrate JEDI significantly outperforms state-of-the-art approaches in circRNA prediction on both isoform level and gene level. Moreover, JEDI also shows promising results on zero-shot backsplicing discovery, where none of the existing approaches can achieve.Availability and implementationThe implementation of our framework is available at https://github.com/hallogameboy/JEDI.Supplementary informationSupplementary data are available at Bioinformatics online.

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 MetaMLAnn, 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 MetaMLAnn based on the public metagenomics dataset collected from multiple locations in the New York and Boston subway systems. The experimental results suggest that MetaMLAnn consistently performs better than other five conventional classifiers under different taxonomic ranks. At genus level, MetaMLAnn can achieve F1 scores of 0.63 and 0.72 on the New York and the Boston datasets, respectively. CONCLUSIONS:By exploiting heterogeneous features, MetaMLAnn captures the hidden interactions between microbial compositions and the urban environment, which enables precise predictions of microbial communities at unmeasured locations.

The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a number of significant diseases. Additionally, revolutions in sequencing technology have led to a rapid increase in publicly-available sequencing data. Consequently, there have been growing efforts to predict disease status from metagenomic sequencing data, with a proliferation of new approaches in the last few years. Some of these efforts have explored utilizing a powerful form of machine learning called deep learning, which has been applied successfully in several biological domains. Here, we review some of these methods and the algorithms that they are based on, with a particular focus on deep learning methods. We also perform a deeper analysis of Type 2 Diabetes and obesity datasets that have eluded improved results, using a variety of machine learning and feature extraction methods. We conclude by offering perspectives on study design considerations that may impact results and future directions the field can take to improve results and offer more valuable conclusions. The scripts and extracted features for the analyses conducted in this paper are available via GitHub:https://github.com/nlapier2/metapheno.

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.

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 GWASs and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4,152 loci with strong evidence of AH. The proportion of all loci with identified AH is 4%-23% in eQTLs, 35% in GWASs of high-density lipoprotein (HDL), and 23% in GWASs of schizophrenia. For eQTLs, we observed a strong correlation between sample size and the proportion of loci with AH (R2 = 0.85, p = 2.2 × 10-16), 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.

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 plantherbivory 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. Key words: Colorado potato beetle, herbivore, infestation, plant-insect interaction, potato. La bete a patate du Colorado (CPB) constitue une peste herbivore devastatrice chez les Solenaceae. En depit de ses impacts economiques, on connait peu de choses sur les interactions moleculaires entre le CPB et ces plantes. En utilisant un microarray 11421 EST de la pomme de terre, les auteurs ont identifie 320 genes a expression differentielle dans des feuilles de pommes de terre, en reaction a l'herbivorie par le CPB. Parmi ceux-ci, on retrouve des genes putatifs codant pour des inhibiteurs de proteinases ainsi que des enzymes des sentiers biosynthetiques de terpenoides, d'alcaloides et de phenylpropanoides, signalant la chimie defensive constituant la defense de la pomme de terre contre l'herbivorie par le CPB. Plusieurs genes, comme ceux codant des inhibiteurs de proteines, representent des mecanismes impliques dans d'autres reactions plante-herbivore, et pourraient correspondre a des strategies generales de defenses chimiques. Dans d'autres cas, les produits de l'expression genetique differentielle peuvent representer une chimie defensive specifique au taxon. Par exemple, les produits alcaloides presumes d'une reductase I de la tropinone sont specifiques a un sousensemble de Solenaceae. Deux genes induits par l'herbivorie, non specifiques a la pomme de terre, sont impliques dans la synthese de substances volatiles, reconnues pour attirer les predateurs des CPB. La comparaison d'une attaque continue vs le retablissement d'une attaque par le CPB indique que moins de genes impliques dans la chimie defensive sont induits apres une attaque soutenue qu'apres une attaque avec retablissement, ce qui suggere que la capacite de la plante a mettre en branle une reaction de defense complete est intensifiee apres une attaque courte vs une attaque prolongee. Mots-cles : bete a patate du Colorado, herbivore, infestation, interaction plante-insecte, pomme de terre. [Traduit par la Redaction], Introduction Plants respond to feeding insects through a complex interaction involving the recognition of signals induced by mechanical wounding, as well as the detection of specific elicitors produced in either [...]

ABSTRACT 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. [ABSTRACT FROM AUTHOR]

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., (© 2011 Blackwell Publishing Ltd.)