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The second ISCB-Asia conference of the International Society for Computational Biology took place December 17-19, 2012, in Shenzhen, China. The conference was co-hosted by BGI as the first Shenzhen Conference on Computational Genomics (SCCG). 45 talks were presented at ISCB-Asia/SCCG 2012. The topics covered included software tools, reproducible computing, next-generation sequencing data analysis, transcription and mRNA regulation, protein structure and function, cancer genomics and personalized medicine. Nine of the proceedings track talks are included as full papers in this supplement. In this report we first give a short overview of the conference by listing some statistics and visualizing the talk abstracts as word clouds. Then we group the talks by topic and briefly summarize each one, providing references to related publications whenever possible. Finally, we close with a few comments on the success of this conference.

Background Efficient use of feed resources for farm animals is a critical concern in animal husbandry. Numerous genetic and nutritional studies have been conducted to investigate feed efficiency during the regular laying cycle of chickens. However, by prolonging the laying period of layers, the performance of feed utilization in the late-laying period becomes increasingly important. In the present study, we measured daily feed intake (FI), residual feed intake (RFI) and feed conversion ratio (FCR) of 808 hens during 81–82 weeks of age to evaluate genetic properties and then used a genome-wide association study (GWAS) to reveal the genetic determinants. Results The heritability estimates for the investigated traits were medium and between 0.15 and 0.28 in both pedigree- and genomic-based estimates, whereas the genetic correlations among these traits were high and ranged from 0.49 to 0.90. Three genome-wide significant SNPs located on chromosome 1 (GGA1) were detected for FCR. Linkage disequilibrium (LD) and conditional GWA analysis indicated that these 3 SNPs were highly correlated with one another, located at 13.55–45.16 Kb upstream of gga-miR-15a. Results of quantitative real-time polymerase chain reaction (qRT-PCR) analysis in liver tissue showed that the expression of gga-miR-15a was significantly higher in the high FCR birds than that in the medium or low FCR birds. Bioinformatics analysis further revealed that gga-mir-15a could act on many target genes, such as forkhead box O1 (FOXO1) that is involved in the insulin-signaling pathway, which influences nutrient metabolism in many organisms. Additionally, some suggestively significant variants, located on GGA3 and GGA9, were identified to associate with FI and RFI. Conclusions This GWA analysis was conducted on feed intake and efficiency traits for chickens and was innovative for application in the late laying period. Our findings can be used as a reference in the genomic breeding programs for increasing the efficiency performance of old hens and to improve our understanding of the molecular determinants for feed efficiency. Electronic supplementary material The online version of this article (10.1186/s12864-017-4092-9) contains supplementary material, which is available to authorized users.

Background In biological systems, metabolomics can not only contribute to the discovery of metabolic signatures for disease diagnosis, but is very helpful to illustrate the underlying molecular disease-causing mechanism. Therefore, identification of disease-related metabolites is of great significance for comprehensively understanding the pathogenesis of diseases and improving clinical medicine. Results In the paper, we propose a disease and literature driven metabolism prediction model (DLMPM) to identify the potential associations between metabolites and diseases based on latent factor model. We build the disease glossary with disease terms from different databases and an association matrix based on the mapping between diseases and metabolites. The similarity of diseases and metabolites is used to complete the association matrix. Finally, we predict potential associations between metabolites and diseases based on the matrix decomposition method. In total, 1,406 direct associations between diseases and metabolites are found. There are 119,206 unknown associations between diseases and metabolites predicted with a coverage rate of 80.88%. Subsequently, we extract training sets and testing sets based on data increment from the database of disease-related metabolites and assess the performance of DLMPM on 19 diseases. As a result, DLMPM is proven to be successful in predicting potential metabolic signatures for human diseases with an average AUC value of 82.33%. Conclusion In this paper, a computational model is proposed for exploring metabolite-disease pairs and has good performance in predicting potential metabolites related to diseases through adequate validation. The results show that DLMPM has a better performance in prioritizing candidate diseases-related metabolites compared with the previous methods and would be helpful for researchers to reveal more information about human diseases.

Background Bacteria are an essential component of the earth`s biota and affect circulation of matters through their metabolic activity. They also play an important role in the carbon and nitrogen cycle in the deep-sea environment. In this paper, two strains from deep-sea sediments were investigated in order to understand nitrogen cycling involved in the deep-sea environment. Results In this paper, the basic genomic information of two strains was obtained by whole genome sequencing. The Cobetia amphilecti N-80 and Halomonas profundus 13 genome sizes are 4,160,095 bp with a GC content of 62.5% and 5,251,450 bp with a GC content of 54.84%. Through a comparison of functional analyses, we predicted the possible C and N metabolic pathways of the two strains and determined that Halomonas profundus 13 could use more carbon sources than Cobetia amphilecti N-80. The main genes associated with N metabolism in Halomonas profundus 13 are narG, narY, narI, nirS, norB, norC, nosZ, and nirD. On the contrast, nirD, using NH4+ for energy, plays a main role in Cobetia amphilecti N-80. Both of them have the same genes for fixing inorganic carbon: icd, ppc, fdhA, accC, accB, accD, and accA. Conclusion In this study, the whole genomes of two strains were sequenced to clarify the basic characteristics of their genomes, laying the foundation for further studying nitrogen-metabolizing bacteria. Halomonas profundus 13 can utilize more carbon sources than Cobetia amphilecti N-80, as indicated by API as well as COG and KEGG prediction results. Finally, through the analysis of the nitrification and denitrification abilities as well as the inorganic carbon fixation ability of the two strains, the related genes were identified, and the possible metabolic pathways were predicted. Together, these results provide molecular markers and theoretical support for the mechanisms of inorganic carbon fixation by deep-sea microorganisms.

Background Motif search is an important step in extracting meaningful patterns from biological data. The general problem of motif search is intractable and there is a pressing need to develop efficient, exact and approximation algorithms to solve this problem. In this paper, we present several novel, exact, sequential and parallel algorithms for solving the (l,d) Edit-distance-based Motif Search (EMS) problem: given two integers l,d and n biological strings, find all strings of length l that appear in each input string with atmost d errors of types substitution, insertion and deletion. Methods One popular technique to solve the problem is to explore for each input string the set of all possible l-mers that belong to the d-neighborhood of any substring of the input string and output those which are common for all input strings. We introduce a novel and provably efficient neighborhood exploration technique. We show that it is enough to consider the candidates in neighborhood which are at a distance exactly d. We compactly represent these candidate motifs using wildcard characters and efficiently explore them with very few repetitions. Our sequential algorithm uses a trie based data structure to efficiently store and sort the candidate motifs. Our parallel algorithm in a multi-core shared memory setting uses arrays for storing and a novel modification of radix-sort for sorting the candidate motifs. Results The algorithms for EMS are customarily evaluated on several challenging instances such as (8,1), (12,2), (16,3), (20,4), and so on. The best previously known algorithm, EMS1, is sequential and in estimated 3 days solves up to instance (16,3). Our sequential algorithms are more than 20 times faster on (16,3). On other hard instances such as (9,2), (11,3), (13,4), our algorithms are much faster. Our parallel algorithm has more than 600 % scaling performance while using 16 threads. Conclusions Our algorithms have pushed up the state-of-the-art of EMS solvers and we believe that the techniques introduced in this paper are also applicable to other motif search problems such as Planted Motif Search (PMS) and Simple Motif Search (SMS). Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2789-9) contains supplementary material, which is available to authorized users.

Background Our knowledge about the ecological role of bacterial antimicrobial peptides (bacteriocins) in the human gut is limited, particularly in relation to their role in the diversification of the gut microbiota during early life. The aim of this paper was therefore to address associations between bacteriocins and bacterial diversity in the human gut microbiota. To investigate this, we did an extensive screening of 2564 healthy human gut metagenomes for the presence of predicted bacteriocin-encoding genes, comparing bacteriocin gene presence to strain diversity and age. Results We found that the abundance of bacteriocin genes was significantly higher in infant-like metagenomes ( Conclusions Our findings indicate that bacteriocins may play a role in the strain diversification during the infant gut microbiota establishment.

Background Piwi-interacting RNAs (piRNAs) have been proven to be closely associated with human diseases. The identification of the potential associations between piRNA and disease is of great significance for complex diseases. Traditional “wet experiment” is time-consuming and high-priced, predicting the piRNA-disease associations by computational methods is of great significance. Methods In this paper, a method based on the embedding transformation graph convolution network is proposed to predict the piRNA-disease associations, named ETGPDA. Specifically, a heterogeneous network is constructed based on the similarity information of piRNA and disease, as well as the known piRNA-disease associations, which is applied to extract low-dimensional embeddings of piRNA and disease based on graph convolutional network with an attention mechanism. Furthermore, the embedding transformation module is developed for the problem of embedding space inconsistency, which is lightweighter, stronger learning ability and higher accuracy. Finally, the piRNA-disease association score is calculated by the similarity of the piRNA and disease embedding. Results Evaluated by fivefold cross-validation, the AUC of ETGPDA achieves 0.9603, which is better than the other five selected computational models. The case studies based on Head and neck squamous cell carcinoma and Alzheimer’s disease further prove the superior performance of ETGPDA. Conclusions Hence, the ETGPDA is an effective method for predicting the hidden piRNA-disease associations.

An amendment to this paper has been published and can be accessed via the original article.

Background Genomic selection (GS) is revolutionizing plant and animal breeding. However, still its practical implementation is challenging since it is affected by many factors that when they are not under control make this methodology not effective. Also, due to the fact that it is formulated as a regression problem in general has low sensitivity to select the best candidate individuals since a top percentage is selected according to a ranking of predicted breeding values. Results For this reason, in this paper we propose two methods to improve the prediction accuracy of this methodology. One of the methods consist in reformulating the GS (nowadays formulated as a regression problem) methodology as a binary classification problem. The other consists only in a postprocessing step that adjust the threshold used for classification of the lines predicted in its original scale (continues scale) to guarantee similar sensitivity and specificity. The postprocessing method is applied for the resulting predictions after obtaining the predictions using the conventional regression model. Both methods assume that we defined with anticipation a threshold, to divide the training data as top lines and not top lines, and this threshold can be decided in terms of a quantile (for example 80%, 90%, etc.) or as the average (or maximum) of the performance of the checks. In the reformulation method it is required to label as one those lines in the training set that are equal or larger than the specified threshold and as zero otherwise. Then we train a binary classification model with the conventional inputs, but using the binary response variable in place of the continuous response variable. The training of the binary classification should be done to guarantee a more similar sensitivity and specificity, to guarantee a reasonable probability of classification of the top lines. Conclusions We evaluated the proposed models in seven data sets and we found that the two proposed methods outperformed by large margin the conventional regression model (by 402.9% in terms of sensitivity, by 110.04% in terms of F1 score and by 70.96% in terms of Kappa coefficient, with the postprocessing methods). However, between the two proposed methods the postprocessing method was better than the reformulation as binary classification model. The simple postprocessing method to improve the accuracy of the conventional genomic regression models avoid the need to reformulate the conventional regression models as binary classification models with similar or better performance, that significantly improve the selection of the top best candidate lines. In general both proposed methods are simple and can easily be adopted for use in practical breeding programs, with the guarantee that will improve significantly the selection of the top best candidates lines.

Background Cross breeding is an important way to improve livestock performance. As an important livestock and poultry resource in Henan Province of China, Bohuai goat was formed by crossing Boer goat and Huai goat. After more than 20 years of breeding, BoHuai goats showed many advantages, such as fast growth, good reproductive performance, and high meat yield. In order to better develop and protect Bohuai goats, we sequenced the whole genomes of 30 BoHuai goats and 5 Huai goats to analyze the genetic diversity, population structure and genomic regions under selection of BoHuai goat. Furthermore, we used 126 published genomes of world-wide goat to characterize the genomic variation of BoHuai goat. Results The results showed that the nucleotide diversity of BoHuai goats was lower and the degree of linkage imbalance was higher than that of other breeds. The analysis of population structure showed that BoHuai goats have obvious differences from other goat breeds. In addition, the BoHuai goat is more closely related to the Boer goat than the Huai goat and is highly similar to the Boer goat. Group by selection signal in the BoHuai goat study, we found that one region on chromosome 7 shows a very strong selection signal, which suggests that it could well be the segment region under the intense artificial selection results. Through selective sweeps, we detected some genes related to important traits such as lipid metabolism (LDLR, STAR, ANGPTL8), fertility (STAR), and disease resistance (CD274, DHPS, PDCD1LG2). Conclusion In this paper, we elucidated the genomic variation, ancestry composition, and selective signals related to important economic traits in BoHuai goats. Our studies on the genome of BoHuai goats will not only help to understand the characteristics of the crossbred but also provide a basis for the improvement of cross-breeding programs.

Background Paeonia ludlowii (Stern & G. Taylor D.Y. Hong) belongs to the peony group of the genus Paeonia in the Paeoniaceae family and is now classified as a “critically endangered species” in China. Reproduction is important for this species, and its low fruiting rate has become a critical factor limiting both the expansion of its wild population and its domestic cultivation. Results In this study, we investigated possible causes of the low fruiting rate and ovule abortion in Paeonia ludlowii. We clarified the characteristics of ovule abortion and the specific time of abortion in Paeonia ludlowii, and used transcriptome sequencing to investigate the mechanism of abortion of ovules in Paeonia ludlowii. Conclusions In this paper, the ovule abortion characteristics of Paeonia ludlowii were systematically studied for the first time and provide a theoretical basis for the optimal breeding and future cultivation of Paeonia ludlowii.

Background Broad-spectrum fluoroquinolone antibiotics are central in modern health care and are used to treat and prevent a wide range of bacterial infections. The recently discovered qnr genes provide a mechanism of resistance with the potential to rapidly spread between bacteria using horizontal gene transfer. As for many antibiotic resistance genes present in pathogens today, qnr genes are hypothesized to originate from environmental bacteria. The vast amount of data generated by shotgun metagenomics can therefore be used to explore the diversity of qnr genes in more detail. Results In this paper we describe a new method to identify qnr genes in nucleotide sequence data. We show, using cross-validation, that the method has a high statistical power of correctly classifying sequences from novel classes of qnr genes, even for fragments as short as 100 nucleotides. Based on sequences from public repositories, the method was able to identify all previously reported plasmid-mediated qnr genes. In addition, several fragments from novel putative qnr genes were identified in metagenomes. The method was also able to annotate 39 chromosomal variants of which 11 have previously not been reported in literature. Conclusions The method described in this paper significantly improves the sensitivity and specificity of identification and annotation of qnr genes in nucleotide sequence data. The predicted novel putative qnr genes in the metagenomic data support the hypothesis of a large and uncharacterized diversity within this family of resistance genes in environmental bacterial communities. An implementation of the method is freely available at http://bioinformatics.math.chalmers.se/qnr/.

We present a report of the 2012 International Conference on Intelligent Biology and Medicine (ICIBM 2012) and the editorial report of the supplement to BMC Genomics that includes 22 research papers selected from ICIBM 2012, which was held on April 22-24, 2012 in Nashville, Tennessee, USA. The conference covered a variety of research areas, including bioinformatics, systems biology, and intelligent computing. It included six sessions, a tutorial - Introduction to Proteome Informatics, a workshop - Next Generation Sequencing, and a poster session. The selected papers in this Supplement issue represent the genomic focus in ICIBM 2012.

Background Speckles in ultrasound imaging affect image quality and can make the post-processing difficult. Speckle reduction technologies have been employed for removing speckles for some time. One of the effective speckle reduction technologies is anisotropic diffusion. Anisotropic diffusion technology can remove the speckles effectively while preserving the edges of the image and thus has drawn great attention from image processing scientists. However, the proposed methods in the past have different disadvantages, such as being sensitive to the number of iterations or low capability of preserving the details of the ultrasound images. Thus a detail preserved anisotropic diffusion speckle reduction with less sensitive to the number of iterations is needed. This paper aims to develop this kind of technologies. Results In this paper, we propose a robust detail preserving anisotropic diffusion filter (RDPAD) for speckle reduction. In order to get robust diffusion, the proposed method integrates Tukey error norm function into the detail preserving anisotropic diffusion filter (DPAD) developed recently. The proposed method could prohibit over-diffusion and thus is less sensitive to the number of iterations Conclusions The proposed anisotropic diffusion can preserve the important structure information of the original image while reducing speckles. It is also less sensitive to the number of iterations. Experimental results on real ultrasound images show the effectiveness of the proposed anisotropic diffusion filter.

Background Biological systems are commonly described as networks of entity interactions. Some interactions are already known and integrate the current knowledge in life sciences. Others remain unknown for long periods of time and are frequently discovered by chance. In this work we present a model to predict these unknown interactions from a textual collection using the vector space model (VSM), a well known and established information retrieval model. We have extended the VSM ability to retrieve information using a transitive closure approach. Our objective is to use the VSM to identify the known interactions from the literature and construct a network. Based on interactions established in the network our model applies the transitive closure in order to predict and rank new interactions. Results We have tested and validated our model using a collection of patent claims issued from 1976 to 2005. From 266,528 possible interactions in our network, the model identified 1,027 known interactions and predicted 3,195 new interactions. Iterating the model according to patent issue dates, interactions found in a given past year were often confirmed by patent claims not in the collection and issued in more recent years. Most confirmation patent claims were found at the top 100 new interactions obtained from each subnetwork. We have also found papers on the Web which confirm new inferred interactions. For instance, the best new interaction inferred by our model relates the interaction between the adrenaline neurotransmitter and the androgen receptor gene. We have found a paper that reports the partial dependence of the antiapoptotic effect of adrenaline on androgen receptor. Conclusions The VSM extended with a transitive closure approach provides a good way to identify biological interactions from textual collections. Specifically for the context of literature-based discovery, the extended VSM contributes to identify and rank relevant new interactions even if these interactions occcur in only a few documents in the collection. Consequently, we have developed an efficient method for extracting and restricting the best potential results to consider as new advances in life sciences, even when indications of these results are not easily observed from a mass of documents.

Background: Analysing gene expression data from microarray technologies is a very important task in biology and medicine, and particularly in cancer diagnosis. Different from most other popular methods in high dimensional bio-medical data analysis, such as microarray gene expression or proteomics mass spectroscopy data analysis, fuzzy rule-based models can not only provide good classification results, but also easily be explained and interpreted in human understandable terms, by using fuzzy rules. However, the advantages offered by fuzzy-based techniques in microarray data analysis have not yet been fully explored in the literature. Although some recently developed fuzzy-based modeling approaches can provide satisfactory classification results, the rule bases generated by most of the reported fuzzy models for gene expression data are still too large to be easily comprehensible. Results: In this paper, we develop some Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) methods for analysing high dimensional bio-medical data sets, such as microarray gene expression data and proteomics mass spectroscopy data. We mainly focus on evaluating our proposed models on microarray gene expression cancer data sets, i.e., the lung cancer data set and the colon cancer data set, but we extend our investigations to other type of cancer data set, such as the ovarian cancer data set. The experimental studies have shown that relatively simple and small fuzzy rule bases, with satisfactory classification performance, can be successfully obtained for challenging microarray gene expression datasets. Conclusions: We believe that fuzzy-based techniques, and in particular the methods proposed in this paper, can be very useful tools in dealing with high dimensional cancer data. We also argue that the potential of applying fuzzy-based techniques to microarray data analysis need to be further explored.

Background Both the protein domains and transcript structures influence protein functional variation. The genomic location of both protein domains and transcript structural features can be described using the genomic coordinates of their encoded sequences. However, the coordinates of protein domains and transcriptional features often differ greatly, and it is difficult to view them in combination at the genome-wide level. In this paper, we describe the development of a new tool that allows users to visualize domains and transcript features together, using either built-in or uploaded genome datasets, and export publication-ready figures. Results We developed a user-friendly, independent R package and Shiny web application named “VisProDom”. VisProDom consists of a genome-wide database containing entire annotated transcripts merged with annotated protein domains from the Pfam database. The built-in dataset includes 82 files, which merge genome general feature format (GFF) annotations with rpsblast tabular outputs from protein sequence searches in the Pfam database. Multiple genomes can be simultaneously screened for protein domains or transcript names. VisProDom includes step-by-step introductions and clickable elements for ease of use. Conclusion VisProDom can display hundreds of transcripts alongside protein domains and export figures in a publication-ready format. This makes it a valuable tool for molecular evolution and comparative genomics.

Background Crossbreeding is an important way to improve production beef cattle performance. Pinan cattle is a new hybrid cattle obtained from crossing Piedmontese bulls with Nanyang cows. After more than 30 years of cross-breeding, Pinan cattle show a variety of excellent characteristics, including fast growth, early onset of puberty, and good meat quality. In this study, we analyzed the genetic diversity, population structure, and genomic region under the selection of Pinan cattle based on whole-genome sequencing data of 30 Pinan cattle and 169 published cattle genomic data worldwide. Results Estimating ancestry composition analysis showed that the composition proportions for our Pinan cattle were mainly Piedmontese and a small amount of Nanyang cattle. The analyses of nucleotide diversity and linkage disequilibrium decay indicated that the genomic diversity of Pinan cattle was higher than that of European cattle and lower than that of Chinese indigenous cattle. De-correlated composite of multiple selection signals, which combines four different statistics including θπ, CLR, FST, and XP-EHH, was computed to detect the signatures of selection in the Pinan cattle genome. A total of 83 genes were identified, affecting many economically important traits. Functional annotation revealed that these selected genes were related to immune (BOLA-DQA2, BOLA-DQB, LSM14A, SEC13, and NAALADL2), growth traits (CYP4A11, RPL26, and MYH10), embryo development (REV3L, NT5E, CDX2, KDM6B, and ADAMTS9), hornless traits (C1H21orf62), and climate adaptation (ANTXR2). Conclusion In this paper, we elucidated the genomic characteristics, ancestry composition, and selective signals related to important economic traits in Pinan cattle. These results will provide the basis for further genetic improvement of Pinan cattle and reference for other hybrid cattle related studies.

The advent of high-throughput next generation sequencing technologies have fostered enormous potential applications of supercomputing techniques in genome sequencing, epi-genetics, metagenomics, personalized medicine, discovery of non-coding RNAs and protein-binding sites. To this end, the 2008 International Conference on Bioinformatics and Computational Biology (Biocomp) – 2008 World Congress on Computer Science, Computer Engineering and Applied Computing (Worldcomp) was designed to promote synergistic inter/multidisciplinary research and education in response to the current research trends and advances. The conference attracted more than two thousand scientists, medical doctors, engineers, professors and students gathered at Las Vegas, Nevada, USA during July 14–17 and received great success. Supported by International Society of Intelligent Biological Medicine (ISIBM), International Journal of Computational Biology and Drug Design (IJCBDD), International Journal of Functional Informatics and Personalized Medicine (IJFIPM) and the leading research laboratories from Harvard, M.I.T., Purdue, UIUC, UCLA, Georgia Tech, UT Austin, U. of Minnesota, U. of Iowa etc, the conference received thousands of research papers. Each submitted paper was reviewed by at least three reviewers and accepted papers were required to satisfy reviewers' comments. Finally, the review board and the committee decided to select only 19 high-quality research papers for inclusion in this supplement to BMC Genomics based on the peer reviews only. The conference committee was very grateful for the Plenary Keynote Lectures given by: Dr. Brian D. Athey (University of Michigan Medical School), Dr. Vladimir N. Uversky (Indiana University School of Medicine), Dr. David A. Patterson (Member of United States National Academy of Sciences and National Academy of Engineering, University of California at Berkeley) and Anousheh Ansari (Prodea Systems, Space Ambassador). The theme of the conference to promote synergistic research and education has been achieved successfully.

As gene expression profile data from DNA microarrays accumulate rapidly, there is a natural need to compare data across labs and platforms. Comparisons of microarray data can be quite challenging due to data complexity and variability. Different labs may adopt different technology platforms. One may ask about the degree of agreement we can expect from different labs and different platforms. To address this question, we conducted a study of inter-lab and inter-platform agreement of microarray data across three platforms and three labs. The statistical measures of consistency and agreement used in this paper are the Pearson correlation, intraclass correlation, kappa coefficients, and a measure of intra-transcript correlation. The three platforms used in the present paper were Affymetrix GeneChip, custom cDNA arrays, and custom oligo arrays. Using the within-platform variability as a benchmark, we found that these technology platforms exhibited an acceptable level of agreement, but the agreement between two technologies within the same lab was greater than that between two labs using the same technology. The consistency of replicates in each experiment varies from lab to lab. When there is high consistency among replicates, different technologies show good agreement within and across labs using the same RNA samples. On the other hand, the lab effect, especially when confounded with the RNA sample effect, plays a bigger role than the platform effect on data agreement.

Background Zinc finger homeodomain (ZHD) protein is a plant-specific transcription factor and a potential regulator of phosphoenolpyruvate carboxylase (PEPCase)-coding genes, and it also participates in plant growth regulation and abiotic stress responses. To study the function of MsZF-HD genes in the alkaline stress response, this paper assessed biological information and performed transcriptome analysis of the MsZF-HD gene family by using the genomes of two different varieties of alfalfa (XinJiangDa Ye and Zhongmu No. 1). Results In total, 49 and 11 MsZF-HD genes were identified in the two different varieties respectively, including the alleles of XinJiangDa Ye. According to their phylogenetic relationships, the 60 MsZF-HD genes were divided into 5 ZHD subfamilies and 1 MIF subfamily. A total of 88.3% of MsZF-HD genes do not contain introns and are unevenly distributed among the 6 chromosomes of alfalfa. A collinearity analysis indicated that 26 genes of XinJiangDa Ye have no orthologous genes in Zhongmu No. 1, although these genes (such as ZHD-X1–2, ZHD-X3–2 and ZHD-X4–2) have homologous genes in Arabidopsis thaliana, Medicago truncatula and Glycine max. Through RNA-seq and qRT–PCR verification, it was found that MsZF-HD genes are downregulated to participate in the alkaline stress response. Conclusion The results of this study may lay the foundation for the cloning and functional study of MsZF-HD genes and provide a theoretical basis for revealing the difference between XinJiangDa Ye and Zhongmu No. 1 at the genome level.

An amendment to this paper has been published and can be accessed via the original article.

Background Calliptamus italicus is a dominant species in the desert and semi-desert grassland. It is widely distributed throughout many regions such as Asia, Europe, North Africa and the Mediterranean, and has enormous destructive potential for agriculture and animal husbandry. The C. italicus overwintering as eggs in the soil through diapause, and the cold tolerance of locust eggs is the key to their ability to survive the winter smoothly to maintain the population. Results Transcriptome analysis of C. italicus eggs was carried out in this paper in constant low temperature acclimation, natural low temperature acclimation and room temperature. The differentially expressed genes related to cold tolerance were screened out, the differences in expression patterns under different low temperature acclimation were analyzed, and the genes in the significantly up-regulated pathways may play an important role in cold tolerance. The results show that different domestication modes can induce C. italicus eggs to express a large number of genes to alleviate low temperature damage, but C. italicus eggs are more sensitive to changes in temperature. Compared with the control, there are 8689 DEGs at constant low temperature and 14,994 DEGs at natural low temperature. KEGG analysis showed that DEGs were mainly enriched in pathways related to metabolism and biological systems under constant low temperature, and were mainly enriched in pathways related to biological systems and environmental information processing under natural low temperature. In addition, RNAi technology was used to further verify the regulation of genes in the significantly enriched up-regulated pathways on C. italicus eggs, and it was confirmed that the hatching rate of C. italicus eggs at low temperature was significantly reduced after interference. Conclusions Transcriptome analysis of C. italicus eggs treated at different temperatures provided a theoretical basis for further understanding the adaptation mechanism of C. italicus eggs to low temperature. In addition, four potential RNAi target genes were verified in the eggs of C. italicus for the first time, providing new ideas for effective control of this species.

Background Recently, erdafitinib (Balversa), the first targeted therapy drug for genetic alteration, was approved to metastatic urothelial carcinoma. Cancer genomics research has been greatly encouraged. Currently, a large number of gene regulatory networks between different states have been constructed, which can reveal the difference states of genes. However, they have not been applied to the subtypes of Muscle-invasive bladder cancer (MIBC). Results In this paper, we propose a method that construct gene regulatory networks under different molecular subtypes of MIBC, and analyse the regulatory differences between different molecular subtypes. Through differential expression analysis and the differential network analysis of the top 100 differential genes in the network, we find that SERPINI1, NOTUM, FGFR1 and other genes have significant differences in expression and regulatory relationship between MIBC subtypes. Conclusions Furthermore, pathway enrichment analysis and differential network analysis demonstrate that Neuroactive ligand-receptor interaction and Cytokine-cytokine receptor interaction are significantly enriched pathways, and the genes contained in them are significant diversity in the subtypes of bladder cancer.

Background Sanghuangporus sanghuang is a well-known traditional medicinal mushroom associated with mulberry. Despite the properties of this mushroom being known for many years, the regulatory mechanisms of bioactive compound biosynthesis in this medicinal mushroom are still unclear. Lysine malonylation is a posttranslational modification that has many critical functions in various aspects of cell metabolism. However, at present we do not know its role in S. sanghuang. In this study, a global investigation of the lysine malonylome in S. sanghuang was therefore carried out. Results In total, 714 malonyl modification sites were matched to 255 different proteins. The analysis indicated that malonyl modifications were involved in a wide range of cellular functions and displayed a distinct subcellular localization. Bioinformatics analysis indicated that malonylated proteins were engaged in different metabolic pathways, including glyoxylate and dicarboxylate metabolism, glycolysis/gluconeogenesis, and the tricarboxylic acid (TCA) cycle. Notably, a total of 26 enzymes related to triterpene and polysaccharide biosynthesis were found to be malonylated, indicating an indispensable role of lysine malonylation in bioactive compound biosynthesis in S. sanghuang. Conclusions These findings suggest that malonylation is associated with many metabolic pathways, particularly the metabolism of the bioactive compounds triterpene and polysaccharide. This paper represents the first comprehensive survey of malonylation in S. sanghuang and provides important data for further study on the physiological function of lysine malonylation in S. sanghuang and other medicinal mushrooms.

Background All diseases containing genetic material undergo genetic evolution and give rise to heterogeneity including cancer and infection. Although these illnesses are biologically very different, the ability for phylogenetic retrodiction based on the genomic reads is common between them and thus tree-based principles and assumptions are shared. Just as the different frequencies of tumor genomic variants presupposes the existence of multiple tumor clones and provides a handle to computationally infer them, we postulate that the different variant frequencies in viral reads offers the means to infer multiple co-infecting sublineages. Results We present a common methodological framework to infer the phylogenomics from genomic data, be it reads of SARS-CoV-2 of multiple COVID-19 patients or bulk DNAseq of the tumor of a cancer patient. We describe the Concerti computational framework for inferring phylogenies in each of the two scenarios.To demonstrate the accuracy of the method, we reproduce some known results in both scenarios. We also make some additional discoveries. Conclusions Concerti successfully extracts and integrates information from multi-point samples, enabling the discovery of clinically plausible phylogenetic trees that capture the heterogeneity known to exist both spatially and temporally. These models can have direct therapeutic implications by highlighting “birth” of clones that may harbor resistance mechanisms to treatment, “death” of subclones with drug targets, and acquisition of functionally pertinent mutations in clones that may have seemed clinically irrelevant. Specifically in this paper we uncover new potential parallel mutations in the evolution of the SARS-CoV-2 virus. In the context of cancer, we identify new clones harboring resistant mutations to therapy.

Background Patient-derived xenografts (PDX) mice models play an important role in preclinical trials and personalized medicine. Sharing data on the models is highly valuable for numerous reasons – ethical, economical, research cross validation etc. The EurOPDX Consortium was established 8 years ago to share such information and avoid duplicating efforts in developing new PDX mice models and unify approaches to support preclinical research. EurOPDX Data Portal is the unified data sharing platform adopted by the Consortium. Main body In this paper we describe the main features of the EurOPDX Data Portal (https://dataportal.europdx.eu/), its architecture and possible utilization by researchers who look for PDX mice models for their research. The Portal offers a catalogue of European models accessible on a cooperative basis. The models are searchable by metadata, and a detailed view provides molecular profiles (gene expression, mutation, copy number alteration) and treatment studies. The Portal displays the data in multiple tools (PDX Finder, cBioPortal, and GenomeCruzer in future), which are populated from a common database displaying strictly mutually consistent views. (Short) Conclusion EurOPDX Data Portal is an entry point to the EurOPDX Research Infrastructure offering PDX mice models for collaborative research, (meta)data describing their features and deep molecular data analysis according to users’ interests.

Background Paris polyphylla is a herb widely used in traditional Chinese medicine to treat various diseases. Stem rot diseases seriously affected the yield of P. polyphylla in subtropical areas of China. Therefore, cost-effective, chemical-free, eco-friendly strategies to control stem rot on P. polyphylla are valuable and urgently needed. Results In this paper, we reported the biocontrol efficiency of Paenibacillus peoriae HJ-2 and its complete genome sequence. Strain HJ-2 could serve as a potential biocontrol agent against stem rot on P. polyphylla in the greenhouse and field. The genome of HJ-2 consists of a single 6,001,192 bp chromosome with an average GC content of 45% and 5,237 predicted protein coding genes, 39 rRNAs and 108 tRNAs. The phylogenetic tree indicated that HJ-2 is most closely related to P. peoriae IBSD35. Functional analysis of genome revealed numerous genes/gene clusters involved in plant colonization, biofilm formation, plant growth promotion, antibiotic and resistance inducers synthesis. Moreover, metabolic pathways that potentially contribute to biocontrol mechanisms were identified. Conclusions This study revealed that P. peoriae HJ-2 could serve as a potential BCA against stem rot on P. polyphylla. Based on genome analysis, the genome of HJ-2 contains more than 70 genes and 12 putative gene clusters related to secondary metabolites, which have previously been described as being involved in chemotaxis motility, biofilm formation, growth promotion, antifungal activity and resistance inducers biosynthesis. Compared with other strains, variation in the genes/gene clusters may lead to different antimicrobial spectra and biocontrol efficacies.

Background With the rapid increase in the amount of Protein-Protein Interaction (PPI) data, the establishment of an event-centered PPI ontology that contains temporal and spatial vocabularies is urgently needed to clarify PPI biological annotations. In this paper, we propose a precisely designed schema - PPIO (PPI Ontology) for representing the biological context of PPIs. Results Inspired by the event model and the distinct characteristics of PPI events, PPIO consists of six core aspects of the information required for reporting a PPI event, including the interactor (who), the biological process (when), the subcellular location (where), the interaction type (how), the biological function (what) and the detection method (which). PPIO is implemented through the integration of appropriate terms from the corresponding vocabularies/ontologies, e.g., Gene Ontology, Protein Ontology, PSI-MI/MOD, etc. To assess PPIO, an approach based on PPIO in developed to extract PPI biological annotations from an open standard corpus “BioCreAtIvE-PPI”. The experiment results demonstrate PPIO’s high performance, a precision of 0.69, a recall of 0.72 and an F-score of 0.70. Conclusions PPIO is a well-constructed essential ontology in the interpretation of PPI biological context. The results of the experiments conducted on the BioCreAtIvE corpus demonstrate that PPIO is able to facilitate PPI annotation extraction from biomedical literature effectively and enrich essential annotation for PPIs.

Background Single molecule measurements of DNA polymerization kinetics provide a sensitive means to detect both secondary structures in DNA and deviations from primary chemical structure as a result of modified bases. In one approach to such analysis, deviations can be inferred by monitoring the behavior of DNA polymerase using single-molecule, real-time sequencing with zero-mode waveguide. This approach uses a Single Molecule Real Time (SMRT)-sequencing measurement of time between fluorescence pulse signals from consecutive nucleosides incorporated during DNA replication, called the interpulse duration (IPD). Results In this paper we present an analysis of loci with high IPDs in two genomes, a bacterial genome (E. coli) and a eukaryotic genome (C. elegans). To distinguish the potential effects of DNA modification on DNA polymerization speed, we paired an analysis of native genomic DNA with whole-genome amplified (WGA) material in which DNA modifications were effectively removed. Adenine modification sites for E. coli are known and we observed the expected IPD shifts at these sites in the native but not WGA samples. For C. elegans, such differences were not observed. Instead, we found a number of novel sequence contexts where IPDs were raised relative to the average IPDs for each of the four nucleotides, but for which the raised IPD was present in both native and WGA samples. Conclusion The latter results argue strongly against DNA modification as the underlying driver for high IPD segments for C. elegans, and provide a framework for separating effects of DNA modification from context-dependent DNA polymerase kinetic patterns inherent in underlying DNA sequence for a complex eukaryotic genome.

An amendment to this paper has been published and can be accessed via the original article.

An amendment to this paper has been published and can be accessed via the original article.

Background It has been a long-standing biological challenge to understand the molecular regulatory mechanisms behind mammalian ageing. Harnessing the availability of many ageing microarray datasets, a number of studies have shown that it is possible to identify genes that have age-dependent differential expression (DE) or differential variability (DV) patterns. The majority of the studies identify "interesting" genes using a linear regression approach, which is known to perform poorly in the presence of outliers or if the underlying age-dependent pattern is non-linear. Clearly a more robust and flexible approach is needed to identify genes with various age-dependent gene expression patterns. Results Here we present a novel model selection approach to discover genes with linear or non-linear age-dependent gene expression patterns from microarray data. To identify DE genes, our method fits three quantile regression models (constant, linear and piecewise linear models) to the expression profile of each gene, and selects the least complex model that best fits the available data. Similarly, DV genes are identified by fitting and comparing two quantile regression models (non-DV and the DV models) to the expression profile of each gene. We show that our approach is much more robust than the standard linear regression approach in discovering age-dependent patterns. We also applied our approach to analyze two human brain ageing datasets and found many biologically interesting gene expression patterns, including some very interesting DV patterns, that have been overlooked in the original studies. Furthermore, we propose that our model selection approach can be extended to discover DE and DV genes from microarray datasets with discrete class labels, by considering different quantile regression models. Conclusion In this paper, we present a novel application of quantile regression models to identify genes that have interesting linear or non-linear age-dependent expression patterns. One important contribution of this paper is to introduce a model selection approach to DE and DV gene identification, which is most commonly tackled by null hypothesis testing approaches. We show that our approach is robust in analyzing real and simulated datasets. We believe that our approach is applicable in many ageing or time-series data analysis tasks.

Background Single-cell (sc) sequencing performs unbiased profiling of individual cells and enables evaluation of less prevalent cellular populations, often missed using bulk sequencing. However, the scale and the complexity of the sc datasets poses a great challenge in its utility and this problem is further exacerbated when working with larger datasets typically generated by consortium efforts. As the scale of single cell datasets continues to increase exponentially, there is an unmet technological need to develop database platforms that can evaluate key biological hypotheses by querying extensive single-cell datasets. Large single-cell datasets like Human Cell Atlas and COVID-19 cell atlas (collection of annotated sc datasets from various human organs) are excellent resources for profiling target genes involved in human diseases and disorders ranging from oncology, auto-immunity, as well as infectious diseases like COVID-19 caused by SARS-CoV-2 virus. SARS-CoV-2 infections have led to a worldwide pandemic with massive loss of lives, infections exceeding 7 million cases. The virus uses ACE2 and TMPRSS2 as key viral entry associated proteins expressed in human cells for infections. Evaluating the expression profile of key genes in large single-cell datasets can facilitate testing for diagnostics, therapeutics, and vaccine targets, as the world struggles to cope with the on-going spread of COVID-19 infections. Main body In this manuscript we describe REVEAL: SingleCell, which enables storage, retrieval, and rapid query of single-cell datasets inclusive of millions of cells. The array native database described here enables selecting and analyzing cells across multiple studies. Cells can be selected using individual metadata tags, more complex hierarchical ontology filtering, and gene expression threshold ranges, including co-expression of multiple genes. The tags on selected cells can be further evaluated for testing biological hypotheses. One such example includes identifying the most prevalent cell type annotation tag on returned cells. We used REVEAL: SingleCell to evaluate the expression of key SARS-CoV-2 entry associated genes, and queried the current database (2.2 Million cells, 32 projects) to obtain the results in Conclusion In this paper, we introduce the REVEAL: SingleCell database that addresses immediate needs for SARS-CoV-2 research and has the potential to be used more broadly for many precision medicine applications. We used the REVEAL: SingleCell database as a reference to ask questions relevant to drug development and precision medicine regarding cell type and co-expression for genes that encode proteins necessary for SARS-CoV-2 to enter and reproduce in cells.

BackgroundGenetic improvement of root system architecture is essential to improve water and nutrient use efficiency of crops or to boost their productivity under stress or non-optimal soil conditions. One hundred ninety-two Ethiopian durum wheat accessions comprising 167 historical landraces and 25 modern cultivars were assembled for GWAS analysis to identify QTLs for root system architecture (RSA) traits and genotyped with a high-density 90 K wheat SNP array by Illumina.ResultsUsing a non-roll, paper-based root phenotyping platform, a total of 2880 seedlings and 14,947 seminal roots were measured at the three-leaf stage to collect data for total root length (TRL), total root number (TRN), root growth angle (RGA), average root length (ARL), bulk root dry weight (RDW), individual root dry weight (IRW), bulk shoot dry weight (SDW), presence of six seminal roots per seedling (RT6) and root shoot ratio (RSR). Analysis of variance revealed highly significant differences between accessions for all RSA traits. Four major (− log10P≥ 4) and 34 nominal (− log10P≥ 3) QTLs were identified and grouped in 16 RSA QTL clusters across chromosomes. A higher number of significant RSA QTL were identified on chromosome 4B particularly for root vigor traits (root length, number and/or weight).ConclusionsAfter projecting the identified QTLs on to a high-density tetraploid consensus map along with previously reported RSA QTL in both durum and bread wheat, fourteen nominal QTLs were found to be novel and could potentially be used to tailor RSA in elite lines. The major RGA QTLs on chromosome 6AL detected in the current study and reported in previous studies is a good candidate for cloning the causative underlining sequence and identifying the beneficial haplotypes able to positively affect yield under water- or nutrient-limited conditions.

BackgroundMicrobe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by aBayesian Network(BN) about a microbial community. Unlike the previously proposedCo-occurrence Networks(CoNs), BNs are based on conditional dependencies and can help in revealing complex associations.ResultsIn this paper, we propose a way of combining a BN and a CoN to construct asigned Bayesian Network(sBN). We report a surprising association between directed edges in signed BNs and knowncolonization orders.ConclusionsBNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order.

Background Pseudotime estimation from dynamic single-cell transcriptomic data enables characterisation and understanding of the underlying processes, for example developmental processes. Various pseudotime estimation methods have been proposed during the last years. Typically, these methods start with a dimension reduction step because the low-dimensional representation is usually easier to analyse. Approaches such as PCA, ICA or t-SNE belong to the most widely used methods for dimension reduction in pseudotime estimation methods. However, these methods usually make assumptions on the derived dimensions, which can result in important dataset properties being missed. In this paper, we suggest a new dictionary learning based approach, dynDLT, for dimension reduction and pseudotime estimation of dynamic transcriptomic data. Dictionary learning is a matrix factorisation approach that does not restrict the dependence of the derived dimensions. To evaluate the performance, we conduct a large simulation study and analyse 8 real-world datasets. Results The simulation studies reveal that firstly, dynDLT preserves the simulated patterns in low-dimension and the pseudotimes can be derived from the low-dimensional representation. Secondly, the results show that dynDLT is suitable for the detection of genes exhibiting the simulated dynamic patterns, thereby facilitating the interpretation of the compressed representation and thus the dynamic processes. For the real-world data analysis, we select datasets with samples that are taken at different time points throughout an experiment. The pseudotimes found by dynDLT have high correlations with the experimental times. We compare the results to other approaches used in pseudotime estimation, or those that are method-wise closely connected to dictionary learning: ICA, NMF, PCA, t-SNE, and UMAP. DynDLT has the best overall performance for the simulated and real-world datasets. Conclusions We introduce dynDLT, a method that is suitable for pseudotime estimation. Its main advantages are: (1) It presents a model-free approach, meaning that it does not restrict the dependence of the derived dimensions; (2) Genes that are relevant in the detected dynamic processes can be identified from the dictionary matrix; (3) By a restriction of the dictionary entries to positive values, the dictionary atoms are highly interpretable. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-021-08276-9).

Background Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer’s disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. Results In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer’s disease, Legionellosis, Pertussis, and Serotonergic synapse. Conclusions The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer’s Disease and will be of value to novel gene discovery and functional genomic studies.

Background Many transcripts have been generated due to the development of sequencing technologies, and lncRNA is an important type of transcript. Predicting lncRNAs from transcripts is a challenging and important task. Traditional experimental lncRNA prediction methods are time-consuming and labor-intensive. Efficient computational methods for lncRNA prediction are in demand. Results In this paper, we propose two lncRNA prediction methods based on feature ensemble learning strategies named LncPred-IEL and LncPred-ANEL. Specifically, we encode sequences into six different types of features including transcript-specified features and general sequence-derived features. Then we consider two feature ensemble strategies to utilize and integrate the information in different feature types, the iterative ensemble learning (IEL) and the attention network ensemble learning (ANEL). IEL employs a supervised iterative way to ensemble base predictors built on six different types of features. ANEL introduces an attention mechanism-based deep learning model to ensemble features by adaptively learning the weight of individual feature types. Experiments demonstrate that both LncPred-IEL and LncPred-ANEL can effectively separate lncRNAs and other transcripts in feature space. Moreover, comparison experiments demonstrate that LncPred-IEL and LncPred-ANEL outperform several state-of-the-art methods when evaluated by 5-fold cross-validation. Both methods have good performances in cross-species lncRNA prediction. Conclusions LncPred-IEL and LncPred-ANEL are promising lncRNA prediction tools that can effectively utilize and integrate the information in different types of features.

Background Horizontal Gene Transfer (HGT) refers to the sharing of genetic materials between distant species that are not in a parent-offspring relationship. The HGT insertion sites are important to understand the HGT mechanisms. Recent studies in main agents of HGT, such as transposon and plasmid, demonstrate that insertion sites usually hold specific sequence features. This motivates us to find a method to infer HGT insertion sites according to sequence features. Results In this paper, we propose a deep residual network, DeepHGT, to recognize HGT insertion sites. To train DeepHGT, we extracted about 1.55 million sequence segments as training instances from 262 metagenomic samples, where the ratio between positive instances and negative instances is about 1:1. These segments are randomly partitioned into three subsets: 80% of them as the training set, 10% as the validation set, and the remaining 10% as the test set. The training loss of DeepHGT is 0.4163 and the validation loss is 0.423. On the test set, DeepHGT has achieved the area under curve (AUC) value of 0.8782. Furthermore, in order to further evaluate the generalization of DeepHGT, we constructed an independent test set containing 689,312 sequence segments from another 147 gut metagenomic samples. DeepHGT has achieved the AUC value of 0.8428, which approaches the previous test AUC value. As a comparison, the gradient boosting classifier model implemented in PyFeat achieve an AUC value of 0.694 and 0.686 on the above two test sets, respectively. Furthermore, DeepHGT could learn discriminant sequence features; for example, DeepHGT has learned a sequence pattern of palindromic subsequences as a significantly (P-value=0.0182) local feature. Hence, DeepHGT is a reliable model to recognize the HGT insertion site. Conclusion DeepHGT is the first deep learning model that can accurately recognize HGT insertion sites on genomes according to the sequence pattern.

Background RNA binding proteins (RBPs) play a vital role in post-transcriptional processes in all eukaryotes, such as splicing regulation, mRNA transport, and modulation of mRNA translation and decay. The identification of RBP binding sites is a crucial step in understanding the biological mechanism of post-transcriptional gene regulation. However, the determination of RBP binding sites on a large scale is a challenging task due to high cost of biochemical assays. Quite a number of studies have exploited machine learning methods to predict binding sites. Especially, deep learning is increasingly used in the bioinformatics field by virtue of its ability to learn generalized representations from DNA and protein sequences. Results In this paper, we implemented a novel deep neural network model, DeepRKE, which combines primary RNA sequence and secondary structure information to effectively predict RBP binding sites. Specifically, we used word embedding algorithm to extract features of RNA sequences and secondary structures, i.e., distributed representation of k-mers sequence rather than traditional one-hot encoding. The distributed representations are taken as input of convolutional neural networks (CNN) and bidirectional long-term short-term memory networks (BiLSTM) to identify RBP binding sites. Our results show that deepRKE outperforms existing counterpart methods on two large-scale benchmark datasets. Conclusions Our extensive experimental results show that DeepRKE is an efficacious tool for predicting RBP binding sites. The distributed representations of RNA sequences and secondary structures can effectively detect the latent relationship and similarity between k-mers, and thus improve the predictive performance. The source code of DeepRKE is available at https://github.com/youzhiliu/DeepRKE/.

BackgroundResearchers discover LncRNA–miRNA regulatory paradigms modulate gene expression patterns and drive major cellular processes. Identification of lncRNA-miRNA interactions (LMIs) is critical to reveal the mechanism of biological processes and complicated diseases. Because conventional wet experiments are time-consuming, labor-intensive and costly, a few computational methods have been proposed to expedite the identification of lncRNA-miRNA interactions. However, little attention has been paid to fully exploit the structural and topological information of the lncRNA-miRNA interaction network.ResultsIn this paper, we propose novel lncRNA-miRNA prediction methods by using graph embedding and ensemble learning. First, we calculate lncRNA-lncRNA sequence similarity and miRNA-miRNA sequence similarity, and then we combine them with the known lncRNA-miRNA interactions to construct a heterogeneous network. Second, we adopt several graph embedding methods to learn embedded representations of lncRNAs and miRNAs from the heterogeneous network, and construct the ensemble models using two ensemble strategies. For the former, we consider individual graph embedding based models as base predictors and integrate their predictions, and develop a method, named GEEL-PI. For the latter, we construct a deep attention neural network (DANN) to integrate various graph embeddings, and present an ensemble method, named GEEL-FI. The experimental results demonstrate both GEEL-PI and GEEL-FI outperform other state-of-the-art methods. The effectiveness of two ensemble strategies is validated by further experiments. Moreover, the case studies show that GEEL-PI and GEEL-FI can find novel lncRNA-miRNA associations.ConclusionThe study reveals that graph embedding and ensemble learning based method is efficient for integrating heterogeneous information derived from lncRNA-miRNA interaction network and can achieve better performance on LMI prediction task. In conclusion, GEEL-PI and GEEL-FI are promising for lncRNA-miRNA interaction prediction.

Background RNA sequencing analysis focus on the detection of differential gene expression changes that meet a two-fold minimum change between groups. The variability present in RNA sequencing data may obscure the detection of valuable information when specific genes within certain samples display large expression variability. This paper develops methods that apply variance and dispersion estimates to intra-group data to identify genes with expression values that diverge from the group envelope. STRING database analysis of the identified genes characterize gene affiliations involved in physiological regulatory networks that contribute to biological variability. Individuals with divergent gene groupings within network pathways can thereby be identified and judiciously evaluated prior to standard differential analysis. Results A three-step process is presented for evaluating biological variability within a group in RNA sequencing data in which gene counts were: (1) scaled to minimize heteroscedasticity; (2) rank-ordered to detect potentially divergent “trendlines” for every gene in the data set; and (3) tested with the STRING database to identify statistically significant pathway associations among the genes displaying marked trendline variability and dispersion. This approach was used to identify the “trendline” profile of every gene in three test data sets. Control data from an in-house data set and two archived samples revealed that 65–70% of the sequenced genes displayed trendlines with minimal variation and dispersion across the sample group after rank-ordering the samples; this is referred to as a linear trendline. Smaller subsets of genes within the three data sets displayed markedly skewed trendlines, wide dispersion and variability. STRING database analysis of these genes identified interferon-mediated response networks in 11–20% of the individuals sampled at the time of blood collection. For example, in the three control data sets, 14 to 26 genes in the defense response to virus pathway were identified in 7 individuals at false discovery rates ≤1.92 E-15. Conclusions This analysis provides a rationale for identifying and characterizing notable gene expression variability within a study group. The identification of highly variable genes and their network associations within specific individuals empowers more judicious inspection of the sample group prior to differential gene expression analysis.

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Background Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. Results This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations. Conclusions Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.

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Background Specific adaptive features including disease resistance and growth abilities in harsh environments are attributed to indigenous cattle breeds of Benin, but these breeds are endangered due to crossbreeding. So far, there is a lack of systematic trait recording, being the basis for breed characterizations, and for structured breeding program designs aiming on conservation. Bridging this gap, own phenotyping for morphological traits considered measurements for height at withers (HAW), sacrum height (SH), heart girth (HG), hip width (HW), body length (BL) and ear length (EL), including 449 cattle from the four indigenous Benin breeds Lagune, Somba, Borgou and Pabli. In order to utilize recent genomic tools for breed characterizations and genetic evaluations, phenotypes for novel traits were merged with high-density SNP marker data. Multi-breed genetic parameter estimations and genome-wide association studies (GWAS) for the six morphometric traits were carried out. Continuatively, we aimed on inferring genomic regions and functional loci potentially associated with conformation, carcass and adaptive traits. Results SNP-based heritability estimates for the morphometric traits ranged between 0.46 ± 0.14 (HG) and 0.74 ± 0.13 (HW). Phenotypic and genetic correlations ranged from 0.25 ± 0.05 (HW-BL) to 0.89 ± 0.01 (HAW-SH), and from 0.14 ± 0.10 (HW-BL) to 0.85 ± 0.02 (HAW-SH), respectively. Three genome-wide and 25 chromosome-wide significant SNP positioned on different chromosomes were detected, located in very close chromosomal distance (±25 kb) to 15 genes (or located within the genes). The genes PIK3R6 and PIK3R1 showed direct functional associations with height and body size. We inferred the potential candidate genes VEPH1, CNTNAP5, GYPC for conformation, growth and carcass traits including body weight and body fat deposition. According to their functional annotations, detected potential candidate genes were associated with stress or immune response (genes PTAFR, PBRM1, ADAMTS12) and with feed efficiency (genes MEGF11 SLC16A4, CCDC117). Conclusions Accurate measurements contributed to large SNP heritabilities for some morphological traits, even for a small mixed-breed sample size. Multi-breed GWAS detected different loci associated with conformation or carcass traits. The identified potential candidate genes for immune response or feed efficiency indicators reflect the evolutionary development and adaptability features of the breeds. Supplementary Information Supplementary information accompanies this paper at 10.1186/s12864-020-07170-0.

An amendment to this paper has been published and can be accessed via the original article.

Background DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem. Results The feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene. Conclusions The results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.

The availability of genome-wide sequence data from a large number of species as well as data from multiple individuals within a species has ushered in the era of phylogenomics. In this era, species phylogeny inference is based on models of sequence evolution on gene trees as well as models of gene tree evolution within the branches of species phylogenies. Parsimony, likelihood, Bayesian, and distance methods have been introduced for species phylogeny inference based on such models. All methods, except for the parsimony ones, assume a common mechanism across all loci as captured by a single value of each branch length of the species phylogeny. In this paper, we propose a ``no common mechanism" (NCM) model, where every gene tree evolves according to its own parameters of the species phylogeny. An analogous model was proposed and explored, both mathematically and experimentally, for sites, or characters, in a sequence alignment in the context of the classical phylogeny problem. For example, a famous equivalence between the maximum parsimony and maximum likelihood phylogeny estimates was established under certain NCM models by Tuffley and Steel. Here we derive an analytically integrated likelihood of both species trees and networks given the gene trees of multiple loci under an NCM model. We demonstrate the performance of inference under this integrated likelihood on both simulated and biological data. The model presented here will afford opportunities for exploring connections among various methods for estimating species phylogenies from multiple, independent loci.