Comparative Genomics Identifies Candidate Genes for Infectious Salmon Anemia (ISA) Resistance in Atlantic Salmon (Salmo salar)

Infectious salmon anemia (ISA) has been described as the hoof and mouth disease of salmon farming. ISA is caused by a lethal and highly communicable virus, which can have a major impact on salmon aquaculture, as demonstrated by an outbreak in Chile in 2007. A quantitative trait locus (QTL) for ISA resistance has been mapped to three microsatellite markers on linkage group (LG) 8 (Chr 15) on the Atlantic salmon genetic map. We identified bacterial artificial chromosome (BAC) clones and three fingerprint contigs from the Atlantic salmon physical map that contains these markers. We made use of the extensive BAC end sequence database to extend these contigs by chromosome walking and identified additional two markers in this region. The BAC end sequences were used to search for conserved synteny between this segment of LG8 and the fish genomes that have been sequenced. An examination of the genes in the syntenic segments of the tetraodon and medaka genomes identified candidates for association with ISA resistance in Atlantic salmon based on differential expression profiles from ISA challenges or on the putative biological functions of the proteins they encode. One gene in particular, HIV-EP2/MBP-2, caught our attention as it may influence the expression of several genes that have been implicated in the response to infection by infectious salmon anemia virus (ISAV). Therefore, we suggest that HIV-EP2/MBP-2 is a very strong candidate for the gene associated with the ISAV resistance QTL in Atlantic salmon and is worthy of further study

Hierarchical maximum likelihood clustering approach

Objective: In this work, we focused on developing a clustering approach for biological data. In many biological analyses, such as multi-omics data analysis and genome-wide association studies (GWAS) analysis, it is crucial to find groups of data belonging to subtypes of diseases or tumors. Methods: Conventionally, the k-means clustering algorithm is overwhelmingly applied in many areas including biological sciences. There are, however, several alternative clustering algorithms that can be applied, including support vector clustering. In this paper, taking into consideration the nature of biological data, we propose a maximum likelihood clustering scheme based on a hierarchical framework. Results: This method can perform clustering even when the data belonging to different groups overlap. It can also perform clustering when the number of samples is lower than the data dimensionality. Conclusion: The proposed scheme is free from selecting initial settings to begin the search process. In addition, it does not require the computation of the first and second derivative of likelihood functions, as is required by many other maximum likelihood based methods. Significance: This algorithm uses distribution and centroid information to cluster a sample and was applied to biological data. A Matlab implementation of this method can be downloaded from the web-link http://www.riken.jp/en/research/labs/ims/med_sci_math/

An integrative machine learning approach for prediction of toxicity - related drug safety

Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading cause of drug withdraws after release to the market. Computational methods capable of predicting these failures can reduce the waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations

DeepInsight: a methodology to transform a non - image data to an image for convolution neural network architecture

It is critical, but difficult, to catch the small variation in genomic or other kinds of data that differentiates phenotypes or categories. A plethora of data is available, but the information from its genes or elements is spread over arbitrarily, making it challenging to extract relevant details for identification. However, an arrangement of similar genes into clusters makes these differences more accessible and allows for robust identification of hidden mechanisms (e.g. pathways) than dealing with elements individually. Here we propose, DeepInsight, which converts non-image samples into a well-organized image-form. Thereby, the power of convolution neural network (CNN), including GPU utilization, can be realized for non-image samples. Furthermore, DeepInsight enables feature extraction through the application of CNN for non-image samples to seize imperative information and shown promising results. To our knowledge, this is the first work to apply CNN simultaneously on different kinds of non-image datasets: RNA-seq, vowels, text, and artificial

Prediction Models of Breast Cancer Outcome

The goal of this study is to establish a method for predicting overall survival (OS ) and disease‐free survival (DFS ) in breast cancer patients after surgical operation. The gene expression profiles of cancer tissues from the patients, who underwent complete surgical resection of breast cancer and were subsequently monitored for postoperative survival, were analyzed using cDNA microarrays. We detected seven and three probes/genes associated with the postoperative OS and DFS , respectively, from our discovery cohort data. By incorporating these genes associated with the postoperative survival into MammaPrint genes, often used to predict prognosis of patients with early‐stage breast cancer, we constructed postoperative OS and DFS prediction models from the discovery cohort data using a Cox proportional hazard model. The predictive ability of the models was evaluated in another independent cohort using Kaplan–Meier (KM ) curves and the area under the receiver operating characteristic curve (AUC ). The KM curves showed a statistically significant difference between the predicted high‐ and low‐risk groups in both OS (log‐rank trend test P = 0.0033) and DFS (log‐rank trend test P = 0.00030). The models also achieved high AUC scores of 0.71 in OS and of 0.60 in DFS . Furthermore, our models had improved KM curves when compared to the models using MammaPrint genes (OS : P = 0.0058, DFS : P = 0.00054). Similar results were observed when our model was tested in publicly available datasets. These observations indicate that there is still room for improvement in the current methods of predicting postoperative OS and DFS in breast cancer

Assessing the feasibility of GS FLX Pyrosequencing for sequencing the Atlantic salmon genome

<p>Abstract</p> <p>Background</p> <p>With a whole genome duplication event and wealth of biological data, salmonids are excellent model organisms for studying evolutionary processes, fates of duplicated genes and genetic and physiological processes associated with complex behavioral phenotypes. It is surprising therefore, that no salmonid genome has been sequenced. Atlantic salmon (<it>Salmo salar</it>) is a good representative salmonid for sequencing given its importance in aquaculture and the genomic resources available. However, the size and complexity of the genome combined with the lack of a sequenced reference genome from a closely related fish makes assembly challenging. Given the cost and time limitations of Sanger sequencing as well as recent improvements to next generation sequencing technologies, we examined the feasibility of using the Genome Sequencer (GS) FLX pyrosequencing system to obtain the sequence of a salmonid genome. Eight pooled BACs belonging to a minimum tiling path covering ~1 Mb of the Atlantic salmon genome were sequenced by GS FLX shotgun and Long Paired End sequencing and compared with a ninth BAC sequenced by Sanger sequencing of a shotgun library.</p> <p>Results</p> <p>An initial assembly using only GS FLX shotgun sequences (average read length 248.5 bp) with ~30× coverage allowed gene identification, but was incomplete even when 126 Sanger-generated BAC-end sequences (~0.09× coverage) were incorporated. The addition of paired end sequencing reads (additional ~26× coverage) produced a final assembly comprising 175 contigs assembled into four scaffolds with 171 gaps. Sanger sequencing of the ninth BAC (~10.5× coverage) produced nine contigs and two scaffolds. The number of scaffolds produced by the GS FLX assembly was comparable to Sanger-generated sequencing; however, the number of gaps was much higher in the GS FLX assembly.</p> <p>Conclusion</p> <p>These results represent the first use of GS FLX paired end reads for <it>de novo </it>sequence assembly. Our data demonstrated that this improved the GS FLX assemblies; however, with respect to <it>de novo </it>sequencing of complex genomes, the GS FLX technology is limited to gene mining and establishing a set of ordered sequence contigs. Currently, for a salmonid reference sequence, it appears that a substantial portion of sequencing should be done using Sanger technology.</p

Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data

Alzheimer’s disease (AD) is the most common subtype of dementia, followed by Vascular Dementia (VaD), and Dementia with Lewy Bodies (DLB). Recently, microRNAs (miRNAs) have received a lot of attention as the novel biomarkers for dementia. Here, using serum miRNA expression of 1,601 Japanese individuals, we investigated potential miRNA biomarkers and constructed risk prediction models, based on a supervised principal component analysis (PCA) logistic regression method, according to the subtype of dementia. The final risk prediction model achieved a high accuracy of 0.873 on a validation cohort in AD, when using 78 miRNAs: Accuracy = 0.836 with 86 miRNAs in VaD; Accuracy = 0.825 with 110 miRNAs in DLB. To our knowledge, this is the first report applying miRNA-based risk prediction models to a dementia prospective cohort. Our study demonstrates our models to be effective in prospective disease risk prediction, and with further improvement may contribute to practical clinical use in dementia

Genomic organization and evolution of the Atlantic salmon hemoglobin repertoire

<p>Abstract</p> <p>Background</p> <p>The genomes of salmonids are considered pseudo-tetraploid undergoing reversion to a stable diploid state. Given the genome duplication and extensive biological data available for salmonids, they are excellent model organisms for studying comparative genomics, evolutionary processes, fates of duplicated genes and the genetic and physiological processes associated with complex behavioral phenotypes. The evolution of the tetrapod hemoglobin genes is well studied; however, little is known about the genomic organization and evolution of teleost hemoglobin genes, particularly those of salmonids. The Atlantic salmon serves as a representative salmonid species for genomics studies. Given the well documented role of hemoglobin in adaptation to varied environmental conditions as well as its use as a model protein for evolutionary analyses, an understanding of the genomic structure and organization of the Atlantic salmon α and β hemoglobin genes is of great interest.</p> <p>Results</p> <p>We identified four bacterial artificial chromosomes (BACs) comprising two hemoglobin gene clusters spanning the entire α and β hemoglobin gene repertoire of the Atlantic salmon genome. Their chromosomal locations were established using fluorescence <it>in situ </it>hybridization (FISH) analysis and linkage mapping, demonstrating that the two clusters are located on separate chromosomes. The BACs were sequenced and assembled into scaffolds, which were annotated for putatively functional and pseudogenized hemoglobin-like genes. This revealed that the tail-to-tail organization and alternating pattern of the α and β hemoglobin genes are well conserved in both clusters, as well as that the Atlantic salmon genome houses substantially more hemoglobin genes, including non-Bohr β globin genes, than the genomes of other teleosts that have been sequenced.</p> <p>Conclusions</p> <p>We suggest that the most parsimonious evolutionary path leading to the present organization of the Atlantic salmon hemoglobin genes involves the loss of a single hemoglobin gene cluster after the whole genome duplication (WGD) at the base of the teleost radiation but prior to the salmonid-specific WGD, which then produced the duplicated copies seen today. We also propose that the relatively high number of hemoglobin genes as well as the presence of non-Bohr β hemoglobin genes may be due to the dynamic life history of salmon and the diverse environmental conditions that the species encounters.</p> <p>Data deposition: BACs S0155C07 and S0079J05 (fps135): GenBank <ext-link ext-link-id="GQ898924" ext-link-type="gen">GQ898924</ext-link>; BACs S0055H05 and S0014B03 (fps1046): GenBank <ext-link ext-link-id="GQ898925" ext-link-type="gen">GQ898925</ext-link></p