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Due to the rapid increase in biological data dimension and acquisition rate, the traditional analysis methods are unable to achieve acceptable accuracy. Recently, Deep learning technologies have shown outstanding results in many domains; especially in pattern recognition in the field of bioinformatics. In this paper, we provide background of what deep learning and its frameworks. In addition, we review...
With the rapid growth of biological technology, large amount of biological data can be produced in few days or months. Many of common-used tools become computation-consuming in analyzing such big biological data. Cloud computing has emerged to provide the huge amount of computing power and play important role in development of bioinformatics tools. We propose a cloud computing framework that is able...
In the 21st century, antibiotic resistance has become a crucial and growing phenomenon in contemporary medicine. Multidrug resistant leads that antibiotics cannot be used to treat infections. In this paper, we propose a novel and efficient method to identify drug resistance genes from raw reads produced by next generation sequencing technology for metagenomes. The experimental results show that the...
High-throughput technology for genotyping has made genome-wide associations possible. Single nucleotide polymorphism (SNP) data derived from array-based technology are usually flawed due to missing data, although they have generally high call rates and good concordance rates across different genotype calling schemes. Missing SNPs can bias the results of association analyses and hence loci with missing...
Proteins perform most important biochemical reactions in organisms, such as the catalysis, signal transduction, and transport of nutrients. The urgent need of automatic annotation is due to the advent of high-throughput sequencing techniques in the post-genomic era. Proteins consist of domains which are elementary building units of protein folding, function, and evolution. The evidence of protein...
After the reference genomes of many organisms are sequenced in this post-genetic era, it has become an extremely important issue that how to do the re-sequencing and assembly for individual genomes from very large amount of reads. In this paper, we will present a re-sequencing tool designed for the Next Generation Sequencing (NGS) data. And these data are composed of a huge amount of short reads which...
Multiple sequence alignment is a scientific tool to assist the study of DNA homology, phylogeny determinations, and conserved motifs identification. Various heuristic MSA methods have been presented to obtain the resulting alignment for multiple sequences. Although these alignment tools are able to align protein, DNA, and RNA sequences successfully, they are not such successful in aligning coding...
Many new sequencing techniques have been proposed in the last few years. A number of tools have been developed for mapping short reads to the genome. However, these tools used the hash look-up table algorithm for reads or for the genome. With more and more reads sequenced and the length of reads increases; these tools will require huge memory usage and may be not practical for biologists and applications...
Multiple sequence alignment is widely applied to discover core conserved regions among query sequences. However, the major deficiency is that alignment accuracy is extremely sensitive to primary sequence identity, which causes alignment of low identity sequences difficult. We propose a feature-integrated model called feature-incorporated alignment (FIA) which integrates relevant biological characteristics...
The classification of protein structures is essential for their function determination in bioinformatics. At present, a reasonably high rate of prediction accuracy has been achieved in classifying proteins into four classes in the SCOP database according to their primary amino acid sequences. However, for further classification into fine-grained folding categories, especially when the number of possible...
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