ISRN bioinformaticsPub Date : 2012-09-04eCollection Date: 2012-01-01DOI: 10.5402/2012/195658
Xinyu Guo, Hong Wang, Vijay Devabhaktuni
{"title":"A Systolic Array-Based FPGA Parallel Architecture for the BLAST Algorithm.","authors":"Xinyu Guo, Hong Wang, Vijay Devabhaktuni","doi":"10.5402/2012/195658","DOIUrl":"https://doi.org/10.5402/2012/195658","url":null,"abstract":"<p><p>A design of systolic array-based Field Programmable Gate Array (FPGA) parallel architecture for Basic Local Alignment Search Tool (BLAST) Algorithm is proposed. BLAST is a heuristic biological sequence alignment algorithm which has been used by bioinformatics experts. In contrast to other designs that detect at most one hit in one-clock-cycle, our design applies a Multiple Hits Detection Module which is a pipelining systolic array to search multiple hits in a single-clock-cycle. Further, we designed a Hits Combination Block which combines overlapping hits from systolic array into one hit. These implementations completed the first and second step of BLAST architecture and achieved significant speedup comparing with previously published architectures. </p>","PeriodicalId":90877,"journal":{"name":"ISRN bioinformatics","volume":"2012 ","pages":"195658"},"PeriodicalIF":0.0,"publicationDate":"2012-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4417556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33173869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ISRN bioinformaticsPub Date : 2012-04-23eCollection Date: 2012-01-01DOI: 10.5402/2012/816402
Chien-Chih Chen, Wen-Dar Lin, Yu-Jung Chang, Chuen-Liang Chen, Jan-Ming Ho
{"title":"Enhancing de novo transcriptome assembly by incorporating multiple overlap sizes.","authors":"Chien-Chih Chen, Wen-Dar Lin, Yu-Jung Chang, Chuen-Liang Chen, Jan-Ming Ho","doi":"10.5402/2012/816402","DOIUrl":"https://doi.org/10.5402/2012/816402","url":null,"abstract":"<p><p>Background. The emergence of next-generation sequencing platform gives rise to a new generation of assembly algorithms. Compared with the Sanger sequencing data, the next-generation sequence data present shorter reads, higher coverage depth, and different error profiles. These features bring new challenging issues for de novo transcriptome assembly. Methodology. To explore the influence of these features on assembly algorithms, we studied the relationship between read overlap size, coverage depth, and error rate using simulated data. According to the relationship, we propose a de novo transcriptome assembly procedure, called Euler-mix, and demonstrate its performance on a real transcriptome dataset of mice. The simulation tool and evaluation tool are freely available as open source. Significance. Euler-mix is a straightforward pipeline; it focuses on dealing with the variation of coverage depth of short reads dataset. The experiment result showed that Euler-mix improves the performance of de novo transcriptome assembly. </p>","PeriodicalId":90877,"journal":{"name":"ISRN bioinformatics","volume":"2012 ","pages":"816402"},"PeriodicalIF":0.0,"publicationDate":"2012-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4417554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33179455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ISRN bioinformaticsPub Date : 2012-04-12eCollection Date: 2012-01-01DOI: 10.5402/2012/564715
J R Deller, Hayder Radha, J Justin McCormick, Huiyan Wang
{"title":"Nonlinear dependence in the discovery of differentially expressed genes.","authors":"J R Deller, Hayder Radha, J Justin McCormick, Huiyan Wang","doi":"10.5402/2012/564715","DOIUrl":"https://doi.org/10.5402/2012/564715","url":null,"abstract":"<p><p>Microarray data are used to determine which genes are active in response to a changing cell environment. Genes are \"discovered\" when they are significantly differentially expressed in the microarray data collected under the differing conditions. In one prevalent approach, all genes are assumed to satisfy a null hypothesis, ℍ 0, of no difference in expression. A false discovery (type 1 error) occurs when ℍ 0 is incorrectly rejected. The quality of a detection algorithm is assessed by estimating its number of false discoveries, 𝔉. Work involving the second-moment modeling of the z-value histogram (representing gene expression differentials) has shown significantly deleterious effects of intergene expression correlation on the estimate of 𝔉. This paper suggests that nonlinear dependencies could likewise be important. With an applied emphasis, this paper extends the \"moment framework\" by including third-moment skewness corrections in an estimator of 𝔉. This estimator combines observed correlation (corrected for sampling fluctuations) with the information from easily identifiable null cases. Nonlinear-dependence modeling reduces the estimation error relative to that of linear estimation. Third-moment calculations involve empirical densities of 3 × 3 covariance matrices estimated using very few samples. The principle of entropy maximization is employed to connect estimated moments to 𝔉 inference. Model results are tested with BRCA and HIV data sets and with carefully constructed simulations. </p>","PeriodicalId":90877,"journal":{"name":"ISRN bioinformatics","volume":"2012 ","pages":"564715"},"PeriodicalIF":0.0,"publicationDate":"2012-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33272806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ISRN bioinformaticsPub Date : 2012-02-15eCollection Date: 2012-01-01DOI: 10.5402/2012/619427
Tiago Grego, Catia Pesquita, Hugo P Bastos, Francisco M Couto
{"title":"Chemical Entity Recognition and Resolution to ChEBI.","authors":"Tiago Grego, Catia Pesquita, Hugo P Bastos, Francisco M Couto","doi":"10.5402/2012/619427","DOIUrl":"https://doi.org/10.5402/2012/619427","url":null,"abstract":"<p><p>Chemical entities are ubiquitous through the biomedical literature and the development of text-mining systems that can efficiently identify those entities are required. Due to the lack of available corpora and data resources, the community has focused its efforts in the development of gene and protein named entity recognition systems, but with the release of ChEBI and the availability of an annotated corpus, this task can be addressed. We developed a machine-learning-based method for chemical entity recognition and a lexical-similarity-based method for chemical entity resolution and compared them with Whatizit, a popular-dictionary-based method. Our methods outperformed the dictionary-based method in all tasks, yielding an improvement in F-measure of 20% for the entity recognition task, 2-5% for the entity-resolution task, and 15% for combined entity recognition and resolution tasks. </p>","PeriodicalId":90877,"journal":{"name":"ISRN bioinformatics","volume":"2012 ","pages":"619427"},"PeriodicalIF":0.0,"publicationDate":"2012-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33272166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ISRN bioinformaticsPub Date : 2011-12-28eCollection Date: 2012-01-01DOI: 10.5402/2012/790452
Issac H K Too, Maurice H T Ling
{"title":"Signal Peptidase Complex Subunit 1 and Hydroxyacyl-CoA Dehydrogenase Beta Subunit Are Suitable Reference Genes in Human Lungs.","authors":"Issac H K Too, Maurice H T Ling","doi":"10.5402/2012/790452","DOIUrl":"https://doi.org/10.5402/2012/790452","url":null,"abstract":"<p><p>Lung cancer is a common cancer, and expression profiling can provide an accurate indication to advance the medical intervention. However, this requires the availability of stably expressed genes as reference. Recent studies had shown that genes that are stably expressed in a tissue may not be stably expressed in other tissues suggesting the need to identify stably expressed genes in each tissue for use as reference genes. DNA microarray analysis has been used to identify those reference genes with low fluctuation. Fourteen datasets with different lung conditions were employed in our study. Coefficient of variance, followed by NormFinder, was used to identify stably expressed genes. Our results showed that classical reference genes such as GAPDH and HPRT1 were highly variable; thus, they are unsuitable as reference genes. Signal peptidase complex subunit 1 (SPCS1) and hydroxyacyl-CoA dehydrogenase beta subunit (HADHB), which are involved in fundamental biochemical processes, demonstrated high expression stability suggesting their suitability in human lung cell profiling. </p>","PeriodicalId":90877,"journal":{"name":"ISRN bioinformatics","volume":"2012 ","pages":"790452"},"PeriodicalIF":0.0,"publicationDate":"2011-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4407196/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33173866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ISRN bioinformaticsPub Date : 2011-11-29eCollection Date: 2012-01-01DOI: 10.5402/2012/982737
John A Springer, Nicholas V Iannotti, Jon E Sprague, Michael D Kane
{"title":"Construction of a drug safety assurance information system based on clinical genotyping.","authors":"John A Springer, Nicholas V Iannotti, Jon E Sprague, Michael D Kane","doi":"10.5402/2012/982737","DOIUrl":"https://doi.org/10.5402/2012/982737","url":null,"abstract":"<p><p>To capitalize on the vast potential of patient genetic information to aid in assuring drug safety, a substantial effort is needed in both the training of healthcare professionals and the operational enablement of clinical environments. Our research aims to satisfy these needs through the development of a drug safety assurance information system (GeneScription) based on clinical genotyping that utilizes patient-specific genetic information to predict and prevent adverse drug responses. In this paper, we present the motivations for this work, the algorithms at the heart of GeneScription, and a discussion of our system and its uses. We also describe our efforts to validate GeneScription through its evaluation by practicing pharmacists and pharmacy professors and its repeated use in training pharmacists. The positive assessment of the GeneScription software tool by these domain experts provides strong validation of the importance, accuracy, and effectiveness of GeneScription. </p>","PeriodicalId":90877,"journal":{"name":"ISRN bioinformatics","volume":"2012 ","pages":"982737"},"PeriodicalIF":0.0,"publicationDate":"2011-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4407205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33173867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bio301: A Web-Based EST Annotation Pipeline That Facilitates Functional Comparison Studies.","authors":"Yen-Chen Chen, Yun-Ching Chen, Wen-Dar Lin, Chung-Der Hsiao, Hung-Wen Chiu, Jan-Ming Ho","doi":"10.5402/2012/139842","DOIUrl":"https://doi.org/10.5402/2012/139842","url":null,"abstract":"<p><p>In this postgenomic era, a huge volume of information derived from expressed sequence tags (ESTs) has been constructed for functional description of gene expression profiles. Comparative studies have become more and more important to researchers of biology. In order to facilitate these comparative studies, we have constructed a user-friendly EST annotation pipeline with comparison tools on an integrated EST service website, Bio301. Bio301 includes regular EST preprocessing, BLAST similarity search, gene ontology (GO) annotation, statistics reporting, a graphical GO browsing interface, and microarray probe selection tools. In addition, Bio301 is equipped with statistical library comparison functions using multiple EST libraries based on GO annotations for mining meaningful biological information. </p>","PeriodicalId":90877,"journal":{"name":"ISRN bioinformatics","volume":"2012 ","pages":"139842"},"PeriodicalIF":0.0,"publicationDate":"2011-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4407203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33173865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}