Chunmei Shi, Junjie Wang, Lingling Zhao, Xiaohong Su, G. Jiang
Cell tracking automatically in time-lapse image sequences is important for understanding the dynamic pattern of micro-cell. In this paper, we present a novel method for tracking cell with shape feature based on the delta generalized labeled multi-Bernoulli (delta-GLMB) filter which is of great research significance. The delta-GLMB filter with cell shape parameters can improve the tracking accuracy. This approach is evaluated and compared with raw detection using the generalized optimal sub-pattern assignment (GOSPA) metric on real N2DH-SIM cell sequences. Experiment results show that the delta-GLMB filter can provide the shape information as well as the better estimation than raw detection and KTH method.
{"title":"[Regular Paper] The Delta Generalized Labeled Multi-Bernoulli Filter for Cell Tracking","authors":"Chunmei Shi, Junjie Wang, Lingling Zhao, Xiaohong Su, G. Jiang","doi":"10.1109/BIBE.2018.00048","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00048","url":null,"abstract":"Cell tracking automatically in time-lapse image sequences is important for understanding the dynamic pattern of micro-cell. In this paper, we present a novel method for tracking cell with shape feature based on the delta generalized labeled multi-Bernoulli (delta-GLMB) filter which is of great research significance. The delta-GLMB filter with cell shape parameters can improve the tracking accuracy. This approach is evaluated and compared with raw detection using the generalized optimal sub-pattern assignment (GOSPA) metric on real N2DH-SIM cell sequences. Experiment results show that the delta-GLMB filter can provide the shape information as well as the better estimation than raw detection and KTH method.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132978730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jhih-Ying Chen, Chia-Min Chen, Pei-Chun Chang, J. Tsai
Cancer is a fatal disease. It is worth noting that the treatment of cancer still lacks effective drugs for cancer resistance. The development of multi-target drugs is an important direction in the future. Human epidermal growth factor receptor (EGFR and HER2) and Heat shock protein 90 (HSP90) have been proven to be useful targets in various cancer cell lines. To develop dual-target drugs for these two proteins may be more effective in cancer treatment. We performed ligand-based QSAR modeling to select potential TCM candidate compounds for HER2/HSP90 inhibition. The results show that cyclokoreanine B, dehydropodophyllotoxin, alloimperatorine, wanpeinine A, zierin, N-demethylnoracronycine, desacetyleupaserrin, dianthramine, gnoscopine, and formononetin might have the potential for HER2/HSP90 inhibition.
{"title":"The Potential Dual-Target Inhibitors for HER2/HSP90 Proteins from Traditional Chinese Medicine","authors":"Jhih-Ying Chen, Chia-Min Chen, Pei-Chun Chang, J. Tsai","doi":"10.1109/BIBE.2018.00061","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00061","url":null,"abstract":"Cancer is a fatal disease. It is worth noting that the treatment of cancer still lacks effective drugs for cancer resistance. The development of multi-target drugs is an important direction in the future. Human epidermal growth factor receptor (EGFR and HER2) and Heat shock protein 90 (HSP90) have been proven to be useful targets in various cancer cell lines. To develop dual-target drugs for these two proteins may be more effective in cancer treatment. We performed ligand-based QSAR modeling to select potential TCM candidate compounds for HER2/HSP90 inhibition. The results show that cyclokoreanine B, dehydropodophyllotoxin, alloimperatorine, wanpeinine A, zierin, N-demethylnoracronycine, desacetyleupaserrin, dianthramine, gnoscopine, and formononetin might have the potential for HER2/HSP90 inhibition.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131942447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Species identification with partial DNA sequences has proved effective for different organisms. DNA barcode is a short genetic marker in an organism's DNA to identify which species it belongs to. In this work, we analyze the effectiveness of supervised machine learning methods to classify species with DNA barcode. We choose specimens from phylogenetically diverse species belonging to the animal, plant and fungus kingdoms. We consider the supervised machine learning methods, simple logistic function, random forest, PART, instance-based k-nearest neighbor, attribute-based classifier, and bagging. The analysis of results on various datasets shows that the classification performances of the selected methods are encouraging, and has an accuracy of 93.66% on average. This result shows 6% improvement compared to the state-of-art DNA barcode classification methods, which have 88.37% accuracy on average.
{"title":"Species Identification Using Partial DNA Sequence: A Machine Learning Approach","authors":"Tasnim Kabir, Abida Sanjana Shemonti, A. Rahman","doi":"10.1109/BIBE.2018.00052","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00052","url":null,"abstract":"Species identification with partial DNA sequences has proved effective for different organisms. DNA barcode is a short genetic marker in an organism's DNA to identify which species it belongs to. In this work, we analyze the effectiveness of supervised machine learning methods to classify species with DNA barcode. We choose specimens from phylogenetically diverse species belonging to the animal, plant and fungus kingdoms. We consider the supervised machine learning methods, simple logistic function, random forest, PART, instance-based k-nearest neighbor, attribute-based classifier, and bagging. The analysis of results on various datasets shows that the classification performances of the selected methods are encouraging, and has an accuracy of 93.66% on average. This result shows 6% improvement compared to the state-of-art DNA barcode classification methods, which have 88.37% accuracy on average.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127986301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alternative splicing of precursor mRNA is an important mechanism for increasing the complexity of gene expression, and it plays a key role in cell differentiation and organism development. Accurate alternative splicing profiles and regulation will affect the cellular functions and destiny. Our previous studies showed that amiloride have a potential effect on alternative splicing, but the high effective concentration of amiloride limits its clinical application. In this study, the molecular docking calculation was performed to estimate the binding affinity between a series synthesized amiloride derivatives and SRPK1 protein in silico and then detect its activity in alternative splicing in vitro. The results showed that amiloride derivatives DS010, DS150, and ES013 have better binding affinity and could regulate the alternative splicing of BCL-X transcripts to increase the proportion of BCL-XL in Huh-7 cells. In addition, the effective concentration of DS010 and ES013 are lower than others. These findings suggested that the amiloride derivative DS010 and ES013 may provide therapeutic potential for future cancer treatment.
{"title":"The Amiloride Derivatives Regulate the Alternative Splicing of Apoptotic Gene Transcripts","authors":"C. Lee, Wen-Hsin Chang, Ting-Yuan Liu, Yu-Chia Chen, Guan-Yu Chen, Yang-Chang Wu, Jan-Gowth Chang","doi":"10.1109/BIBE.2018.00069","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00069","url":null,"abstract":"Alternative splicing of precursor mRNA is an important mechanism for increasing the complexity of gene expression, and it plays a key role in cell differentiation and organism development. Accurate alternative splicing profiles and regulation will affect the cellular functions and destiny. Our previous studies showed that amiloride have a potential effect on alternative splicing, but the high effective concentration of amiloride limits its clinical application. In this study, the molecular docking calculation was performed to estimate the binding affinity between a series synthesized amiloride derivatives and SRPK1 protein in silico and then detect its activity in alternative splicing in vitro. The results showed that amiloride derivatives DS010, DS150, and ES013 have better binding affinity and could regulate the alternative splicing of BCL-X transcripts to increase the proportion of BCL-XL in Huh-7 cells. In addition, the effective concentration of DS010 and ES013 are lower than others. These findings suggested that the amiloride derivative DS010 and ES013 may provide therapeutic potential for future cancer treatment.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128865177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
His-Chung Kung, Rong-Ming Chen, J. Tsai, Rouh-Mei Hu
Gut microbiome plays an important role on our health and disease development. In the recent decade, many research papers reported the correlation between alternations of microbiome pattern and the occurrence/severity of diseases. However, the human microbiome is very complex and divergent between individuals. Little is known about the whole spectrum of healthy human microbiome. Using data from human microbiome project (HMP) database (n= 325), we 1) classify sample by hierarchical clustering; 2) Identification the core taxes of each class and the differential microbe cross classes; 3) examine and compare the within-sample microbial diversity (alpha-diversity) and between-person diversity (beta-diversity); 4) built a SVM-based classifier for stool microbiome classification. The results showed that 1) healthy stool microbiome can be classified into 4 classes; 2) Firmicutes and Bacteroidete are the two dominant phyla, and Bacteroides and Prevotella are the most predominant genera. Alistipes, Oscillibacter and Ruminococcus were the major taxa in certain cases; 3) Classes were differed in their microbial composition and complexity; 4) SVM-based gut microbiome classifier yield a very good classification accuracy, sensitivity and specificity.
{"title":"Stratification of Human Gut Microiome and Building a SVM-Based Classifier","authors":"His-Chung Kung, Rong-Ming Chen, J. Tsai, Rouh-Mei Hu","doi":"10.1109/BIBE.2018.00011","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00011","url":null,"abstract":"Gut microbiome plays an important role on our health and disease development. In the recent decade, many research papers reported the correlation between alternations of microbiome pattern and the occurrence/severity of diseases. However, the human microbiome is very complex and divergent between individuals. Little is known about the whole spectrum of healthy human microbiome. Using data from human microbiome project (HMP) database (n= 325), we 1) classify sample by hierarchical clustering; 2) Identification the core taxes of each class and the differential microbe cross classes; 3) examine and compare the within-sample microbial diversity (alpha-diversity) and between-person diversity (beta-diversity); 4) built a SVM-based classifier for stool microbiome classification. The results showed that 1) healthy stool microbiome can be classified into 4 classes; 2) Firmicutes and Bacteroidete are the two dominant phyla, and Bacteroides and Prevotella are the most predominant genera. Alistipes, Oscillibacter and Ruminococcus were the major taxa in certain cases; 3) Classes were differed in their microbial composition and complexity; 4) SVM-based gut microbiome classifier yield a very good classification accuracy, sensitivity and specificity.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125514544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heart rates have normal values ranging from 60 to 80 beats per minute (bpm) for adults. RR distances have normal durations between 0.75 and 1 second. The complexes QRS durations have normal durations of less than 0.1 second. However, heart rate and RR distances also depend on age (adult or child), the patient's status (rest or stress), sex (male or female) and the patient's conditions (healthy or pathological). Heart rates, RR distances and QRS durations are not sufficient to determine whether ECGs are normal or pathological. Recently, two novel metrics have been calculated to reflect the regularity of RR distances and the QRS complexes durations irrespective of the patient's age, sex and heart rate. In this paper, these novel parameters were tested and validated on the arrhythmia (MIT-BIH), Abdominal and Direct Fetal ECG (ADFECGDB) and BIDMC Congestive Heart Failure (CHFDB) databases.
{"title":"Novel Parameters for ECG Signal Analysis Irrespective of Patient's Age, Sex and Heart Rate","authors":"S. Hamdi, A. Abdallah, M. Hedi","doi":"10.1109/BIBE.2018.00056","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00056","url":null,"abstract":"Heart rates have normal values ranging from 60 to 80 beats per minute (bpm) for adults. RR distances have normal durations between 0.75 and 1 second. The complexes QRS durations have normal durations of less than 0.1 second. However, heart rate and RR distances also depend on age (adult or child), the patient's status (rest or stress), sex (male or female) and the patient's conditions (healthy or pathological). Heart rates, RR distances and QRS durations are not sufficient to determine whether ECGs are normal or pathological. Recently, two novel metrics have been calculated to reflect the regularity of RR distances and the QRS complexes durations irrespective of the patient's age, sex and heart rate. In this paper, these novel parameters were tested and validated on the arrhythmia (MIT-BIH), Abdominal and Direct Fetal ECG (ADFECGDB) and BIDMC Congestive Heart Failure (CHFDB) databases.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126075011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Strauch, L. Mukunda, Alja Ludke, C. Galizia, D. Merhof
The ensemble of odorant receptors on the antenna of the fruit fly Drosophila melanogaster acts as an encoder for chemical molecules. Chemically similar odorants elicit activity in similar subsets of the receptors, spanning a so-called chemotopic feature space that enables chemical similarity search. A compound signal of receptor activity can be read out by calcium imaging of the antenna, yet without revealing corresponding receptors on different antennae. Employing Canonical Correlation Analysis (CCA) for multiple sets, we show that a consensus feature space can nevertheless be recovered from a group of variable antenna sensors that all respond to a common sequence of odorants. In the chemotopic consensus feature space, properties of novel odorants can be inferred, demonstrating how fruit fly antenna chemosensors may be employed as an alternative to electronic noses.
{"title":"[Regular Paper] Recovering a Chemotopic Feature Space from a Group of Fruit Fly Antenna Chemosensors","authors":"M. Strauch, L. Mukunda, Alja Ludke, C. Galizia, D. Merhof","doi":"10.1109/BIBE.2018.00039","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00039","url":null,"abstract":"The ensemble of odorant receptors on the antenna of the fruit fly Drosophila melanogaster acts as an encoder for chemical molecules. Chemically similar odorants elicit activity in similar subsets of the receptors, spanning a so-called chemotopic feature space that enables chemical similarity search. A compound signal of receptor activity can be read out by calcium imaging of the antenna, yet without revealing corresponding receptors on different antennae. Employing Canonical Correlation Analysis (CCA) for multiple sets, we show that a consensus feature space can nevertheless be recovered from a group of variable antenna sensors that all respond to a common sequence of odorants. In the chemotopic consensus feature space, properties of novel odorants can be inferred, demonstrating how fruit fly antenna chemosensors may be employed as an alternative to electronic noses.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114223389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cancer treatments have been shown to be more effective if the cancer is detected and treated at an early stage. Current detection methods include imaging a tissue and blood sample testing. These methods are expensive and invasive for patients, thus scientists have been driven to develop new alternatives to detect cancer. Biomimetic Pattern Recognition (BPR) is a technique that constructs a hyper-dimensional (HD) geometric body by mimicking a biological system and uses it for classification. BPR is derived from the Principle of Homology-Continuity, which assumes elements of the same class are biologically evolved and continuously connected. In other words, between any two elements of the same class, there is a gradual connection. These connecting branches form HD line segments or hyper-surfaces. The resulting topological structure, known as a biomimetic structure, mimics a biological class. In recent years, BPR has been successfully used in voice, facial, and iris recognition software. Here, we developed new BPR algorithms and classification schemes to detect specific cancers using DNA microarray data. We investigated the performance of the proposed BPR methods based on bladder, colon, leukemia, liver, and prostate cancers. Results indicate that the proposed BPR has an increase in recognition rate when compared to previous techniques. BPR has shown to be a promising approach for cancer detection using DNA microarray data.
{"title":"Cancer Screening Using Biomimetic Pattern Recognition with Hyper-Dimensional Structures","authors":"Leonila Lagunes, Charles H. Lee","doi":"10.1109/BIBE.2018.00046","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00046","url":null,"abstract":"Cancer treatments have been shown to be more effective if the cancer is detected and treated at an early stage. Current detection methods include imaging a tissue and blood sample testing. These methods are expensive and invasive for patients, thus scientists have been driven to develop new alternatives to detect cancer. Biomimetic Pattern Recognition (BPR) is a technique that constructs a hyper-dimensional (HD) geometric body by mimicking a biological system and uses it for classification. BPR is derived from the Principle of Homology-Continuity, which assumes elements of the same class are biologically evolved and continuously connected. In other words, between any two elements of the same class, there is a gradual connection. These connecting branches form HD line segments or hyper-surfaces. The resulting topological structure, known as a biomimetic structure, mimics a biological class. In recent years, BPR has been successfully used in voice, facial, and iris recognition software. Here, we developed new BPR algorithms and classification schemes to detect specific cancers using DNA microarray data. We investigated the performance of the proposed BPR methods based on bladder, colon, leukemia, liver, and prostate cancers. Results indicate that the proposed BPR has an increase in recognition rate when compared to previous techniques. BPR has shown to be a promising approach for cancer detection using DNA microarray data.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120905193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The gene BRCA1 is a human tumor suppressor gene found in all humans, and it was reported that around 5-10% of people with breast cancer have a mutation of their BRCA1 genes. In this study, we investigated potential long non-coding RNA biomarkers for BRCA1 mutation carriers breast cancer cases. From differential expression analysis of RNA-Seq data, we identified 6 significant long non-coding genes (p-value < 0.05). There are three long non-coding genes which were reported as potential biomarkers in breast cancer: AC008268.1, AC091013.1 and AL021395.1. Then the other three genes (AC008592.5, AC090204.1, LINC02570) are novel candidates reported in this study.
{"title":"Identification of Potential Long Non-coding RNA Biomarkers for Breast Cancer Patients with Somatic BRCA1 Mutations from RNA-Seq Datasets","authors":"Jia-Hua Cai, Yu-Ching Chen, H. Chu, J. Tsai","doi":"10.1109/BIBE.2018.00060","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00060","url":null,"abstract":"The gene BRCA1 is a human tumor suppressor gene found in all humans, and it was reported that around 5-10% of people with breast cancer have a mutation of their BRCA1 genes. In this study, we investigated potential long non-coding RNA biomarkers for BRCA1 mutation carriers breast cancer cases. From differential expression analysis of RNA-Seq data, we identified 6 significant long non-coding genes (p-value < 0.05). There are three long non-coding genes which were reported as potential biomarkers in breast cancer: AC008268.1, AC091013.1 and AL021395.1. Then the other three genes (AC008592.5, AC090204.1, LINC02570) are novel candidates reported in this study.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121028856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to improve the accuracy of indel detection, micro-assembly is used in multiple variant callers, such as the GATK HaplotypeCaller to reassemble reads in a specific region of the genome. Assembly is a computationally intensive process that causes runtime bottlenecks. In this paper, we propose a GPU-based de Bruijn graph construction algorithm for micro-assembly in the GATK HaplotypeCaller to improve its performance. Various synthetic datasets are used to compare the performance of the GPU-based de Bruijn graph construction implementation with the software-only baseline, which achieves a speedup of up to 3x. An experiment using two human genome datasets is used to evaluate the performance shows a speedup of up to 2.66x.
{"title":"An Efficient GPU-Based de Bruijn Graph Construction Algorithm for Micro-Assembly","authors":"Shanshan Ren, Nauman Ahmed, K. Bertels, Z. Al-Ars","doi":"10.1109/BIBE.2018.00020","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00020","url":null,"abstract":"In order to improve the accuracy of indel detection, micro-assembly is used in multiple variant callers, such as the GATK HaplotypeCaller to reassemble reads in a specific region of the genome. Assembly is a computationally intensive process that causes runtime bottlenecks. In this paper, we propose a GPU-based de Bruijn graph construction algorithm for micro-assembly in the GATK HaplotypeCaller to improve its performance. Various synthetic datasets are used to compare the performance of the GPU-based de Bruijn graph construction implementation with the software-only baseline, which achieves a speedup of up to 3x. An experiment using two human genome datasets is used to evaluate the performance shows a speedup of up to 2.66x.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116542139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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