Scientific Program

Conference Series LLC Ltd invites all the participants across the globe to attend International Conference on Computational Biology and Bioinformatics Tokyo, Japan.

Day 1 :

Computational Biology 2018 International Conference Keynote Speaker Wen-Lian Hsu photo

Dr. Wen-Lian Hsu 's earlier contribution was on the design of graph algorithms and he has applied similar techniques to tackle computational problems in biology and natural language. In 1993, he developed a Chinese input software, GOING, which has since revolutionized Chinese input on computer. Dr. Hsu is particularly interested in applying natural language processing techniques to understanding DNA sequences as well as protein sequences, structures and functions and also to biological literature mining. Alignment algorithm is often used to compare sequence similarity for both biological sequences and natural language texts. Since biological sequences are exceptionally long and abundant, speed is the major concern. On the other hand, for natural language text, discovering similar phrases, sentences, and paragraphs are of utmost importance. Dr. Hsu has designed ultra-efficient alignment algorithms for biological sequences, flexible approximate matching and clustering algorithms for natural language text.



We present an ultra-efficient global alignment algorithm for comparing similar genomes, and for read mapping in next generation sequencing (NGS), which can process long reads as fast as short reads. Furthermore, it can tolerate much higher error rates. Our parallel read mapping algorithm, KART, is 3 to 10 times faster than the well-known Bowtie2 and BWA-MEM algorithm. On pairwise alignment of human genome sequences, the extended KART is 260 times faster than current methods. The same idea has also been applied to RNA-seq, producing DART, a quick and accurate mapping algorithm. These two results are published in Bioinformatics and the source codes can be downloaded: (1) Kart: a divide-and-conquer algorithm for NGS read alignment: (2) DART: a fast and accurate RNA-seq mapper with a partitioning strategy Besides getting high quality alignment efficiently, our algorithm can simultaneously perform variant calling in about the same amount of time.

To achieve the abovementioned objectives, we design a divide-and-conquer alignment strategy: giving a query sequence P and one reference sequence Q; identifying all locally maximal exact matches as simple region pairs in sequence Q with sequence P, and then clustering the simple region pairs (simple pairs) according to their coordinates in the database to form the bases of global alignment; and fixing the overlaps between adjacent simple region pairs and then filling gaps between adjacent simple region pairs by inserting normal region pair (normal pairs) to produce a complete alignment. The crux of the algorithm is that simple pairs can be aligned in linear time, and all simple pairs and normal pairs can be aligned independently and in parallel. After dividing the query sequence P sufficiently, those pairs that require gapped alignment only have an average length of 21.


Keynote Forum

Wen-Hsiung Li

Biodiversity Research Center, Academia Sinica, Taipei, 115 Taiwan

Keynote: A Comparative Transcriptomics Method with Applications

Time : -

Computational Biology 2018 International Conference Keynote Speaker Wen-Hsiung Li photo

Wen‐Hsiung Li’s expertise is in bioinformatics, molecular evolution and genomics. He has developed many methods for DNA and protein sequence analysis, genomics data analysis and transcriptomics study. His lab has also developed methods for predicting the binding sites of transcription factors and  for  constructing  gene  regulatory networks.  His talk  will  present  a  new  method  for  analyzing  time‐series transcriptomes obtained under two different conditions. This method can deal with heterogeneity of samples, unequal numbers of time points, and uneven time period lengths between studies. This is the first method to deal with these three issues.




Transcriptomes obtained from the same tissue under different conditions can provide massive data for identifying genes differentially expressed between conditions. Moreover, transcriptomes from time‐ series experiments provide dynamic information to profile gene expressions over time. Such three dimensional (3‐D) (gene expression, condition and time) data are very useful for studying dynamic gene regulatory networks and biological processes  (Note that “conditions” can be replaced by “species” or “strains” and “time series” can be replaced by “tissues” or “sources”.) However, three issues, i.e., heterogeneity of samples, unequal numbers of time points, and uneven time period lengths between studies, made it difficult to analyze the data. The first issue affects the determination of gene expression differences between conditions. The second and third issues require transformation of the original time‐series transcriptomes for cross‐condition comparisons. Although methods have been developed for analyzing 3‐D data, there is still no method to deal with all of these issues. In this study, we developed a comparative gene coexpression network (GCN) method to analyze 3‐D data. To illustrate our method, we applied it to two sets of time‐series transcriptomes of maize embryonic leaf development under the normal light/dark (LD) cycle and under total darkness (TD). As a C4 plant, maize leaves exhibit the Kranz anatomy, which is crucial for C4 photosynthesis. Since Kranz anatomy develops under both LD and TD, we applied our method to compare the two types of transcriptomes to obtain a time‐ordered light‐independent GCN. This GCN should include all regulators of Kranz anatomy development. Indeed, from this GCN we inferred and experimentally validated a number of upstream regulators of a key Kranz anatomy regulator, SHR (SHOOTROOT). In addition, we also obtained a light‐specific GCN and a darkness‐specific GCN. From these three GCNs, we inferred light‐independent, light‐ preferred and darkness‐preferred genes. Moreover, from the darkness‐specific GCN, we could also explain why embryonic leaf cells first divide faster but then more slowly under TD than under LD. As will be explained, our method can be applied to other types of data.


Computational Biology 2018 International Conference Keynote Speaker Satoru Miyano photo

Satoru Miyano, PhD, is the Director of Human Genome Center, Institute of Medical Science, University of Tokyo. He received the B.S. (1977), M.S. (1979) and PhD (1984), all in Mathematics from Kyushu University, Japan. He has been working in the field of Bioinformatics and joined Human Genome Center as a professor in 1996. His research mission is to develop “Computational Medical Systems Biology towards Genomic Personalized Medicine”, in particular, cancer research and clinical sequence informatics. He has been involved as PI with MEXT Scientific Research on Innovative Areas “Systems Cancer Project”, “Systems Cancer in Neodimension”, the International Cancer Genome Consortium, MEXT Large-Scale Data Analysis with K computer, and Post-K Computer Project. He is an ISCB Fellow 2013 (The International Society for Computational Biology: 2016 Uehara Award for cancer genomics.



We present our computational methods and their analyses in Cancer Systems Biology that use the supercomputers at Human Genome Center of The University of Tokyo and K computer at RIKEN Advanced Institute of Computational Science. The first challenge is a pipeline Genomon ( (Fig. 1) for cancer genome analysis that is a suite of bioinformatics tools for analyzing cancer genome data (WGS, WES, RNA-seq). It enables us to perform very sensitive and accurate detection of most types of genomic variants (single nucleotide variants, short indels, mid-size indels and large scale structural variations), and transcriptomic changes (gene fusions, aberrant splicing patterns). It adopts an efficient job scheduling framework that enables us easily analyzing several hundreds of genome and transcriptome sequencing data simultaneously. We present some of our recent contributions to cancer genomics with Genomon [1-2]. 

     The second is computational strategy for unraveling gene networks and their diversity lying over genetic variations, mutations, environments and diseases from gene expression profiles of cancer cells. We developed methods for exhibiting how gene networks vary from patient to patient according to a modulator, which is any score representing characteristics of cells, e.g. survival, drug resistance [3-4]. We also developed a microRNA/mRNA gene network analysis with Bayesian network method that revealed subnetworks with hub genes that may switch cancer survival [5]. On-going cancer research is also introduced, including a discovery of the first lncRNA modulating MYC gene regulation using K computer.