Wing-Kin Sung

Bio: Wing-Kin Sung is an academic researcher from National University of Singapore. The author has contributed to research in topics: Gene & Chromatin immunoprecipitation. The author has an hindex of 64, co-authored 327 publications receiving 26116 citations. Previous affiliations of Wing-Kin Sung include University of Hong Kong & Yale University.

Faster Algorithms for Computing the R* Consensus Tree

Abstract: The fastest known algorithms for computing the R* consensus tree of k rooted phylogenetic trees with n leaves each and identical leaf label sets run in $$O(n^{2} \sqrt{\log n})$$O(n2logn) time when $$k = 2$$k=2 (Jansson and Sung in Algorithmica 66(2):329---345, 2013) and $$O(k n^{3})$$O(kn3) time when $$k \ge 3$$kź3 (Bryant in Bioconsensus, volume 61 of DIMACS series in Discrete Mathematics and Theoretical Computer Science. American Mathematical Society, pp 163---184, 2003). This paper shows how to compute it in $$O(n^{2})$$O(n2) time for $$k = 2, O(n^{2} \log ^{4/3} n)$$k=2,O(n2log4/3n) time for $$k = 3$$k=3, and $$O(n^{2} \log ^{k+2} n)$$O(n2logk+2n) time for unbounded k.

Finding the Cyclic Covers of a String

Abstract: We introduce the concept of cyclic covers, which generalizes the classical notion of covers in strings. Given any nonempty string X of length n, a factor W of X is called a cyclic cover if every position of X belongs to an occurrence of a cyclic shift of W. Two cyclic covers are distinct if one is not a cyclic shift of the other. The cyclic cover problem requires finding all distinct cyclic covers of X. We present an algorithm that solves the cyclic cover problem in $$\mathcal {O}(n \log n)$$ time. This is based on finding a well-structured set of standard occurrences of a constant number of factors of a cyclic cover candidate W, computing the regions of X covered by cyclic shifts of W, extending those factors, and taking the union of the results.

Algorithms for building consensus MUL-trees

Abstract: A MUL-tree is a generalization of a phylogenetic tree that allows the same leaf label to be used many times. Lott et al. [9,10] recently introduced the problem of inferring a so-called consensus MUL-tree from a set of conflicting MUL-trees and gave an exponential-time algorithm for a special greedy variant. Here, we study strict and majority rule consensus MUL-trees , and present the first ever polynomial-time algorithms for building a consensus MUL-tree. We give a simple, fast algorithm for building a strict consensus MUL-tree. We also show that although it is NP-hard to find a majority rule consensus MUL-tree, the variant which we call the singular majority rule consensus MUL-tree is unique and can be constructed efficiently.

Genome assembly and chemogenomic profiling of National Flower of Singapore Papilionanthe Miss Joaquim ‘Agnes’ reveals metabolic pathways regulating floral traits

Abstract: Abstract Singapore’s National Flower, Papilionanthe ( Ple .) Miss Joaquim ‘Agnes’ (PMJ) is highly prized as a horticultural flower from the Orchidaceae family. A combination of short-read sequencing, single-molecule long-read sequencing and chromatin contact mapping was used to assemble the PMJ genome, spanning 2.5 Gb and 19 pseudo-chromosomal scaffolds. Genomic resources and chemical profiling provided insights towards identifying, understanding and elucidating various classes of secondary metabolite compounds synthesized by the flower. For example, presence of the anthocyanin pigments detected by chemical profiling coincides with the expression of ANTHOCYANIN SYNTHASE (ANS) , an enzyme responsible for the synthesis of the former. Similarly, the presence of vandaterosides (a unique class of glycosylated organic acids with the potential to slow skin aging) discovered using chemical profiling revealed the involvement of glycosyltransferase family enzymes candidates in vandateroside biosynthesis. Interestingly, despite the unnoticeable scent of the flower, genes involved in the biosynthesis of volatile compounds and chemical profiling revealed the combination of oxygenated hydrocarbons, including traces of linalool, beta-ionone and vanillin, forming the scent profile of PMJ. In summary, by combining genomics and biochemistry, the findings expands the known biodiversity repertoire of the Orchidaceae family and insights into the genome and secondary metabolite processes of PMJ.

Learning Chromatin Interaction Using Hi-C Datasets.

Abstract: Although our genome consists of a set of linear polymers of nucleotides, they form 3 dimensional structure through chromatin interaction. Understanding the 3 dimensional structure of our genome is important since recent research showed that the 3 dimensional structure helps to explain tissue-specific expression profile and helps to explain how mutations in inter-genic region affecting the expression of oncogenes and tumor-suppressor genes in diseases.

Fast and accurate short read alignment with Burrows–Wheeler transform

Abstract: Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: [email protected]

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

Abstract: Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.

An integrated encyclopedia of DNA elements in the human genome

Abstract: The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Model-based Analysis of ChIP-Seq (MACS)

Abstract: We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.